Reviewerly/exHarmony huggingface dataset:
Plausible Argumentation in Eikotic Arguments: The Ancient Weak versus Strong Man Example

In this paper it is shown how plausible reasoning of the kind illustrated in the ancient Greek example of the weak and strong man can be analyzed and evaluated using a procedure in which the pro evidence is weighed against the con evidence using formal, computational argumentation tools. It is shown by means of this famous example how plausible reasoning is based on an audience’s recognition of situations of a type they are familiar with as normal and comprehensible in their shared common knowledge. The paper extends previous work on this example by using three new multiagent argumentation schemes closely related to the scheme for argument from negative consequences.
A New Method for Automating Voice Calls Routing Using Data Preprocessing Techniques for Supervised Learning

In spite of the massive technological development in the telecommunications field globally, to transit and terminate international phone calls that are originated from one country to another is a process that requires much time and resources, which poses a great challenge. This paper proposes a new method for automated voice calls routing which automates the selection process for the appropriate carrier/channel of calls by creating a class label for quality statistics using data preprocessing for supervised learning while ensuring it complies with the requirements and conditions of the operator with its targeted quality of services. Data preprocessing tasks were performed on international calls duration records to automate the routing decision-making process in terms of sorting and prioritizing the routes of carriers who deliver the calls to the rightful destination. The methods used in the research were data cleaning to get rid of the noisy data, manual fill-in for the missing values, aggregation for data transformation, attribute subset selection in data reduction and finally creating a class label to determine the quality of each call and prepare the preprocessed data for supervised learning. The experimental results proved the efficiency of the proposed method and solved a great challenge that is facing telecom operators by saving operational costs and time.
On the 100th anniversary of Alexander Grigoryevich Zakharov, the first director of the Library for Natural Sciences of RAS

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Simulation of Fast Transformation Procedures in Galois Fields to Estimate Efficiency of Perspective Signals Generation and Processing Devices

The object of consideration is design automation and prototyping of complex signals and their generation and processing devices. The subject of the analysis is the substantiation of the structure, composition and implementation method of a modeling system aimed at modeling and prototyping promising devices for the generation and processing of complex signals based on fast transformations in Galois fields. The purpose of the analysis is a reasonable choice of methods and tools for computational, simulation and prototyping that achieve high display accuracy, performance, flexibility and ease of modeling. The solutions are based on the principles and mathematical models developed by the authors: methods and algorithms for fast transformations in Galois fields, an algebraic representation model and the structure of fast delay acquisition devices for a subclass of sequences. The article is devoted to the development of a software and hardware modeling systems that allows the most accurate mapping of fast algebraic transformations in Galois fields to algorithmic models of various levels of representation to ensure high performance, flexibility and ease of modeling, as well as prototyping of promising devices for the generation and processing of complex signals. The results of the work will be useful in the design of promising devices of various information and communication systems, scientific research, as well as in the educational process.
Proposal of a Wearable Personal Concierge System with Healthcare Using Speech Dialogue Technology

The following topics are dealt with: learning (artificial intelligence); Internet of Things; neural nets; feature extraction; Internet; security of data; mobile computing; cloud computing; image colour analysis; object detection.
Real-Time Frame-to-Frame Jitter Removing Video Stabilization Technique

Users that use low-cost handheld cameras are untrained, and they suffer from intense handshakes and jitters to the videos taken. To acquire video sequences by removing these unavoidable jitters and handshakes, video stabilization algorithm is needed. This paper examined these pitfalls to enhance the accuracy of the images. Different techniques are used to protect the frames which are recorded which relies on the simple motion modules on different schemes. This work focuses on various new processes of video stabilization and offers background on existing methods. Paper successfully implemented a proposed algorithm to obtain a video sequence where jitter has effectively been frame to frame eliminated by using inbuilt mobile sensors. To produce a smoothened camera motion, adaptive filter algorithms are used and frame-to-frame jitter can be reduced using smoothing camera motion. The proposed application can be implemented on any android smartphone which having inbuilt accelerometer and gyroscope sensors.
A clique-free pseudorandom subgraph of the pseudo polarity graph

We provide a new family of Kk-free pseudorandom graphs with edge density Θ(n−1/(k−1)), matching a recent construction due to Bishnoi, Ihringer and Pepe [2]. As in the former result, the idea is to use large subgraphs of polarity graphs, which are defined over a finite field Fq. While their construction required q to be odd, we will give the first construction with q a power of 2.
A Case Study on the Evolution of Configuration Options of a Highly-Configurable Software System

