Reviewerly/exHarmony huggingface dataset:
Text Classification of Gujarati Newspaper Headlines

Text classification is an extremely important area of Natural Language Processing (NLP). This paper studies various methods for embedding and classification in the Gujarati language. The dataset comprises of Gujarati News Headlines classified into various categories. Different embedding methods for Gujarati language and various classifiers are used to classify the headlines into given categories. Gujarati is a low resource language. This language is not commonly worked upon. This paper deals with one of the most important NLP tasks - classification and along with it, an idea about various embedding techniques for Gujarati language can be obtained since they help in feature extraction for the process of classification. This paper first performs embedding to get a valid representation of the textual data and then uses already existing robust classifiers to perform classification over the embedded data. Additionally, the paper provides an insight into how various NLP tasks can be performed over a low resource language like Gujarati. Finally, the research paper carries out a comparative analysis between the performances of various existing methods of embedding and classification to get an idea of which combination gives a better outcome.
Presentation of a Feeling of Presence Using an Electrostatic Field

With the spread of infectious diseases, the importance of remote communication is increasing in people's lives. We focused on a "feeling of presence," which is considered to be an important factor in remote communication. The feeling of presence is an implicit indication of the presence of others. In this paper, we propose a system that presents a presence-like sensation using an electrostatic field. Through experiments, we showed that the system can present a presence-like sensation and investigated the distance that is most suitable for inducing a presence-like sensation.
Optimal Attacking Strategy Against Online Reputation Systems with Consideration of the Message-Based Persuasion Phenomenon

The past decades witness the rise and proliferation of online reputation systems. These reputation systems are vulnerable to malicious attacks, and most recent studies have focused on how to better defend the system. This paper aims to analyze the optimal attacking strategy, especially when considering the "message-based persuasion" phenomenon where users’ ratings tend to be influenced by earlier ones. Based on a simple model of users’ herding behavior in reputation systems, we study how attackers can explore this phenomenon to attack the system more effectively, and quantitatively analyze the optimal attacking strategies. This investigation is critical to the design of defensive mechanisms, and to the protection of online reputation systems.
Self-supervised Adaptive Aggregator Learning on Graph

Neighborhood aggregation is a key operation in most of the graph neural network-based embedding solutions. Each type of aggregator typically has its best application domain. The single type of aggregator for aggregation adopted by most existing embedding solutions may inevitably result in information loss. To keep the diversity of information during aggregation, it is mandatory to use the most appropriate different aggregators for specific graphs or subgraphs. However, when and what aggregators to be used remain mostly unsolved. To tackle this problem, we introduce a general contrastive learning framework called Cooker, which supports self-supervised adaptive aggregator learning. Specifically, we design three pretext tasks for self-supervised learning and apply multiple aggregators in our model. By doing so, our algorithm can keep the peculiar features of different aggregators in node embeddings and minimize the information loss. Experiment results on node classification and link prediction tasks show that Cooker outperforms the state of the art baselines in all three compared datasets. A set of ablation experiments also demonstrate that the integration of more types of aggregators generally improves the algorithm’s performance and stability.
Real-Time End-to-End 3D Human Pose Prediction on AI Edge Devices

This paper tackles the problem of predicting 3D Human Pose on AI Edge Devices. An AI edge device is an AI-enabled Internet of Things (IoT) device that can run Deep Learning (DL) models right on the device without connecting to the Internet. In recent years, researchers have proposed numerous DL-based models for 3D human pose estimation (HPE), but no work focuses on solving this task in edge devices. Building a DL model for an edge device has unique challenges such as limited computation capacity, small memory, and low power. This paper investigates how to optimize a big, heavy-computing 3D pose estimation DL model into a light-weight, small-size model that can run efficiently in a device. Specially, we propose an End-to-End pipeline to run 3D human pose prediction (HPP) in Real-time on AI edge devices. Furthermore, our proposed end-to-end pipeline is general and can be employed by other AI edge device based real-world applications.
Prior Knowledge about Attributes: Learning a More Effective Potential Space for Zero-Shot Recognition

