Reviewerly/exHarmony huggingface dataset:
Classification mechanisms for digitalized information in web application development, taking into account cloud server deployment trends

With the transition from natural to virtual communication, there are new technologies for presenting information, the perception of which depends on its presentation by modern multimedia. Visual perception plays an important role in everyday life, helping in learning and communicating with other people. Behind the most effective perception is a complex process of understanding the information obtained. Visual perception of information includes the processes of seeing and reading. Building assumptions on what we see, we rely on expectations, beliefs, past knowledge and previous experience. Reading is a complex activity that involves both the process of perception and thought. At the very beginning of the formation of reading, understanding is not common, but only after perception, as the development and automation of reading skills, understanding begins to outpace the process of perception, manifested in the emergence of semantic assumptions, guessing meaning within words. Perception of visual image is accompanied by the actualization of its auditory-motor image, without which it is impossible to recognize. Reading is accompanied by inner speech. The same information can be understood by different people with different degrees of depth, which entails the existence of factors that affect the process of perception and understanding of texts. Such factors can be divided into the following groups: characteristics of the individual who acts as a subject of perception and understanding; parameters of the text and the reality that is reflected in this text; features of the situation in which the process of perception and understanding takes place. Text in a virtual environment, ie media information - is more accurately and quickly perceived by a person and it depends on various factors influencing perception, as well as the type and method of presenting information.&#x0D; The purpose of the study is to analyze the perception of digitalized information, the formation of recommendations for professionals with UI / UX, which will be further formed into a prototype of a possible web directory. The aim is also to develop a possible tool model that can be used not only by the above specialists, but also by SMM-managers, business analysts and others. The methods of research were chosen analysis - the study of individual parts of the objects of study, comparing different approaches or their individual components to obtain a holistic picture of the subject area.
Ensuring the security for cloud storage data using a novel ADVP protocol by multiple auditing

The increasing growth of storage data in the cloud and virtual reality allows it a significant challenge to maintain the security of data that is outsourced by the data owners. The existing protocols for the auditing of cloud storage normally use post-quantum cryptography to monitor data integrity to solve this issue. Nevertheless, these protocols use strong cryptography to create data tags that reduce their reliability and extensibility. In this research work, we propose a novel protocol named "Advanced Distribution Verification Protocol (ADVP)" to design secure cloud storage to track the quality of cloud-saved data with the help of "Multiple-Third Party Auditors" (mTPAs) and not a "Single-TPA" (sTPA). This protocol requires several SUBTPAs that operate throughout the single TPA, which then needs to be spread equally throughout the SUBTPAs to guarantee that every SUBTPA is checked throughout the entire section. It then has strict safety evidence of malicious cloud resistance and a promise of privacy. It would also broaden the suggested protocol and include additional implementation scenarios to enable data dynamics and batch audit. It sums up a structured process to establish security as a further contribution.
Perspectives Generation via Multi-Head Attention Mechanism and Common-Sense Knowledge

Consideration of multiple viewpoints on a contentious issue is critical for avoiding bias and assisting in the formulation of rational decisions. We observe that the current model imposes a constraint on diversity. This is because the conventional attention mechanism is biased toward a single semantic aspect of the claim, whereas the claim may contain multiple semantic aspects. Additionally, disregarding common-sense knowledge may result in generating perspectives that violate known facts about the world. The proposed approach is divided into two stages: the first stage considers multiple semantic aspects, which results in more diverse generated perspectives; the second stage improves the quality of generated perspectives by incorporating common-sense knowledge. We train the model on each stage using reinforcement learning and automated metric scores. The experimental results demonstrate the effectiveness of our proposed model in generating a broader range of perspectives on a contentious subject.
Factors Affecting Mobile Phone Usage by Farmers As A Source of Agricultural Information In Sharqia Governorate, Egypt

