Search

The novelty of additive manufacturing (AM) involves new requirements, restrictions and rules, that are considerably different to those of conventional manufacturing methods. Therefore, designers often lack experience and knowledge about identifying AM-suited components. However, to ensure profitability, it is essential to choose components, that are well suited for additive manufacturing. State-of-the-art user-support methods for identifying AM potential widely focus on either economic potential or manufacturability but fail to address both aspects. While machine learning solutions are considered highly efficient, the assessment outcome lacks process transparency, inhibiting troubleshooting, plausibility checks and problem-oriented considerations. This paper proposes a holistic, yet detailed and transparent approach to identify conventionally manufactured parts for AM from an existing product portfolio, enabling decision-making based on quantifiable results. It combines and advances state-of-the-art methods, considering manufacturability, economic profitability and socio-ecological aspects. Besides evaluating AM potential, the method additionally assesses the components' potential for re-design-based enhancement for AM suitability. Besides understanding product functions and present production processes, users are expected to have a basic understanding of company goals. The approach involves inquiries regarding company- and product-specific priorities, enabling a weighted assessment. The weights are determined based on individual company philosophies regarding AM value propositions such as differing stakeholder interests and priorities. Additionally, the approach allows users to investigate different development goals by weighting opportunistic and restrictive assessment. The method application is demonstrated via an assembly comprising 11 parts in a scenario focusing on serviceability, eventually determining suitability statements. [ABSTRACT FROM AUTHOR]

In the process of facial expression recognition, face detection is the prerequisite, image preprocessing is the foundation, facial expression feature extraction is the key, and facial expression classification is the target. Effective feature extraction in this process can improve the accuracy of facial expression classifications. On the other hand, traditional facial expression recognition methods are not only complicated in the feature extraction process, but also unable to obtain more in-depth high-semantic features and deep features from the original image. To solve the above problems, this paper proposes a facial expression recognition method based on multi-channel fusion and lightweight neural network. First, a cascade classifier based on Haar features is used to detect the face region of the facial expression image. Second, the local binary pattern (LBP) is used to extract the local texture features from the face region. Third, face edge features are simultaneously obtained by performing edge detection in the face region based on the Canny edge detection algorithm. Fourth, the obtained face image, LBP texture feature image, and edge detection Canny image are fused, and the fused image is input into the constructed lightweight neural network for training and recognition. Experiments are carried out on the public image databases Facial Expression Recognition 2013 (Fer2013) and extended Cohn–Kanade (CK +) using the hold-out cross-validation method. The experimental results show that the proposed method effectively extracts more complete image features by combining traditional feature extraction algorithms with deep learning feature extraction algorithms, improves the accuracy and robustness of facial expression recognition, and has better recognition rate and generalization ability compared to other mainstream methods. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is evolving rapidly and has made many theoretical breakthroughs while widely applied in various fields. ML allows systems the ability to access data and use it to enable computers to execute cognitive processes such as learning and improving from previous experiences and solving complicated issues. Many first-order stochastic optimization methods have been used to solve the optimization model of ML. These algorithms adopt Barzilai-Borwein (BB) step size instead of fixed or diminishing step size to improve performance. However, the BB step size format involves fractional calculation, which inevitably leads to a zero denominator, especially when the objective function is non-convex. The BB technique will be violated if the denominator is near 0 or even negative. To improve the computation of the step size, a Positive Defined Stabilized Barzilai-Borwein (PDSBB) approach is introduced in this paper. Integrating PDSBB with the stochastic variance reduced gradient (SVRG) approach, a new method SVRG-PDSBB is proposed. Numerical experiments have shown that the new algorithm has stabilized the performance of the new step size, which successfully avoiding zero denominators and effectively solving the common problems in machine learning. The convergence of SVRG-PDSBB is theoretically and numerically proven, and the effectiveness of the new algorithm is shown by comparison with other algorithms. [ABSTRACT FROM AUTHOR]

Transfer learning is proposed to solve a general problem in practical applications faced by traditional machine learning methods, that is, the training and test data have different distributions. This paper provides a novel transfer subspace learning method combining low-rank representation (LRR) and feature selection for unsupervised domain adaptation. The core of the proposed method is to map both the source and target data into a latent subspace by a projection such that the discrepancy between domains is reduced. Specifically, by using LRR, a low-rank constraint is imposed on the reconstruction coefficient matrix, and thus the global structure of data can be preserved. Moreover, a structured sparsity-inducing norm based regularization term is introduced into the domain adaptation, which leads to imposing a row-sparsity constraint on the projection matrix. This constraint can enforce rows of the projection matrix corresponding to inessential feature attributes to be all zeros, and thus select relevant features across two domains. As a result, the proposed method has good interpretability and can adaptively perform feature selection. Furthermore, taking into account that the projected samples should be close to each other in the shared subspace if they belong to the same class, regardless of which domain they originally come from, we introduce graph embedding to characterize the local manifold structures of data so as to preserve the relationships between examples in the subspace. Finally, we mathematically formulate the proposed method and derive an iterative algorithm to solve the corresponding problem. The exhaustive experimental evaluations on public datasets confirm the effectiveness of the proposed method in comparison with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Learning to Rank (L2R) methods that utilize machine learning techniques to solve the ranking problems have been widely studied in the field of information retrieval. Existing methods usually concatenate query and document features as training input, without explicit understanding of relevance between queries and documents, especially in pairwise based ranking approach. Thus, it is an interesting question whether we can devise an algorithm that effectively describes the relation between queries and documents to learn a better ranking model without incurring huge parameter costs. In this paper, we present a Gaussian Embedding model for Ranking (GERank), an architecture for co-embedding queries and documents, such that each query or document is represented by a Gaussian distribution with mean and variance. Our GERank optimizes an energy-based loss based on the pairwise ranking framework. Additionally, the KL-divergence is utilized to measure the relevance between queries and documents. Experimental results on two LETOR datasets and one TREC dataset demonstrate that our model obtains a remarkable improvement in the ranking performance compared with the state-of-the-art retrieval models. [ABSTRACT FROM AUTHOR]

Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies. [ABSTRACT FROM AUTHOR]

Background: Collaborative problem solving (CPS) is important for success in the 21st century, especially for teamwork and communication in technology‐enhanced environments. Measurement of CPS skills has emerged as an essential aspect in educational assessment. Modern research in CPS relies on theory‐driven measurements that are usually carried out as manual annotations over recorded logs of collaborative activities. However, manual annotation has limited scalability and is not conductive towards CPS assessments at scale. Objective: We explore possibilities for automated annotation of actions in collaborative‐teams, especially chat messages. We evaluate two approaches that employ machine learning for automated classification of CPS events. Method: Data were collected from engineering, physics and electronics students' participation in a simulation‐based task on electronics concepts, in which participants communicated via text‐chat messages. All task activities were logged and time stamped. Data have been manually classified for the CPS skills, using an ontology that includes both social and cognitive dimensions. In this article, we describe computational linguistic methods for automatically classifying the CPS skills from logged data, with a view towards automating CPS assessments. Results: We applied two machine learning methods to our data. A Naïve Bayes classifier has been previously used in CPS research, but it is only moderately successful on our data. We also present a k‐nearest‐neighbours (kNN) classifier that uses distributional semantic models for measuring text similarity. This classifier shows strong agreement between automated and human annotations. The study also demonstrates that automatic spelling correction and slang normalization of chat texts are useful for accurate automated annotation. Implications: Our results suggest that a kNN classifier can be very effective for accurate annotation of CPS events. It achieves reasonably strong results even when trained on only half of the available data. This shows a promise towards reduction of manual data annotation for CPS measurement. Lay Description: What is already known about this topic: Collaborative problem solving (CPS) is important for success in the 21st century, especially when teamwork and communication occur in technology‐enhanced environments.Measurement of CPS skills requires classification and annotation of team interactions, in particular their communications, such as chat messages. What this paper adds: We explore possibilities for automated classification of CPS activities using machine learning approaches. Implications for practice: One of the approaches we explored is particularly effective and can help reduce the amount of manual annotations needed for CPS measurements. [ABSTRACT FROM AUTHOR]

The International Classification of Diseases (ICD) code is a disease classification method formulated by the World Health Organization(WHO). ICD coding usually requires clinicians to manually allocate ICD codes to clinical documents, which is labor-intensive, expensive, and error-prone. Therefore, many methods have been introduced for automatic ICD coding. However, most of the methods have ignored or cannot combine two essential features well: long-tailed label distribution and label correlation. In this paper, we propose a novel end-to-end Joint Attention Network (JAN) to solve these two problems. JAN includes Document-based attention and Label-based attention to capture semantic information from clinical document text and label description, respectively, which helps solve the classification of dense and sparse data in long-tailed label distribution. Besides, an Adaptive fusion layer and CorNet block are presented to adaptively adjust the weight of these two attentions and exploit label co-occurrence relations, respectively. Experiments on the MIMIC-III and MIMIC-II datasets demonstrate that our proposed JAN outperformed previous state-of-art methods achieving Micro-F1 of 0.553, Micro-AUC of 0.989 and precision at top 8(P@8) of 0.735. Finally, we also provide attention and label correlation visualization to verify the effectiveness of our model and improve the interpretation of our deep learning-based method. [ABSTRACT FROM AUTHOR]

In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely and accurate acquisition of wheat spike number is of great practical significance for yield prediction. In actual production; the method of using an artificial field survey to count wheat spikes is time-consuming and labor-intensive. Therefore, this paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of the wheat spikes. This method introduces an efficient channel attention module (ECA) in the C3 module of the backbone structure of the YOLOv5s network model; at the same time, the global attention mechanism module (GAM) is inserted between the neck structure and the head structure; the attention mechanism can be more Effectively extract feature information and suppress useless information. The result shows that the accuracy of the improved YOLOv5s model reached 71.61% in the task of wheat spike number, which was 4.95% higher than that of the standard YOLOv5s model and had higher counting accuracy. The improved YOLOv5s and YOLOv5m have similar parameters, while RMSE and MEA are reduced by 7.62 and 6.47, respectively, and the performance is better than YOLOv5l. Therefore, the improved YOLOv5s method improves its applicability in complex field environments and provides a technical reference for the automatic identification of wheat spike numbers and yield estimation. [ABSTRACT FROM AUTHOR]

The availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development of algorithms for use in applied and theoretical quantum settings. In this paper, we introduce such a dataset, the QDataSet, a quantum dataset designed specifically to facilitate the training and development of quantum machine learning algorithms. The QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise. The datasets are structured to provide a wealth of information to enable machine learning practitioners to use the QDataSet to solve problems in applied quantum computation, such as quantum control, quantum spectroscopy and tomography. Accompanying the datasets on the associated GitHub repository are a set of workbooks demonstrating the use of the QDataSet in a range of optimisation contexts. Measurement(s) Simulations of one- and two-qubit quantum systems evolving in the presence and absence of noise and distortion Technology Type(s) Simulated measurement using Python packages Sample Characteristic - Organism Simulated quantum systems Sample Characteristic - Environment Quantum systems in noisy and noiseless environments [ABSTRACT FROM AUTHOR]

