Reviewerly/exHarmony huggingface dataset:
Feature Engineering Techniques to Improve Identification Accuracy for Offline Signature Case-Bases

Handwritten signatures have been widely acclaimed for personal identification viability in educated human society. But, the astronomical growth of population in recent years warrant developing mechanized systems to remove the tedium and bias associated with manual checking. Here the proposed system, performing identification with Nearest Neighbor matching between offline signature images collected temporally. The raw images and their extracted features are preserved using Case Based Reasoning and Feature Engineering principles. Image patterns are captured through standard global and local features, along with some profitable indigenously developed features. Outlier feature values, on detection, are automatically replaced by their nearest statistically determined limit values. Search space reduction possibilities within the case base are probed on a few selected key features, applying Hierarchical clustering and Dendogram representation. Signature identification accuracy is found promising when compared with other machine learning techniques and a few existing well known approaches.
How do we solve the problem of ransomware?

The US oil pipeline that shut down after a cyberattack is just the latest victim in a growing wave, reports Matthew Sparkes
Regression differentiation and regression integration of finite series

Data analysis is a complex and multi-dimensional concept. It is explained by both the objective complexity of the data itself and the subjective nature of the expert analyzing them. Therefore, adequate formalization of this requires a completely new apparatus, on the one hand, capable of overcoming the objective complexity of data (irregularity and inaccuracy), and, on the other hand, the vague nature of expert judgment. The development of Discrete Mathematical Analysis (DMA) is an important step in this direction. DMA is highly expert oriented and occupies an intermediate position in data analysis between strict mathematical methods (statistical analysis, TFA, etc.) and soft combinatorial methods (simulation modeling, neural networks, etc.). This paper proposes new mathematical constructions of regression derivatives and regression integrals for discrete time series defined in general on an irregular grid. An important role in their study is played by the recently created by the authors projection method for solving systems of linear algebraic equations described at the end of this paper. The obtained constructions of regression derivatives and regression integrals have a hierarchical character in the spirit of wavelet and fractal analysis. The results of this work define another direction in further studies, namely, the propagation of regression differentiation and regression integration in finite mathematics under scenarios of the classical one.
THE ROLE OF IoT iN THE RELATIONSHIP BETWEEN STRATEGIC LOGISTICS MANAGEMENT AND OPERATIONAL PERFORMANCE

ABSTRACT Purpose: Against the backdrop of the transaction costs and theoretical strands of thought in authors of the strategic logistics management (SLM), operational performance (OP), and the internet of things (IoT), the purpose of this study is to verify if the IoT performs a mediating effect on the relationship between SLM and OP. Originality/value: The bibliometric study indicated that studies on the IoT aimed at the business area are still scarce. Therefore, there is a prospect of expansion of the knowledge frontier of the administration field. This article seeks to show whether the IoT has a mediating influence on the relationship between SLM and OP, in addition to highlighting the most significant variables of each construct. This study can help business managers and researchers to identify the benefits of adopting IoT. Design/method/approach: For that, a quantitative research was carried out. The research collected data through a semi-structured questionnaire with a sample of 76 companies. Data were treated by descriptive statistics and multivariate statistics with the modeling technique in structural equations. Before the application of the modeling technique in structural equations, data were submitted to tests of convergent and discriminant validity. Findings: The IoT plays a mediating but partial role in the relationship between SLM and OP. Implementing the IoT provides better efficiency by enabling proactive decisions, interconnecting resources, automating, streamlining, remotely controlling, generating customer information, and eliminating errors.
Improving Training of Generative Adversarial Networks

Optimization algorithms and objective functions play an important role in the training of deep learning networks. This paper explores the impact of using various optimization algorithms and objective functions for the training of different Generative Adversarial Networks. The paper first summarizes various Generative Adversarial Networks available in the literature. Various Generative Adversarial Networks are then evaluated for different objective functions and optimization algorithms. Empirically, Generative Adversarial Networks are analyzed here based on generator loss, discriminator loss, and accuracy metrics. The training of various Generative Adversarial Networks is analyzed on the MNIST dataset. The results indicate that Adam optimization algorithm and conditional objective function is a good choice for improved training of Generative Adversarial Networks.
Repetitive Nearest Neighbor Based Simulated Annealing Search Optimization Algorithm for Traveling Salesman Problem