Evolving configurable software poses unique challenges, due to dependencies between the configuration knowledge and the assets that implement the system. Despite a lot of research in the area of configurable software evolution many open challenges subsist, which hamper industrial adoption of different techniques. For instance, there is not much research on the co-evolution of tests and the configuration space. A challenge for this is that the configuration space is often not clearly defined and reasoning over its evolutions and the implication from it is difficult. In this paper, we study the configuration space of the web-based bug tracker Bugzilla, a highly configurable system, replicating the analysis of previous research. In addition, we analyze the evolution of the configuration space over several versions of the system to investigate the changes that were made over the revisions spanning the development of several years and their implications and open challenges.
New Textural Features for Handwritten Signature Image Verification

In this paper, a new textural feature for solving offline handwritten signature verification is proposed. A new textural features method is developed by combining a Local Binary Patterns (LBP) method and a Gradient Quantization Angle (GQA) method. This proposed method is called Local Binary Patterns with Gradient Quantization Angle (LBPGQA), as developed by heuristic method to improve the precision of verification the offline signature image. The hypothesis for this study is to classify the distinctive handwritten signature individually with the actual signature angle and refraction for enhancing the signature fraud detection. The verification step is achieved by Artificial Neural Network (ANN) classifier and trained on genuine signatures. Furthermore, the test stage is performed on genuine signatures and skilled forgeries. The experiments are conducted on CEDAR datasets. The experimental results show that in the LBPGQA method outperforms classical features such as Histogram of oriented gradients and local binary patterns. Conclusively, this proposed method can verify the individual and distinctive handwritten signature and help to protect the signature fraud by skilled forgeries.
Frame-Rate-Aware Aggregation for Efficient Video Super-Resolution

Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, recently draws increasing attention. In contrast to the previous works that perform explicit motion estimation and compensation, we propose a novel deep neural network which performs implicit motion estimation with frame-rate-based temporal aggregation. Specifically, the input frames are first aggregated by a frame-rate-aware 3D convolution layer, where neighboring frames are integrated with the reference frame according to the corresponding frame rate. Then, the aggregated features are fed into several branches for further aggregation. Different branches correspond to a kind of motion rate, which provides complementary information to recover missing details in the reference frame. Extensive experiments demonstrate that our method is able to handle various motion types and achieves state-of-the-art performance on several benchmarks. In addition, our model is light-weight and requires an extremely less computational load than other state-of-the-art methods.
English Teaching Ability Evaluation Based on Big Data Fuzzy k-means Clustering Algorithm

In recent years, various data acquisition methods have produced a large number of data acquisition methods from multiple perspectives. Mufti view data clustering is of great significance and challenge in large-scale data analysis. In particular, the importance of clustering analysis in data mining is discussed. Based on the practical application and theoretical research of data mining, the research status of data mining technology at home and abroad is analyzed. The fuzzy membership method is used to describe the probability that the sample belongs to a certain class. Firstly, the constraint parameter analysis model is established. The evaluation model suitable for modern teaching mode such as "micro course" and "flipped classroom" is constructed, and fuzzy clustering technology is adopted. The number of clusters depends on the distance between the cluster node and the whole cluster.
Ontology Learning of New Concepts combining Textural Knowledge and Visual Analysis for Service Robot Applications

This paper describes a method for the automatic learning of new ontological concepts oriented to service robot applications. We propose the combination of textural knowledge and visual analysis to determine the new concept's correct placement in the ontology structure. We aim to make the robot able to extend its ontological knowledge autonomously. We conducted experiments to analyze the applicability of the presented method for learning new concepts.
Research on Evaluation and Mechanism of Power Grid Reserve Project Based on Matter Element Model

In view of the increasing number of power grid reserve projects, how to reasonably evaluate power grid reserve projects and formulate power grid reserve project management mechanisms is of great significance for improving project management levels, optimizing resource allocation, and realizing the sustainable development of power grid enterprises. This paper proposes an evaluation method for power grid reserve projects based on the matter-element model. First, considering the influence factors of technology, economy, environment, etc., this paper constructs an evaluation index system for reserve projects considering policy, safety, and benefits; The method of weighting determines the index weight, and evaluates the project in combination with the matter-element model, laying a foundation for the research of the project reserve mechanism, and finally uses examples to verify the effectiveness of the method.
Assessing time-based and range-based strategies for commit assignment to releases