Routing is one of the most important and time-consuming stages of physical design. As the process node of semiconductors keeps scaling down, the routing process faces increasing challenges, and the traditional solutions are not sufficiently efficient. In recent years, machine learning has aroused much interest in this context, and an increasing number of algorithms have introduced advanced machine learning techniques to help solve the routing problem. In this paper, we survey the recent development of machine learning-based routing algorithms. • This paper provides an overview of recent published machine learning-based routing algorithms. • Some trends on the development of machine learning-based routing algorithms are revealed. • The possible advantages and disadvantages on machine learning-based routing methods are discussed. [ABSTRACT FROM AUTHOR]

Scheduling problems are a subclass of combinatorial problems consisting of a set of tasks/activities/jobs to be processed by a set of resources usually to minimize a time criterion. Some optimization methods used to solve these problems are hybridized with knowledge discovery techniques to extract information during the optimization process and enhance it. However, most of these hybrid techniques are custom‐designed and lack generalization. In this paper, a module for knowledge extraction in Stochastic Local Searches is designed, aiming to be problem independent and plugged into optimization methods that relies on multiple Stochastic Local Search replications. The objective is to prune parts of the search space for which the exploration is likely to lead to poor solutions. This is performed through the extraction of high‐quality patterns occurring in locally optimal solutions. Benchmarked on two well‐known scheduling problems, the Job‐shop Problem and the Resource Constrained Project Scheduling Problem, the results show both a speed up in the convergence and the reaching of better local optima solutions. [ABSTRACT FROM AUTHOR]

As a complex nonlinear system, the first-order incremental relationship between the state variables of the beam and ball system (BABS) is asymmetric in the definition domain of the variables, and the characteristics of the system do not satisfy the superposition theorem. Studying the balance control of the BABS can help to better grasp the relevant characteristics of the nonlinear system. In this paper, the deep reinforcement learning method is used to study the BABS based on a visual sensor. First, the detail-reward function is designed by observing the control details of the system, and the rationality of the function is proved based on Q-function; secondly, considering and comparing the applicability of image processing methods in ball coordinate location, an intelligent location algorithm is proposed, and the location effects between the algorithms are compared and analyzed; then, combining the nonlinear theory and LQR theory, a reinforcement learning policy model is proposed to linearize near the equilibrium point, which significantly improves the control effect. Finally, experiments are designed to verify the effectiveness of the above methods in the control system. The experimental results show that the design scheme can be effectively applied to the control system of the BABS. It is verified that the introduction of detail-reward mechanism into a deep reinforcement learning algorithm can significantly reduce the complexity of the nonlinear control system and iterative algorithm, and effectively solve nonlinear control problems. [ABSTRACT FROM AUTHOR]

This paper considers the offline assignment of fixed priorities in partitioned preemptive real-time systems where tasks have precedence constraints. This problem is crucial in this type of systems, as having a good fixed priority assignment allows for an efficient use of the processing resources while meeting all the deadlines. In the literature, we can find several proposals to solve this problem, which offer varying trade-offs between the quality of their results and their computational complexities. In this paper , we propose a new approach, leveraging existing algorithms that are widely exploited in the field of Machine Learning: Gradient Descent, the Adam Optimizer, and Gradient Noise. We show how to adapt these algorithms to the problem of fixed priority assignment in conjunction with existing worst-case response time analyses. We demonstrate the performance of our proposal on synthetic task-sets with different sizes. This evaluation shows that our proposal is able to find more schedulable solutions than previous heuristics, approximating optimal but intractable algorithms such as MILP or brute-force, while requiring reasonable execution times. [ABSTRACT FROM AUTHOR]

Pose measurement is a necessary technology for UAV navigation. Accurate pose measurement is the most important guarantee for a UAV stable flight. UAV pose measurement methods mostly use image matching with aircraft models or 2D points corresponding with 3D points. These methods will lead to pose measurement errors due to inaccurate contour and key feature point extraction. In order to solve these problems, a pose measurement method based on the structural characteristics of aircraft rigid skeleton is proposed in this paper. The depth information is introduced to guide and label the 2D feature points to eliminate the feature mismatch and segment the region. The space points obtained from the marked feature points fit the space linear equation of the rigid skeleton, and the UAV attitude is calculated by combining with the geometric model. This method does not need cooperative identification of the aircraft model, and can stably measure the position and attitude of short-range UAV in various environments. The effectiveness and reliability of the proposed method are verified by experiments on a visual simulation platform. The method proposed can prevent aircraft collision and ensure the safety of UAV navigation in autonomous refueling or formation flight. [ABSTRACT FROM AUTHOR]

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper, we study this problem for finite-state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input–output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. We also carry out a performance comparison against two existing tools (LearnLib and flexfringe). Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample. [ABSTRACT FROM AUTHOR]

