Reviewerly/exHarmony huggingface dataset:
An Improved C<sup>2</sup>oDE based on Stochastic Ranking

How to balance diversity and convergence as well as constraint violation and objective function in Constrained Optimization Problems (COPs) is an important and key issue when designing a new Constrained Optimization Evolutionary Algorithms (COEAs). C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> oDE set a good example on this point, and obtains promising results. In C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> oDE, the two Constraint Handling Techniques (CHTs) are adopted fixed in two phases. To verify whether there are some other more efficient combinations, Stochastic Ranking (SR) is adopted to balance the combination. Experimental results show a good performance, which also give some inspiration for other research.
Normalisation and subformula property for a system of intuitionistic logic with general introduction and elimination rules

Abstract This paper studies a formalisation of intuitionistic logic by Negri and von Plato which has general introduction and elimination rules. The philosophical importance of the system is expounded. Definitions of ‘maximal formula’, ‘segment’ and ‘maximal segment’ suitable to the system are formulated and corresponding reduction procedures for maximal formulas and permutative reduction procedures for maximal segments given. Alternatives to the main method used are also considered. It is shown that deductions in the system convert into normal form and that deductions in normal form have the subformula property.
CNN-based framework for classifying temporal relations with question encoder

Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well due to the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time-event entities) with various window sizes to focus on context words around the entities as a filter. We refer to this module as "question encoder." In our approach, this kind of prior information can extract task-related information from simple sentence embedding. Our experimental results on a publicly available Timebank-Dense corpus demonstrate that our approach outperforms some state-of-the-art techniques, including CNN-, LSTM-, and BERT-based temporal relation classifiers.
Analysis of Single and Ensemble Machine Learning Classifiers for Phishing Attacks Detection

Phishing attacks have been used in different ways to harvest the confidential information of unsuspecting internet users. To stem the tide of phishing-based attacks, several machine learning techniques have been proposed in the past. However, fewer studies have considered investigating single and ensemble machine learning-based models for the classification of phishing attacks. This study carried out performance analysis of selected single and ensemble machine learning (ML) classifiers in phishing classification. The focus is to investigate how these algorithms behave in the classification of phishing attacks in the chosen dataset. Logistic Regression and Decision Trees were chosen as single learning classifiers while simple voting techniques and Random Forest were used as the ensemble machine learning algorithms. Accuracy, Precision, Recall and F1-score were used as performance metrics. Logistic Regression algorithm recorded 0.86 as accuracy, 0.89 as precision, 0.87 as recall and 0.81 as F1-score. Similarly, the Decision Trees classifier achieved an accuracy of 0.87, 0.83 for precision, 0.88 for recall and 0.81 for F1-score. In the voting ensemble, accuracy of 0.92 was achieved. 0.90 was obtained for precision, 0.92 for recall and 0.92 for F1-score. Random Forest algorithm recorded 0.98, 0.97, 0.98 and 0.97 as accuracy, precision, recall and F1-score respectively. From the experimental analyses, Random Forest algorithm outperformed simple averaging classifier and the two single algorithms used for phishing URL detection. The study established that the ensemble techniques that were used for the experimentations are more efficient for phishing URL identification compared to the single classifiers.
Classification of eye condition based on electroencephalogram signals using extreme learning machines algorithm (ELM)

Electroencephalography or EEG signals is a biosignal that is rife in current research topics. EEG signals have many benefits such as the detection of epilepsy, sleep disorders, or input in a computer application. One input that can be detected based on EEG signals is the state of the eye. However, to be used as input in an application a classification with adequate performance is required. Therefore a study was conducted in which one method of learning Artificial Neural Networks, Extreme Learning Machine (ELM) will be implemented to classify eye conditions based on EEG signals.
The Use of Human Voice and Speech for Development of Language Technologies: the EU and Russian Data-protection Law Perspectives

The global character of research and business related to the language-technology sector requires those producing applications of technology in this domain to comply with relevant regulation – pertaining to intellectual property, personality rights, and data protection – applicable in multiple jurisdictions. The paper reports on research aimed at evaluating and defining conditions for the compatibility of various legal frameworks for the use of voice and speech in development and dissemination of language-technology applications from the EU and the Russian data-protection regulation perspective. The research fills a gap that is of particular relevance, in that the compatibility of Russian data-protection law with the General Data Protection Regulation (GDPR) with regard to the field of language technology has not been explored extensively. The authors draw from prior research to examine the implications in greater depth, with two foci. The first part of the article addresses the legal nature of human voice and speech. In the second part of the paper, the conditions for the development of language technologies are analysed.
A new blockchain system design to improve the supply chain of engineering, procurement and construction (EPC) companies – a case study in the oil and gas sector

