Reviewerly/exHarmony huggingface dataset:
An Upper Bound on the Reliability Function of the DMC

We derive a new upper bound on the reliability function for channel coding over discrete memoryless channels. Our bounding technique relies on two main elements: (i) adding an auxiliary genie-receiver that reveals to the original receiver a list of codewords including the transmitted one, which satisfy a certain type property, and (ii) partitioning (most of) the list into subsets of codewords that satisfy a certain pairwise-symmetry property, which facilitates lower bounding of the average error probability by the pairwise error probability within a subset. We compare the obtained bound to the Shannon-Gallager-Berlekamp straight-line bound, the sphere-packing bound, and an amended version of Blahut's bound. Our bound is shown to be at least as tight for all rates, with cases of stricter tightness in a certain range of low rates, compared to all three aforementioned bounds. Our derivation is performed in a unified manner which is valid for any rate, as well as for a wide class of additive decoding metrics, whenever the corresponding zero-error capacity is zero. We further present a relatively simple function that may be regarded as an approximation to the reliability function in some cases. We also present a dual form of the bound, and discuss a looser bound of a simpler form, which is analyzed for the case of the binary symmetric channel with maximum likelihood decoding.
Word-by-Word Generation of Visual Dialog Using Reinforcement Learning

The task of visual dialog generation requires an agent holding a conversation referencing question history, putting the current question into context, and processing visual content. While previous research focused on arranging questions to form dialog, we tackle the more challenging task of arranging questions from words, and dialog from questions. We develop our model in a simple “Guess which?” game scenario where the agent needs to predict an image region that has been selected by an oracle by asking questions to the oracle. As a result, the reinforcement learning agent arranges words to refer to the image features strategically to acquire the required information from the oracle, memorizing it and giving the correct prediction with an accuracy well above 80%. Imposing costs on the number of questions asked to the oracle leads to a strategy using few questions, while imposing costs on the number of words used leads to more but shorter questions. Our results are a step towards making goal-directed dialog fully generic by assembling it from words, elementary constituents of language.
Szövegnyelvészeti szempontok a műfordításkritikában. Két francia szövegrészlet magyar fordításának példája

The study aims to present a possible way of applying the interdisciplinary text approach in literary translation criticism through the comparison of two French literary texts and their Hungarian translation.Instead of traditional literary criticism, often based on subjective impressions, the study presents a complex comparative text analysis approach, in which text linguistics, especially semiotic textology and functional stylistics complement each other and together ensure the objective character of the equivalence analysis of the source and target language texts.The comparative text analysis aims to demonstrate as a practical example that text linguistics can provide theoretical and methodological viewpoints that contribute to the practice of literary translation criticism according to scientific criteria.
The Influence of Education Curriculum, Motivation, and Parenting Patterns on The Competence of Cadets in Banten Shipping Polytechnic

The quality of human resources, one of which is determined by the education factor, has a very important strategic role in determining the progress and success of a country. Talking about Indonesia's development, of course, cannot be separated from Indonesian education in the eyes of the world. Education is one of the important factors of the authority of a country. With a good education, of course, it will produce the next generation of people who are smart and competent in their respective fields. So that the state of the country will continue to improve along with the emergence of successors of the state from all walks of life. This study aims to determine and analyze the effect of educational curriculum, motivation, and parenting patterns on the Competence of the Banten Shipping Polytechnic Cadets. The research was conducted at the Banten Shipping Polytechnic with a research time of March 2022 to June 2022. The method used is a quantitative analysis approach through data collection techniques using questionnaires. The population studied was the Banten Shipping Polytechnic cadets in 2020, with a sample of 394 respondents. The sampling method used probability sampling with a simple random sampling technique—the method of analysis for hypothesis testing with the help of software SmartPLS 3.2.9. The analysis results show that the educational curriculum, the motivation, and the pattern of caregivers have a positive and significant effect on the competence of the Banten Shipping Polytechnic cadets. The original sample results show the influential factors, where the competence of the cadets is strongly influenced by motivation. Motivation is much influenced by the encouragement of learning needs in the Parenting Pattern. It is concluded that the education curriculum, especially the competency approach, is highly related to the educational curriculum. The highest motivation comes from the need to learn, which means cadets are motivated by the need to learn. The pattern of caregivers, especially suggestive (the method used to provide encouragement or support in the form of views, suggestions, and advice for a more interactive communicative atmosphere), has a high relationship with the pattern of caregivers.
KNOWLEDGE AND ATTITUDES OF SCHOOL-AGE CHILDREN TOWARDS VEGETABLE AND FRUIT CONSUMPTION AT SD NEGERI 9 PEKANBARU