Algorithmic age is the actual time context where automated technologies and decision-making systems are starting to influence world scale transport economies and in particular the sector of maritime transport. Algorithms emerging from Artificial Intelligence (AI) and machine learning applications are suited to solve demanding problems or improving our ability to accomplish timeconsuming tasks of maritime companies. This paper presents a comparative analysis of technology advancements met in three case studies of artificial intelligence assisted platforms and tools useful for enhancing maritime business in the algorithmic age. The case studies can be further used in qualitative studies on the societal impact of artificial intelligence technologies and for the understanding of the framework of digital evolution in the maritime sector. [ABSTRACT FROM AUTHOR]

The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems. [ABSTRACT FROM AUTHOR]

The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a "black box problem." That is, machine learning algorithms are "opaque" to human users, failing to be "interpretable" or "explicable" in terms that would render categorization procedures "understandable." The purpose of this paper is to challenge the widespread agreement about the existence and importance of a black box problem. The first section argues that "interpretability" and cognates lack precise meanings when applied to algorithms. This makes the concepts difficult to use when trying to solve the problems that have motivated the call for interpretability (etc.). Furthermore, since there is no adequate account of the concepts themselves, it is not possible to assess whether particular technical features supply formal definitions of those concepts. The second section argues that there are ways of being a responsible user of these algorithms that do not require interpretability (etc.). In many cases in which a black box problem is cited, interpretability is a means to a further end such as justification or non-discrimination. Since addressing these problems need not involve something that looks like an "interpretation" (etc.) of an algorithm, the focus on interpretability artificially constrains the solution space by characterizing one possible solution as the problem itself. Where possible, discussion should be reformulated in terms of the ends of interpretability. [ABSTRACT FROM AUTHOR]

Summary: The inconsistency in the mass production of lithium‐ion battery (LIB) packs stem from the inconsistency in the capacity, voltage and internal resistance of single batteries that compose packs. The inconsistency issue of these battery packs can greatly reduce the output performance of a large power pack. This paper proposed the machine learning approach based on self‐organization mapping (SOM) neural networks for establishing the consistency of LIBs. This method comprehensively compares and analyzes the real‐LIB parameters (internal resistance, capacity and voltage) data obtained during charging and discharging to form the clusters of similar performing LIBs. Experimental result validated the clustering analysis and it indicates that the performance of clustered battery pack typically precedes than that of original. The capacity of clustered battery pack increased 1.9% compared with brand‐new pack. The temperature distribution of the battery pack assembled after screening is consistent. The peak temperature is 4°‐5° lower than the ordinary battery, and the temperature fluctuation is reduced by 2.6°. In addition, the application of cluster analysis is expanded and some key research directions are pointed out. [ABSTRACT FROM AUTHOR]

Regression via Classification (RvC) is a process to solve a regression problem by using a classifier. An ensemble consists of many models, in which the final result is the combination of the results of these individual models. In this paper, two RvC ensemble methods are proposed. In the first ensemble method, the output of the ensemble method is modified to achieve the final output. A formula is derived in this paper for this purpose. In the second method, a new approach is proposed to compute the output of each model of an ensemble. It is shown that an accurate binary classifier can be transformed into an accurate regression method with the proposed methods. It is also shown experimentally, by using popular Random Forests as a classifier in the proposed ensemble methods against Random Forests as a regression method, the effectiveness of the proposed RvC ensemble methods. [ABSTRACT FROM AUTHOR]

The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms. [ABSTRACT FROM AUTHOR]

They develop a Bayesian machine learning-based demand forecasting methodology for these models in both class-based and class-free settings that explicitly considers competitive market information. In January 2018, an AI initiative was established at Sabre to identify industry-relevant problems suitable for AI-based solutions, raise internal awareness and accelerate adoption. Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. [Extracted from the article]

Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approaches to solving this problem. Due to the scarcity of recently updated labeled data, the performance of detection systems degrades when exposed to a new dataset. Therefore, semi-supervised learning (SSL) techniques can improve performance, using both labeled and unlabeled examples. In this paper, we propose a framework based on the multi-view graph attention mechanism using a transfer learning (TL) approach, to predict social bots. We called the framework 'Bot-MGAT', which stands for bot multi-view graph attention network. The framework used both labeled and unlabeled data. We used profile features to reduce the overheads of the feature engineering. We executed our experiments on a recent benchmark dataset that included representative samples of social bots with graph structural information and profile features only. We applied cross-validation to avoid uncertainty in the model's performance. Bot-MGAT was evaluated using graph SSL techniques: single graph attention networks (GAT), graph convolutional networks (GCN), and relational graph convolutional networks (RGCN). We compared Bot-MGAT to related work in the field of bot detection. The results of Bot-MGAT with TL outperformed, with an accuracy score of 97.8%, an F1 score of 0.9842, and an MCC score of 0.9481. [ABSTRACT FROM AUTHOR]

This paper overviews and discusses the relationship between Reinforcement Learning (RL) and the recently developed Dual Control for Exploitation and Exploration (DCEE). It is argued that there are two related but quite distinctive approaches, namely, control and machine learning, in tackling intractability arising in optimal decision making/control problems. In the control approach, the original problems (of an infinite horizon) are approximated by finite horizon problems and solved online by taking advantage of the availability of computing power. In the machine learning approach, the optimal solutions are approximated through iterations, or (offline) training through trials when models are not available. When dealing with unknown environments, DCEE as a technique developed from the control approach could potentially solve similar problems as RL while offering a number of advantages, most notably, coping with uncertainty in environment/tasks, high efficiency in learning through balancing exploitation and exploration, and potential in establishing its formal properties like stability. The links between DCEE and other relevant methods like dual control, Model Predictive Control and particularly Active Inference in neuroscience are discussed. The latter provides a strong biological endorsement for DCEE. The methods and discussions are illustrated by autonomous source search using a robot. It is concluded that DCEE provides a promising, complementary approach to RL, and more research is required to develop it as a generic theory and fully realise its potential. The relationships revealed in this paper provide insights into these relevant methods and facilitate cross fertilisation between control, machine learning and neuroscience for developing autonomous control under uncertain environments. [ABSTRACT FROM AUTHOR]

