Search

Purpose: This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach: The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on "how exactly the matching process is done for evaluating the document." Finally, the e-learners are classified based on their grades. Findings: On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value: This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors' knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners. [ABSTRACT FROM AUTHOR]

• Explainable framework for meta-learning. • Efficiency and high causality. • Intervention and counterfactual. With the growing convergence of artificial intelligence and daily life scenarios, the application scenarios for intelligent decision methods are becoming increasingly complex. The development of various machine learning algorithms has benefited all disciplines of study, but choosing which algorithm is most suitable for a certain problem among a large number of algorithms is a challenge that every field must overcome. Another challenge at the practical application level is that machine learning algorithms currently trained with large amounts of data are primarily black-box and uninterpretable. This indicates that these methods pose potential risks and are difficult to rely on, thus hindering their application in sensitive fields such as finance and healthcare. The first challenge can be overcome by using meta-learning to combine data and prior knowledge to efficiently and automatically select the machine learning models. The second challenge remains to be addressed due to the lack of interpretability of traditional meta-learning techniques and deficiencies in transparency and fairness. Achieving the interpretability of meta-learning in autonomous algorithm selection for classification is crucial to balance the need for high accuracy and transparency of machine learning models in practical application scenarios. This paper proposes EFFECT , an interpretable meta-learning framework that can explain the recommendation results of meta-learning algorithm selection and provide a more complete and accurate explanation of the recommendation algorithm's performance on specific datasets combined with business scenarios. Extensive experiments have demonstrated the validity and correctness of this framework. [ABSTRACT FROM AUTHOR]

• Explainable framework for meta-learning. • Efficiency and high causality. • Intervention and counterfactual. With the growing convergence of artificial intelligence and daily life scenarios, the application scenarios for intelligent decision methods are becoming increasingly complex. The development of various machine learning algorithms has benefited all disciplines of study, but choosing which algorithm is most suitable for a certain problem among a large number of algorithms is a challenge that every field must overcome. Another challenge at the practical application level is that machine learning algorithms currently trained with large amounts of data are primarily black-box and uninterpretable. This indicates that these methods pose potential risks and are difficult to rely on, thus hindering their application in sensitive fields such as finance and healthcare. The first challenge can be overcome by using meta-learning to combine data and prior knowledge to efficiently and automatically select the machine learning models. The second challenge remains to be addressed due to the lack of interpretability of traditional meta-learning techniques and deficiencies in transparency and fairness. Achieving the interpretability of meta-learning in autonomous algorithm selection for classification is crucial to balance the need for high accuracy and transparency of machine learning models in practical application scenarios. This paper proposes EFFECT , an interpretable meta-learning framework that can explain the recommendation results of meta-learning algorithm selection and provide a more complete and accurate explanation of the recommendation algorithm's performance on specific datasets combined with business scenarios. Extensive experiments have demonstrated the validity and correctness of this framework. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is rapidly evolving and changing the way we live and work. The goal of this technology is to create systems that can mimic certain human capabilities such as, for example, natural language processing (NLP, Natural Language Processing), generating content, composing music etc. through the use of advanced algorithms and large amounts of data. In the educational sector, AI systems such as chatbots and AI-based learning platforms are raising many inter- questionings on how they can be potentially used. This paper will present some of the most recent national and international news articles that show how heated and counterintuitive the debate is, still in its infancy, on the use of AI in school and academic tracks. [ABSTRACT FROM AUTHOR]

Purpose: The purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain. Design/methodology/approach: This study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare. Findings: Robotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications. Originality/value: This study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector. [ABSTRACT FROM AUTHOR]

This paper designs and simulates a circular heatsink (HSK) with a novel geometry. A number of cylindrical pin-fins (PIFs) are placed on the HSK. The flow of alumina/water nanofluid (NF) enters from the middle of the HSK, passes through the PIFs, goes toward the outer curvature, and exits from the HSK. Constant thermal flux is applied at the bottom of the HSK. The variables include the length of PIFs changing from 5 to 20 mm, the distance between PIFs varying from 10 to 15 mm, and the diameter of PIFs changing from 1 to 4 mm. The effect of these variables on the maximum of the HSK, the average temperature (T-AV) of the HSK is examined. Finally, numerical optimization is done on the results using machine learning and artificial intelligence in terms of the minimum HSK temperature. The flow of NF is simulated using a two-phase model and the finite element method (FEM). An increment in the length of the PIFs from 5 to 20 reduces the T-AV by 11.42 K. An increment in the diameter of the PIFs from 1 to 4 reduces the T-AV of the HSK by 5.98 K. [ABSTRACT FROM AUTHOR]

Investigating the entropy production (ETP) and inertial number can help to improve the efficiency of heat transfer devices, for this reason, in this paper, the study of the ETP of nanofluid (NNF) flow in a two-dimensional cavity was studied. The NNF flow is affected by the horizontal magnetic field (MGD). Also, the Bejan number (Be) of the flow was also studied for different states of the mentioned parameters. The cavity had two hot and cold walls and two insulated walls, the cold wall above which was also movable. On the lower warm wall, there are 3 blades in the shape of a triangle, the height of each of which was changed individually from 0.1 to 0.7 and its effect on entropy changes and Be was investigated. Finally, using artificial intelligence, the best conditions for having the lowest Be and ETP for the range of variables were checked. The NNF was simulated in two phases and the simulation was done in FORTRAN software using SIMPLE algorithm. The results of this study showed that the largest amount of Be was present in the longest length of the triangular obstacles, but the lowest value of the Be was in the low values of the height of theobstacles and did not necessarily occur in the lowest values of the heights. The highest amount of entropy was produced at the lowest height of the left obstacle and the highest height of the middle obstacle. Also, the amount of ETP at higher altitudes on the right side of the obstacle was greater than its lower altitudes. [ABSTRACT FROM AUTHOR]

