Reviewerly/exHarmony huggingface dataset:
Quadratic Privacy-Signaling Games and the MMSE Information Bottleneck Problem for Gaussian Sources

We investigate a privacy-signaling game problem in which a sender with privacy concerns observes a pair of correlated random vectors which are modeled as jointly Gaussian. The sender aims to hide one of these random vectors and convey the other one whereas the objective of the receiver is to accurately estimate both of the random vectors. We analyze these conflicting objectives in a game theoretic framework with quadratic costs where depending on the commitment conditions (of the sender), we consider Nash or Stackelberg (Bayesian persuasion) equilibria. We show that a payoff dominant Nash equilibrium among all admissible policies is attained by a set of explicitly characterized linear policies. We also show that a payoff dominant Nash equilibrium coincides with a Stackelberg equilibrium. We formulate the information bottleneck problem within our Stackelberg framework under the mean squared error distortion criterion where the information bottleneck setup has a further restriction that only one of the random variables is observed at the sender. We show that this MMSE Gaussian Information Bottleneck Problem admits a linear solution which is explicitly characterized in the paper. We provide explicit conditions on when the optimal solutions, or equilibrium solutions in the Nash setup, are informative or noninformative.
Finding Formations for the Non-prehensile Object Transportation with Differentially-Driven Mobile Robots

This paper proposes a formation-finding procedure for the non-prehensile transportation of arbitrarily-shaped polygonal objects with differentially-driven mobile robots. The proposed procedure is conceptually novel by taking a multibody system dynamics perspective, explicitly taking into account the robots’ non-holonomic constraints. Being based on proper first principles such as Jourdain’s principle, if the modeling assumptions are met, the approach nominally guarantees to only produce formations that are actually useful to manipulate the object in a given dynamic situation. As a byproduct, the scheme can directly provide a set of robot propulsion forces fitting the desired object motion. Simulation results confirm the chosen formations’ favorable qualities.
Orthogonal Design-Based Control Vector Parameterization Combined With Improved Seagull Optimization Algorithm for Dynamic Optimization Problems

This paper proposes an orthogonal design-based control vector parameterization (OCVP, for short) and a Gaussian distribution-based seagull optimization algorithm (GSOA) for dynamic optimization problems (DOPs).OCVP uses orthogonal experimental design to analyze the dynamic model to capture the fluctuation characteristics of the optimal control trajectory.Then using the ranges obtained in the orthogonal experiment to guide the construction of the time grid.Based on the seagull optimization algorithm (SOA), GSOA introduces the initialization idea based on Gaussian distribution and the dimension-order mutation operator based on Gaussian distribution.The initialization idea cleverly uses Gaussian distribution to generate the initial population that conforms to the chemical process.The mutation operator uses the dimension-order mutation method to improve the optimization performance of SOA.OCVP and GSOA are combined to form a new optimization method, named OCVP-GSOA.In the application of four typical chemical DOPs, the simulation results show that OCVP-GSOA can achieve similar or even higher solution accuracy.Furthermore, OCVP and control vector parameterization are compared, and GSOA and other meta-heuristic algorithms are compared.The results show that OCVP can achieve higher solution accuracy in most cases, and GSOA can achieve better performance.
SPAKT: A Self-Supervised Pre-TrAining Method for Knowledge Tracing

Knowledge tracing (KT) is the core task of computer-aided education systems, and it aims at predicting whether a student can answer the next exercise (i.e., question) correctly based on his/her historical answer records. In recent years, deep neural network-based approaches have been widely developed in KT and achieved promising results. More recently, several researches further boost these KT models via exploiting plentiful relationships including exercise-skill relations (E-S), the exercise similarity (E-E) as well as skill similarity (S-S). However, these relationship information are frequently absent in many real-world educational applications, and it is a labor-intensive work for human experts to label it. Inspired by recent advances in natural language processing domain, we propose a novel pre-training approach, namely as SPAKT, and utilize self-supervised learning to pre-train exercise embedding representation without the need for expensive human-expert annotations in this paper. Contrary to existing pre-training methods that highly rely on manually labeling knowledge about the E-E, S-S, or E-S relationships, the core idea of the proposed SPAKT is to design three self-attention modules to model the E-S, E-E, and S-S relationships, respectively, and all of these three modules can be trained in the self-supervised setting. As a pre-training approach, our SPAKT can be effortlessly incorporated into existing deep neural network-based KT frameworks. We experimentally show that, even without using expensive annotations about the aforementioned three kinds of relationships, our model achieves competitive performance compared with state-of-the-arts. Our algorithm implementations have been made publicly available at https://github.com/Vinci-hp/pretrainKT.
Towards Explainable Artificial Intelligence in Financial Fraud Detection: Using Shapley Additive Explanations to Explore Feature Importance