Mobile phones are one of the most important ICTs that contribute to farmers’ adoption of agricultural innovations around the world, this study was conducted to identify the factors influencing mobile phone usage as a source of agricultural information for farmers in Sharqia Governorate, Egypt. A random sample of 355 respondents was interviewed during the period from January to April 2021. The results showed the diversity of sources that farmers depend on to obtain agricultural information such as fertilizer and supplies dealers, experienced farmers, relatives, and neighbors which were the main sources of agricultural information for respondents. Results indicated that only 9.9% of the respondents had a high mobile phone usage as a source of agricultural information, while 69.3% of them had a low degree of usage. A negative statistically significant relationship was found between the total degree of respondents' use of mobile phones as a source of agricultural information and their age at a 0.01 level of significance. while the relationship was positive with variables such as educational level, farm size, possession of a smartphone, and membership in social organizations. The mobile network's weak coverage in the village, lack of knowledge about agricultural applications, lack of knowledge about mobile phone operation, and the high cost of internet services were at the forefront of these problems facing the surveyed farmers who use mobile phones for agricultural purposes. Therefore, this study recommends the agricultural extension to make greater efforts to spread the use of ICT tools, including mobile phones technology among farmers.
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility

This article studies the infinite-width limit of deep feedforward neural networks whose weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden node of the network is assigned a nonnegative random variable that controls the variance of the outgoing weights of that node. We make minimal assumptions on these per-node random variables: they are iid and their sum, in each layer, converges to some finite random variable in the infinite-width limit. Under this model, we show that each layer of the infinite-width neural network can be characterised by two simple quantities: a non-negative scalar parameter and a L\'evy measure on the positive reals. If the scalar parameters are strictly positive and the L\'evy measures are trivial at all hidden layers, then one recovers the classical Gaussian process (GP) limit, obtained with iid Gaussian weights. More interestingly, if the L\'evy measure of at least one layer is non-trivial, we obtain a mixture of Gaussian processes (MoGP) in the large-width limit. The behaviour of the neural network in this regime is very different from the GP regime. One obtains correlated outputs, with non-Gaussian distributions, possibly with heavy tails. Additionally, we show that, in this regime, the weights are compressible, and some nodes have asymptotically non-negligible contributions, therefore representing important hidden features. Many sparsity-promoting neural network models can be recast as special cases of our approach, and we discuss their infinite-width limits; we also present an asymptotic analysis of the pruning error. We illustrate some of the benefits of the MoGP regime over the GP regime in terms of representation learning and compressibility on simulated, MNIST and Fashion MNIST datasets.
https://irojournals.com/aicn/AllVolumes.html

Most of the traditional recommendation algorithm models are recommended based on the user's own historical preferences, although it can recommend POI for users to a certain extent. But in real life, people are more willing to ask their friends what they think when they have a difficult decision. Therefore, a word2vec-based social relationship point of interest recommendation model (W-SimTru) is proposed, which combines the similarity of friends based on cosine similarity with the friend trust recommendation algorithm based on TF-IDF to improve the model recommendation effect. In addition, before modeling the similarity of users, word2vec is used to process the user's historical check-in behavior to solve the problem of inaccurate recommendation due to sparse check-in data. Finally, experiments are carried out on three datasets of Los Angeles, Washington and NYC in Gowalla, and the experimental results show that the proposed W-SimTru recommendation algorithm outperforms the algorithms of the three comparative experiments.
AI Based Chess Solver

The aim of this project is to create an artificial intelligence that can play chess and analyze how it has improved with the different iterations of the engine. To achieve this goal different approaches to chess engineswill be explored. These different algorithms will be implemented into Python with the aim of creating a lowto midlevel chess engine
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but prone to produce artifacts upon challenging cases in real-world scenarios, since joint participation in the same degradation branch will impact final performance due to huge domain gap between real-world and synthetic LR ones obtained by generators. To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch. Our Semi-Cycled Generative Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the domain gap between the real-world LR face images and the synthetic LR ones, and to achieve accurate and robust face SR performance by the shared restoration branch regularized by both the forward and backward cycle-consistent learning processes. Experiments on two synthetic and two real-world datasets demonstrate that, our SCGAN outperforms the state-of-the-art methods on recovering the face structures/details and quantitative metrics for real-world face SR. The code will be publicly released at https://github.com/HaoHou-98/SCGAN.
Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers

Performance of text classification models tends to drop over time due to changes in data, which limits the lifetime of a pretrained model. Therefore an ability to predict a model's ability to persist over time can help design models that can be effectively used over a longer period of time. In this paper, we provide a thorough discussion into the problem, establish an evaluation setup for the task. We look at this problem from a practical perspective by assessing the ability of a wide range of language models and classification algorithms to persist over time, as well as how dataset characteristics can help predict the temporal stability of different models. We perform longitudinal classification experiments on three datasets spanning between 6 and 19 years, and involving diverse tasks and types of data. By splitting the longitudinal datasets into years, we perform a comprehensive set of experiments by training and testing across data that are different numbers of years apart from each other, both in the past and in the future. This enables a gradual investigation into the impact of the temporal gap between training and test sets on the classification performance, as well as measuring the extent of the persistence over time.
MEDICAL IMAGE ENCRYPTION USING DNA ENCODING AND MODIFIED CIRCULAR SHIFT

This paper proposes a new encryption method for the encryption of medical images. The method is used to divide the image into several blocks and then scramble the image blocks using DNA chains and then shift the pixels in a circle with certain rules. To provide a more secure result, the input key contains a DNA chain and is equipped with complementary rules, and is converted into a hexadecimal number using a DNA coding table. Experimental results and values of NPCR and UACI show that the scheme achieves good encryption and decryption results.
Gaussian Processes and Bayesian Optimization for High Precision Experiments

High-precision measurements require optimal setups and analysis tools to achieve continuous improvements. Systematic corrections need to be modeled with high accuracy and known uncertainty to reconstruct underlying physical phenomena. To this end, we present Gaussian processes for modeling experiments and usage with Bayesian optimization, on the example of an electron energy detector, achieving optimal performance. We demonstrate the method's strengths and outline stochastic variational Gaussian processes for physics applications with large data sets, enabling new solutions for current problems.
A Practical Dental Image Enhancement Network for Early Diagnosis of Oral Dental Disease

Abstract Intelligent robotics and expert system applications in dentistry suffer from identification and detection problems due to the non-uniform brightness and low contrast in the captured images. Moreover, during the diagnostic process, exposure of sensitive facial parts to ionized radiations (e.g., X-Rays) has several disadvantages and provides a limited angle for the view of vision. Capturing high-quality medical images with advanced digital devices is challenging, and processing on these images distorts the contrast and visual quality. It curtails the performance of potential intelligent and expert systems and disincentives the early diagnosis of oral and dental diseases. The traditional enhancement methods are designed for specific conditions, and network-based methods rely on large-scale datasets with limited adaptability towards varying conditions. This paper proposed a novel and adaptive dental image enhancement strategy based on a small dataset, and proposed a paired branch Denticle-Edification network (Ded-Net). The input dental images are decomposed into reflection and illumination in a multilayer Denticle network (De-Net). The subsequent enhancement operations are performed to remove the hidden degradation of reflection and illumination. The adaptive illumination consistency is maintained through the Edification network (Ed-Net). The network is regularized following the decomposition congruity of the input data and provides user-specific freedom of adaptability towards desired contrast levels. The experimental results demonstrate that the proposed method improves visibility and contrast and preserves the edges and boundaries of the low contrast input images. It proves that the proposed method is suitable for intelligent and expert system applications for future dental imaging.
Moral and psychological readiness for pedagogical activities

The article examines the moral and psychological readiness for pedagogical activity as a result of the strategy of training future teachers on the principles of student centrism. The purpose of the article is theoretical substantiation and experimental verification of the effectiveness of the principles of student centrism in the formation of moral and psychological readiness of future teachers for professional activity. Using a set of research methods identified the resource possibilities of training future teachers on the principles of student-centrism in the formation of moral and psychological readiness, which positively affects the effectiveness of students' pedagogical actions as an indicator of the quality of their education. The results obtained experimentally make it possible to assert that the strategy of student-centrism in university education is implemented through a personality-oriented individualization of training, practically aimed at the formation of students' moral and psychological readiness.
Implanting Vocabulary for Long Terms Memory through Memrise and Quizlet Applications

The student's ability to understand and use academic and research vocabulary was lacking. Therefore, this research tries to enhance the students' vocabulary achievement by applying Memrise and Quizlet Applications. This research applies Classroom Action Research using four procedures: Planning, Action, Observation, and Reflection. Researchers obtained data from tests and questionnaires. The results showed that vocabulary planting through the Memrise and Quizlet applications increased student achievement. In addition, students also showed a positive attitude towards the application of Memrise and Quizlet for vocabulary learning. Lecturers or teachers can use the Memrise and Quizlet Applications as a medium to motivate students and provide fun and independent learning. Vocabulary achievement improved after learning vocabulary using Memrise and Quizlet applications from the results presented above. This study also used the Memrise and Quizlet applications to instil vocabulary. Therefore, the researcher suggests that teachers or lecturers who teach use the Memrise and Quizlet applications as alternative media to instil word material in students in the long term.
TASK APPROACH AND PRINCIPLE PRINCIPLE PROSPECTS IN THE PEDAGOGICAL PROCESS - BASIS OF ACTIVATION OF PROFESSIONAL POSITION STUDENTS