Purpose This paper aims to propose a new blockchain system design to improve engineering, procurement and construction (EPC) companies’ supply chain for constructing oil and gas infrastructure, by mitigating cost and time inefficiencies. Design/methodology/approach A case study analyses the supply chain of a sample EPC company. First, a literature review is conducted to explore the subject in academic literature. Second, information flows are mapped using responsible, accountable, consulted and informed analysis and cross-functional process mapping. Third, inefficiencies are identified. Fourth, the root causes of the inefficiencies are pinpointed using fishbone and five-times-why analysis. Fifth, a comparison is made between the linear and the blockchain information system via force-field analysis. Sixth, a specific blockchain system design is identified based on three external expert interviews. Finally, the new system is designed and a cost-benefit analysis is conducted. Findings Major cost and time inefficiencies in oil and gas infrastructure developments are caused by a poor information flow in the supply chain. The new blockchain system design is a feasible solution, reducing cost inefficiencies by 12.4% and operation lead-times by 36.5%. Research limitations/implications The confidentiality of the sample EPC company’s information represents a limitation. Originality/value The research introduces a new blockchain system design, reducing cost and time inefficiencies in the project-development supply chain, including implementation processes.
On Correctness and Completeness of an <i>n</i> Queens Program

Abstract Thom Frühwirth presented a short, elegant, and efficient Prolog program for the n queens problem. However, the program may be seen as rather tricky and one may not be convinced about its correctness. This paper explains the program in a declarative way and provides proofs of its correctness and completeness. The specification and the proofs are declarative, that is they abstract from any operational semantics. The specification is approximate, it is unnecessary to describe the program’s semantics exactly. Despite the program works on non-ground terms, this work employs the standard semantics, based on logical consequence and Herbrand interpretations. Another purpose of the paper is to present an example of precise declarative reasoning about the semantics of a logic program.
Compression of Neural Networks for Specialized Tasks via Value Locality

Convolutional Neural Networks (CNNs) are broadly used in numerous applications such as computer vision and image classification. Although CNN models deliver state-of-the-art accuracy, they require heavy computational resources that are not always affordable or available on every platform. Limited performance, system cost, and energy consumption, such as in edge devices, argue for the optimization of computations in neural networks. Toward this end, we propose herein the value-locality-based compression (VELCRO) algorithm for neural networks. VELCRO is a method to compress general-purpose neural networks that are deployed for a small subset of focused specialized tasks. Although this study focuses on CNNs, VELCRO can be used to compress any deep neural network. VELCRO relies on the property of value locality, which suggests that activation functions exhibit values in proximity through the inference process when the network is used for specialized tasks. VELCRO consists of two stages: a preprocessing stage that identifies output elements of the activation function with a high degree of value locality, and a compression stage that replaces these elements with their corresponding average arithmetic values. As a result, VELCRO not only saves the computation of the replaced activations but also avoids processing their corresponding output feature map elements. Unlike common neural network compression algorithms, which require computationally intensive training processes, VELCRO introduces significantly fewer computational requirements. An analysis of our experiments indicates that, when CNNs are used for specialized tasks, they introduce a high degree of value locality relative to the general-purpose case. In addition, the experimental results show that without any training process, VELCRO produces a compression-saving ratio in the range 13.5–30.0% with no degradation in accuracy. Finally, the experimental results indicate that, when VELCRO is used with a relatively low compression target, it significantly improves the accuracy by 2–20% for specialized CNN tasks.
On the Complexity of Preferred Semantics in Argumentation Frameworks with Bounded Cycle Length

Argumentation frameworks are a core formalism in the field of formal argumentation, with several semantics being proposed in the literature. Among them, preferred semantics is one of the most popular but comes with relatively high complexity. In fact, deciding whether an argument is skeptically accepted, i.e. contained in each preferred extension, is Pi^P_2-complete. In this work we study the complexity of this problem w.r.t. the length of the cycles in the considered AF. Our results show which bounds are necessary to decrease the complexity to coNP and P, respectively. We also consider argumentation frameworks with collective attacks and achieve Pi^P_2-hardness already for cycles of length 4.
MR-CapsNet: A Deep Learning Algorithm for Image-Based Head Pose Estimation on CapsNet