The traveling salesman problem (TSP) is the most popular and most studied non-deterministic polynomial (NP) hard problem that has been used in various fields of science and technology.Due to the NP-hard nature, it is very difficult to solve this problem effectively and efficiently.For this reason, diverse appropriate optimization algorithms have been designed and developed in the last few decades.Among these algorithms, heuristic algorithms are much more suitable to tackle with this complex problem.In this paper, we propose a hybrid heuristic algorithm to solve the symmetric TSP problem by combining the search mechanism of repetitive nearest neighbor (RNN) heuristic and simulated annealing (SA) heuristic algorithms.In fact, a set of better routes are generated step by step by the RNN algorithm and these routes are improved through the iterative improvement process of the SA algorithm.The proposed algorithm is tested on a set of benchmark symmetric TSP datasets and compared with the basic RNN and SA algorithms as well as some other hybrid algorithms existing in the literature.It is demonstrated by the experimental results that the proposed algorithm is more effective than both the basic RNN and SA algorithms, and the obtained optimum results are in good agreement with the corresponding best-known optimum results.In addition, the proposed algorithm outperforms some other hybrid algorithms in terms of solution quality.
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

Real world multi-agent tasks often involve varying types and quantities of agents and non-agent entities; however, agents within these tasks rarely need to consider all others at all times in order to act effectively. Factored value function approaches have historically leveraged such independences to improve learning efficiency, but these approaches typically rely on domain knowledge to select fixed subsets of state features to include in each factor. We propose to utilize value function factoring with random subsets of entities in each factor as an auxiliary objective in order to disentangle value predictions from irrelevant entities. This factoring approach is instantiated through a simple attention mechanism masking procedure. We hypothesize that such an approach helps agents learn more effectively in multi-agent settings by discovering common trajectories across episodes within sub-groups of agents/entities. Our approach, Randomized Entity-wise Factorization for Imagined Learning (REFIL), outperforms all strong baselines by a significant margin in challenging StarCraft micromanagement tasks.
An Improved Adaptive Differential Evolution Approach for Constrained Optimization Problems

As the complexity of the real-world engineering problems increases, numerous efficient constraint-handling methods and optimization algorithms have emerged recently. However, the majority of the research consider the constraint-handling method and the optimization algorithm independently. In this paper, we propose a constraint-based mutation operator, in which the constraint violation and objective function are considered simultaneously. We define the pbest individuals as the best in top 5% constraint violators if all the individuals are infeasible. In this way, we could guide the population move towards the feasible region. Two real-world engineering applications are used to test the performance of the IεJADE. Compared with the state-of-the-art algorithms, the experimental results illustrate the effectiveness of the IεJADE algorithm, which also exhibits a fast convergence rate in terms of computation efficiency.
Examining Students' Reading Habit and Gender Differences behind Students' Academic Writing Achievement

Students’ reading habit and gender differences are believed as two influential factors on students’ academic writing achievement. The purposes of this study were to find out the effect of the two factors on students’ academic writing achievement and to find out the relationship among them. 43 English students of UIN Imam Bonjol Padang in the fourth semester registered in academic year 2020/2021 were involved. Reading habit questionnaire and essay writing test were administered. To analyze the data, simple regression test, multiple regression test, and Pearson’s correlation test were applied. It was revealed that reading habit had no effect on students’ academic writing achievement; they had very weak correlation, whereas gender affected students’ academic writing achievement; they had weak relationship. Moreover, students’ reading habit and gender affected students’ academic writing achievement as many as 10.4 %.
A Study on Blended Learning Mode Applied in Foreign Languages Teaching as Exemplified by Microlecture

Prof. He Kekang put forward that “The blended learning mode integrates strengths of both traditional teaching and network teaching”. Teaching modes optimization and revolution is fueled by rapid development of modern web-based teaching technologies promoted by the “internet plus” era. Micro-chat, Micro-blog, Micro-movie, Micro-shop, Micro-video and the like have portrayed a “micro epoch” when students in universities pay more and more attention to “micro” which refers to features of “short, essential and fast”. Consequently, short-term effective micro-lecture with essential contents exactly fits for cognitive characteristics of modern students. Blended learning mode flexibly integrates foreign language micro-lecture and traditional teaching would remarkably improves the effect of foreign language teaching no matter students learn from movable platform or from websites using personal computer.
Break-It-Fix-It: Unsupervised Learning for Program Repair