Release is a ubiquitous concept in software development, referring to grouping multiple independent changes into a deliverable piece of software. Mining releases can help developers understand the software evolution at coarse grain, identify which features were delivered or bugs were fixed, and pinpoint who contributed on a given release. A typical initial step of release mining consists of identifying which commits compose a given release. We could find two main strategies used in the literature to perform this task: time-based and range-based. Some release mining works recognize that those strategies are subject to misclassifications but do not quantify the impact of such a threat. This paper analyzed 13,419 releases and 1,414,997 commits from 100 relevant open source projects hosted at GitHub to assess both strategies in terms of precision and recall. We observed that, in general, the range-based strategy has superior results than the time-based strategy. Nevertheless, even when the range-based strategy is in place, some releases still show misclassifications. Thus, our paper also discusses some situations in which each strategy degrades, potentially leading to bias on the mining results if not adequately known and avoided.
A Differential Evolution Algorithm with Adaptive Strategies for Constrained Optimization Problem

Constndned optimization problems are widely used in real-world applications as optimization models. Due to the complexity of the objective itself as well as too tight constraints, it is difficult to obtain the global optimal solution to these problems. In this manuscript, an improved differential evolutionary algorithm is proposed from the perspective of operator design and constraint handling. Firstly, in order to enhance the exploration ability of the algorithm, a heuristic mutation operator with better point information is constructed. Secondly, an improved dynamic epsilon constraint handling method is developed, in which the value of the epsilon decreases as the iteration number increases. The method can increase effectively the feasible individual in populations. Finally, the simulation results on 10 benchmark functions show that the proposed algorithm is effective and robust when compared with similar algorithms.
Enhancing Semantic Segmentation of Aerial Images with Inhibitory Neurons

In a Convolutional Neural Network, each neuron in the output feature map takes input from the neurons in its receptive field. This receptive field concept plays a vital role in today's deep neural networks. However, inspired by neuro-biological research, it has been proposed to add inhibitory neurons outside the receptive field, which may enhance the performance of neural network models. In this paper, we begin with deep network architectures such as VGG and ResNet, and propose an approach to add lateral inhibition in each output neuron to reduce its impact on its neighbours, both in fine-tuning pre-trained models and training from scratch. Our experiments show that notable improvements upon prior baseline deep models can be achieved. A key feature of our approach is that it is easy to add to baseline models; it can be adopted in any model containing convolution layers, and we demonstrate its value in applications including object recognition and semantic segmentation of aerial images, where we show state-of-the-art result on the Aeroscape dataset. On semantic segmentation tasks, our enhancement shows 17.43% higher mIoU than a single baseline model on a single source (the Aeroscape dataset), 13.43% higher performance than an ensemble model on the same single source, and 7.03% higher than an ensemble model on multiple sources (segmentation datasets). Our experiments illustrate the potential impact of using inhibitory neurons in deep learning models, and they also show better results than the baseline models that have standard convolutional layer.
User Perceptions of Defensive Techniques Against Keystroke Timing Attacks During Password Entry

To protect the password from visual attacks, most password entry screens use a password masking scheme that displays a series of placeholder characters (e.g., dots and asterisks) instead of the actual password. Recent research has however shown the security provided by this form of password masking to be weak against keystroke timing-analytics attacks. The underlying idea behind these attacks is that, even when a password is masked as described above, the timing between consecutive placeholder characters gives away information about the password since the relative locations of characters on the keyboard dictate how fast fingers move between them. In this paper we argue that, for security-sensitive applications, password masking mechanisms ought to hide the true intervals between password characters in order to overcome these kinds of attacks. Making adjustments to these timings however has the potential to pose usability issues given the fact that the typing would not perfectly align with the display of typed content. The paper proposes 3 different password masking schemes and undertakes a usability evaluation on them. Our early results suggest that user receptiveness to two of the schemes is not much worse than that seen with the conventional (insecure) scheme.
DEVELOPMENT OF COGNITIVE INTERESTS OF YOUNGER SCHOOL CHILDREN IN OUT-OF-LESSON ACTIVITIES WHEN CONDUCTING EXCURSION CLASSES

This article is devoted to the problems of the development of the cognitive interests of younger schoolchildren in extracurricular activities during excursion classes. A brief description of the available research on this issue is given, as well as some developments by different authors. So, the distinctive features of cognitive interest, aspects of conducting excursion classes, as well as stages of conducting an excursion in order to develop the cognitive interest of younger students are presented.
The Role of Cycle Consistency for Generating Better Human Action Videos from a Single Frame