The article discusses the impact of COVID-19 on the educational sector, specifically focusing on teaching statistics and data science during the pandemic. It highlights three papers that describe different approaches to teaching during this time. The article also includes other papers on various topics such as causal inference, qualitative methods for investigating data science skills, and personalized education. Additionally, the article announces the registration for the 2024 Annual Symposium on Risks and Opportunities of AI in Pharmaceutical Medicine, which aims to address the challenges and opportunities of AI in pharmaceutical medicine and promote collaboration among industry, academia, regulatory agencies, and professional associations. The symposium will feature panel discussions and a keynote address by the director of the Center for Drug Evaluation and Research at the US Food and Drug Administration. [Extracted from the article]

To address the challenge of facilitating overall problem solving and improving the overall efficiency of intelligent systems efforts through group decision making (GDM) by experts at all stages of the systems, this paper proposes an approach that focuses on improving consistency and enhancing local consensus. The proposed approach specifically considers the probabilistic linguistic preference relations (PLPRs) and incorporates personalized individual semantics (PISs) of decision makers (DMs). For the consistency procedure, we construct an expectation-additive consistency-driven semantic model to acquire PISs of different DMs. Secondly, the preference relation (PR) which does not satisfy the consistency is improved based on a minimum adjustment model. In particular, according to the expected value with PISs this paper, DMs offer PLPRs can be transformed into fuzzy preference relations (FPRs) correspondently for making decisions. For the FPRs, a virtue consensus measure is explored for the consensus reaching process (CRP) from two levels to combine the average value and variance of pair similarities, which effectively improves the accuracy of the consensus measurement. Next, a collective consensus level is calculated to replace the consensus threshold objectively, and then the DMs and alternative pairs that do not reach consensus are identified locally from two levels. Subsequently, an optimisation model that combines the two objectives is developed to improve the consensus level while ensuring consistency. Finally, our method is applied to a publicly available air quality dataset, and the consensus and ranking results are discussed in comparison with other advanced methods to confirm the superiority of the established method. [ABSTRACT FROM AUTHOR]

Location factor of recommender systems has been extensively studied in the past decade. However, there is no research thoroughly analyzing location's role in news recommendation. In this paper, a comprehensive exploration on role of location in news recommendation is presented. First of all, based on analysis of real news datasets, we find that news recommendation differs from spatial item recommendation. Location affects news consumption behaviors of users with two-fold aspects including geographic feature and semantic feature. Regarding geographic feature, location influences news recommendation according to region rather than latitude-longitude level. Furthermore, interesting news topics are also impacted by semantic feature of location. Semantic feature may play a more positive role than geographic feature. The novel findings consistently manifest that, as non-spatial items, news differ from spatial items in that location influences users' selection in terms of different pattern and degree. In summary, geographic and semantic features influence reading preference through mapping locations into special topics. Changing of location topics leads to varying of reading preference. The news datasets in this paper belong to check in data. NewsREEL dataset is from a company, and it is provided by German researcher. The location data in Twitter dataset is also check in data. NetEase news dataset are collected from NetEase news websites, and the type of location data is city or region. [ABSTRACT FROM AUTHOR]

The main aim of the paper is to discuss the architecture for the future Intelligent Systems (IS's) and Decision Support Systems (DS's) dealing with complex phenomena such as supporting medical decisions (diagnosis and therapy) and to emphasize challenges in designing such systems. More precisely, the paper presents arguments for developing a specialized computing model based on the interactive granular computing paradigm which can help to design IS's and DS's more close to the prototypes of real life decision making. In this regard, the paper brings to the fore different experiences faced during designing other medical IS's or DS's.As a starting step, the paper considers the experience of developing the OvuFriend platform and outlines some possible extension of it in the framework of the proposed architecture on the basis of Interactive Granular Computing (IGrC) model. Specifically, our attempt is to analyze a scheme, which is being used in the platform of OvuFriend for determining health risks and possibilities of a woman to conceive a child, from the perspective of IGrC. The target of the paper is two fold. Firstly, to show how the underlying AI algorithm of this scheme can be related with the notion of computing in the context of IGrC. Secondly, to identify possible extensions of the existing scheme so that it becomes more dynamic, interactive, and close to personalized medicine. [ABSTRACT FROM AUTHOR]

• The paper presents a lightweight model with satisfactory performance in complex industrial noise environments. • Data fusion using data from multiple sensors for more complete fault information. • Design a unique grayscale feature map based on IMF components to obtain multi-sensor and multi-frequency band fault features. • Design a UL-GoogLeNet lightweight fault diagnosis model based on GoogLeNet and ULSAM. As artificial intelligence and sensor technology develop rapidly, intelligent fault diagnosis methods based on deep learning are widely used in industrial production. However, in practical industrial applications, the complex noise environment affects the performance of the diagnostic model, and the huge model parameters cannot meet the requirements of low cost and high performance in industrial production. To address the above problems, this paper proposes a lightweight intelligent fault diagnosis model using multi-sensor data fusion that not only meets the lightweight requirements of "small, light, and fast", but also realizes high accuracy diagnosis in noisy environments. Firstly, the vibration signals from different sensors of rolling bearings are processed using the variational mode decomposition (VMD) to design a unique method of constructing grayscale feature maps based on each intrinsic modal function (IMF) component. Then, the ultra-lightweight GoogLeNet model (UL-GoogLeNet) is constructed to adjust the traditional GoogLeNet structure, while the Ultra-lightweight subspace attention module (ULSAM) is introduced to reduce the model parameters and enhance the feature extraction capability. UL-GoogLeNet is trained and tested by dividing the grayscale feature maps into training and testing sets to realize the intelligent recognition of different fault types in rolling bearings. Experiments are conducted on two datasets and compared with multiple methods, and the final experimental results prove the effectiveness and superiority of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Purpose: Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper. Design/methodology/approach: Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively. Findings: Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved. Originality/value: This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an interface with the physical world that usually operates in random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips: power consumption, power supply, artificial intelligence (AI), small-signal acquisition, and evaluation criteria. To overcome these challenges, many works recently aimed at IoT system design have emerged. This work reviews the architecture and circuit innovations that have contributed to IoT developments. This paper does not cover security of IoT. Event-driven architectures and nonuniform sampling ADCs significantly reduce the long-term average power. Besides, embedding AI engines in IoT nodes (AIoT) is one critical trend. The computing-in-memory technique improves the energy efficiency of the AI engine. Asynchronous spike neural networks (ASNNs) AI engines show low power potential. In addition to data processing, small-signal acquisition is also critical. The charge-domain analog-front-end (AFE) techniques such as floating inverter-based amplifiers improve energy efficiency. In addition to the above low power and high energy efficiency technologies, energy harvesting can also enhance the lifetime of AIoT devices. This article discusses recent ambient RF and natural energy harvesting approaches and high-efficiency DC-DC with a wide load range. Finally, novel evaluation criteria are introduced to establish benchmark standards for AIoT chips. [ABSTRACT FROM AUTHOR]