The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. This involves the development of models capable of processing and analyzing the multimodal information uniformly. Unstructured real-world data can inherently take many forms, also known as modalities, often including visual and textual content. Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning. In this paper, we seek to improve the understanding of key concepts and algorithms of deep multimodal learning for the computer vision community by exploring how to generate deep models that consider the integration and combination of heterogeneous visual cues across sensory modalities. In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains. Finally, we highlight the limitations and challenges of deep multimodal learning and provide insights and directions for future research. [ABSTRACT FROM AUTHOR]

Emission inspection of motor vehicles (emission inspection) is a crucial player in solving the problem of motor vehicle exhaust pollution, and research on the features affecting emission inspection results and their importance is a basis for optimizing the environmental management of motor vehicles. However, there is no study on the multi-feature impact analysis of the emission inspection results. This hinders the emission inspection from playing a better guiding role in the policy formulation of motor vehicle management. In this paper, the ensemble learning algorithm and interpretable machine learning theory are used. Nineteen feature indicators and over 400,000 vehicle mass analysis system (VMAS) detection data in Chengdu were selected from the emission inspection database to construct prediction models for emission inspection results. Moreover, the factors affecting emission inspection results and their ranks by importance were also obtained. The results revealed that the environment has a strong influence on the outcomes from emission inspections (accounting for about one-third of the total effect). Besides, the following eight feature indicators displayed great effects on emission inspection results in sequence: emission inspection agency (18.38%), world manufacturer code (15.01%), vehicle usage days (9.60%), transmission type (9.41%), accumulated mileage (9.21%), emission standard (5.82%), temperature (5.54%), and driving mode (5.50%). In this study, prediction models for emission inspection results are established, and the results are interpreted based on the interpretable machine learning theory. It is considered that more attention should be paid to the effect of inspection differences among emission inspection agencies on fairness, as well as the effects of differences in world manufacturer and transmission type on vehicle deterioration in future research. The supervision of emission inspection agencies, training of inspectors, elimination of obsolete vehicles, and government-guided purchase should be strengthened. This study provides empirical support for optimizing the formulation of motor vehicle environmental management policies. Implications: Emission inspection of motor vehicles (emission inspection) is a crucial player in solving the problem of motor vehicle exhaust pollution. In this work, prediction models for emission of motor vehicles inspection results are established. The results revealed that following eight feature indicators displayed great effects on emission inspection results in sequence: emission inspection agency (18.38%), world manufacturer code (15.01%), vehicle usage days (9.60%), transmission type (9.41%), accumulated mileage (9.21%), emission standard (5.82%), temperature (5.54%), and driving mode (5.50%). It is considered that more attention should be paid to the effect of inspection differences among emission inspection agencies on fairness, as well as the effects of differences in world manufacturer and transmission type on vehicle deterioration in future research. The supervision of emission inspection agencies, training of inspectors, elimination of obsolete vehicles, and government-guided purchase should be strengthened. This study provides empirical support for optimizing the formulation of motor vehicle environmental management policies. [ABSTRACT FROM AUTHOR]

The globalization of the integrated circuit (IC) industry has raised concerns about hardware Trojans (HT), and there is an urgent need for efficient HT-detection methods of gate-level netlists. Machine learning (ML) is a powerful tool for this purpose. A Trojan-detection framework is proposed in this paper to solve the data imbalance and low accuracy problems of existing ML-based HT-detection algorithms. To solve the problem of data imbalance, we propose the node-filtering algorithm, which extracts structure templates from HT circuits and removes most normal nodes based on them. To enhance the identification of unknown HT payload, we propose the load-expansion algorithm, which expands the identified HT nodes based on their fanout features. We evaluate the framework using different ML algorithms. The results show that the framework significantly improves the Trojan-detection rate of the original algorithms, and achieves a 10% improvement in true positive rate compared to the original algorithms. [ABSTRACT FROM AUTHOR]

Image clustering is a key and challenging task in the field of machine learning and computer vision. Technically, image clustering is the process of grouping images without the use of any supervisory information in order to retain similar images within the same cluster. This paper proposes a novel image clustering method based on coupled convolutional and graph convolutional network. It solves the problem that the deep clustering method usually only focuses on the useful features extracted from the sample itself, and seldom considers the structural information behind the sample. Experimental results show that the proposed algorithm can effectively extract more discriminative deep features, and the model achieves good clustering effect due to the combination of attribute information and structure information of samples in GCN. [ABSTRACT FROM AUTHOR]

