Reviewerly/exHarmony huggingface dataset:
Activation Functions Instituted Clustering for Automated Generation of Programming Code Contracts

Several object-oriented analysis methods require constraints in form of programming constraints to reflect rules for extensive verification of concurrent software with higher degree of polymorphism and inheritance relationships. To address the need of software verification in time-restrained environment, the proposed work builds such contracts in an automated way with minimal involvement by developers. The behavioral and structural information of concurrent Java software is extracted to derive the behavioral dependency measure as constraints in form of a decision tree and thereafter transform them as automated code contracts. Then, optimization of derived contracts is performed with modified particle swarm optimization algorithm to identify the viable contracts. In order to validate the effectiveness of swarm optimization in contracts generation, enhanced k-means clustering with neural networks-associated activation functions, namely tangent hyperbolic and variants of rectified linear units, is applied on contracts derived from behavioral dependency measure, forming defined clusters of similar contracts, and results of both methods are compared. The results obtained show the efficiency of contracts generated by both optimization and clustering techniques. The work also compares the performances of the applied clustering algorithms to assess the correctness of contracts generation by each clustering. The findings indicated the tangent hyperbolic function guided k-means clustering accurately and grouped the programming contracts with efficient time and memory resources usage.
Realization of Synchronized Movement between Caregiver and Electric Wheelchair

A caregiver has an important role to not only help patients with high levels of disability to move around, but also be aware and manage the patient's state of health. Visual cues on the patient's expression is an especially important source of information on the patient's well-being, especially for those with high levels limitations on cue sending, such as Amyotrophic Lateral Sclerosis (ALS) patients. Thus, this paper proposes a system that enables the caregiver to control the movement of the wheelchair while waking alongside the wheelchair where they can also interact with the patient directly. The proposed approach has been validated through experiments.
Image Encryption Method based on Elliptical Curve Cryptography over XOR and Scrambling Techniques

Security and Privacy are the major concern in today’s communication and messaging network system.  The existing algorithm has various side effects especially for the side channel attacks. In this research a novel and robust Image Cryptography techniques is proposed. This paper proposes a unique hybrid methodology based on Elliptical Curve Cryptography over XOR and Scrambling Techniques for Image encryption and decryption process. This algorithm provides greater performance and efficient security compared to public-key cryptography techniques like RSA, Diffie Hellman algorithm. We have also tested the proposed system by performing various attacks. The outcome of the implementation clearly demonstrates that the proposed system is highly resilience and provides effective security mechanism against most popular cryptographic attacks like side channels etc. The decrypted image has negligible loss of information, but the original image is clearly visible for the human eye.
Block cutting and searching method of fractured rock mass based on topological matrix

Aiming at the problem of block cutting and searching in fractured rock mass, a method of block cutting and searching in fractured rock mass based on topological matrix is proposed. According to the structural characteristics of fractured rock mass, the data structure of the model is established, which only contains node coordinates (vectors) and a numbering system; the line segments are divided into line segment units, which only intersect at the boundary by the algorithm of line segment intersection and discretization. The number matrix describing the topological relationship of points and lines system is constructed with the intersection and line segment as the core. According to the number matrix and geometric characteristics of spatial line segments, the number matrix for block search is obtained by cutting a non-closed polygon; without any geometric operation, only the index of the number in the matrix is used to search the blocks and form a seamless rock mass model; according to the spatial relationship between the clipped line segment and the block, the block reflecting the internal fracture is generated to form the final fractured rock mass model. Results indicate that this method can identify the distribution of complex blocks and their internal joints and fissures. It also has a great application prospect in the analysis of fractured rock mass.
Named Entity Recognition of Wa Cultural Information Resources Based on Attention Mechanism

Aiming at the problem that the entities in the field of Wa cultural information resources have long length, parallel entities, and no public data set, this paper uses the text information in the Chinese ethnic dictionary Wa nationality volume as the data set, and uses the BERT model to pre-trained the word vector, then extracting the semantic features with the attention mechanism based on the BiLSTM network model, finally, the CRF model is used to predict and output the optimal tag sequence. A method of named entity recognition of Wa cultural information resources based on the attention mechanism is proposed. The experimental results show that the model can effectively identify the entities in the Wa cultural information resources and alleviate the problem of inconsistent entity labels. The recognition accuracy, recall rate, and F value of the Wa cultural information resources corpus are 92.67%, 90.06%, and 91.34% respectively.
Two-hand Pose Estimation from the non-cropped RGB Image with Self-Attention Based Network