• This paper proposes a human–machine integration ECG intelligent annotation system. • The ECG intelligent annotation system can free the annotation experts from the heavy manual annotation work. • This paper proposes an accurate beat generation model suitable for all beat types. • The generated beat data can be used as a supplement to labelled data. • This paper proposes an intelligent beat pre-annotation model based on intelligent simulation generated data. • The intelligent beat pre-annotation model achieved the best performance. An electrocardiogram (ECG) consists of complex segments, such as P-QRS-T waves. Manual ECG annotation is challenging and time-consuming, even for specialist physicians. The shortage of labelled ECG data is one of the essential factors that affect ECG intelligent analysis's long-term development. This study proposes an intelligent ECG-assisted annotation system, that not only supplements labelled data, but also significantly reduces the workload compared with manual annotation. Since beat annotation is the most basic and important part, a GAN-based generation model that can generate 14 types of simulation beats and a CNN-based beat pre-annotation model are proposed. The experimental results show that the simulation beat has high similarity to real beat and the accuracy of the pre-annotation model on the test set of 14 classes of beats is 99.28%. The proposed ECG intelligent annotation system's self-learning mechanism could improve pre-annotation performance and annotation efficiency by generating more labelled data. The proposed annotation system can also be extended to other data annotation applications. [ABSTRACT FROM AUTHOR]

• Explanation Sets, a new framework that unifies counterfactuals and semifactuals. • Counterfactuals and semifactuals are defined in terms of a similarity measure. • Restrictions and preferences over the explanations are defined using a neighborhood. • A taxonomy for set-based representations. Explainable Machine Learning (ML) is an emerging field of Artificial Intelligence that has gained popularity in the last decade. It focuses on explaining ML models and their predictions, enabling people to understand the rationale behind them. Counterfactuals and semifactuals are two instances of Explainable ML techniques that explain model predictions using other observations. These techniques are based on the comparison between the observation to be explained and another one. In counterfactuals, their prediction is different, and in semifactuals, it is the same. Both techniques have been studied in the Social Sciences and Explainable ML communities, and they have different use cases and properties. In this paper, the Explanation Set framework, an approach that unifies counterfactuals and semifactuals, is introduced. Explanation Sets are example-based explanations defined in a neighborhood where most observations satisfy a grouping measure. The neighborhood allows defining and combining restrictions. The grouping measure determines if the explanations are counterfactuals (dissimilarity) or semifactuals (similarity). Besides providing a unified framework, the major strength of the proposal is to extend these explanations to other tasks such as regression by using an appropriate grouping measure. The proposal is validated in a regression and classification task using several neighborhoods and grouping measures. [ABSTRACT FROM AUTHOR]

Measuring risk is at the center of modern financial risk management. As the world economy is becoming more complex and standard modelling assumptions are violated, the advanced artificial intelligence solutions may provide the right tools to analyse the global market. In this paper, we provide a novel approach for measuring market risk called Encoded Value-at-Risk (Encoded VaR), which is based on a type of artificial neural network, called Variational Auto-encoders (VAEs). Encoded VaR is a generative model which can be used to reproduce market scenarios from a range of historical cross-sectional stock returns, while increasing the signal-to-noise ratio present in the financial data, and learning the dependency structure of the market without any assumptions about the joint distribution of stock returns. We compare Encoded VaR out-of-sample results with twelve other methods and show that it is competitive to many other well-known VaR algorithms presented in the literature. [ABSTRACT FROM AUTHOR]

As the basis of many artificial intelligence tasks, text similarity measurement has received extensive attention in current studies. However, few of them focus on the resource-limited scenes (i.e., limited computational resources and few training datasets), which are becoming increasingly popular and challenging with the development of the Internet of Things. Worse still, popular methods such as the deep-neural-network-based methods may lose their power in such scenes, since they typically require considerable computational resources. As for most current traditional methods, they also have issues of not effectively exploiting the semantic information in the sentences. As an alternative, this paper proposes a lightweight and semantically rich text similarity measurement model named the TES-TK model. In this model, a sentence is first transformed into a tree structure called TES-Tree with the integration of syntactic information, semantic knowledge, and topic distribution, aiming to comprehensively represent the multidimensional semantics of sentences. Afterward, a modified tree kernel model is designed to calculate the similarity between each pair of TES-Trees. In this way, the similarity score between the two related sentences can be retrieved. Experiments on 19 public benchmark datasets (STS2012–2015) demonstrate that the proposed approach exhibits significantly better performance than the compared eight peer methods on most datasets. Especially in resource-limited scenes, our approach achieved highly competitive results compared with the latest methods, such as BERT. [ABSTRACT FROM AUTHOR]

A common challenge in science is the human capability to evaluate the real impact of an observation and a data set. This is a complex task due to having only partial information and/or to the complexity of the problem, requiring different fields to be combined. In order to overcome these important limitations, we need to be able to review all the available data and interpretations. This would allow us to evaluate the global distribution of a specific process or phenomenon of interest. The increasing number of scientific publications prevents scientists from being able to keep up with all the available literature especially when scientific papers cross disciplines. These challenges prevent us from evaluating the global impact of a certain process and are particularly relevant today given the impact of our scientific assessment on one of the most pressing issues of our time, which is climate change and its impact on society. We present here an application of artificial intelligence to geosciences: We conduct a systematic analysis of geoscience literature through a hybrid machine-human approach. Such applications are more common in other fields such as biomedicine and are in their infancy in the geosciences because of various difficulties the machines encounter in parsing geologic literature. We describe here some of these limitations and how we overcame them. We then use the following case study as an example to test our approach: We ask whether climate is influenced by volcanism in the geologic past. Our case study results show, as expected, that most analyzed literature in this experiment conclude that volcanism influences climate change in deep time, but there is no complete consensus on this question. Similarly, any question of potential global significance, such as the impact of human activities on climate change, can be posed as an interrogating technique for our vast and fast-growing literature in the field of geosciences. Such an approach has the potential to be applied to a variety of complex problems, hence addressing some of the major limitations with cross-disciplinary research. [ABSTRACT FROM AUTHOR]

