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In this paper, we introduce a new approach to fix the validation of a block and the assignment of a new block in a blockchain infrastructure by using a novel negotiation procedure. The block validation and assignment are reached thanks to negotiation procedures based on an extended probability environment. Also, by using a multiscale approach (typical of Complexity Theory) and Quantum and Relativistic Mechanics, the result appears to solve some of the most relevant questions in the Blockchain context, which are the democracy and the randomness of the validator of a block and the assignment of the new one. The selection of actors to mine is invariant concerning the number of addresses, i.e., the coins of owners, which have more chance to be selected generally. This work is the companion of CQKD (Computational Quantum Key Distribution), as we will see in the introduction, where we considered the infrastructural question of the key distribution; also, it is a very effective application of the decision and reasoning in incompleteness or uncertainty conditions as described in the previous and prodromic paper as described in the introduction too.

This paper proposes a decision support framework for defining planning parameters for national crop production. The proposed framework addresses the gaps in policymaking, the role of all stakeholders, and uses historical data of crop production in different sectors of land in optimizing the profits that shall meet the defined constraints including national requirement, and export demand of different crops. There are many agro-economy-based countries where agriculture is the main contributor to their GDP, while there are others who always struggle to meet their national need while optimizing the agro-economy component of their earnings. Crops are not always produced with keeping the demand and production balance. Rather, the crops are mostly produced according to the farmer’s ease and last year’s prices for profit maximization leading to excessive production of one type of crop, lowering of profit margins, and shortage of some key crops for national need. This paper presents a decision aiding tool that can be used for farmer’s awareness for crop production planning that meets the national and export needs while maximizing farmer’s earnings. The model is tested on historical data of different segments of cultivated lands in Pakistan for validation, with the first experiment performed on single farmland with multi-fields while the second performed for multi-locations and multi-fields. The results are promising and provide estimates of net profit, expected production against the demand for each crop, and analysis that aids in crop planning, before and after the application of our model while meeting all necessary constraints.

This paper proposes a novel technique for the automatic detection of dementia based on the attentional matrices test (AMT) for selective attention assessment. The original test provides three matrices, of increasing difficulty, and the test taker is asked to mark target digits assigned. In our proposal, AMT was developed on a digitizing tablet, equipped with an electronic pen. Tablet technology enables the acquisition of additional measures to those that can be obtained by observing the execution of the traditional paper-based test. These measures reflect the dynamics of the handwriting process, particularly the pauses and hesitations while the pen is not in contact with the pad surface. Handwriting measures can then serve as an input to machine learning algorithms to automatize disease detection. In contrast to the traditional approach, dynamic handwriting analysis can provide a means to better evaluate the visual search of the patient, as well as motor planning. To evaluate the effectiveness of the proposal, a classification study was carried out involving 29 healthy control subjects and 36 demented patients. We employed different machine learning algorithms and an ensemble scheme. We observed the first matrix to be the most discriminating, while the ensemble of the best classification models over the three matrices provided the best classification performance [i.e., an area under the ROC curve (AUC) of 87.30% and a sensitivity of 86.11%]. Our proposal has the potential to provide a cost-effective and easy-to-use diagnostic tool, which may also support mass screening of the population.

The demand for more developed and agile urban taxi drones is increasing rapidly nowadays to sustain crowded cities and their traffic issues. The critical factor for spreading such technology could be related to the safety criteria that must be considered. One of the most critical safety aspects for such vertical and/or Short Take-Off and Landing (V/STOL) drones is related to safety during the landing stage, in which most of the recent flight accidents have occurred. This paper focused on solving this issue by proposing decentralized processing cores that could improve the landing failure rate by depending on a Fuzzy Logic System (FLS) and additional Digital Signal Processing (DSP) elements. Also, the proposed system will enhance the safety factor during the landing stages by adding a self-awareness feature in case a certain sensor malfunction occurs using the proposed Adaptive Prognostic Malfunction Unit (APMU). This proposed coarse-grained Autonomous Landing Guidance Assistance System (ALGAS4) processing architecture has been optimized using different optimization techniques. The ALGAS4 architecture has been designed completely using VHDL, and the targeted FPGA was the INTEL Cyclone V 5CGXFC9D6F27C7 chip. According to the synthesis findings of the INTEL Quartus Prime software, the maximum working frequency of the ALGAS4 system is 278.24 MHz. In addition, the proposed ALGAS4 system could maintain a maximum computing performance of approximately 74.85 GOPS while using just 166.56 mW for dynamic and I/O power dissipation.