Estimating the pose of two hands is a crucial problem for many human-computer interaction applications. Since most of the existing works utilize cropped images to predict the hand pose, they require a hand detection stage before pose estimation or input cropped images directly. In this paper, we propose the first real-time one-stage method for pose estimation from a single RGB image without hand tracking. Combining the self-attention mechanism with convolutional layers, the network we proposed is able to predict the 2.5D hand joints coordinate while locating the two hands regions. And to reduce the extra memory and computational consumption caused by self-attention, we proposed a linear attention structure with a spatial reduction attention block called SRAN block. We demonstrate the effectiveness of each component in our network through the ablation study. And experiments on public datasets showed the competitive result with the state-of-the-art method.
The Relationship Between Logical Entropy and Shannon Entropy

This chapter is focused on developing the basic notion of Shannon entropy, its interpretation in terms of distinctions, i.e., the minimum average number of yes-or-no questions that must be answered to distinguish all the “messages.” Thus Shannon entropy is also a quantitative indicator of information-as-distinctions, and, accordingly, a “dit-bit transform” is defined that turns any simple, joint, conditional, or mutual logical entropy into the corresponding notion of Shannon entropy. One of the delicate points is that while logical entropy is always a non-negative measure in the sense of measure theory (indeed, a probability measure), we will later see that for three or more random variables, the Shannon mutual information can be negative. This means that Shannon entropy can in general be characterized only as a signed measure, i.e., a measure that can take on negative values.
An Ethereum Based Distributed Application for Ensuring the Integrity of Stored E-Voting Data

E-Voting system generally adopts the client-server architecture where the server is the responsible entity of all data in the system. Such a system poses several issues regarding information security such as data integrity of the election result, the confidentiality of voter identity with its vote and the auditability of transactions. This research presents an implementation of blockchain-based Ethereum on the E-Voting system to protect the information security with a distributed storage. The e-Voting system is implemented with three main components: private blockchain network, smart contract, and web service as an interface to manager and voters. The functional evaluation on the data store, display, update, delete, verify voters, authenticate voters, and the integrity of the election process in the private blockchain network are all well-functioning, even without an overvote. The nonfunctional evaluation shows that the system can guarantee the data integrity of the election result, the confidentiality of voter identity with its selection, the auditability of transactions, and the maximum scale of load that system can handle. Based on these results, E-Voting system with a blockchain-based Ethereum give a better alternative than those implemented with a client-server architecture.
A Chatterbot Based on Genetic Algorithm: Preliminary Results

Chatterbots are programs that simulate an intelligent conversation with people. They are commonly used in customer service, product suggestions, e-commerce, travel and vacations, queries, and complaints. Although some works have presented valuable studies by using several technologies including evolutionary computing, artificial intelligence, machine learning, and natural language processing, creating chatterbots with a low rate of grammatical errors and good user satisfaction is still a challenging task. Therefore, this work introduces a preliminary study for the development of a GA-based chatterbot that generates intelligent dialogues with a low rate of grammatical errors and a strong sense of responsiveness, so boosting the personals satisfaction of individuals who interact with it. Preliminary results show that the proposed GA-based chatterbot yields 69% of “Good” responses for typical conversations regarding orders and receipts in a cafeteria.
Predicting Next Dialogue Action in Emotionally Loaded Conversation

This paper reports on creating a neural network model for prediction of the next action in a dialogue considering conversation history, i.e. entities, context variables and emotion indicators marking emotionally loaded user utterances. Several experiments were performed to see how the information about emotions affects the accuracy of the model. For the purposes of these experiments, a dataset containing 206 dialogs in Latvian in the transport inquiry domain was created containing both neutral and emotionally loaded utterances. To see if the proposed next dialogue action prediction model architecture is suitable for other languages, the original Latvian utterances were translated into English and a separate model was trained with English data. Some experiments were performed training the model with the data in one language and testing with the data in another language as well as training a single model using data in both languages. Experiments were performed with several fastText and Transformer pre-trained embedding models. Models for both languages Latvian and English achieved 0.91 accuracy on 10-fold cross-validation.
Frontier Application and Development Trend of Artificial Intelligence in New Media in the AI Era