• An AI algorithm for data enhanced encryption at the end and the intermediate nodes of autonomous IoT systems is proposed. • An AI access strategy is designed for reducing the calculation cost of data encryption. • A data shared matrix is proposed to share the encrypted data to achieve the (k, n) threshold strategy. Aiming at the security issues during the multi-types data storage and data transmission in autonomous Internet of Things (IoT) systems, this paper proposes an AI algorithm for data enhanced encryption used in the ends and the intermediate nodes of IoTs. The algorithm in this paper first constructs a three-dimensional Arnold transformation matrix for data unit value encryption in the end of IoTs, and designs a quantum logic intelligent mapping that effectively diffuses the encrypted data units to reduce the linear correlation of the image data and to improve the security performance of IoT edge data. Furthermore, the algorithm designs an AI access strategy for scrambling sequence nodes and builds a random-access route for the elements of the scrambling sequence which can reduce the calculation cost and improve the operating efficiency of IoT system in the ends and intermediate nodes. Finally, the data shared matrix is used to share the encrypted data to achieve the (k, n) threshold strategy. Experimental results prove that the algorithm has high plaintext and key sensitivity and can effectively resist brute force attacks, statistical analysis and differential attacks. The algorithm in this paper provides an AI solution for data security encryption in the ends and the intermediate nodes of autonomous IoT systems. [ABSTRACT FROM AUTHOR]

In a previous paper (Kim, 2019), an analytical framework based on the constraint satisfaction problems was proposed to reveal the characteristics of households using event logs from smart door lock systems. This work provides a more rigorous justification for the previous approach. This paper proposes a novel parameter estimation method called the maximum feasibility estimation (MFE). The MFE does not rely on any assumption about the parametric family of probability densities from which a random observation is drawn. Instead, we assume that constraints are imposed on observations and that some of the constraints are a function of a parameter of interest. The proposed estimator maximizes the feasible region, a set of all possible observations that satisfy those constraints. The method proposed is validated using synthetic data as well as real streaming event log data. [ABSTRACT FROM AUTHOR]

Purpose: To be more effective, artificial intelligence (AI) requires a broad overall view of the design and transformation of enterprise architecture and capabilities. Maturity models (MMs) are the recognized tools to identify strengths and weaknesses of certain domains of an organization. They consist of multiple, archetypal levels of maturity of a certain domain and can be used for organizational assessment and development. In the case of AI, quite a few numbers of MMs have been proposed. Generally, the links between AI technology, AI usage and organizational performance stay unclear. To address these gaps, this paper aims to introduce the complete details of the AI maturity model (AIMM) for AI-driven platform companies. The associated AI-Driven Platform Enterprise Maturity framework proposed here can help to achieve most of the AI-driven platform companies' objectives. Design/methodology/approach: Qualitative research is performed in two stages. In the first stage, a review of the existing literature is performed to identify the types, barriers, drivers, challenges and opportunities of MMs in AI, Advanced Analytics and Big Data domains. In the second stage, a research framework is proposed to align company value chain with AI technologies and levels of the platform enterprise maturity. Findings: The paper proposes a new five level AI-Driven Platform Enterprise Maturity framework by constructing a formal organizational value chain taxonomy model that explains a vast group of MM phenomena related with the AI-Driven Platform Enterprises. In addition, this study proposes a clear and precise description and structuring of the information in the multidimensional Platform, AI, Advanced Analytics and Big Data domains. The AI-Driven Platform Enterprise Maturity framework assists in identification, creation, assessment and disclosure research of AI-driven platform business organizations. Research limitations/implications: This research is focused on the basic dimensions of AI value chain. The full reference model of AI consists of much more concepts. In the last few years, AI has achieved a notable drive that, if connected appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in machine learning, especially in deep neural networks, the entire community stands in front of the barrier of explainability. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models in industry. Our prospects lead toward the concept of a methodology for the large-scale implementation of AI methods in platform organizations with fairness, model explainability and accountability at its core. Practical implications: AI-driven platform enterprise maturity framework can be used for better communicate to clients the value of AI capabilities through the lens of changing human-machine interactions and in the context of legal, ethical and societal norms. Social implications: The authors discuss AI in the enterprise platform stack including talent platform, human capital management and recruiting. Originality/value: The AI value chain and AI-Driven Platform Enterprise Maturity framework are original and represent an effective tools for assessing AI-driven platform enterprises. [ABSTRACT FROM AUTHOR]

In this paper, we present the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information, where 'deduction' is used for defeasible 'forward' inference. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Our IG-formalism is intended to facilitate the construction of formal representations within AI systems by serving as an intermediary formalism between analyses performed using informal reasoning tools and formalisms that allow for formal evaluation. In this paper, we investigate the use of the IG-formalism as an intermediary formalism in facilitating Bayesian network (BN) graph construction. We propose a structured approach for automatically constructing from an IG a directed BN graph, together with qualitative constraints on the probability distribution represented by the BN. Moreover, we prove a number of formal properties of our approach and identify assumptions under which the construction of an initial BN graph can be fully automated. [ABSTRACT FROM AUTHOR]

Nowadays, information technology, through increasingly intelligent systems, has invaded all sectors and fields of our life. The devices we use (excluding computers, smartphones, tablets and similar) are increasingly smart: cars with advanced infotainment systems, smart TVs that are real computers, household appliances connected to the network. All these devices, in some way, use software that have a certain level of intelligence. In this context, agents are autonomous, reactive and proactive software systems, capable of interacting with both the environment in which they reside and other entities. This paper describes in broad terms what are the agents, how they are utilized in cinematography and how their use has contributed to the production of some great films of the last twenty years, such as the trilogy of The Lord of the Rings. [ABSTRACT FROM AUTHOR]