This paper addresses the challenging problem of generating videos with human action semantics. Unlike previous work which predict future frames in a single forward pass, this paper introduces the cycle constraints in both forward and backward passes in the generation of human actions. This is achieved by enforcing the appearance and motion consistency across a sequence of frames generated in the future. The approach consists of two stages. In the first stage, the pose of a human body is generated. In the second stage, an image generator is used to generate future frames by using (a) generated human poses in the future from the first stage, (b) the single observed human pose, and (c) the single corresponding future frame. The experiments are performed on three datasets: Weizmann dataset involving simple human actions, Penn Action dataset and UCF-101 dataset containing complicated human actions, especially in sports. The results from these experiments demonstrate the effectiveness of the proposed approach.
Research progress of face recognition based on deep learning

Compared with static face recognition, dynamic face recognition has more practical value and research value.However, there are still some difficulties and challenges in dynamic face recognition in video.Dynamic faces obtained under video and unconstrained conditions are subject to complex interference factors such as posture change, expression and side face, which makes it more difficult to recognize them.To solve the above problems, based on the deep learning method, the dynamic face recognition method is further studied.
Isolations of cubic lattices from their proper sublattices

A (positive definite and integral) quadratic form is called an isolation of a quadratic form f if it represents all subforms of f except for f itself. The minimum rank of isolations of a quadratic form f is denoted, if it exists, by Iso(f). In this article, we show that Iso(I2)=5 and Iso(I3)=6, where In=x12+…+xn2 is the sum of n squares for any positive integer n. After proving that there always exists an isolation of In for any positive integer n, we provide some explicit lower and upper bounds for Iso(In). In particular, we show that Iso(In)∈Ω(n32−ϵ) for any ϵ>0.
Similarity-Principle-Based Machine Learning Method for Clinical Trials and Beyond

With recent success in supervised learning, artificial intelligence (AI) and machine learning (ML) can play a vital role in precision medicine. Deep learning neural networks have been used in drug discovery when larger data is available. However, applications of machine learning in clinical trials with small sample size (around a few hundreds) are limited. We propose a Similarity-Principle-Based Machine Learning (SBML) method, which is applicable for small and large sample size problems. In SBML, the attribute-scaling factors are introduced to objectively determine the relative importance of each attribute (predictor). The gradient method is used in learning (training), that is, updating the attribute-scaling factors. We evaluate SBML when the sample size is small and investigate the effects of tuning parameters. Simulations show that SBML achieves better predictions in terms of mean squared errors for various complicated nonlinear situations than full linear models, optimal and ridge regressions, mixed effect models, support vector machine and decision tree methods.
PROFESSIONAL TRAINING FOR FUTURE BACHELORS IN SOCIAL WORK

The purpose of the work is to theoretically substantiate the system of professional training of futurebachelors of social work and analyze the main trends in modern education in Russia. On the basis of theoretical and empirical research methods, the author presents a comparative characteristic of qualification requirements in federal state educational standards, identifies trends that provide modern professional training for bachelors of social work. The results obtained create the basis for scientific and methodological support of the educational process aimed at optimizing the system of professional training of future bachelors of social work, and can be included in the educational practice of Russian universities that provide professional training for bachelors of social work. The article is addressed to teachers leading the training of students studying in the areas of a social profile, as well as specialists working in institutions of social protection and social services for the population.
Advances in Computerized Adaptive Testing

Computerized adaptive testing (CAT) is a testing mode based on measurement theory and computer technology. Firstly, this article briefly introduces the definition and advantages of computerized adaptive testing. Moreover, this study uses web of science retrieval tools and CiteSpace software to analyze the literature related to CAT technology from 1990 to 2020, and the problems and countermeasures in CAT application are briefly described, Finally, the future development trend of computer adaptive testing is depicted.
Evaluation of Source to Target and Target to Source Word Alignment for English to Hindi

Word alignment is the process of recognizing the semantically counterpart words in the translation of a sentence in one natural language into another natural language. Word alignment is a critical step in machine translation. Quality of word alignment determines the quality of translation. The word alignments are language dependent. Grammatical structure of Hindi language is very much complex than English language. The structural difference between English and Hindi language makes the word alignment a difficult task. The translation model of phrase-based statistical machine translation (PBSMT) approach used in this experimentation heavily depends on word alignments. This paper discusses the effect of source to target and target to source heuristics of word alignment symmetrization on the quality of English to Hindi translation. Freely available n-gram-based BLEU metric and TER metric are used for evaluating the results. It is observed that, source to target direction of world alignment is more effective and produces 8.75% higher BLEU score than target to source direction of alignment.