In recent years, deep algorithms and big data and other technical means have risen rapidly, and the application of artificial intelligence has become the future development trend of various industries. In the news industry, AI is involved in all aspects of news gathering, content production, distribution and consumer consumption. This paper mainly studies the cutting-edge application and development trend of artificial intelligence in the field of new media in the era of AI. In this paper, the news media industry takes advantage of the application advantage of artificial intelligence and puts forward a news recommendation algorithm based on neural network. In this paper, the collaborative filtering method is used to recommend news by using cyclic neural network (RNN). However, in the face of the problem that the RNN network structure is repeated and difficult to recommend in parallel in collaborative filtering, this paper applies Slices Neural Network (SRNN) to the collaborative filtering recommendation system to improve the training and operating efficiency of the system.
Professional Activity Practices of the Scientific and Teaching Community of the Baby Boomer Generation

The purpose of the study is to identify characteristics and factors of identity formation in the scientific and teaching community of the Baby Boomer Generation in the context of professional activity areas. The paper examines daily practices in the professional activity of representatives of the scientific and teaching community belonging to the same generation designated in the theory of generations as “baby boomers”. Scientific novelty consists in determining factors and conditions for identity formation in the scientific and teaching community of the Baby Boomer Generation, in creating and introducing into scientific use new sources. As a result of the study, similar practices of professional and non-professional activities in different areas of a teacher’s activity have been identified.
A Novel Approach to Preserve DRM for Content Distribution over P2P Networks

The Digital revolution has made the sharing of information very easy. Peer-to-Peer (P2P) technology has found an important and significant role through which a large number of files are being exchanged by millions of users concurrently. However, due to the significant growth of P2P file sharing, even copyrighted files are actively exchanged among the users. This caused illegal sharing of copyrighted contents and hence violating the copyright law. So, copyright infringement has become a serious issue. In this paper, a DRM-protected content distribution system is proposed that uses a Web Browser-based P2P system for data transfer. The system provides hassle-free transfer of digital media content such as Audio/Video/Movies through the use of Web Real-Time Communications (WebRTC) API. This system aims to provide the best user experience by appropriate selection of the peer in terms of minimizing the latency in digital content sharing while imposing secured and restricted distribution to enforce Digital Rights Management (DRM).
The renewal equation in nonlinear approximation

The family of Markov processes are considered in the article. We study the multidimensional renewal equation in nonlinear approximation. The purpose of the work is to find the limit of renewal function.
Ranks in the family of hyperelliptic Jacobians of y2 = x5 + ax II

This paper is a continuation of our previous one under the same title. In both articles, we study the hyperelliptic curves [Formula: see text] defined over [Formula: see text], and their Jacobians [Formula: see text] (without loss of generality a is a nonzero 8th power free integer). Previously, we considered the case when the polynomial [Formula: see text] is irreducible in [Formula: see text] and obtained (under certain conditions on the quartic field [Formula: see text]) upper bounds for the [Formula: see text]; in particular, we found infinite subfamilies of [Formula: see text] with rank zero. Now we consider all cases when [Formula: see text] is reducible in [Formula: see text] and prove analogous results. First we obtain (under mild conditions on some quadratic fields) upper bounds for the ranks in a rather general situation, then we restrict to a several infinite subfamilies of [Formula: see text] (when 2a has two primes divisors) and get the best possible bounds or even the exact value of rank (if it is zero). We deduce as conclusions the complete lists of rational points on [Formula: see text] in such cases.
Analysis and Design of College Sports Information System Based on Network

Abstract This paper mainly studies the design and development of university sports information management system. First of all, research significance of the sports information management system in Colleges and universities, research background, research status at home and abroad, the main contents and methods are described, in addition to the relevant technical information system are introduced, especially the C/S and B/S model are discussed, and introduces its working principle, two hierarchical structure model and its characteristics. Analysis of the advantages and disadvantages of two modes. In addition, in the management information system, database technology plays an important role, from three aspects of database, database management system and database system are introduced in detail.
Big Data: Impact on University Education Management

The paper attempts to comprehend the possibilities of using big data technology in the activities of universities. Based on the analysis of Russian and foreign sources, the understanding and significance of big data are presented. A reasoned opinion is expressed that the period of the pandemic has become a catalyst for universities to make managerial decisions in the educational sphere based on big data. Based on the results of sociological research, it has been shown that the transition from the “analog” to the digital level of understanding educational reality in the context of the COVID-19 pandemic, including the development of local acts, was carried out under the influence of big data. The results of the author’s questionnaire are given, indicating that the respondents are aware of the technology of big data, their positive attitude to the use of its potential for building the educational process at the university. The conclusion is made about the transition in large educational organizations from small to big data in the aspect of making management decisions based on the analysis of such data, which became possible due to the formation by the efforts of scientific and pedagogical workers and specialists of technical departments of the electronic information and educational environment of the university. The circumstances that negatively affect the full-fledged use of big data technology in the field of education are revealed. Prospects for the further development of big data at universities are outlined.
P-459 Coping strategies of hospital nurses in the COVID-19 pandemic context