As the number of organizations and their complexity have increased, a tremendous amount of manual effort has to be invested to detect financial fraud. Therefore, powerful machine learning methods have become a critical factor to reduce the workload of financial auditors. However, as most machine learning models have become increasingly complex over the years, a significant need for transparency of artificial intelligence systems in the accounting domain has emerged. In this paper, we propose a novel approach using Shapley additive explanations to improve the transparency of models in the field of financial fraud detection. Our information systems engineering procedure follows the cross industry standard process for data mining including a systematic literature review of machine learning methods in fraud detection, a systematic development process and an explainable artificial intelligence analysis. By training a downstream Logistic Regression, Support Vector Machine and eXtreme Gradient Boosting classifier on a dataset of publicly traded companies convicted of financial statement fraud by the United States Securities and Exchange Commission, we show how the key items for financial statement fraud detection and their directionality can be identified using Shapley additive explanations. Finally, we contribute to the current state of research with this work by increasing model transparency and by generating insights on important financial statement fraud detection variables.
Improved Adaptive Komodo Mlipir Algorithm

In order to improve the global search performance of the Komodo Mlipir Algorithm, this paper proposed two adaptive Komodo Mlipir Algorithms with variable fixed parameters (IKMA-1; IKMA-2). Among them, IKMA-1 adaptively controls the parthenogenesis radius of female Komodo dragons to achieve more efficient conversion of global search and local search. Second, IKMA-2 introduces adaptive weighting factors to the &#x201C;mlipir&#x201D; movement formula of Komodo dragons to improve the local search performance. Both IKMA-1 and IKMA-2 were tested on 23 benchmark functions in CEC2013 and compared with the other seven optimization algorithms. The Wilcoxon rank-sum test and Friedman rank test were used to compare the performance of different algorithms. Furthermore, IKMA-1 and IKMA-2 are applied to two constrained engineering optimization problems to verify the engineering applicability of the improved algorithm. The results show that both IKMA-1 and IKMA-2 have better convergence accuracy than the initial KMA. In terms of the benchmark function simulation results, IKMA-1 improves the performance by 17.58&#x0025; compared to KMA; IKMA-2 improves by 10.99&#x0025;. Both IKMA-1 and IKMA-2 achieve better results than other algorithms for engineering optimization problems, and IKMA-2 outperforms IKMA-1.
Uncertainty-Aware and Multigranularity Consistent Constrained Model for Semi-Supervised Hashing

Recently, deep semi-supervised hashing methods have attracted increasing attention, which can significantly improve retrieval performance by leveraging abundant unlabeled data. These methods usually generate surrogate supervision signals to learn with unlabeled data, such as neighborhood information and augmentation invariant requirements. However, an essential issue of these methods is that the supervised signals are not always reliable, which may damage the performance. In this paper, we propose a novel Uncertainty-Aware and Multi-Granularity Consistent Constrained Semi-Supervised Hashing (UMCSH) method to alleviate the negative effects of noisy supervised signals and enlarge the inter-class distance. Specifically, our UMCSH mainly consists of an Uncertainty-Aware Instance-Level Consistency (UAILC) model and a Cluster-Based Class-Level Consistency (CBCLC) model. UAILC introduces an uncertainty estimation method to select reliable supervised signals to extract discriminative features for each unlabeled data. CBCLC establishes connections between labeled data and unlabeled data by encouraging each unlabeled sample to be close to the hash center (calculated with the labeled data) according to its pseudo-label. Extensive experimental results demonstrate the superior performance of our proposed approach compared with several state-of-the-art semi-supervised hashing methods.
Constructions for several classes of few-weight linear codes and their applications

&lt;p style='text-indent:20px;'&gt;In this paper, for any odd prime &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ p $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and an integer &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ m\ge 3 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, several classes of linear codes with &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ t $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-weight &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ (t = 3,5,7) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are obtained based on some defining sets, and then their complete weight enumerators are determined explicitly by employing Gauss sums and quadratic character sums. Especially for &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ m = 3 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, a class of MDS codes with parameters &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ [p,3,p-2] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are obtained. Furthermore, some of these codes can be suitable for applications in secret sharing schemes and &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ s $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-sum sets for any odd &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ s&amp;gt;1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Parameterised State Estimation Approach for 2-Dimensional Underwater Bearings only Target Tracking