The increasing amount of data from web systems data is becoming one of the most valuable resources for information retrieval and knowledge discovery. The huge content of information makes it an important area for data mining research. To analyze the dependencies of the outcoming data, expressed as query scenarios, we present a new approach for evaluating the behavior of interactive web systems by applying different data mining techniques to solve the problem. We propose tools that take outcoming logs as input, analyze them, and provide information about web client actions. Qualitative and quantitative automatic evaluation of the data can explain the connections between the most significant parameters of the system in particular scenarios. In this paper, we propose a new method, which can be used to efficiently verify the type of client behavior of a web system or design of the system. The analysis of results demonstrates the possibility of efficient pattern search. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) in the wild is very challenging due to occlusion, posture, illumination, and other uncontrolled factors. Learning discriminant features for FER using Convolutional Neural Networks is a momentous task for the significant class imbalance, wrong labels, inter-class similarities, and intra-class variations. The traditional method utilizes the Cross entropy loss function to optimize the convolutional network to obtain discriminative features for classification. However, this loss function cannot effectively solve the above problems in practice and cannot contribute to obtaining highly discriminant facial features for further analysis. Center loss improves the learning efficiency by reducing the intra-class distance of similar expressions, while the improvement of inter-class similarity, class imbalance, and generalization is insufficient. In this paper, we propose a lightweight Effective Attention Feature Reconstruction loss (EAFR loss), which can further optimize the feature space and enhance the discriminability of expression. The loss model is composed of the Focal Smoothing loss (FS loss) and the Aggregation-Separation loss (AS loss). Firstly, the FS loss can improve the poor recognition performance caused by imbalanced classes and prevent paranoid knowledge learning behaviors. Meanwhile, AS loss further accurately condenses the intra-class expression features and expands the inter-class distance, which is achieved by using progressive stage max-pooling channel and position attention mechanism and lightweight asymmetric autoencoder model for feature reconstruction. Finally, the EAFR loss joins the above two loss functions to more comprehensively solve the above typical problems for FER in the wild. We validate the proposed loss function on three most commonly used large-scale wild expression datasets (RAF-DB, FERPlus, and AffectNet), and the results show that our model achieves superior performance to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multimodal tasks based on attention mechanism and language face numerous problems. Based on multimodal hierarchical attention mechanism and genetic neural network, this paper studies the application of image segmentation algorithm in data completion and 3D scene reconstruction. The algorithm refers to the process of concentrating attention that humans subjectively pay attention to and calculates the difference between each pixel in the genetic neural network test image in the color space and the average value of the target image, which solves the problem of static feature maps and dynamic feature maps of image sequences. In addition, in view of the problem that the number of attention enhancement feature extraction modules is too large and the parameters are too large, the recursive mechanism is used as the feature extraction branch, and new model parameters are not added when the network depth is increased. The simulation results show that the accuracy of the improved image saliency detection algorithm based on the attention mechanism reaches 89.7%, and the difference between the average value of the single-point pixel and the target image is reduced to 0.132, which further promotes the practicability and reliability of the image segmentation model. [ABSTRACT FROM AUTHOR]

In computational electromagnetism there are manyfold advantages when using machine learning methods, because no mathematical formulation is required to solve the direct problem for given input geometry. Moreover, thanks to the inherent bidirectionality of a convolutional neural network, it can be trained to identify the geometry giving rise to the prescribed output field. All this puts the ground for the neural meta-modeling of fields, in spite of different levels of cost and accuracy. In the paper it is shown how CNNs can be trained to solve problems of optimal shape synthesis, with training data sets based on finite-element analyses of electric and magnetic fields. In particular, a concept of multi-fidelity model makes it possible to control both prediction accuracy and computational cost. The shape design of a MEMS design and the TEAM workshop problem 35 are considered as the case studies. [ABSTRACT FROM AUTHOR]

Many machine learning algorithms depend on weights that quantify row and column similarities of a data matrix. The choice of weights can dramatically impact the effectiveness of the algorithm. Nonetheless, the problem of choosing weights has arguably not been given enough study. When a data matrix is completely observed, Gaussian kernel affinities can be used to quantify the local similarity between pairs of rows and pairs of columns. Computing weights in the presence of missing data, however, becomes challenging. In this paper, we propose a new method to construct row and column affinities even when data are missing by building off a co‐clustering technique. This method takes advantage of solving the optimization problem for multiple pairs of cost parameters and filling in the missing values with increasingly smooth estimates. It exploits the coupled similarity structure among both the rows and columns of a data matrix. We show these affinities can be used to perform tasks such as data imputation, clustering, and matrix completion on graphs. [ABSTRACT FROM AUTHOR]

The goal of the recommender system is to learn the user's preferences from the entity (user–item) historical interaction data, so as to predict the user's ratings on new items or recommend new item sequences to users. There are two major challenges: (1) Datasets are usually sparse. The item side is often accompanied by some auxiliary information, such as attributes or context; it can help to slightly improve its representation. However, the user side is usually presented in the form of ID due to personal privacy. (2) Due to the influences of confounding factors, such as the popularity of items, users' ratings on items often have bias that cannot be recognized by the traditional recommendation methods. In order to solve these two problems, in this paper, (1) we explore the use of a graph model to fuse the interactions between users and common rating items, that is, incorporating the "neighbor" information into the target user to enrich user representations; (2) the d o () operator is used to deduce the causality after removing the influences of confounding factors, rather than the correlation of the data surface fitted by traditional machine learning. We propose the EGCI model, i.e., enhanced graph learning for recommendation via causal inference. The model embeds user relationships and item attributes into the latent semantic space to obtain high-quality user and item representations. In addition, the mixed bias implied in the rating process is calibrated by considering the popularity of items. Experimental results on three real-world datasets show that EGCI is significantly better than the baselines. [ABSTRACT FROM AUTHOR]

Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness. [ABSTRACT FROM AUTHOR]