<h3>Introduction</h3> Occupational stress is one of the negative factors present in nursing work, especially in the hospital context; that factor was exasperated by the Covid-19 pandemic. Organizational strategies are very useful to reduce stress at work, individual coping behaviors can also contribute to the understanding of this factor. <h3>Objective</h3> This study aims to understand coping strategies of hospital nurses with occupational stress in the Covid-19 pandemic context. <h3>Method</h3> This is a qualitative, descriptive and exploratory study, which was developed at a university hospital located in the state of São Paulo, Brazil. The target population was male and female nurses who were employed in the research hospital. Data were collected through virtual individual interviews, based on a previously defined semi-structured questionnaire, and analyzed according to thematic content analysis. Intentional sampling was used and the participants were recruited until data saturation. The project was approved by study scenario and Ethics Committee for Research with Human Beings at Federal University of São Carlos; all ethical precepts were observed. <h3>Partial Results</h3> Thirteen nurses participated in the data collection: 10 (76.92%) women and 3 (23.07%) men, aged between 31 and 55 years old. Working as a nurse: 1 (7.69%) worked between 1 to 5 years, 3 (23.07%) between 5.1 to 10 years, 6 (46.15%) between 10.1 to 20 years and 3 (23.07%)&gt; 20 years. For the duration of work experience at the studied institution: 1 (7.69%) &lt; 1 year, 9 (69.23%) between 1 to 5 years and 3 (23.07%) between 5.1 to 10 years. It was expected, with the results of this research, to understand the coping strategies used by hospital nurses to cope with occupational stress in the current pandemic scenario of COVID-19.
Federated Learning: Optimizing Objective Function

A universal server coordinates the training of a single model on a largely distributed network of computers in federated learning. This setting can easily be expanded to a multi-task learning system in order to manage real-world federated datasets with high statistical heterogeneity across devices. Federated learning is very useful as a framework for real-world data and federated multi-task learning has been applied to convex models. This research work discusses and evaluates possibility of sparser gradient changes to outperform the existing state-of-the-art for federated learning on real-world federated datasets as well as imputed data values. The experiments investigate the effect of rolling data or data randomization and adaptive global frequency update scheduling on the convergence of the federated learning algorithm. The results show that convergence speed and gradient curve are considerably affected by number of contact rounds between worker and aggregator and is unaffected by data heterogeneity or client sampling. The research is the core part of an extended experimental setup that will follow to better understand the behavior of distributed learning, by developing a simulation to track weights and loss function gradients during the training.
Qgen: An Automatic Question Paper Generator

Education is a process of gaining knowledge and is important for all as it plays a vital role in shaping the life of a student. The knowledge that we gain through education is evaluated through examinations that the teachers conduct periodically. Performance in an examination is an indication of a student's proficiency in a particular subject. Courses of an educational curriculum are defined with learning objectives. Question papers consist of questions belonging to different cognitive levels. Generation of a quality question paper is essential as performance in it can influence the career decisions the students take in their life. It is difficult for the teachers to maintain the same level of complexity across the set of question papers that are generated. In this paper, we present a model where the questions are tagged automatically to their respective cognitive levels. It also helps in creating different sets of question papers containing unique questions and also provides a pictorial representation of the percentage of various cognitive levels present in the question paper. The pictorial representation helps the evaluation panel to have an overview of the distribution of cognitive levels in the question paper.
Sarcasm Analysis and Mood Retention Using NLP Techniques

Sarcasm detection in written texts is the Achilles’ heel of research areas in sentiment analysis, especially with the absence of the rightful verbal tone, facial expression or body gesture that leads to random misinterpretations. It is crucial in sectors of social media, advertisements and user feedbacks on services that require proper interpretation for service evaluation and improvisation of their products. The objective here thereby is to identify sarcasm within a given text by experimenting with the original predicted mood of the text and work on its transformation with the several variations in combination of the standard sarcastic elements present in the corresponding writing. Here standard NLP techniques are used for identification and interpretation. This involves detecting primary connotation of the given text (e.g. positive/neutral/negative), followed by detecting elements of sarcasm. Then, under the presence of the sarcasm indicator algorithm, the rightful interpretation of the previously detected mood is attempted.