Return on Equity (ROE) is an important factor from the perspective of formulating and implementing a company's financial strategies. It is also one of its evaluation criteria. It presents to investors the effectiveness of using their capital. Increasing profitability may be treated as a symptom of shareholder wealth, while its reduction may be a signal indicating a deterioration of the financial situation of the company. An investment in a company will be attractive to investors if the results obtained by it will enable the benefit from the dividend to be paid and if the share prices will show an upward trend. Therefore, profit and profitability are categories dependent on the company and affect the wealth of its owners. The ROE ratio is synthetic and is linked to, among others with the size of sales, asset use activity, and the size of the company's debt. However, the decisions regarding the capital structure of a company should be made not only by purely economic and financial analyses but also should take into account the social and environmental effects of economic activities. To take into account not only short-term financial goals but also long-term sustainable development goals during the decision-making process, we need intelligent and creative multi-criteria decision support tools. Bio-inspired artificial intelligence techniques-such as evolutionary algorithms, deep neural networks, or swarm algorithms, to give only a few examples-are gaining more and more popularity in the recent years. Evolutionary algorithms are optimization techniques that are modeled on the processes of evolution that are taking place in natural populations. They can find approximate solutions to the NP-hard global, multi-modal, and multi-objective optimization problems. In this paper, we propose an innovative approach-an agent-based bio-inspired system supporting decisions in the area of corporate finance, which takes into account not only financial goals but also the sustainable development goals. The system will allow for multi-objective optimization with the use of bio-inspired algorithms. In this paper, we will concentrate on one module of the proposed system-the evolutionary algorithm optimizing the ROE factor. During the experiments, we will verify the ability of the proposed algorithm to provide decision makers with reasonable, useful, and, at the same time, also innovative and non-obvious solutions concerning the desired capital structure of a given company, which usually operates in a rapidly changing environment. The proposed system will allow for taking into account more than one criteria and perform multi-objective optimization with the use of an evolutionary algorithm or an agent-based co-evolutionary algorithm, soit will be possible to include also the long-term goals of sustainable development in the future.

The aim of this study is the computerization of the argument Delphi method. The Delphi method is mainly designed for qualitative prediction within a group of experts, where the experts make predictions and a facilitator controls these predictions until the experts end up with a level of consensus. Argument Delphi, as opposed to the classical Delphi model, is built on the contradictions of the ideas of the experts. Argument Delphi mainly focuses on a discussion topic and asks experts to create new arguments and criticize other arguments from other experts. After a certain level of contradiction, the method yields an amount of contradictory, criticized arguments and builds a decision over these antitheses, as in the Hegelian approach. This is the first time the argument Delphi method has been modeled in a graph of arguments and the problem of qualitative decision has been transferred into a graph problem using Delphi method. This paper is also the first time that argument aggregation and evaluation methods have been proposed. Moreover, the computerized version of argument Delphi is applied to real-world problems using crowd involvement through Facebook. The problem is defined as the prediction of petroleum prices for the end of year and more than 100 contributors from all around the world argued and criticized each other. This paper also discusses the findings of this case study.

Elicitation of the elements of Unified Modelling Language (UML) analysis and design models from sentences written in scripted English is essential in the production of analysis and design models. The correct elicitation of these elements depends on the intuitive, manually defined set of linguistic heuristics, which is used to map a word in the sentence to its correct semantics in the domain of UML analysis and design models. This paper proposes a Genetic Algorithm-based classification rule discovery approach and a developed Enhanced Intuitive Linguistic Heuristics (EILH) dataset to automate the definition of the intuitive linguistic heuristics set to elicit five elements of UML analysis and design models from English sentences. These elements are the use case, the actor, the sender, the receiver, and the message. The automatically defined intuitive linguistic heuristics set was evaluated by developing an Artificial Neural Network (ANN) to recognize the elements of the UML analysis and design models using both manually defined and automatically defined sets. This comparison shows the superiority of the automatically defined set over the manually defined one.