There are more cases of malnutrition than other cases, such as stunted growth of children and loss of vitamins and minerals. Lack of consumption of vegetables and fruit causes children to be susceptible to malnutrition. The novelty of this study is because it examines the knowledge and attitudes of school-age children towards vegetable and fruit consumption at SD Negeri 9 pekanbaru The purpose of this study was to determine the relationship between knowledge and attitudes towards vegetable consumption in school-age children. This research is a quantitative type of research using cross sectional method which was conducted in July 2015 to determine the knowledge and attitude scores. This research was conducted at the State Elementary School 9 Pekanbaru in a school environment where school children are easier to lack nutrients such as vitamins and minerals during their growth period. Of the 88 respondents studied, there is a significant relationship between knowledge of consumption of vegetables and fruit indicated by a P Value of 0.008 0.05 (OR: 4.667. CI: 1.570 – 13.874). For attitudes, there is a relationship between attitudes towards consumption of vegetables and fruit in school children. P value 0.004 0.05 (OR : 6.500, CI : 2.049 – 20.619) From the results of this study, consumption of vegetables and fruit in children is still lacking. Consumption of vegetables and fruit can be increased through the role of parents and teachers in schools so as to reduce the problem of malnutrition. In addition, facilities and infrastructure in schools can also support children to be able to consume enough vegetables and fruit every day.Keywords: Knowledge and attitudes of school-age children; consumption of vegetables and fruit for school-age children.
Determination of accuracy and probability in the analysis of large-scale biomedical data

The paper below introduces an image classification workflow concept for biomedical purposes and present the process of determination of accuracy and probabilities in the analysis of large-scale biomedical data. The workflow is based on image features extraction through deep network embedding. The distance computation in the workflow is accomplished using Cosine distance. The experiments are conducted on the base of the Logistic Regression, Random Forest and Naïve Bayes and are directed on accuracy and probability in the analysis of large-scale data. The performed analysis shows the advantage of the Logistic Regression results which are considered to be the most reliable compared to the results generated through the Random Forest and Naïve Bayes.
Ontology-based approach in digital healthcare recruitment

Digital healthcare industry faces huge talent gaps and shortages, and is expected to worsen over the next few years. Since many roles in healthcare require advanced degrees and certifications, it makes sense to focus on identifying promising talent early. There are many jobs and many medical job titles in the health sector and there is a growing need for people to fill these jobs. Some of them have clinical functions, such as doctors, surgeons and nurses. Others are technical jobs such as radiologists, healthcare support that involve helping other doctors, and administrative jobs. In this article, an ontology-based approach is presented not only to find suitable candidates for current positions, but also candidates who could be potential candidates in the future, and will advise them in a personalized manner about the right training they need to acquire for qualifying for a certain job and/or organization / team. This approach is developed from the point of view of the employer, and the candidate or even the student who is still considering opting for a medical/healthcare profession. On the operational level, the new approach will keep track of the job seekers’ competencies even if they don't fit the current positions, and will advise about the right training and skills they need. On the strategic level, the discussion is about creating a common vocabulary, which makes the process more efficient. Healthcare strategies and ontologies: clinical, technical, support and administrative are described.
Design and development challenges for building an interactive app for cultural and historical heritage

Mobile application development is rapidly increasing its popularity due to the extensive use of mobile devices all over the world. The objective of this article is to outline the challenges that are faced during the design and development of such a mobile application focused on the travel and tourism industry. From choosing the right tech stack to supporting all screen sizes and ensuring data security, mobile development becomes more and more complex in a world of ever increasing mobile usage.
Dynamic code generation for CRUD operations in web development

In this paper, an architecture based on ASP .NET MVC is considered, providing dynamic user interface generation (runtime scaffolding) for operations on creating, reading, updating and deleting data. The architecture provides the flexibility to build an MVC application, reducing the amount of code used and simplifying the process of maintaining applications compared to classical approaches.
Biomedical image classification algorithms evaluation

