Reviewerly/exHarmony huggingface dataset:
Terminological, semantic and lexicological aspects of management

The paper deals with the lexicological basis for the creation of a dictionary to be used by pedagogical staff in the management of the educational process. These starting points respect the results of implemented qualitative semantics as well as the analysis of teachers' educational needs. Actually, the individual words should provide a better understanding of the educational reality. Due to current curricular reform, the lexicon used in pedagogical practice is characterized by variability and sometimes terminological inconsistency. It is, therefore, important to use relevant theoretical and empirical data to create a glossary aimed at managing the educational process. At the same time, the study provides practical experience with the creation of a dictionary and with the possibilities of its use in practice by pedagogical staff in support of their self-development and improvement of the educational process as a whole.
Reaching a Better Trade-Off Between Image Quality and Attack Success Rates in Transfer-Based Adversarial Attacks

We study transfer-based adversarial attacks that introduce perturbations in an image that are large enough to make an unknown CNN wrongly classify it. These perturbations should also be small enough to not be perceived by others. We denote this problem as mitigating the trade-off between image quality (or imperceptibility) and obtaining a high attack success rate (ASR). Our proposed methods explicitly integrate perceptual models into the attacks. We first propose a “spatial” perceptual-aware attack which allows the introduction of higher perturbations in the perceptually insignificant image regions, and less perturbations in the visually sensitive ones. We then propose a novel frequency-perceptual attack utilizing frequency perceptual models. Both types of attacks are independent of the gradient estimation, thus they can be directly incorporated into existing gradient-based attacks. Experiments demonstrate their effectiveness in mitigating such trade-offs.
Criteria for Fuzzy Rule-Based Systems and Its Applicability on Examples

Classification allows us to handle the large amount of data that is available nowadays. In our work, we use the classification features to divide employees into the several classes and examine the differences between the classical and flexible classification. We also emphasize the advantages of classical classification as well as the disadvantages, and how we can solve them by fuzzy logic. Fuzzy rule-based systems are explainable and therefore interpretable because the rules are defined by linguistic variables. Design of a more complex system is a tedious task. To resolve this, we examine interpretability criteria for fuzzy rule-based systems. We examine this topic on the examples with two classification attributes because it is easily illustrated graphically. To use more attributes is mathematically possible, but it is harder to visualize for users in a three and more dimensional spaces. In our work, we propose how to create an explainable design for classification and propose possibilities how to expand it.
Usability Evaluation of Mobile Application Software Mockups

The impact of Information and Communication Technologies (ICT’s) at the economic, political, social, etc. level and their multiple possibilities of integration in industry, education, commerce, health, among others, have allowed a positive growth in the development of different software applications. However, to develop a software product it is necessary to perform a good software design, since it is considered as a key factor to improve usability when interacting with the final software product. In this article, usability is evaluated in the mockups of a mobile application, using user testing and questionnaire methods. Data collection instruments were established for each method. For the execution of the tests, the participation of 30 male and female users between 17 and 50 years of age was considered. The results obtained for the user testing method regarding effectiveness and efficiency are 40% and 163.5 s respectively. As for the questionnaire method, the average value for each question is less than 5.5 points. These results have made it possible to identify the usability level of the mockups, make the necessary adjustments before moving on to the development stage, optimize resources and obtain a higher level of acceptance by the end user.
(AR)e These Products Making Sense? Resolving Product Schema-Incongruity with Augmented Reality

This paper investigates two distinct yet potentially complementary issues that when put together, may be able to solve each other. First, businesses are pressured to roll out innovative products to stay ahead of the competition, but product innovation is risky as it can sometimes violate consumer expectations due to schema incongruity. Second, while augmented reality (AR) technology has been examined in adoption studies in retail and advertising, and in practice, we have seen the occasional application of AR in marketing campaigns, AR remains a blackbox, in that both researchers and practitioners have little understanding of how the unique AR attributes can be maximised as well as in what contexts. Our study brings these two dilemmas together by examining how AR product presentations can be designed to improve consumer evaluations of innovative, yet schema-incongruent products. We propose the design of a field experiment that leverages two key attributes of AR, namely the superimposition of virtual items into real-life environments and the controllability of these virtual items. We propose to investigate how the styles of the virtual items (cartoon vs. realistic) and the levels of controllability (with control vs. without control) within an AR product presentation can facilitate incongruity resolution for schema-incongruent products, which could lead to positive product evaluations and behavioural intentions. We anticipate the findings of our experiment to provide novel insights into cognitive processes pertaining to the use of AR and to provide practical implications for the design of AR for incongruity resolution.
Developing a Game in Python