In this paper, parameterised state estimation approach has been discussed for a typical passive 2-D bearings only target tracking (BoT) problem. Existing nonlinear state estimation algorithms may not provide good estimation performance in case of initial uncertainty in the range and speed of the target since uncertain and way-off initial conditions can lead to loss of accuracy. Here, the target velocity parameterisation along with range is embedded in extended Kalman filter (EKF), unscented Kalman filter (UKF), new sigma point Kalman filter (NSKF) and shifted Rayleigh filter (SRF). This approach involves partitioning the initial target range and velocity estimates into a number of sub-areas. Then separate weighted filter is applied to each sub-area to estimate the target position and speed. The likelihood of each sub-area is computed based on a Gaussian assumption for the residuals, and the final estimate is calculated by the weighted sum of each filter estimates over all the sub-areas. This overcomes the practical large initial range variation scenarios usually encountered in underwater tracking problems. Then, comparison analysis is performed on the basis of track loss and root mean square error (RMSE) in position.
Aircarft Signal Feature Extraction and Recognition Based on Deep Learning

Radio signal recognition has a wide application in future communication systems and the vehicular communication, whose core is the extraction of signal features such as electromagnetic fingerprints. With the rapid development of artificial intelligence technology, deep learning has made amazing breakthroughs in image recognition, speech recognition and other fields. Deep learning is applied to electromagnetic fingerprint extraction in this paper. Firstly, thousands of the downlink aircraft communications addressing and reporting system (ACARS) signals used for communication between civil aircraft and airport tower are collected and generated. Then a pre-transformation network suitable for electromagnetic signals is constructed to convert one-dimensional signals into two-dimensional feature maps, and afterwards the feature maps are input into the convolutional neural network (CNN) for classification. By adopting the attention modules, the classification results were improved by a few percentage points over the baseline with a little cost. The method proposed in this paper achieves an accuracy rate of 94.1% and can obtain the aircraft type in a shorter time than traditional method. Moreover, the robustness of the proposed model in response to additive Gaussian white noise (AWGN) and phase deviation is studied and tested.
Metrics for Software Process Quality Assessment in the Late Phases of SDLC

The software systems worldwide increase in a density on a daily basis. The success in nowadays competitive market requires sustainable and quality software product. Controversially to the quantity of software products, the quality and cost of the software are tend to depend on several aspects. However, they are not fully inculcated yet as a fundamentally essential. The full control over the software quality requires software metrics to be introduced. By effective usage of software quality metrics one can monitor the software development process, minimize the cost, track the resource usage and maintain the expected results. This paper reviews the late phases and the existing software quality models to track software process quality metrics in these late phases. And based on the summarized studies we describe our system architecture in the way to evaluate the software quality with embedded external systems. This paper find outs additional metrics we can measure with the help of our framework.
Is There Still a Need to Regulate the Television Industry in the Digital Age?

In media industry, the digital-led convergence supported by satellite network and the Internet has facilitated new opportunities and new market, especially in television industry, which is the most significant and indispensable in human's everyday life.Under the complicated media environment, it is more than essential to discuss whether it is necessary to regulate the television industry as the public media channel.This article firstly explores the diversity of TV programs with examples, then the impact of the Internet and new media platforms are mentioned, and finally the intrinsic characteristics of television industry and the limitation of market control are introduced respectively.
Hybrid Production: Enabled By Controlling The Output Sequence Of A Matrix Production Using Answer Set Programming

Combining a line production with a matrix production into a hybrid production is a vivid field of research to cope with the challenges of mass personalization. Nevertheless, to implement a hybrid system, several challenges have to be overcome. For a line production, a predefined sequence of products is necessary in order to handle variants at all. In a matrix production, workstations can be skipped or variants have different processing times that result in non-deterministic sequences. This leads to the main challenge in such a hybrid system, to control the output sequence of the flexible system in order to continue the production in a line segment. Answer set programming is a declarative problem-solving approach, presenting a foundation for solving highly constrained combinatorial problems. Based on a formal description of the problem, a solver generates all its possible solutions. This paper presents a novel approach for the production scheduling of a flexible system using answer set programming. The developed approach is able to create a production schedule for a matrix production that obeys the necessary output sequence for the input of the subsequent production line. The result is a production schedule allocating workstations, employees, components, and automated guided vehicles to each operation of a specific job. Furthermore, it is now possible to have two production lines entering the flexible segment and merging them into one outgoing production line and vice versa, while value-adding operations are performed.
Mongolian Word Segmentation Based on BiLSTM-CNN-CRF Model