The main contribution of this paper is a numerical ship motion model of NTNU’s research vessel Gunnerus, capturing the surge, sway, roll, and yaw dynamics when sailing in uniform and steady currents. The model utilizes a crossflow drag formulation for the transverse viscous loads, and it includes a nonlinear formulation for the propulsion and steering loads provided by two azipod thrusters. A wide range of experimental data obtained from sea trials are used for model calibration and validation. The model is intended for development of Decision Support Systems (DSS) that provide the helmsman with recommendations for safe maneuvers. As a demonstration, the model is used to generate input to a previously proposed DSS solution, which uses offline simulations to create a database of the critical navigation area for different encounter scenarios. Additionally, we propose a DSS solution that uses online simulations to predict the future ship trajectory under guidance of a virtual autopilot. The virtual autopilot is designed using a novel hybrid control barrier function formulation to predict the need of evasive maneuvers for collision avoidance.

Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility.

More than half of all commercial aircraft operation accidents could have been prevented by executing a go-around. Making timely decision to execute a go-around manoeuvre can potentially reduce overall aviation industry accident rate. In this paper, we describe a cockpit-deployable machine learning system to support flight crew go-around decision-making based on the prediction of a hard landing event. This work presents a hybrid approach for hard landing prediction that uses features modelling temporal dependencies of aircraft variables as inputs to a neural network. Based on a large dataset of 58177 commercial flights, the results show that our approach has 85% of average sensitivity with 74% of average specificity at the go-around point. It follows that our approach is a cockpit-deployable recommendation system that outperforms existing approaches.

Bayesian Networks are probabilistic graph models that can be used for classification, prediction, diagnosis and parameter learning. Probabilities can be inferred from the models and missing values can be imputed, based on probability theory. Missing person cases place a strain on the already overstretched resources of Police Forces. Such cases predominantly come from at risk groups such as children in care and people suffering from depression or dementia. Current approaches for dealing with such cases are manual and rely upon empirical studies and domain knowledge. This paper proposes the use of a Bayesian Network model, which can be used to predict the likely location of a missing person (misper) for a number of at risk groups. The model is evaluated using a set of misper cases and results compare very favourably with those of the manual processes currently used by UK Police forces. The novel approach described provides both a theoretical foundation and a practical framework for the future development of a decision support system. In addition to the model, a contribution is made through guidelines, which recount experiences in learning a Bayesian Network from data.

With the rapid development of improved breeding equipment and information technology, computer-aided decision-making in plant breeding evaluation can help solve the problems associated with high-throughput demand and insufficient experience of breeders in modern large-scale field breeding experiments. Many linear models have made great contributions to the development of breeding evaluation although they are based on a wrong assumption of attribute independence. This paper proposes a unified coupled representation that integrates intra-coupled and inter-coupled relationships to capture the interdependence among quantitative traits by addressing coupling context and coupling weights. Moreover, a hybrid scheme of the linear correlation and ordinal relation is introduced to express the coupling relationship with a preset parameter that balances the contributions so as to capture both relative and absolute performance in cultivar selection and breeding evaluation. A framework that includes data preprocessing, coupled data representation, feature selection, prediction model construction, and assisted decision-making is our overall solution for the plant breeding evaluation task. Experiments on real plant breeding data sets demonstrated the effectiveness of coupled representation for elucidating the quantitative phenotypic traits and the advantages of the proposed plant breeding evaluation algorithm compared with benchmark algorithms.