Zero-shot learning (ZSL) aims to recognize unseen classes accurately by learning seen classes and known attributes, but correlations in attributes were ignored by previous study which lead to classification results confused. To solve this problem, we build an Attribute Correlation Potential Space Generation (ACPSG) model which uses a graph convolution network and attribute correlation to generate a more discriminating potential space. Combining potential discrimination space and user-defined attribute space, we can better classify unseen classes. Our approach outperforms some existing state-of-the-art methods on several benchmark datasets, whether it is conventional ZSL or generalized ZSL.
Smart Approach to Optical Character Recognition and Ubiquitous Speech Synthesis Using Real-Time Deep Learning Algorithms

Cell phones have been known as most usually utilized electronic gadgets in everyday life today. As equipment inserted in cell phones can perform significantly more assignment than customary telephones, the cell phones are not, at this point only a specialized gadget yet additionally considered as a ground-breaking processing gadget which is ready to catch pictures, record recordings, surf the web and so on. With the headway of innovation, it is conceivable to apply a few strategies to perform content discovery and interpretation. Consequently, an application that permits cell phones to catch a picture and concentrate the content from it to extract its language and information values and discourse it out is not, at this point a dream. In this paper, we address an Android application that is created by coordinating firebase machine learning kit and the WaveNet API with an approach of reinforcement learning to vocalize the texts using advanced speech synthesis algorithms. By utilizing this application, travelers who visit a remote nation ready to comprehend messages depicted in various languages as well as for the users who want to grab the information on the go by listening to the article.
Strategic coalitions in stochastic games

Abstract The article compares two different approaches of incorporating probability into coalition logics. One is based on the semantics of games with stochastic transitions and the other on games with the stochastic failures. The work gives an example of a non-trivial property of coalition power for the first approach and a complete axiomatization for the second approach. It turns out that the logical properties of the coalition power modality under the second approach depend on whether the modal language allows the empty coalition. The main technical results for the games with stochastic failures are a strong completeness theorem for the logical system without the empty coalition and an incompleteness theorem which shows that there is no strongly complete logical system in the language with the empty coalition.
An Evaluation Algorithm for Expert in Community Question Answering by Combining Topics and Behaviors

In this paper, we propose an algorithm to evaluate the influence of experts in community question answering (CQA). In the current research of influence evaluation algorithm, the main research method is to establish the network between users and problems, and use centrality algorithm such as PageRank to evaluate the influence of users. However, Existing algorithms tend to favor users with more followers. In this paper, we propose an evaluation algorithm based on user's content combined with link relationship. The experimental results show that our method can give higher ratings to users who have low number of followers but can provide high-quality content. At the same time, we add the topic relevance to the evaluation algorithm, so that the expert score will change according to the different topics.
Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal Agent

Reinforcement learners are agents that learn to pick actions that lead to high reward. Ideally, the value of a reinforcement learner's policy approaches optimality-where the optimal informed policy is the one which maximizes reward. Unfortunately, we show that if an agent is guaranteed to be “asymptotically optimal” in any (stochastically computable) environment, then subject to an assumption about the true environment, this agent will be either “destroyed” or “incapacitated” with probability 1. Much work in reinforcement learning uses an ergodicity assumption to avoid this problem. Often, doing theoretical research under simplifying assumptions prepares us to provide practical solutions even in the absence of those assumptions, but the ergodicity assumption in reinforcement learning may have led us entirely astray in preparing safe and effective exploration strategies for agents in dangerous environments. Rather than assuming away the problem, we present an agent, Mentee, with the modest guarantee of approaching the performance of a mentor, doing safe exploration instead of reckless exploration. Critically, Mentee's exploration probability depends on the expected information gain from exploring. In a simple non-ergodic environment with a weak mentor, we find Mentee outperforms existing asymptotically optimal agents and its mentor.
The Place of Public Relations in Management System of Higher Education

The paper deals with the analysis the place of public relations in management system of higher education. The distinctive feature of public relations activities in the field of education is considerable public confidence in information transmitted through various communication channels. However, we observe the absence of systematic public relations activities at the government level and at the level of universities. We focused on the implementation of effective activities for the formation of public opinion, which requires the definition of the marketing goals of an educational institution, the choice of messages and means of information transfer, the careful implementation of the intended plan and evaluation of its results. Thus, we made the conclusion that the special role of the system of management of the university during the reformation of the national education system belongs to the organization of public relations activities, involving the planning and implementation of contacts and external information about the university based on flexible interaction with target audiences.
Central Bank Digital Currency: Gaps in Existing Literature