This article is devoted to forming of active professional position of future teachers by means of using role perspective principle and problem approach during pedagogical training.
What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning

Understanding spatial relations is essential for intelligent agents to act and communicate in the physical world. Relative directions are spatial relations that describe the relative positions of target objects with regard to the intrinsic orientation of reference objects. Grounding relative directions is more difficult than grounding absolute directions because it not only requires a model to detect objects in the image and to identify spatial relation based on this information, but it also needs to recognize the orientation of objects and integrate this information into the reasoning process. We investigate the challenging problem of grounding relative directions with end-to-end neural networks. To this end, we provide GRiD-3D, a novel dataset that features relative directions and complements existing visual question answering (VQA) datasets, such as CLEVR, that involve only absolute directions. We also provide baselines for the dataset with two established end-to-end VQA models. Experimental evaluations show that answering questions on relative directions is feasible when questions in the dataset simulate the necessary subtasks for grounding relative directions. We discover that those subtasks are learned in an order that reflects the steps of an intuitive pipeline for processing relative directions.
ANALYSIS OF THE EFFECTIVENESS OF THE USE OF ADAPTIVE TRAINING PLATFORMS IN SECONDARY VOCATIONAL EDUCATION

The article considers the tasks and features of mathematics training for studentsof secondary vocational education. Special attention is paid to the need to solve the problemof adaptation of students to the conditions of study in college and the organization ofindependent work. In this regard, the authors propose to make wider use of the practiceof adaptive learning as innovative pedagogical tools. The article considers the concept ofthe effectiveness of adaptive personalized learning and suggests the directions by which itcan be evaluated. As an example, the experience of implementing an adaptive educationalcourse “Mathematics”, designed in the Articulate Storyline platform, is analyzed. The moduleis designed to organize and support adaptive learning of students of the Department ofInformation Systems by means of adaptive educational technologies. The results of thetraining are analyzed, and the possibilities of the Articulate Storyline platform in ensuringthe independent work of students are presented. The main part of the article is devotedto evaluating the effectiveness of e-learning using an adaptive educational platform. Withthe help of questionnaires and tools of the Articulate Storyline platform, an assessment ofthe educational result achieved was made, the degree of motivation of students to masterthe discipline of mathematics was analyzed, and the attitude of students to the process ofe-learning using an adaptive educational platform was investigated.
Populism and its Economic Impact: What Does the Evidence Say?

Populism is an essentially contested phenomenon due to its complex nature. Even more difficult is to study its economic consequences. This paper aims to study populism as an economic phenomenon by reviewing literature devoted to studying its economic impacts. The paper starts with a brief overview of the concept of populism and its history. Further, it delves into economic literature that has so far tried to study its empirical effects on economies around the world. The paper seeks to investigate whether the long-held conceptions of economic theory are corroborated by empirical evidence or not.n act of redemption, but his sacrificial death is far from being suicidal because he did not allow the powers of temptations to lead him astray.
Improving College Assistance with the Help of Richer Human Computer Interaction and Speech Recognition

Abstract: Chat bots are intelligent systems that interpret and react to users questions in their native language. In a conversation, the chat bot reacts in the same way as a human would. It functions as a virtual assistant, and its accuracy is assessed by determining a correlation between user questions and chat bot responses. For a better user experience, the implemented Chat bot has two modes: text mode and audio mode. It provides an interactive approach of answering through voice messages when in audio mode. There is a long line at the inquiry window during the Institute's Academic Admission procedure. Even more challenging is the situation for parents who live in various cities, states, and nations. The purpose of the system is to give students and parents a place to ask questions and get answers using simple English language text messages or audio commands. Instead of queueing at an information desk to ask questions about the admission process students and parents will collaborate with the bot. The proposed application is easily available to students and responds to user any time. Keyword: Chat Bot, College bots, Interactive bots, Dialogue based bots, NLP, Text based bots