Head pose estimation based on a single image is a challenging endeavor because of the complex background conditions and characteristics of the human face.In this report, we propose a Multi stage Regression-Capsule Network (MR-CapsNet) to predict head posture based on a single image input.In the study, we used the residual attention block and squeeze-and-excitation block to extract features in three levels.CapsNet overcomes the shortcomings of the traditional convolutional neural network and implements module aggregation to describe the spatial relationship of features after aggregation, in addition to realizing a compact and robust model using a multi-stage regression scheme.We tested our method on the AFLW2000 and BIWI datasets obtaining mean absolute errors of 4.26% and 3.95%, respectively.In addition, we discuss the accuracy of our method in the case of eye or mouth occlusion.The results of comprehensive experiments reveal that our method can accurately predict head posture.
Solving Gauss's Law on Digital Quantum Computers with Loop-String-Hadron Digitization

This paper describes the qubit digitization of a loop-string-hadron formulation of SU(2) lattice gauge theories coupled to staggered fermions.
Complete Path Planning That Simultaneously Optimizes Length and Clearance

This paper considers a fundamental, optimal path planning problem that requires simultaneously minimizing path length and maximizing obstacle clearance. We show that in even simple planar settings with point and disc obstacles, the set of alternative solutions such that no one is clearly better than another (the set of Pareto-optimal solutions) is uncountably infinite. In spite of this difficulty, we introduce a complete, efficient algorithm that computes the Pareto front and a data structure that finitely represents the complete set of all Pareto- optimal paths. Particular optimal paths can then be selected from the computed data structure during execution, based on any additional conditions or considerations.
Towards News Aggregation in Russian: a BERT-based Approach to News Article Similarity Detection

This paper presents neural models developed for the Dialogue Evaluation 2021: Russian News Clustering shared task.The participants were offered the news clustering task, but solutions based on the classification were also accepted.We applied a classification approach and implemented a hard-voting ensemble of pretrained language models fine-tuned on the official competition dataset.Our solution ranked third among 17 teams at the Russian News Clustering shared task in both private and public leaderboards and obtained 95.73% of F1-score (private LB) and 96.5% (public LB).
Integrative Approach in Teaching English for Professional Purposes

The article discusses the need for an integrative approach in teaching Business English, aimed at forming and developing professional competencies of a future graduate in the field of management. The applied aspect of the integrative approach means implementing the principle of integration in all parts of the educational process. In this study, it concerns the formation of professional competencies during the process of learning a foreign language. The basis of the successful implementation of an integrative approach at all levels is adding a reflective component, forming of reflective position of a student. It helps analyze and order all the elements and stages of the studying activity from goal setting to result achieving; it also helps students realize themselves as active participants of the studying process. Today, due to various factors and reasons, the potential of the discipline “Business Foreign Language” in forming professional competencies often remains unrealized. The course developed allows to fully reveal its opportunities of forming professional competencies for a future manager. The course is focused on developing skills in a foreign language and professional training of students in such field of study as “Management”. This contributes to the students’ personal and professional growth. This is achieved due to the course’s content and structure, when the general concept of management and its functions being considered sequentially and logically. Including the reflective component into the studying process allows not only the teacher but also the students themselves follow the level of progress and the degree of achieving the goals set by them.
ITUTime: Turkish Temporal Expression Extraction and Normalization

We introduce a rule based tagger of temporal expressions, ITUTime, for detecting and normalizing temporal expressions in Turkish language. The proposed system is morphologically aware and does not require any preprocessing steps, since ITUTime operates on free text. We also establish the first temporally annotated dataset of Turkish language. The work presented here locates itself as a baseline for tagging Turkish temporal expressions. The proposed system is evaluated on manually annotated test dataset and achieved 0.89 F1 score on recognizing and 0.89 F1 score on normalization.
Quantum Markov monogamy inequalities

Markovianity lies at the heart of communication problems. This in turn makes the information-theoretic characterization of Markov processes worthwhile. Data-processing inequalities are ubiquitous in this sense, assigning necessary conditions for all Markov processes. We address here the problem of the information-theoretic analysis of constraints on Markov processes in the quantum regime. We show the existence of a class of quantum data-processing inequalities called here quantum Markov monogamy inequalities. This class of necessary conditions on quantum Markov processes is inspired by its counterpart for classical Markov processes, thus providing a strong link between classical and quantum constraints on Markovianity. We go on to construct a family of multitime quantum Markov monogamy inequalities, based on the process tensor formalism and that exploits multitime correlations. We then show, by means of an explicit example, that the Markov monogamy inequalities can be stronger than the usual quantum data-processing inequalities.
Text Classification by Genre Based on Rhythm Features