In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published. However, these models share the same problems of rarely taking important features of KG like attribute features, other than relation triples, into account, while dealing with the heterogeneous, complex and incomplete COVID-19 medical data. To address the above issue, we propose a graph feature collection network (GFCNet) for COVID-19 KGE task, which considers both neighbor and attribute features in KGs. The extensive experiments conducted on the COVID-19 drug KG dataset show promising results and prove the effectiveness and efficiency of our proposed model. In addition, we also explain the future directions of deepening the study on COVID-19 KGE task. [ABSTRACT FROM AUTHOR]

Information modeling and handling in uncertain environments is an important topic in the field of modern artificial intelligence. In practical applications of classification problems, the data harvested by the agent is usually not precise. Based on multi-valued mapping of probabilities expressed by Basic Probability Assignment (BPA), Dempster-Shafer Theory (DST) has a strong ability to model and handle uncertain information. In this paper, we propose a method of fusing attributes to enhance the quality of uncertain data under the framework of DST. The fusion method is based on proposed uncertainty and dissimilarity measures, which performs consistent transformations on belief information in DST. We simulate uncertain data by adding different noises to precise datasets and classify the improved data using common classifiers. With the increasing uncertainty degree of data, the proposed method has higher accuracy and robustness than other methods. [ABSTRACT FROM AUTHOR]

The rapid integration of intelligent processes and methods into information systems in the Artificial Intelligence (AI) era has led to a substantial shift towards autonomous software decision-making. This evolution necessitates robust human oversight, especially in critical domains like Healthcare, Education, and Energy. Human trust in AI plays a vital role in influencing decision-making processes of users interacting with AI. This paper presents VIRTSI (V ariability and I mpact of R eciprocal T rust S tates towards I ntelligent systems), a novel rigorous computational model for human-AI Interaction. VIRTSI simulates human trust states, spanning from overtrust to distrust, through user modelling. It comprises: 1. A trust dynamics representational model based on Deterministic Finite State Automata (DFAs), illustrating transitions among cognitive trust states in response to AI-generated replies. 2. A trust evaluation model based on Confusion Matrices, originating from machine learning and Accuracy Metrics, providing a quantitative framework for analysing human trust dynamics. As a result, this is the first time that trust dynamics have been thoroughly traced in a representational model and a method has been developed to assess the impact of possibly harmful states like overtrust and distrust. An empirical study on the recently launched Large Language Model of generative AI, ChatGPT (version 3.5), provides a radical underexplored AI-generated platform for evaluating the human-AI interaction through VIRTSI. The study involved 1200 interactions of real users as well as AI experts together with experts in two very different domains of evaluation, namely software engineering and poetry. This study traces trust dynamics and the emerging human-AI interaction, in concrete examples of real user synergies with generative AI. The research reveals the vital role of maintaining normal trust states for optimal human-AI interaction and that both AI and human users need further steps towards this goal. The real-world implications of this research can guide the creation and evaluation of user interfaces with AI and the incorporation of functionalities in the development of generative AI chatbots in terms of trust by providing a new rigorous DFA representational method of trust dynamics and a corresponding new perspective of confusion matrix evaluation method of the dynamics' impact in the efficiency of human-AI dialogues. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network (ℂ -RSN), an innovative variant of the Recurrent Spectral Network (RSN) model. The ℂ -RSN model introduces localized non-linearity, complex fixed eigenvalues, and a distinct separation of memory and input processing functionalities. These features enable the ℂ -RSN to evolve towards a dynamic, oscillating final state that bear some degree of similarity with biological cognition. The model's ability to classify data through a time-dependent function, and the localization of information processing, is demonstrated by using the MNIST dataset. Remarkably, distinct items supplied as a sequential input yield patterns in time which bear the indirect imprint of the insertion order (and of the separation in time between contiguous insertions). • The C-RSN overcomes some limitations in existing neural network models. • C-RSN introduces localized non-linearity, complex eigenvalues and a separated memory. • The network evolves towards a dynamic, oscillating final state. • The model's efficacy is demonstrated through empirical evaluation. • C-RSN is able to process sequential inputs keeping track of the insertion order. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence for decision support and automation has shown tremendous potential in many areas. The ability to explain the decisions made by a machine learning algorithm is fundamental to facilitating the widespread use of this type of tool. There are many important real-world problems where the cost of the decisions depends on the characteristics of each example: these are called example-dependent cost (EDC) problems. For this type of classification problem, an appropriate formulation that takes into account the decision costs is fundamental both for the design of the classifier and for the explanation of its decisions. In this paper, we propose COCOA, an explanation method designed for EDC problems based on a Bayesian discriminant. The proposed method can provide counterfactual samples generated by considering decision costs. The COCOA method provides valid and plausible counterfactuals with a high success rate, which can be actionable, diverse, and sparse, achieving a remarkable improvement in terms of cost over five state-of-the-art methods on six real-world datasets. • COCOA provides actionable, plausible, sparse and diverse counterfactuals. • Decision costs are taken into account when generating counterfactuals. • The obtained counterfactuals improve cost savings. • The method is presented for MLPs, but other architectures can be used. [ABSTRACT FROM AUTHOR]

The VFS Redstone Chapter recently hosted a successful two-day Technical Meeting on the development, affordability, and qualification of complex systems in Huntsville, Alabama. The meeting featured keynote presentations by the US Army's Program Executive Office for Aviation and covered topics such as complex aircraft systems, the Future Vertical Lift program, uncrewed aircraft systems, artificial intelligence, and digital engineering. The event attracted approximately 80 attendees and included discussions on new standards, processes, methods, and tools relevant to the qualification and fielding of complex systems. The meeting's technical papers and presentations are available for access on the VFS website. The Redstone Chapter is now planning its next conference, the 2025 Handling Qualities Technical Meeting. [Extracted from the article]