We consider repair tasks: given a critic (e.g., compiler) that assesses the quality of an input, the goal is to train a fixer that converts a bad example (e.g., code with syntax errors) into a good one (e.g., code with no syntax errors). Existing works create training data consisting of (bad, good) pairs by corrupting good examples using heuristics (e.g., dropping tokens). However, fixers trained on this synthetically-generated data do not extrapolate well to the real distribution of bad inputs. To bridge this gap, we propose a new training approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use the critic to check a fixer's output on real bad inputs and add good (fixed) outputs to the training data, and (ii) we train a breaker to generate realistic bad code from good code. Based on these ideas, we iteratively update the breaker and the fixer while using them in conjunction to generate more paired data. We evaluate BIFI on two code repair datasets: GitHub-Python, a new dataset we introduce where the goal is to repair Python code with AST parse errors; and DeepFix, where the goal is to repair C code with compiler errors. BIFI outperforms existing methods, obtaining 90.5% repair accuracy on GitHub-Python (+28.5%) and 71.7% on DeepFix (+5.6%). Notably, BIFI does not require any labeled data; we hope it will be a strong starting point for unsupervised learning of various repair tasks.
Machine Learning Meets the Semantic Web

Remarkable progress in research has shown the efficiency of Knowledge Graphs (KGs) in extracting valuable external knowledge in various domains. A Knowledge Graph (KG) can illustrate high-order relations that connect two objects with one or multiple related attributes. The emerging Graph Neural Networks (GNN) can extract both object characteristics and relations from KGs. This paper presents how Machine Learning (ML) meets the Semantic Web and how KGs are related to Neural Networks and Deep Learning. The paper also highlights important aspects of this area of research, discussing open issues such as the bias hidden in KGs at different levels of graph representation.
A Study on Correlation of Subjects on Electrical Engineering Course Using Artificial Neural Network (ANN)

Abstract: This paper presents a study of correlation between subjects of Diploma in Electrical Engineering (Electronics/Power) at Universiti Teknologi MARA(UiTM) Cawangan Terengganu using Artificial Neural Network (ANN). The analysis was done to see the effect of mathematical subjects (Pre-calculus and Calculus 1) and core subject (Electric Circuit 1) on Electronics 1. Electronics 1 is found to be a core subject with the history of high failure rate percentage (more than 25%) in previous semesters. This research has been conducted on current final semester students (Semester 5). Seven (7) models of ANN are developed to observe the correlation between the subjects. In order to develop an ANN model, ANN design and parameters need to be chosen to find the best model. In this study, historical data from students’ database were used for training and testing purpose. Total number of datasets used are 58 sets. 70% of the datasets are used for training process and 30% of the datasets are used for testing process. The Regression Coefficient, (R) values from the developed models was observed and analyzed to see the effect of the subject on the performance of students. It can be proven that Electric Circuit 1 has significant correlation with the Electronics 1 subject respected to the highest R value obtained (0.8100). The result obtained proves that student’s understanding on Electric Circuit 1 subject (taken during semester 2) has direct impact on the performance of students on Electronics 1 subject (taken during semester 3). Hence, early preventive measures could be taken by the respective parties. &#x0D; &#x0D; Keywords: Artificial neural network, Diploma in Electrical Engineering, Graduate on time, Correlation.
Comparative Analysis of Deep Learning Approaches for Scientific Literature Classification

Automatic document categorization has emerged as a significant application of supervised learning. Deep learning has among other fields is revolutionizing document categorization. Recently, deep learning has been applied to classify scientific literature. This paper presents a comparative analysis of various deep learning-based text classification approaches for scientific literature classification. The purpose of this study is to explore the applicability of such algorithms on the task of classifying scientific literature. We describe and apply deep learning-based text classification algorithms on various datasets. The experimental results show that deep learning-based text classification algorithms are applicable and perform well on the task.
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning

How can neural networks trained by contrastive learning extract features from the unlabeled data? Why does contrastive learning usually need much stronger data augmentations than supervised learning to ensure good representations? These questions involve both the optimization and statistical aspects of deep learning, but can hardly be answered by analyzing supervised learning, where the target functions are the highest pursuit. Indeed, in self-supervised learning, it is inevitable to relate to the optimization/generalization of neural networks to how they can encode the latent structures in the data, which we refer to as the feature learning process. In this work, we formally study how contrastive learning learns the feature representations for neural networks by analyzing its feature learning process. We consider the case where our data are comprised of two types of features: the more semantically aligned sparse features which we want to learn from, and the other dense features we want to avoid. Theoretically, we prove that contrastive learning using $\mathbf{ReLU}$ networks provably learns the desired sparse features if proper augmentations are adopted. We present an underlying principle called $\textbf{feature decoupling}$ to explain the effects of augmentations, where we theoretically characterize how augmentations can reduce the correlations of dense features between positive samples while keeping the correlations of sparse features intact, thereby forcing the neural networks to learn from the self-supervision of sparse features. Empirically, we verified that the feature decoupling principle matches the underlying mechanism of contrastive learning in practice.
Tensor-Based Reliable Multiview Similarity Learning for Robust Spectral Clustering on Uncertain Data