The article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are markers of the text rhythm. On their basis, statistical features were calculated that describe quantitatively and structurally these rhythm features. The resulting text model was visualized for statistical analysis using boxplots and heat maps that showed differences in the rhythm of texts of different genres. The boxplots showed that almost all genres differ from each other in terms of the overall density of rhythm features. Heatmaps showed different rhythm patterns across genres. Further, the rhythm features were successfully used to classify texts into six genres. The classification was carried out in two ways: a binary classification for each genre in order to separate a particular genre from the rest genres, and a multi-class classification of the text corpus into six genres at once. Two text corpora in English and Russian were used for the experiments. Each corpus contains 100 fiction novels, scientific articles, advertisements and tweets, 50 reviews and political articles, i.e. a total of 500 texts. The high quality of the classification with neural networks showed that rhythm features are a good marker for most genres, especially fiction. The experiments were carried out using the ProseRhythmDetector software tool for Russian and English languages. Text corpora contains 300 texts for each language.
Generating Abstractive Summaries of Lithuanian News Articles Using a Transformer Model

In this work, we train the first monolingual Lithuanian transformer model on a relatively large corpus of Lithuanian news articles and compare various output decoding algorithms for abstractive news summarization. We achieve an average ROUGE-2 score 0.163, generated summaries are coherent and look impressive at first glance. However, some of them contain misleading information that is not so easy to spot. We describe all the technical details and share our trained model and accompanying code in an online open-source repository, as well as some characteristic samples of the generated summaries.
Wander: A breath-control Audio Game to Support Sound Sleep

Overuse of mobile phones pre-sleep is one of the main factors that cause sleep disorders. Although medical research proves that excessive blue light from the smartphone leads to a decrease in sleepiness, the widespread use of cell phones makes it difficult for people to disconnect from them entirely before bedtime. Regarding this dilemma, we explore the potential of treating mobile phones as sleep helpers. In this paper, we proposed a novel interaction mode and designed a breath-control audio game, which guides mobile users to reduce the use of smartphones and take breathing exercises that improve sleep quality near bedtime based on gamification theory. Our game's metaphor promotes smartphone users to beware that excessive use of mobile phones before bedtime will adversely affect sleep, demonstrating the opportunities of utilizing smartphones to support sound sleep. Finally, through our practice, we show that gamification is a potential method to cultivate healthy habits and can be generalized to the formation of other ones in the future.
Deploying and scaling distributed parallel deep neural networks on the Tianhe-3 prototype system

Due to the increase in computing power, it is possible to improve the feature extraction and data fitting capabilities of DNN networks by increasing their depth and model complexity. However, the big data and complex models greatly increase the training overhead of DNN, so accelerating their training process becomes a key task. The Tianhe-3 peak speed is designed to target E-class, and the huge computing power provides a potential opportunity for DNN training. We implement and extend LeNet, AlexNet, VGG, and ResNet model training for a single MT-2000+ and FT-2000+ compute nodes, as well as extended multi-node clusters, and propose an improved gradient synchronization process for Dynamic Allreduce communication optimization strategy for the gradient synchronization process base on the ARM architecture features of the Tianhe-3 prototype, providing experimental data and theoretical basis for further enhancing and improving the performance of the Tianhe-3 prototype in large-scale distributed training of neural networks.
Implementation of Choice of Jurisdiction and Law in Private International Law by PROLEG Meta-interpreter

Private International law (also called Conflict of laws) treats international affairs which involves legal systems of multiple countries. In the domain of private international law, choice of jurisdiction (the choice of country whose court can have a competence to treat the affairs) and choice of law (the choice of law on which the judgement of the affair is based) are main questions. In this paper, we give an implementation of both questions using extending PROLEG meta-interpreter. PROLEG is our legal knowledge representation language which consists of general rules and exceptions in one legal system. We extend PROLEG to handle different legal systems and reference of other legal systems within reasoning about international affairs.
Application of Graph Convolutions in a Lightweight Model for Skeletal Human Motion Forecasting

Prediction of movements is essential for successful cooperation with intelligent systems.We propose a model that integrates organized spatial information as given through the moving body's skeletal structure.This inherent structure is exploited in our model through application of Graph Convolutions and we demonstrate how this allows leveraging the structured spatial information into competitive predictions that are based on a lightweight model that requires a comparatively small number of parameters.* This research was