The object of the following paper is a concept for biomedical image classification. On its base, two alorithms for Covid-19 image classification are designed. For the image decomposition and feature vectors a wavelet transform is used and in particular the Dual Tree Complex Wavelet Transform. For the first algorithm, wavelet transform is performed at the third level and the second algorithm is designed at the second transform level. A comparative analysis on both algorithms is accomplished based on the evaluation metrics accuracy, precision and recall. The results are depicted graphically.
HANDWRITING TEXT RECOGNITION USING NEURAL NETWORK

This project introduces a way to recognize text or handwritten information covert into machine readable text in a form the computer can process, store and edit as a text file or as a part of a data entry and manipulation software.Then it converts the machine readable text to speech.People who suffer from low vision, sight and visual impairments, who are not able to see words and letters in ordinary newsprint, books and notes were the ones this proposal was meant to help.Moreover this project also can be capstone for research and development of artificially intelligent virtual assistants that require a glimpse into the human world for information such as street signs and receipts.Using a combination of Convolution neural networks and Recurrent Neural Networks the project successfully came about to recognize text and hand written information.Another deep learning network, WaveNet was employed to convert the text which is recognized from the handwriting to covert into a raw audio file.Hence using this complete system one can pass an image containing text, have it recognized by the computer and also be read out to the user.
RAILS: A Robust Adversarial Immune-inspired Learning System

Adversarial attacks against deep neural networks (DNNs) are continuously evolving, requiring increasingly powerful defense strategies. We develop a novel adversarial defense framework inspired by the adaptive immune system: the Robust Adversarial Immune-inspired Learning System (RAILS). Initializing a population of exemplars that is balanced across classes, RAILS starts from a uniform label distribution that encourages diversity and uses an evolutionary optimization process to adaptively adjust the predictive label distribution in a manner that emulates the way the natural immune system recognizes novel pathogens. RAILS' evolutionary optimization process explicitly captures the tradeoff between robustness (diversity) and accuracy (specificity) of the network, and represents a new immune-inspired perspective on adversarial learning. The benefits of RAILS are empirically demonstrated under eight types of adversarial attacks on a DNN adversarial image classifier for several benchmark datasets, including: MNIST; SVHN; CIFAR-10; and CIFAR-10. We find that PGD is the most damaging attack strategy and that for this attack RAILS is significantly more robust than other methods, achieving improvements in adversarial robustness by $\geq 5.62\%, 12.5\%$, $10.32\%$, and $8.39\%$, on these respective datasets, without appreciable loss of classification accuracy. Codes for the results in this paper are available at https://github.com/wangren09/RAILS.
INTERNET PLATFORMS AS A FACTOR OF EMOTIONAL DISCOURSE OF VIGILANTS COMMUNITIES

This article identifies similarities and differences in emotional discourse in the communication of subscribers of vigilante Internet resources located on different technological platforms. As part of this study, API was used to upload comments over the past two years to materials posted in typical vigilante communities (Vkontakte API – «Lev Vs», «StopHam», «Piggy vs», «Anti-dealer»; Youtube API - the most popular videos «Lev Vs», «StopHam», «Piggy vs», «Sober Yard»). The sample size was 169,923 comments on Vkontakte and 175,096 comments on YouTube. The emoticon emoji served as the unit of analysis. Fixing the amount of emojis used was implemented with the assistance of a special Python script. Based on available data, a network analysis was carried out using the Gephi program (Fruchterman-Reingold layout). The authors concluded that it is not the goal direction of the vigilante group, but the communication tool that becomes the most important factor for the formation of a paralinguistic emotional discourse. Vigilante media, presented in the form of public and channels, act as mass media, and viewers and readers of their communities are the audience consuming this information. This argument is confirmed by the constant use of emojis that emphasize the «entertainment» of the presented content. Thus, most often, representatives of vigilante communities form not adherents who are ready to take part in their actions, but consumers.
Centralization vs. Decentralization - supply chain networks and blockchain in the agri-food business