Similarity graph learning is the most key technique for multiview spectral clustering. However, existing methods fail when applied to uncertain data contaminated with various types of noise in an open environment. Due to the damaged structure by noise, unreliable similar relationships are learned, which extends similarity inconsistency among views. Moreover, the high-order correlation hidden in graphs are ignored generally. To address these problems, we propose a reliable similarity learning scheme for multiview clustering on uncertain data. This method can significantly improve spectral clustering performance in a noisy environment, and the contributions of our scheme include the following three aspects: 1) Uncertain data subspace reconstruction and adaptive graph learning are combined to construct a view-specific graph from high-quality recovered data, thus improving robustness. 2) A low-rank tensor constraint is utilized to facilitate multiview fusion, where the latent high-order correlation among view graphs will be fully explored when learning the consensus graph structure. 3) Data recovery, view-specific graphs, and latent consensus tensor structure are assembled into a unified framework, to be optimized jointly for mutual benefit. Our study also develops an efficient algorithm for obtaining overall solutions. The experimental results on several datasets demonstrate that our proposed approach shows significant improvements in robustness and evaluation metrics over the comparison methods.
Digital literacy self-efficacy scale: A scale development study

This study aims to develop a scale in order to determine the digital literacy self-efficacy level of primary school students. In line with this purpose, firstly, open-ended questions, which were created and asked for opinions on digital literacy, were directed to field experts and primary school teachers. The draft scale form was created as a result of the responses to open-ended questions and the literature review was presented to the field experts and the items in the draft scale were finalized in the line with the feedback. The draft scale form was applied to a total of 175 4th grade students studying in the central district of Afyonkarahisar in the 2019-2020 academic year determined by purposeful methods. The data were analyzed by Exploratory Factor Analysis (EFA) and it resulted in a structure with 21 items and four subdimensions named ‘Collaboration in Digital Environments’, ‘Emotion Management in Digital Environments’, ‘Information Management in Digital Environments’ and ‘Awareness in Digital Environments’. It was confirmed that the subscales constituted a model by the first level and second level Confirmatory Factor Analysis (CFA). The Cronbach’s alpha value for the scale was found to be .944.
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches and top professional players in a live event. Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays. In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.
Sentence-structure errors of machine translation into Slovak

Abstract The study identifies, classifies and analyses errors in machine translation (MT) outputs of journalistic texts from English into Slovak, using error analysis. The research results presented in the study are pioneering, since the issue of machine translation – with its strong interdisciplinary character and novelty – has not yet been studied in the Slovak academic environment. The evaluation of the errors is based on a framework for classification of MT errors devised by Vaňko, which was arranged for the Slovak language. The study discusses and explains the issues of sentence structure, including predicativeness, syntactic-semantic correlativeness, and a modal and communication sentence framework. We discovered that the majority of the errors are related to the categories of agreement, word order and nominal morpho-syntax. This fact clearly correlates with features of journalistic texts, in which nominal structures and nouns in all realizations are used to a great extent. Moreover, there are some serious differences between the languages which limit and affect the quality of translation.
KuiLeiXi: a Chinese Open-Ended Text Adventure Game

There is a long history of research related to automated story generation, dating back as far as the 1970s.Recently, the rapid development of pre-trained language models has spurred great progresses in this field.Equipped with GPT-2 and the latest GPT-3, AI Dungeon has been seen as a famous example of the powerful text generation capabilities of large-scale pre-trained language models, and a possibility for future games.However, as a game, AI Dungeon lacks incentives to players and relies entirely on players to explore on their own.This makes players' enthusiasm decline rapidly.In this paper, we present an openended text adventure game in Chinese, named as KuiLeiXi 1 .In KuiLeiXi, players need to interact with the AI until the pre-determined plot goals are reached.By introducing the plot goals, players have a stronger incentive to explore ways to reach plot goals, while the AI's abilities are not abused to generate harmful contents.This limited freedom allows this game to be integrated as a part of a romance simulation mobile game, Yu Jian Love 2 .Since KuiLeiXi was launched, it has received a lot of positive feedbacks from more than 100,000 players.