As a new type of money, central bank digital currencies (CBDC) have gained strong interest among central banks as well as researchers. Driven from the Covid-19 pandemic, recent trends of online communication and collaboration, online banking and shopping are expected to increase attention on CBDC. Hence, this paper aims to review the current literature on CBDC so as to reveal gaps in this area as a motivation for further researches. The paper is expected to carry importance for policy makers as well by introducing relevant literature for their decision making.
Towards Secure E-Voting Using Blockchain

Voting in this country is the most tedious job to be handled, involving all kinds of corrupted and illegal deeds. Elections in India are conducted exclusively using EVM’s developed over the past two decades by a group of government-owned companies. These devices, known in India as EVMs, have been adopted greatly for their simple design, ease of conduct, and robustness. However, recently they have also been marked prey following widespread reports of election irregularities. Despite this criticism, many details of the mechanism have never been publicly discussed, and they have not been subjected to a stringent, independent security evaluation. We conclude that in spite of the machines’ simplicity and software trusted computing base, they are vulnerable to indigenous attacks that can modify election results and violate the law of the election commission. Most of the attacks done are physical, by changing the electricals, but if the machine is connected real time to a cloud server and involves an independent screen which shows the confirmation of choice symbolically before placing the final vote, it can help in detecting problems and maintain the integrity of the system.
Research on Integer Ambiguity Algorithm of GNSS under Different Monitoring Modes

According to the different requirements of GNSS deformation monitoring in terms of time and accuracy, based on the traditional periodic repeated monitoring mode, two GNSS monitoring modes, fixed continuous monitoring mode and real-time dynamic monitoring mode, are adopted, and the corresponding double difference integer ambiguity processing algorithm is mainly studied, so as to form a GNSS model to meet the monitoring requirements of different accuracy levels and time resolution, and through the analysis of examples to verify the correctness of the two methods.
The implementation of the gender approach in the educational organization

The genderalization of the learning process is currently of key importance for the formation of the academic and political foundations of pedagogical innovations in the education system. This article presents the results of a study aimed at defining the concept of modern pedagogical innovations in the field of gender education. The analysis of the obtained data allowed us to identify seven different concepts of the concept of gender pedagogical innovations, formulate an updated definition and propose a cycle of the pedagogical innovation concept. The article analyzes the factors contributing to the successful implementation of gender-based innovative pedagogical practices in the education system. Considers the issues of configuration and level of intensity of the influence of different gender factors involved in the implementation of innovative pedagogical practices, the relationship between the level of intensity of the identified factors affecting the dynamics of pedagogical innovation and change level of the education system as a whole.
Movilizer Application with Genre and Rating Classification Using NW-KNN Method

Information about movies can be easily seen in cyberspace. However, not all film sites present relevant and accurate information as examples of high rating films but have bad comments. In addition, there is a review that has not been accompanied by ratings and the genre is unknown. The classification of input data in the form of text will be processed and classified into the same or similar class using the Neighbor-Weighted K-Nearest Neighbor (NW-KNN) method. The NW-KNN method is able to classify well for data that is not evenly distributed by giving weights to each class in the system. The description text of the film will be classified into 10 classes with the number of training data as many as 1028, while the movie review text will be classified into 5 classes with the number of training data as many as 10032. The results of system testing indicate that the NW-KNN method produces an accuracy of 96.6% film genre and 86.85% to classify film reviews into movie ratings.
Komparasi Model Peramalan Debit Sungai Menggunakan Ann – Som Ann Pada Sub Das Tapung Kiri