Three-way decision is about thinking, problem-solving, and computing in threes or through triads. Its philosophy and practice of triadic thinking appear everywhere. In particular, there are a number of three-world or triworld models in different fields and disciplines, where a complex system, a complicated issue, or an intricate concept is explained and understood in terms of three interrelated worlds, with each world enclosing a group of elements or representing a particular view. This paper examines various triadic metaphors and structures in general and three-world metaphors and conceptions in particular for building and explaining basic concepts of three-way decision. The three major objectives are (a) to formulate a TAO (Triading-Acting-Optimizing) framework of three-way decision, (b) to discuss the Dao, the Way of three-way decision, and (c) to articulate the Dao, the Way of three-world thinking. Three-world conceptions offer more insights into three-way decision with new viewpoints, methods, and modes. An emphasis of the paper is on the philosophy and the power of three-way decision for building high-level conceptual models. By using three-part structures, by focusing on only three things, or by considering three basic ingredients, we may build a theory, a model, or a method that is simple-to-understand, easy-to-remember, and practical-to-use. [ABSTRACT FROM AUTHOR]

Research on brain-like models with bio-rationality promotes the development of artificial intelligence. However, the topology of a brain-like model still lacks bio-rationality. Bio-brains have self-adaptive robustness. The purpose of this paper is to investigate a more bio-rational brain-like model verified by anti-damage capabilities. Thus, this paper proposes a new spiking neural network (SNN) constrained by a functional brain network based on human-brain functional magnetic resonance imaging (fMRI-SNN). Then, we investigate the anti-damage capabilities of our fMRI-SNN, and discuss the mechanism of these anti-damage capabilities. Our results indicate the following: (i) The fMRI-SNN has anti-damage capabilities under random attacks evaluated by two anti-damage indicators. Based on the relevance analysis, our discussion implies that the intrinsic element of the anti-damage capabilities is the synaptic plasticity. (ii) The anti-damage capabilities of our fMRI-SNN outperform those of the scale-free SNN and the small-world SNN. Our discussion on dynamic topological characteristics further implies that the network topology is an element that impacts the performance level of the anti-damage capabilities. • A more bio-rational brain-like model (fMRI-SNN) is proposed. • The performance of fMRI-SNN is verified by anti-damage capabilities. • The anti-damage mechanism of fMRI-SNN is discussed. [ABSTRACT FROM AUTHOR]

The article explores Clarivate's Institute for Scientific Information (ISI) proposed a new Collaborative CNCI (Collab-CNCI) indicator to analyze the credit authors of academic papers receive via citations. In its report, "Making It Count: Research Credit Management in a Collaborative World," ISI confirms that highly collaborative papers can distort summary results at a national and institutional level.

We develop an agent-based artificial general intelligent system that can be implemented in compact and power-efficient electronic hardware. The hardware under development is called the Street Engine, which is a hardware-based cognitive architecture for implementing agent-based artificial intelligence. In this paper, we introduce an agent-based system to replicate simple cognitive behaviours. In the processes of this system, numerical data are converted into fuzzy symbolic representations of the surrounding environment, and reasoning rules are included in a modified Fuzzy Inference System to support the cognitive decision-making. We use a case study example, the homing behaviour of the honey bee, to demonstrate constructing production rules and implementing the cognitive and reasoning capabilities of agents. The low level cognitive behaviour is converted into a rule-based fuzzy system, and hardware-based experiments have been conducted to verify the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services, which estimates and traces the degree of learner's mastery of concepts based on students' responses to learning resources. The existing mainstream KT models, only attribute learners' feedback to the degree of knowledge mastery and leave the influence of mental ability factors out of consideration. Although ability is an essential component of the problem-solving process, these knowledge-centered models cause a contradiction between data fitting and rationalization of the model decision-making process, making it difficult to achieve high precision and readability simultaneously. In this paper, an innovative KT model, a bility b oosted k nowledge t racing (ABKT) 1 1 Our implementations are available in https://github.com/ccnu-mathits/ABKT. is proposed, which introduces the ability factor into learning feedback attribution to enable the model to analyze the learning process from two perspectives, knowledge and ability, simultaneously. Based on constructive learning theory, continuous matrix factorization (CMF) model is proposed to simulate the knowledge internalization process, following the initiative growth and stationarity principles. In addition, the linear graph latent ability (LGLA) model is proposed to construct learner and item latent ability features, from graph-structured learner interaction data. Then, the knowledge and ability dual-tracing framework is constructed to integrate the knowledge and ability modules. Experimental results on four public databases indicate that the proposed methods perform better than state-of-the-art knowledge tracing algorithms in terms of prediction accuracy in quantitative assessments, displaying some advantages in model interpretability and intelligibility. [ABSTRACT FROM AUTHOR]

Feature importance and interaction are among the main issues in explainable artificial intelligence or interpretable machine learning. To measure feature importance and interaction, several methods, such as H-statistic and partial dependency, have been proposed. However, it is difficult to understand the practical implications of importance and interaction. In this paper, a new method for measuring feature importance and interaction is proposed. For the classification model, we observed correctly predicted cases in a predictive model and grouped them according to the characteristics of the cases. We derived a method for feature importance and interaction from group information. For the regression model, we grouped cases according to the change in the size of the prediction error. The proposed method supports the same rationale for feature importance and interaction. It also supports the decomposition of feature importance to feature power and feature interactions. To implement the proposed method, three visualization tools, including a feature interaction graph, are implemented. Through the proposed work, we can better understand the working mechanism of a predictive model. [ABSTRACT FROM AUTHOR]

• Introduce a new algorithm for Gradient Boosting Decision Trees. • The method allows the exact sequence of decisions of the ensemble to be calculated. • This method allows the thresholds and features of each decision to be obtained. Gradient Boost Decision Trees (GBDT) is a powerful additive model based on tree ensembles. Its nature makes GBDT a black-box model even though there are multiple explainable artificial intelligence (XAI) models obtaining information by reinterpreting the model globally and locally. Each tree of the ensemble is a transparent model itself but the final outcome is the result of a sum of these trees and it is not easy to clarify. In this paper, a feature contribution method for GBDT is developed. The proposed method takes advantage of the GBDT architecture to calculate the contribution of each feature using the residue of each node. This algorithm allows to calculate the sequence of node decisions given a prediction. Theoretical proofs and multiple experiments have been carried out to demonstrate the performance of our method which is not only a local explicability model for the GBDT algorithm but also a unique option that reflects GBDTs internal behavior. The proposal is aligned to the contribution of characteristics having impact in some artificial intelligence problems such as ethical analysis of Artificial Intelligence (AI) and comply with the new European laws such as the General Data Protection Regulation (GDPR) about the right to explain and nondiscrimination. [ABSTRACT FROM AUTHOR]