This chapter deals with the development of a real video game. No game engines will be used because that topic is too advanced, but the Python language is used and explained, especially its module, called Pygame.
Clinico-aetiological Analysis of Anaemia along with Haematological Parameters in Children and Adolescents: A Retrospective Study from a Tertiary Care Hospital, Chhattisgarh, India

Introduction: Anaemia is defined as a decreased concentration of blood haemoglobin. World Health Organization (WHO) estimates that 42% of children less than 5 years of age worldwide are anaemic and prevalence of anaemia in India in children 6-59 months for year 2019 was 53.4%. Childhood anaemia not only affects physical health but cognitive and social development as well. Aim: To determine various causes of anaemia in children in age groups 1-5 years, 6-12 years and 13 to 18 years and to determine clinical features and haematological investigations in moderate to severe anaemia. Materials and Methods: This retrospective, observational study was conducted in Department of Paediatrics, Shri Shankaracharya Institute of Medical Sciences, Bhilai, Charttisgarh, India, from September 2021 to December 2021. The data collection was done retrospectively over a period of 2 years, from September 2019 to September 2021 from patients case records which were available from Medical Record Department. A total of 300 patients from age 1 year to 18 years who were admitted in the paediatric ward with anaemia were included for study. Anaemia was defined and severity categorised based on WHO definition. The aetiology of anaemia was analysed based on Complete Blood Count (CBC) and investigations including serum ferritin, vitamin B12 level, bone marrow examination, High Performance Liquid Chromatography (HPLC) and other relevant investigations. Data was entered in a excel sheet and analysed using software Statistical Package for the Social Sciences (SPSS) version 21.0. Statistical test used was student’s t-test for paired and unpaired data for continuous variables. The p-value &lt;0.05 was considered significant. Results: Out of total 300 patients, 148 (49.3%) were males and 152 (50.7%) were females. Most of the patients were in 1-5 years of age (45.6%), followed by 13-18 years age (29.4%) and 6-12 years (25%). Total 95 (31.6%) patients had mild, 112 (37.3%) had moderate and 93 (31%) had severe anaemia. The three most common causes of anaemia were iron deficiency in 77 patients (37.5%), haemolytic anaemia in 54 patients (26.3%) and vitamin B12 deficiency in 34 patients (16.5%). Mean Red Cell Distribution Width (RDW )(31.1±10.1%) values and mean of Mean Corpuscular Volume (MCV) (98.2±15.2 fL) were higher in vitamin B12 deficiency patients as compared to iron deficiency patients mean RDW (17.2±3.5%) and mean of MCV (58.14±7.3 fL), this difference was found to be significant with p-value &lt;0.001. Conclusion: Most of the patients had moderate anaemia. Iron deficiency was most common cause of anaemia overall in children and especially below 13 years followed by haemolytic anaemia mainly sickle cell anaemia and Vitamin B12 deficiency anaemia. The MCV and RDW values from CBC can be used to identify two most common causes of nutritional anaemia.
Arctangent-Based Inertia Weight for Particle Swarm Optimization

Aiming at the premature convergence of particle swarm optimization (PSO) and the shortcomings of existing inertia weight adjustment strategies, this paper proposes a new inertia weight adjustment strategy based on the arctangent function (ATWPSO), which introduces the arctangent function of trigonometric function by being transformed into particle swarm optimization. The standard PSO, linear decreasing weight PSO, quadratic decreasing weight PSO and three kinds of PSO with adaptive inertia weight are evaluated to compare with the efficiency of ATWPSO. 10 complex CEC2017 benchmark functions are utilized to verify the promising performance of ATWPSO, which includes 4 types such as unimodal functions, simple multimodal hybrid and composition functions. Besides, ATWPSO is applied to vehicle routing problem with time windows, and be verified by Solomon benchmark instances. The experimental results show that the ATWPSO strategy proposed in this paper has better performance in solving accuracy and standard deviation, and that especially in high-dimensional problems, it is easier to obtain higher search accuracy.
MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation

Non-autoregressive translation (NAT) predicts all the target tokens in parallel and significantly speeds up the inference process.The Conditional Masked Language Model (CMLM) is a strong baseline of NAT.It decodes with the Mask-Predict algorithm which iteratively refines the output.Most works about CMLM focus on the model structure and the training objective.However, the decoding algorithm is equally important.We propose a simple, effective, and easy-to-implement decoding algorithm that we call MaskRepeat-Predict (MR-P).The MR-P algorithm gives higher priority to consecutive repeated tokens when selecting tokens to mask for the next iteration and stops the iteration after target tokens converge.We conduct extensive experiments on six translation directions with varying data sizes.The results show that MR-P significantly improves the performance with the same model parameters.Specifically, we achieve a BLEU increase of 1.39 points in the WMT'14 En-De translation task.Our code is available at https://github.com/chynphh/MaskRepeat-Predict.
Redundancy and Uncertainty-Based Algorithms for Computation Planning