Automatic decision and lane-keeping tasks are the most fundamental but important tasks in robot controlling researches. Concerned about the computing limitations of mobile robot platforms, an easily trainable method with low computational consumption and low latency is needed to solve this problem. An unsupervised conditional reflex learning network was proposed in this paper, which uses the conditional pattern to learn the conditional pattern and make decisions in an unsupervised manner. We used the convolutional spiking neural network to extract hidden features of road lanes, and then used the dopamine modulation mechanism to learn the decision-making information from the acquired features. In order to evaluate the quality of automatic decision-making models, two metrics were designed which are total deviation distance per second (TDDPS) and target achievement rate during training (TAR). In the process of training, neither labels were given to the convolutional spiking neural network, nor artificial decision-making information was assigned to the dopamine modulation layer. Simulation experiments showed that the proposed model has a state-of-the-art performance in a relatively complicated scenario and solves three limitations mentioned in previous works. Our work brought more biological inspiration into decision support systems, with the hope that the proposed method can promote the development of the bionic decision support system in hardware, especially in neuromorphic hardware.

This paper proposes the creation of an intelligent system for assessing the levels of professional competencies of students in e-learning. Mathematical models and methods for assessing the formation of professional competencies of students at the level of disciplines, modules and the entire educational program are built. Algorithms for calculating the assessment of levels of competency in the discipline, module, and educational program are developed. These estimates can be used in the formation of inference rules for making decisions on changing the structure and content of disciplines, modules and educational programs.

Many applications are hosted on cloud databases where several applications share the same database instance. Such database management system exhibits periodic behavior in terms of data references. For example, U.S. customers access data at a particular time while Japanese customers access data at some other time. The periodicity of data references is translated into periodic block references. This periodicity of block references can be used to improve cache performance by improving the block replacement policy. This paper proposes a novel cache replacement policy by considering periodicity of references of database blocks. It also proposes how to estimate the probability of a block reference in a specified time interval using “Parzen Windows,” and determines a working set based on this probability and defines the cache management policy using this working set. The experimentation shows considerable improvement of the hit ratio as the performance measure of the buffer cache management as compared with the other state-of-art algorithms.

Loan status prediction is an effective tool for investment decisions in peer-to-peer (P2P) lending market. In P2P lending market, most borrowers fulfill the repayment plan; however, some of them fail to pay back their loans. Therefore, an imbalanced classification method can be utilized to discriminate such default borrowers. In this context, the aim of this paper is to propose an investment decision model in P2P lending market which consists of fully paid loans classified via the instance-based entropy fuzzy support vector machine (IEFSVM). IEFSVM is a modified version of the existing entropy fuzzy support vector machine (EFSVM) in terms of an instance-based scheme. IEFSVM can reflect the pattern of nearest neighbors entropy with respect to the change of its size instead of fixing it in unified neighborhood size. Therefore, IEFSVM allows the class change of nearest neighbors in the determination of fuzzy membership. Applying the model to the lending club dataset, we determine loans that are predicted to be fully paid. Then, we also provide a multiple regression model to generate an investment portfolio based on non-default loans that are predicted to yield high returns. Throughout the experiment, the empirical results reveal that IEFSVM outperforms not only EFSVM but also the six other state-of-the-art classifiers including the cost-sensitive adaptive boosting, cost-sensitive random forest, EasyEnsemble, random undersampling boosting, weighted extreme learning machine, and cost-sensitive extreme gradient boosting in terms of loan status classification. Also, the investment performance of the multiple regression model using IEFSVM is higher and more robust than that of two other benchmarks. In this regard, we conclude that the proposed investment model is a decent and practical approach to support decisions in the P2P lending market.

The main goal of software testing is to detect as many hidden bugs as possible in the final software product before release. Generally, a software product is tested by executing a set of test cases, which can be performed manually or automatically. The number of test cases which are required to test a software product depends on several parameters such as the product type, size, and complexity. Executing all test cases with no particular order can lead to waste of time and resources. Test optimization can provide a partial solution for saving time and resources which can lead to the final software product being released earlier. In this regard, test case selection, prioritization, and scheduling can be considered as possible solutions for test optimization. Most of the companies do not provide direct support for ranking test cases on their own servers. In this paper, we introduce, apply, and evaluate sOrTES as our decision support system for manual integration of test scheduling. sOrTES is a Python-based supportive tool which schedules manual integration test cases which are written in a natural language text. The feasibility of sOrTES is studied by an empirical evaluation which has been performed on a railway use-case at Bombardier Transportation, Sweden. The empirical evaluation indicates that around 40 % of testing failure can be avoided by using the proposed execution schedules by sOrTES, which leads to an increase in the requirements coverage of up to 9.6%.