Iris image-based biometric systems are commonly used in applications that demand security, authentication, recognition and faster login access. In solving these real-time problems, the impact of soft computing techniques which employ cognitive skills is very high. Although this system has been commercialized, the scope for improvement is still plenty. This paper introduces the reader to different segments of an iris recognition system and reviews the techniques involved with each segment. It reports on how research articles validate the robustness of an iris-based recognition system. As these systems are fallible, it also shows the vulnerabilities associated with each segment and provides insights to develop much better intelligent and robust techniques which will make the system more accurate. This paper also shows that in spite of versatility of soft computing techniques, it is not fully exploited for iris recognition systems. The present challenges and directions for future research are also discussed. [ABSTRACT FROM AUTHOR]

Imbalanced data classification has received much attention in machine learning, and many oversampling methods exist to solve this problem. However, these methods may suffer from insufficient noise filtering, overlap between synthetic and original samples, etc., resulting in degradation of classification performance. To this end, we propose a hybrid sampling with two-step noise filtering (HSNF) method in this paper, which consists of three modules. In the first module, HSNF denoises twice according to different noise discrimination mechanisms. Note that denoising mechanism is essentially based on the Euclidean distance between samples. Then in the second module, the minority class samples are divided into two categories, boundary samples and safe samples, respectively, and a portion of the boundary majority class samples are removed. In the third module, different oversampling methods are used to synthesize instances for boundary minority class samples and safe minority class samples. Experimental results on synthetic data and benchmark datasets demonstrate the effectiveness of HSNF in comparison with several popular methods. The code of HSNF will be released. [ABSTRACT FROM AUTHOR]

The interaction between machine learning and quantum physics has given rise to an emerging frontier of quantum machine learning research. In this line, quantum classifiers have received significant attention recently as a quantum device designed to solve the classification problem in machine learning. In this paper, we propose a new variational quantum multi-class classifier that uses l o g 2 N qubits to represent N labels, converts the labels into different quantum states, and optimizes the circuit parameters by the fidelity between the true label state and the output state. Our method effectively reduces the width of the circuit and lowers the number of auxiliary particles needed from N to l o g 2 N . We conducted simulation experiments on several datasets. On the MNIST handwritten digits dataset, we achieved 99.8% accuracy for 4 classifications and 97% for 8 classifications. On the CIFAR-10 dataset, we obtained 85.3% accuracy for 8 classifications. Finally, on the CIFAR-100 dataset, we reached 76% accuracy for 16 classifications. [ABSTRACT FROM AUTHOR]

Matrix decomposition is a widely used tool in machine learning with many applications such as dimension reduction or visualization. In this paper we consider decomposing X, a matrix of size n × m , to a product WS where we require that S, a matrix of size n × k , needs to have consecutive ones property. More specifically, we require that each row of S needs to be in the form of 0 , ... , 0 , 1 , ... , 1 , 0 , ... , 0 . Such decompositions are particularly meaningful if X is a matrix where each row represents a time series; in such a case the ones in each row in S represent a time segment. We show that the optimization problem is inapproximable. To solve the problem we propose 5 different algorithms. The first two algorithms are based on solving iteratively S while keeping W fixed and then solving W while keeping S fixed. The next two algorithms are based on greedily optimizing a single row in S and the corresponding column in W. The last algorithm first finds the optimal decomposition of with 2 k - 1 non-overlapping rows, and then greedily combines the rows until k rows remain. We compare the algorithms experimentally, focusing on the quality of the decomposition as well as the computational time. We show experimentally that our algorithms yield interpretable results in practical time. [ABSTRACT FROM AUTHOR]

On-board system fault knowledge base (KB) is a collection of fault causes, maintenance methods, and interrelationships among on-board modules and components of high-speed railways, which plays a crucial role in knowledge-driven dynamic operation and maintenance (O&M) decisions for on-board systems. To solve the problem of multi-source heterogeneity of on-board system O&M data, an entity matching (EM) approach using the BERT model and semi-supervised incremental learning is proposed. The heterogeneous knowledge fusion task is formulated as a pairwise binary classification task of entities in the knowledge units. Firstly, the deep semantic features of fault knowledge units are obtained by BERT. We also investigate the effectiveness of knowledge unit features extracted from different hidden layers of the model on heterogeneous knowledge fusion during model fine-tuning. To further improve the utilization of unlabeled test samples, a semi-supervised incremental learning strategy based on pseudo labels is devised. By selecting entity pairs with high confidence to generate pseudo labels, the label sample set is expanded to realize incremental learning and enhance the knowledge fusion ability of the model. Furthermore, the model's robustness is strengthened by embedding-based adversarial training in the fine-tuning stage. Based on the on-board system's O&M data, this paper constructs the fault KB and compares the model with other solutions developed for related matching tasks, which verifies the effectiveness of this model in the heterogeneous knowledge fusion task of the on-board system. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is one of the most efficient machine learning tools, and it is fast, simple to use, reliable, and provides accurate classification results. Despite its generalization capability, SVM is usually posed as a quadratic programming (QP) problem to find a separation hyperplane in nonlinear cases. This needs huge quantities of computational time and memory for large datasets, even for moderately sized ones. SVM could be used for classification tasks whose number of samples is limited but does not scale well to large datasets. The idea is to solve this problem by a smoothing technique to get a new smaller dataset representing the original one. This paper proposes a fast and less time and memory-consuming algorithm to solve the problems represented by a nonlinear support vector machine tool, based on generating a Gaussian pyramid to minimize the size of the dataset. The reduce operation between dataset points and the Gaussian pyramid is reformulated to get a smoothed copy of the original dataset. The new dataset points after passing the Gaussian pyramid will be closed to each other, and this will minimize the degree of nonlinearity in the dataset, and it will be 1/4 of the size of the original large dataset. The experiments demonstrate that our proposed techniques can reduce the classical SVM tool complexity, more accurately, and are applicable in real time. [ABSTRACT FROM AUTHOR]