The description of a physical problem through a model necessarily involves the introduction of parameters. Hence, one wishes to have a solution of the problem that is a function of all these parameters: a parametric solution. However, the construction of such parametric solutions exhibiting localization in space is only ensured by costly and time-consuming tests, which can be both numerical or experimental. Numerical methodologies used classically imply enormous computational efforts for exploring the design space. Therefore, parametric solutions obtained using advanced nonlinear regressions are an essential tool to address this challenge. However, classical regression techniques, even the most advanced ones, can lead to non physical interpolation in some fields such as fluid dynamics, where the solution localizes in different regions depending on the problem parameters choice. In this context, Optimal Transport (OT) offers a mathematical approach to measure distances and interpolate between general objects in a, sometimes, more physical way than the classical interpolation approach. Thus, OT has become fundamental in some fields such as statistics or computer vision, and it is being increasingly used in fields such as computational mechanics. However, the OT problem is usually computationally costly to solve and not adapted to be accessed in an online manner. Therefore, the aim of this paper is combining advanced nonlinear regressions with Optimal Transport in order to implement a parametric real-time model based on OT. To this purpose, a parametric model is built offline relying on Model Order Reduction and OT, leading to a real-time interpolation tool following Optimal Transport theory. Such a tool is of major interest in design processes, but also within the digital twin rationale. [ABSTRACT FROM AUTHOR]

Planning in stochastic and partially observable environments is a central problem in artificial intelligence. To address this issue, an accurate model or a black-box simulator of the environment is usually needed in the literature. Although some recent approaches have been proposed for learning optimal behaviors under model uncertainty, prior knowledge about the environment is still required to guarantee the performance of the proposed algorithms. With the benefits of the Predictive State Representations (PSRs) approach for state representation and model prediction, in this paper, we introduce an approach for planning under partial observability with no prior domain knowledge, where an offline PSR model is firstly learned and then combined with online Monte-Carlo tree search for planning under model uncertainty. Furthermore, we also showed that with the proposed framework, the PSR models learned via other techniques, e.g., the online PSR model learning approach, can be integrated straightforwardly. By comparing with the state-of-the-art approach of planning under model uncertainty, we demonstrated the effectiveness of the proposed approaches along with the proof of their convergence. [ABSTRACT FROM AUTHOR]

In Probabilistic Abductive Logic Programming we are given a probabilistic logic program, a set of abducible facts, and a set of constraints. Inference in probabilistic abductive logic programs aims to find a subset of the abducible facts that is compatible with the constraints and that maximizes the joint probability of the query and the constraints. In this paper, we extend the PITA reasoner with an algorithm to perform abduction on probabilistic abductive logic programs exploiting Binary Decision Diagrams. Tests on several synthetic datasets show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Purpose: Academic success and failure are relevant lifelines for economic success in the knowledge-based economy. The purpose of this paper is to predict the propensity of students' academic performance using early detection indicators (i.e. age, gender, high school exam scores, region, CGPA) to allow for timely and efficient remediation. Design/methodology/approach: A machine learning approach was used to develop a model based on secondary data obtained from students' information system in a Nigerian university. Findings: Results revealed that age is not a predictor for academic success (high CGPA); female students are 1.2 times more likely to have high CGPA compared to their male counterparts; students with high JAMB scores are more likely to achieve academic success, high CGPA and vice versa; students from affluent and developed regions are more likely to achieve academic success, high CGPA and vice versa; and students in Years 3 and 4 are more likely to achieve academic success, high CGPA. Originality/value: This predictive model serves as a classifier and useful strategy to mitigate failure, promote success and better manage resources in tertiary institutions. [ABSTRACT FROM AUTHOR]

The article discusses bias found in AI algorithmic systems -- known as Algorithmic Bias -- which returned results with errors related to skin color or gender. One paper by researchers Joy Buolamwini and Timnit Gebru documented substantial discrepancies across skin types and the reliability with which three commercial face recognition systems could classify gender. A followup study by the same researchers found that those companies that were originally audited released new classifiers with reduced error rates while other companies had much higher error rates.

TECH Large language models (LLMs) are deep-learning algorithms that can "understand" and produce text, or as you and I might put it, read and write. But Brian Christian, a researcher, programmer and author of several books about AI, describes LLMs as "auto-complete on steroids." In a paper published May 2022 in Daedalus, Blaise Agüera y Arcas, a machine-intelligence expert at Google, wrote that he thinks LLMs may be the foundation for general AI, and eventually may achieve consciousness. [Extracted from the article]

Progress in the field of Non-Intrusive Load Monitoring (NILM) has been attributed to the rise in the application of artificial intelligence. Nevertheless, the ability of energy disaggregation algorithms to disaggregate different appliance signatures from aggregated smart grid data poses some privacy issues. This paper introduces a new notion of disclosure risk termed energy disaggregation risk. The performance of Sequence-to-Sequence (Seq2Seq) NILM deep learning algorithm along with three activation extraction methods are studied using two publicly available datasets. To understand the extent of disclosure, we study three inference attacks on aggregated data. The results show that Variance Sensitive Thresholding (VST) event detection method outperformed the other two methods in revealing households' lifestyles based on the signature of the appliances. To reduce energy disaggregation risk, we investigate the performance of two privacy-preserving mechanisms based on microaggregation and Discrete Fourier Transform (DFT). Empirically, for the first scenario of inference attack on UK-DALE, VST produces disaggregation risks of 99%, 100%, 89% and 99% for fridge, dish washer, microwave, and kettle respectively. For washing machine, Activation Time Extraction (ATE) method produces a disaggregation risk of 87%. We obtain similar results for other inference attack scenarios and the risk reduces using the two privacy-protection mechanisms. [ABSTRACT FROM AUTHOR]

The article focuses on the integration of artificial intelligence (AI) in chemistry, showcasing how AI can rapidly design and conduct experiments, leading to accelerated scientific discoveries and improved experimental reliability. Topics include success stories like AI-designed experiments and the challenges of maintaining scientific integrity amid concerns and the proliferation of fake papers, highlighting the need for careful oversight and the preservation of human intelligence in research.