Tapung Kiri Sub river Basin is part of siak river basin which is categorized as a critical basin river and a flood prone area. The potential for flooding is caused by high rainfall in the middle, upstream and along basin river that have undergone land use changes. So it is necessary to do debit forecasting to find out when floods will occur in the future.There have been many hydrological models that have been developed for this discharge prediction using both physical and softcomputing models. One of the softcomputing models that have received attention in hydrological modeling recently is the Artificial Neural Network model. The backpropagation algorithm is an algorithm commonly used in the ANN method to solve problems related to forecasting. However, the drawback of this algorithm is the determination of the initial random weight so that the network training is not optimal, so the Self Organizing Map is used so that the result obtained are more optimal.This research is expected to provide an overview of which method has more advantages for use as an early warning system.The result showed that the SOM-ANN method had a correlation coefficient of 0,987, better than the ANN method which has a coefficient value of 0,897 in the network formation and modeling process. The use of the SOM – ANN method is more stable than the ANN method because in the SOM – ANN method the data comes from the source ( AWLR ) in the cluster first so that the data pattern becomes good for training, testing and validation.Keywords : Artificial Neural Network, correlation coefficient, discharge forecasting, Self Organizing Map
Specific Motion Pattern Detection: State-of-the-Art and Challenges

Detecting motion patterns in video constitutes a field which requires complex computations as processing of moving frame (video) is to be done. The field requires detection of patterns in motion by evaluating each frame pixel by pixel. Since we are mostly interested in detecting only specific patterns, it is possible that patterns that are not specific could be discarded. Applications of motion pattern detection cut across several verticals including surveillance, storage optimization, video compression, tracking of objects and determining the general direction of motion of multiple objects. In the present work, we have explored the state-of-the-art techniques and algorithms for detection of motion patterns, along with the associated challenges.
Interactive Global Mosaic Stitching from Mesentery Video Sequences

Mosaicing or large scale panoramic image generation is a crucial task in medical imaging especially examining cellular features or microstructures. Earlier we have proposed an algorithm, Deformable Normalized Cross Cor-relation (DNCC) [1] to mosaic feature-poor, motion-blurred frog mesentery video sequences. DNCC algorithm generates a single mosaic from each sequence establishing broad structure morphology. Unfortunately, a single mosaic cannot incorporate all information captured from all the sequences together. In another word, we aim to generate a global mosaic combining or fusing all the single mosaics from mesentery sequences. Global mosaic is desirable for comprehensive observation and exploration of the target region in microscopic resolution. Unfortunately, registration of single mosaics to a global mosaic is challenging due to the perspective distortion, scale change and visible seam of single mosaics which are introduced during stitching. Traditional feature based or correlation based method do not work well in such cases. To handle these challenges, we propose a new fused descriptor combining the strengths of both feature based and correlation based registration method for fusing/stitching the single mosaics into one global mosaic or panorama.
A Novel Superpixels Image Segmentation Algorithm for PCBA Components

The visual location accuracy of assembly robot is affected directly by the accuracy of image segmentation. Most traditional algorithms couldn&#x2019;t meet the industrial manufacture rhythm of Printed Circuit Board Assembly (PCBA) components. As a result, this paper proposed a novel superpixels image segmentation algorithm for PCBA components. Firstly, the salient feature was extracted by Histogram-based Contrast (HC) algorithm. Then, the salience map was extracted according to the Simple Non-Iterative Clustering (SNIC) superpixels segmentation algorithm. A new method was proposed to obtain information entropy by creating a new superpixels image. Ultimately, the segmented image was obtained according to the information entropy. According to the experimental results, the F-measure, the MAE and the time were 0.7979, 0.040567 and 179.12851s respectively, showing that the proposed algorithm was better than the other six algorithms.
Reducing Memory Requirements of Convolutional Neural Networks for Inference at the Edge

The main focus of this paper is to use post training quantization to analyse the influence of using lower precision data types in neural networks, while avoiding the process of retraining the networks in question. The main idea is to enable usage of high accuracy neural networks in devices other than high performance servers or super computers and bring the neural network compute closer to the device collecting the data. There are two main issues with using neural networks on edge devices, the memory constraint and the computational performance. Both of these issues could be diminished if the usage of lower precision data types does not considerably reduce the accuracy of the networks in question.
Theoretical and Practical Training of Future Teachers of the Humanities for Socially Oriented Educational Work

The article discusses the theoretical and practical foundations of training future teachers for socially oriented educational work. The article also highlights the topics of experimental lectures, general requirements for their conduct, leading methods, as well as types of student’s independent work, criteria of social education, the topic of the seminar. The authors of the article believe that the experimental results of this study confirm the validity of the scientific-theoretical approach to the study of the problem, the thoroughness of the theoretical-pedagogical and theoretical-practical training of future teachers for the social and educational work.