Traditional supervised learning algorithms do not satisfactorily solve the classification problem on imbalanced data sets, since they tend to assign the majority class, to the detriment of the minority class classification. In this paper, we introduce the Bayesian network-based over-sampling method (BOSME), which is a new over-sampling methodology based on Bayesian networks. Over-sampling methods handle imbalanced data by generating synthetic minority instances, with the benefit that classifiers learned from a more balanced data set have a better ability to predict the minority class. What makes BOSME different is that it relies on a new approach, generating artificial instances of the minority class following the probability distribution of a Bayesian network that is learned from the original minority classes by likelihood maximization. We compare BOSME with the benchmark synthetic minority over-sampling technique (SMOTE) through a series of experiments in the context of indirect cost-sensitive learning, with some state-of-the-art classifiers and various data sets, showing statistical evidence in favor of BOSME, with respect to the expected (misclassification) cost. [ABSTRACT FROM AUTHOR]

Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with imbalanced classes is one of the most important issues in pattern recognition and machine learning. In this paper, a method is proposed in the form of a cost-sensitive optimization problem which implements the process of selecting and extracting the features simultaneously. The feature extraction phase is based on reducing error and maintaining geometric relationships between data by solving a manifold learning optimization problem. In the feature selection phase, the cost-sensitive optimization problem is adopted based on minimizing the upper limit of the generalization error. Finally, the optimization problem which is constituted from the above two problems is solved by adding a cost-sensitive term to create a balance between classes without manipulating the data. To evaluate the results of the feature reduction, the multi-class linear SVM classifier is used on the reduced data. The proposed method is compared with some other approaches on 21 datasets from the UCI learning repository, microarrays and high-dimensional datasets, as well as imbalanced datasets from the KEEL repository. The results indicate the significant efficiency of the proposed method compared to some similar approaches. [ABSTRACT FROM AUTHOR]

End-to-end aspect-based sentiment analysis aims to complete aspect terms extraction and aspect sentiment classification simultaneously. Most existing methods ignore the sematic connection between the two subtasks. In this paper, we solve the problem by inducing constituents from input sentences, and propose a novel model based on sentence constituent-aware attention mechanism for end-to-end aspect-based sentiment analysis. Our framework mainly involves three layers. The first layer gets word representations by the pre-trained language model. Followed by the proposed sentence constituent-aware attention layer to induce constituents from the input sentence. With the operation of inducing constituents, the words in the same constituent are constrained to attend to each other, making the aspect term pay more attention to its corresponding opinion. Finally, a simple linear classification layer is adopted to predict the unified tags. Experimental results demonstrate that the proposed model outperforms other baselines on four benchmark datasets. [ABSTRACT FROM AUTHOR]

Real‐time facial expression recognition is the basis for computers to understand human emotions and detect abnormalities in time. To effectively solve the problems of server overload and privacy information leakage, a real‐time facial expression recognition method based on iterative transfer learning and efficient attention network (EAN) for edge resource‐constrained scenes is proposed in this paper. Firstly, an EAN is designed with its parameter number and computation amount strictly limited by depth separable convolution and local channel attention mechanism. Then, the soft labels of facial expression data were obtained by EAN based on the idea of knowledge distillation, so as to provide more supervision information for the training process. Finally, an iterative transfer learning method of teacher‐student (T‐S) network was proposed; it refines the soft labels of the teacher network and further improves the recognition accuracy of the student network. The tests on the public datasets, FER2013 and RAF‐DB, show that this method can significantly reduce the model complexity and achieve high recognition accuracy. Compared with other advanced methods, the proposed method strikes a good balance between complexity and accuracy, and well meets the real‐time deployment requirements of facial expression recognition technology for edge resource‐constrained scenes. [ABSTRACT FROM AUTHOR]

$k$ k -means method using Lloyd heuristic is a traditional clustering method which has played a key role in multiple downstream tasks of machine learning because of its simplicity. However, Lloyd heuristic always finds a bad local minimum, i.e., the bad local minimum makes objective function value not small enough, which limits the performance of $k$ k -means. In this paper, we use coordinate descent (CD) method to solve the problem. First, we show that the $k$ k -means minimization problem can be reformulated as a trace maximization problem, then a simple and efficient coordinate descent scheme is proposed to solve the maximization problem. Two interesting findings through theory are that Lloyd cannot decrease the objective function value of $k$ k -means produced by our CD further, and our proposed method CD to solve $k$ k -means problem can avoid produce empty clusters. In addition, according to the computational complexity analysis, it is verified CD has the same time complexity with original $k$ k -means method. Extensive experiments including statistical hypothesis testing, on several real-world datasets with varying number of clusters, varying number of samples and varying number of dimensions show that CD performs better compared to Lloyd, i.e., lower objective value, better local minimum and fewer iterations. And CD is more robust to initialization than Lloyd whether the initialization strategy is random or initialization of $k$ k -means++. [ABSTRACT FROM AUTHOR]