Hierarchically organized businesses have been dominating our economy for centuries. We follow this structure for so long now because it is very efficient to allocating resources, performing coordination activities, and building relationships. However, potentially disruptive technologies such as Blockchain Technology (BCT), Artificial Intelligence (AI), and Internet of Things (IoT) have the potential to transform the centralized agri-food supply chain networks (SCN) into decentralized digital ecosystems, which will impact business processes and workflows alike. Still today the traditional agri-food SCN is characterized by a pyramid-hierarchic structure where decisions are being made by a central authority. It is typically headed by a focal firm which is responsible for the coordination of the network while determining the strategy and goals of the SCN. We investigated BCT use cases both in the agri-food industry and across several industries. Our literature- and use case-based research unveiled that supply chain use cases which have significantly higher operational implementations compared to their peers exhibit predominantly the following characteristics: they operate in a vertically coordinated ecosystem, they are dominated by a focal firm, and participation of suppliers is mandated by a central authority. The use cases displaying the mentioned characteristics have been found predominantly with consortium type of BCT platform implementations. As agri-food SCNs operate in vertically coordinated ecosystems we suggest that solutions such as provenance or traceability in agri-food SCNs should be implemented based on consortium BCTs as those seem to support best the traditional agri-food SCN structure.
First-Order Modal $ξ$-Calculus: On the Aspects of Application and Bisimulation

This paper proposes first-order modal $\xi$-calculus as well as genealogical Kripke models. Inspired by modal $\mu$-calculus, first-order modal $\xi$-calculus takes a quite similar form and extends its inductive expressivity onto a different dimension. We elaborate on several vivid examples that demonstrate this logic's profound utility, especially for depicting genealogy of concurrent computer processes. Bisimulation notion for the logic has also been thoroughly examined.
Sports Camera Pose Refinement Using an Evolution Strategy

This paper presents a robust end-to-end method for sports cameras extrinsic parameters optimization using a novel evolution strategy. First, we developed a neural network ar-chitecture for an edge or area-based segmentation of a sports field. Secondly, we implemented the evolution strategy, which purpose is to refine extrinsic camera parameters given a single, segmented sports field image. Experimental comparison with state-of-the-art camera pose refinement methods on real-world data demonstrates the superiority of the proposed algorithm. We also perform an ablation study and propose a way to generalize the method to additionally refine the intrinsic camera matrix.
Reducing the Number of Training Cases in Genetic Programming

In the field of Machine Learning, one of the most common and discussed questions is how to choose an adequate number of data observations, in order to train our models satisfactorily. In other words, find what is the right amount of data needed to create a model, that is neither underfitted nor overfitted, but instead is able to achieve a reasonable generalization ability. The problem grows in importance when we consider Genetic Programming, where fitness evaluation is often rather slow. Therefore, finding the minimum amount of data that enables us to discover the solution to a given problem could bring significant benefits. Using the notion of entropy in a dataset, we seek to understand the information gain obtainable from each additional data point. We then look for the smallest percentage of data that corresponds to enough information to yield satisfactory results. We present, as a first step, an example derived from the state of art. Then, we question a relevant part of our procedure and introduce two case studies to experimentally validate our theoretical hypothesis.
Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design

Many games are reliant on creating new and engaging content constantly to maintain the interest of their player-base. One such example are puzzle games, in such it is common to have a recurrent need to create new puzzles. Creating new puzzles requires guaranteeing that they are solvable and interesting to players, both of which require significant time from the designers. Automatic validation of puzzles provides designers with a significant time saving and potential boost in quality. Automation allows puzzle designers to estimate different properties, increase the variety of constraints, and even personalize puzzles to specific players. Puzzles often have a large design space, which renders exhaustive search approaches infeasible, if they require significant time. Specifically, those puzzles can be formulated as quadratic combinatorial optimization problems. This paper presents an evolutionary algorithm, empowered by expert-knowledge informed heuristics, for solving logical puzzles in video games efficiently, leading to a more efficient design process. We discuss multiple variations of hybrid genetic approaches for constraint satisfaction problems that allow us to find a diverse set of near-optimal solutions for puzzles. We demonstrate our approach on a fantasy Party Building Puzzle game, and discuss how it can be applied more broadly to other puzzles to guide designers in their creative process.
Construction of A Class of Minimal Linear Codes

Minimal linear codes are a special subclass of linear codes and have significant applications in secret sharing and secure two-party computation. In this paper, based on the relationship of the defining set and the Walsh spectrum of its characteristic functions, the authors obtain a class of linear codes with a few weights and present the weight distribution. Results show that almost all the new codes obtained in this paper are minimal codes, which can be used to design a secret sharing scheme with good access structure as they are fail to match the A-B condition.