The shift to a new era of dealing with big data has driven continuous progress and development in computer science, artificial intelligence and machine learning. This change has led to the application of advanced techniques in the realm of decision science, particularly in the area of large-scale group decision-making (LSGDM). However, although these existing techniques have become the core of LSGDM methods, they are still limited in solving problems facing incomplete data. In addition, due to the rise of social media platforms such as Weibo, WeChat and Twitter, which build bridges for communication between decision makers (DMs), this brings new opportunities and challenges for consensus research. To address this set of issues, this study develops a consensus architecture that combines dynamic social network and opinion evolution in the context of an incomplete multi-attribute LSGDM. It is worth mentioning that the proposed consensus framework is a novel decision-making system that can be used to complete the estimation of the missing values and the consensus reaching process (CRP) by simulating the realistic decision-making scenarios. Firstly, considering the size of the trust value and the length of the path, a new trust propagation method is designed to achieve a more reliable estimation of the unknown trust value. Secondly, this paper establishes a missing value estimation method by virtue of the improved DeGroot model, which is able to obtain complete evaluation information by simulating the opinion formation process of DMs. Next, a hierarchical clustering algorithm with stronger robustness is constructed, which not only can adaptively complete the clustering process, but also integrally considers two attributes of trust and opinion similarity. In light of the above research, this study designs an opinion evolution and dynamic trust-driven consensus model, referred to as the DSN-DG-LSGDM model. Finally, the sensitivity analysis and experiments on a real dataset verify the significant superiority of the constructed DSN-DG-LSGDM model compared with the extant LSGDM consensus models. • A DeGroot model based on self-confidence level is designed. • An opinion evolution and dynamic trust-driven consensus framework is developed. • An adaptive clustering algorithm with dependence levels is constructed. • A more reliable trust propagation method is proposed. • A new dynamic subgroup weight calculation method is introduced. [ABSTRACT FROM AUTHOR]

While probabilistic graphical models are a central tool for reasoning under uncertainty in AI, they are in general not as expressive as deep neural models, and inference is notoriously hard and slow. In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions and ensure tractable inference, but still lack the expressive power of intractable models based on deep neural networks. In this paper, we introduce conditional SPNs (CSPNs)—conditional density estimators for multivariate and potentially hybrid domains—and develop a structure-learning approach that derives both the structure and parameters of CSPNs from data. To harness the expressive power of deep neural networks (DNNs), we also show how to realize CSPNs by conditioning the parameters of vanilla SPNs on the input using DNNs as gate functions. In contrast to SPNs whose high-level structure can not be explicitly manipulated, CSPNs can naturally be used as tractable building blocks of deep probabilistic models whose modular structure maintains high-level interpretability. In experiments, we demonstrate that CSPNs are competitive with other probabilistic models and yield superior performance on structured prediction, conditional density estimation, auto-regressive image modeling, and multilabel image classification. In particular, we show that employing CSPNs as encoders and decoders within variational autoencoders can help to relax the commonly used mean field assumption and in turn improve performance. [ABSTRACT FROM AUTHOR]

In the symbolic artificial intelligence community, abstract argumentation with its semantics, i.e. approaches for defining sets of valid conclusions (extensions) that can be derived from argumentation graphs, is considered a promising method for non-monotonic reasoning. However, from a sequential perspective, abstract argumentation-based decision-making processes typically do not guarantee an alignment with common formal notions to assess consistency; in particular, abstract argumentation can, in itself, not enforce the satisfaction of relational principles such as reference independence (based on a key principle of microeconomic theory) and cautious monotony. In this paper, we address this issue by introducing different approaches to ensuring reference independence and cautious monotony in sequential argumentation: a reductionist , an expansionist , and an extension-selecting approach. The first two approaches are generically applicable, but may require comprehensive changes to the corresponding argumentation framework. In contrast, the latter approach guarantees that an extension of the corresponding argumentation framework can be selected to satisfy the relational principle by requiring that the used argumentation semantics is weakly reference independent or weakly cautiously monotonous , respectively, and also satisfies some additional straightforward principles. To highlight the relevance of the approach, we illustrate how the extension-selecting approach to reference independent argumentation can be applied to model (boundedly) rational economic decision-making. [ABSTRACT FROM AUTHOR]

Technology has grown rapidly in recent years, and new solutions that rely on Machine Learning (ML) and Artificial Intelligence (AI) are introduced every day. With such fast-paced advancement, inspecting and fully comprehending how given models make decisions is becoming problematic. The complex decision-making process of these models has become a black box, making it challenging to unravel how they work; therefore, eXplainable Artificial Intelligence (XAI) methods are crucial for further development. This paper discusses how state-of-the-art techniques determine classifications and why they need to be revised to understand the prediction-generating process fully. It compares those existing solutions with the new method called Principal Image Sections Mapping - PRISM, which relies on Principal Component Analysis and allows visualising the most significant features recognised by a given Convolutional Neural Network. PRISM is implemented in a piece of software called TorchPRISM that can generate and present the clustering based on the method's output. The result can indicate ambiguous classes discrimination; thus, the possibility of automating the output analysis process is also discussed. The paper's main objective is to examine how PRISM enhances the current understanding of the decision-making process and introduce a tool that can facilitate analysing the output. PRISM implementation (TorchPRISM) can be found in the public GitHub repository: https://github.com/szandala/TorchPRISM • PRISM method can explain CNN's decisions in an understandable way for various users. • PRISM uses PCA to point features and highlights them using an RGB-coloured image mask. • PRISM's effectiveness is assessed based on qualitative and quantitative measures. • TorchPRISM software allows the identification of ambiguous classes in large datasets. • PRISM overcomes the deficiencies of existing eXplainable AI and saliency map methods. [ABSTRACT FROM AUTHOR]