Nowadays, the development and use of workflow-based applications (distributed applied software packages) are some of the key challenges in terms of preparing and carrying out large-scale scientific experiments in distributed environments with heterogeneous computing resources. The environment resources can be represented by clusters of personal computers, supercomputers, and private or public cloud platforms and differ in their computational characteristics. Moreover, the composition and characteristics of resources change in dynamics. Therefore, computations planning and resource allocation in the considered environments are important problems. In this regard, we propose new algorithms for computation planning taking into account redundancy and uncertainty in such distributed applied software packages. Compared to other algorithms of a similar purpose, the proposed algorithms use evaluations of workflow execution makespan obtained in the process of continuous integration, delivery, and deployment of applied software. The proposed algorithms provide the construction of redundant problem-solving schemes that allow us to adapt them to the dynamic characteristics of computational resources and improve distributed computing reliability. The algorithms are based on a theory of conceptual modeling computational processes. We demonstrate the process of constructing problem-solving schemes on model examples. In addition, we show the utility in using redundancy for increasing the distributed computing reliability In comparison with some traditional meta-schedulers.
Forensic Sketch Reconnaissance Using Deep Learning

Contemporary society is passing an increase in ordinary crime. To fight this, law enforcement agencies must accelerate the entire process and find a way to impeach culprits against justice. One similar system could be using facial recognition technology to identify and corroborate culprits. Conventionally, forensic artists use hand- drawn sketches to identify culprits and contemporizing this system requires relating culprits by comparing the sketches to law-enforcement databases. Taking this approach will pose a number of limitations to the current technology, as there are fairly many felonious artists available compared to the growing number of crimes. Our idea is to speed up the process for law enforcement departments by creating a standalone platform that can be used to directly sketch a suspect without backing from a forensic sketch artist and with no special training or cultural chops. Sketches can be created with drag-and- drop in operations with colorful facial rudiments, and synthetic face sketches drawn using deep literacy and pall structure can be automatically counterplotted to law-enforcement databases much briskly and more efficiently.
Automated Bug Triaging in a Global Software Development Environment: An Industry Experience

Bug triaging is a crucial task in development and maintenance of software projects. In this context, the ability to quickly assign bug reports to specialized technical groups (TG) is essential to save time, reduce costs and gain competitiveness. An issue wrongly assigned increases the risk of negative impact in a project’s budget and schedule. This paper presents an analysis of automated approaches to recommend specialized TGs to fix a new bug. The experiments show the effectiveness of the analyzed method in finding the target TG in the first rank positions, improving the Accuracy@1 in 7% and the Mean Reciprocal Rank (MRR) in 3% compared with our previous work. More significantly, the new method reduces the model’s training time from 5 h to 22 min. These results were validated in a real-world dataset composed of 410,631 bug reports obtained from the projects of a global smartphone manufacturer.
Intelligent classroom perception system based on Artificial Intelligence

Through the construction of intelligent campus classroom prediction system, the realization of intelligent management and intelligent teaching can not only greatly improve the management efficiency and teaching quality of schools in the region, but also greatly reduce the cost of school management and teaching.At the same time, it can also increase the social transparency of educational resources, and realize the intensification, standardization and Standardization development.
Application-Level Protocols

A client and a server need to exchange information via messages. TCP and UDP provide the transport mechanisms to do this. The two processes also need to have a protocol in place so that message exchange can take place meaningfully. A protocol defines what type of conversation can take place between two components of a distributed application by specifying messages and data types, encoding formats, and so on. This chapter looks at some of the issues involved in this process and gives a complete example of a simple client-server application.
CAeSaR: An Online Payment Anti-Fraud Integration System With Decision Explainability