The macroeconomic indicators play a major role in all the stock markets, and they vary from nation to nation. This paper identifies the influence of macroeconomic indicators on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE) of India. Total of forty-four macroeconomic indicators for eight years from the year 2011 to 2018 are considered in this study. The macroeconomic factors are aggregated and considered in average monthly form. The proposed method finds the correlation matrix of all considered macroeconomic indicators. The need for dimensionality reduction and the existence of multicollinearity are proven using validation techniques such as the Kaiser-Meyer-Olkin and Bartlett tests. The Principal Component Analysis (PCA) method is used to reduce the dimensionality to seven factors and then PCA with the varimax rotation method is applied to find factors with maximum variation. In addition, the influence of these seven factors on the NSE Nifty and BSE SENSEX indices are analyzed using regression. Finally, an Artificial Neural Network is used to predict stock market movement with the help of macroeconomic indicators. Accuracy of 92% and 87% are obtained on NSE NIFTY and BSE SENSEX respectively.

Comprehensive measures for the estimation performance evaluation (EPE) has become increasingly prominent. This paper proposed a new radar chart evaluation method to measure the estimation performance. Firstly, the new radar chart index, which is composed of several popular incomprehensive measures, are presented, and the method of the weight of the each index is calculated based on vector ranking method. Secondly, the new comprehensive measures for the EPE is designed according to the fan area and the fan arc length. Finally, two cases study are provided to verify the effectiveness of this method.

The GreenSoul project introduces an innovative energy-efficient platform which enhances traditional public-use buildings with various technologies, such as smart adaptors, energy analyzers, an occupant aware decision support engine, mobile applications, and interactive ambient interfaces. These enhancement aims to directly improve the interactions between occupants and energy-consuming assets in their environment. The GreenSoul framework is further enriched by the deployment of lightweight edgecomputing GreenSoul-ed devices, which reduce energy consumption by cooperating with other devices, smart metering equipment and, very importantly, with eco-aware users. The decision making process is supported by a socio-economic behavioral model, which provides the necessary understanding of occupant indoor behavior toward transforming energy consuming devices into active pro-sustainability agents that inform users how energy-efficiently they operate them, provide notifications as to improve that aspect, and even adapt their own functioning to reduce energy waste. These eco-wise devices, which are coined as GreenSoul-ed Things, are explained in detail throughout this paper as well as the sensor-based architecture that supports their operation. The deployment of the framework across six pilot buildings is addressed, as well as the designed experimental setting to corroborate the potential of such a collaborative approach to enhance energy efficiency in office buildings.

The use of computer-based and online education systems has made new data available that can describe the temporal and process-level progression of learning. To date, machine learning research has not considered the impacts of these properties on the machine learning prediction task in educational settings. Machine learning algorithms may have applications in supporting targeted intervention approaches. The goals of this paper are to: 1) determine the impact of process-level information on machine learning prediction results and 2) establish the effect of type of machine learning algorithm used on prediction results. Data were collected from a university level course in human factors engineering (n=35), which included both traditional classroom assessment and computer-based assessment methods. A set of common regression and classification algorithms were applied to the data to predict final course score. The overall prediction accuracy as well as the chronological progression of prediction accuracy was analyzed for each algorithm. Simple machine learning algorithms (linear regression, logistic regression) had comparable performance with more complex methods (support vector machines, artificial neural networks). Process-level information was not useful in post-hoc predictions, but contributed significantly to allowing for accurate predictions to be made earlier in the course. Process level information provides useful prediction features for development of targeted intervention techniques, as it allows more accurate predictions to be made earlier in the course. For small course data sets, the prediction accuracy and simplicity of linear regression and logistic regression make these methods preferable to more complex algorithms.