In natural language processing tasks, word segmentation is an important fundamental task, and the accuracy and rationality of Mongolian word segmentation will alleviate the data sparsity problem and directly affect the subsequent work of Mongolian information processing. A BiLSTM-CNN-CRF-based deep learning Mongolian word segmentation model is developed to address the stickiness characteristics of Mongolian and the rich and complex morphological changes of words. The model introduces a convolutional neural network (CNN) algorithm in a bidirectional long- and short-term memory network (BiLSTM) to realize the local features of Mongolian character sequences. The model’s overall performance for Mongolian word segmentation is improved by combining the extraction of local features and long-range time-dependent features and then by using a conditional random field (CRF) model for constrained correction. The LSTM model, BiLSTM model, and BiLSTM-CRF model were selected for comparison experiments. It was verified that the BiLSTM-CNN-CRF-based neural network model performed best on the test data set when the word vector dimension was 100 and the dropout value was 20%, and the introduction of CNN as the local feature extractor of the sequence effectively improved the overall performance of the model. The introduction of CNN as a local feature extractor effectively improved the overall labeling effect of the model, and the F value reached 99.47%.
Automated Tools for Usability Evaluation: A Systematic Mapping Study

Usability is one of the most critical indicators in determining the quality of a software product. It corresponds to how users can use a software system to achieve specific objectives with effectiveness, efficiency, and satisfaction. A usability evaluation is necessary to ensure that the software system is usable, but this has certain disadvantages (e.g., a high cost of time and budget for the evaluation to be implemented). While these disadvantages can be a bit daunting despite the benefits they provide, some tools can automatically generate and support usability testing. We conducted a systematic mapping study to identify the tools that support automatic usability evaluation. We identified a total of 15 primary studies. In addition, we classify the tools into four categories: measure usability, support usability evaluation, detect usability problems, and correct usability problems. We identified that the automatic evaluation of the usability of web platforms and mobile devices is the most interesting.
BiSHM: Evidence detection and preservation model for cloud forensics

Abstract The cloud market is growing every day. So are cloud crimes. To investigate crimes that happen in a cloud environment, an investigation is carried out adhering to the court of law. Forensics investigations require evidence from the cloud. Evidence acquisition in the cloud requires formidable efforts because of physical inaccessibility and the lack of cloud forensics tools. Time is very crucial in any forensic investigation. If the evidence is preserved before the cloud forensic investigation, it can give the investigators a head start. To identify and preserve such potential evidence in the cloud, we propose a system with an artificial intelligence (AI)-based agent, equipped for binary classification that monitors and profiles the virtual machine (VM) from hypervisor level activities. The proposed system classifies and preserves evidence data generated in the cloud. The evidence repository module of the system uses a novel blockchain model approach to maintain the data provenance. The proposed system works at the hypervisor level, which makes it robust for anti-forensics techniques in the cloud. The proposed system identifies potential evidence reducing the effective storage space requirement of the evidence repository. Data provenance incorporated in the proposed system reduces trust dependencies on the cloud service provider (CSP).
Multiobjective Harris Hawks Optimization With Associative Learning and Chaotic Local Search for Feature Selection

In the classification problem, datasets often have a large number of features, but not all features are useful for classification. A lot of irrelevant features may even reduce the performance. Feature selection is to remove irrelevant features by minimizing the number of the feature subset and minimizing the classification error rate.So it can be regarded as a multi-objective optimization problem. Beacuse of its simple structure and easy implementation, Harris Hawks Optimization algorithm(HHO) is widely employed in optimization problems.In this paper, the multi-objective HHO is applied to address the feature selection problem. In order to improve the search ability of the algorithm, associative learning, grey wolf optimization and chaotic local search are introduced into it. An external repository is used to save non-dominant solution set. The results of feature selection on the sixteen University of CaliforniaIrvine (UCI) datasets show that the proposed method can effectively remove redundant features and improve the classification performance of the algorithm.
Benefits and use of blockchain technology to support supply chain during COVID-19

Blockchain is a decentralized digital ledger, structured as a chain of blocks, containing chronologically related transactions regulated by a system of cryptographic algorithms and rules. COVID-19 has highlighted the critical issues related to the management of a pandemic situation, reaffirming the crucial role of the supply chain: First in the identification of outbreaks of contagion and then to the supplying and the distribution of personal protective equipment (PPE), to finish with the problematic campaign of administering anti COVID-19 vaccines. All these criticalities can be managed effectively by adopting innovative technologies such as the blockchain. In this chapter, the basic blockchain concepts will be analyzed, and the use of this technology in addressing the COVID-19 emergency will be discussed. Finally, a blockchain-based model for vaccine supply chain will be studied.