In data-driven anti-fraud engineering for online payment services, the integration of proper function modules is an effective way to further improve detection performance by overcoming the inability of single-function methods to cope with complex and varied frauds. However, a qualified integration is really inaccessible under multiple demanding requirements, i.e., improving detection performance, ensuring decision explainability, and limiting processing latency and computing consumption. In this work, we propose a qualified integration system, named CAeSaR, that can simultaneously meet all of the above requirements. This satisfactory result is achieved by the cooperation of two innovative techniques. The first is a novel three-way taxonomy of function division, called TRTPT, according to the temporal positions of transactions relative to a reference fraudulent transaction. Based on TRTPT, CAeSaR can introduce three kinds of anti-fraud function modules which collaboratively cover all types of frauds theoretically. The second is an effective integration scheme, called TELSI. It generates the candidate decision strategies by combining the judgments of three function modules by only two simple logical connectives, which essentially ensures the decision explainability. Particularly, TELSI can assign the most effective decision strategy to the corresponding transaction adaptively by a devised stacking-based multi-classification. The advantages of CAeSaR are validated in practice over real-life data from a prestigious bank.
Using Software Programs, SDM-Assist and SnapGene, to Maximize Learning Benefits of Performing Site Directed Mutagenesis while Minimizing Logistical Challenges

Site Directed Mutagenesis (SDM) is a biological multi-step laboratory technique that introduces a point mutation in a gene, usually to mutate an individual amino acid.Implementing SDM in a Course Based Undergraduate Research Experience course comes with advantages and disadvantages.It is advantageous because it can easily accommodate research-backed educational guidelines including student ownership and decision-making while incorporating and connecting fundamental skills.For example, with SDM a student can decide the amino acid for mutagenesis.Initial steps require designing primers with one or more mismatches for the amino acid mutation as well as an additional mismatch to create/remove a restriction enzyme site in the plasmid, which can later be used to verify mutagenesis.The choice in amino acid influences which restriction enzymes sites will be available, thereby influence experimental results and create opportunities for a student to play a larger role in the process while using their "own" data.A disadvantage in using SDM is that it can be technically challenging to guide large classrooms of students through this process to arrive at their own decisions in a scientifically sound manner.For example, some amino acids are more technically challenging to mutate than others.In this workshop participants will learn how to use two freely available software programs, SDM-assist and SnapGene, to develop a student activity to maximize student decision-making while minimizing the technical challenges associated with the initial steps of this type of work.Participants will learn how each student can easily use SDM-assist to generate mutagenic primers and use SnapGene to predict the results of the mutagenesis.Participants will learn how these two programs have been incorporated in an upper-level Biology CURE.Real examples of student data will be shown and discussed.
Adaptive Kernel Smote-Svm Algorithm for Imbalanced Data Classification

Abstract : Imbalance data classification has become a challenging task in recent years, and the synthetic minority oversampling technique (SMOTE) combined with a support vector machine (SVM) is a well-known algorithm (SMOTE-SVM) for dealing with class-imbalance problems. However, SMOTE utilizes a linear operation to generate new samples for SVM for classification by projecting a dataset into a high-dimensional space, which may then become noise samples and reduce the classification accuracy. In this study, we propose a SMOTE-SVM-related method. First, the algorithm introduces support vectors (SVs) as borderline samples to generate new samples in the kernel space. According to the class distribution of neighbors, the SVs are first labeled as “safe” or “dangerous”, and new samples are then adaptively generated for different label samples by outward or inward calculation. The outward calculation generates new samples in the low-density areas of minority samples, which helps to improve the classification surface by improving the diversity of new minority samples. The Gram matrix is then designed, and the SVM decision function can be constructed without the explicit expression of new samples in the kernel space. The improved SMOTE-SVM algorithm unifies sample generation and classification in the same kernel space, which improves the credibility of the new samples. Experiments on multiple datasets show that the proposed algorithm can effectively improve the classification results on imbalanced datasets.
Interpreting Randomly Wired Graph Models for Chinese NER

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks.However, most existing approaches only focus on improving the performance of models but ignore their interpretability.In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER.Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks.Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden states of RWGNN.
Sound and Music in Video Games: Relevant Issues

The article examines the main current issues of sound and, in particular, soundtrack in the modern video games. The immense popularity of this new genre layer of modern musical culture deserves serious scholarly understanding. So the author names and highlights such important issues as the general characteristic features of sound image of video games, diverse sounds and original musical compositions. The music for the game is presently being written by such famous composers as Hans Zimmer, Jeremy Soul, Harry Gregson-Williams, Akira Yamaoka, and Jesper Kyd. Recently, music in video games has become more in demand by a wide audience than movie music, due to its artistic merit and capabilities. The observation of the functions of music in video games seems to be relevant to the author — their system with various links to well-known products is described in the article. Moreover, the most important issue of the theoretical, aesthetic characteristic features of composers’ original music, which is practically not touched upon in modern literature in Russia and in other countries, is also posed and briefly highlighted. To illustrate such a formulation of the question, various opinions about the musical language, style and stylistic features are presented by the illustrative example of the American composer Paul Anthony Romero. Certain important positions have been made about the entirely new format of composing technique — the use of special network libraries.