Reviewerly/exHarmony huggingface dataset:
Digital Forensic Analysis of Telegram Messenger App in Android Virtual Environment

The paper provides an in-depth analysis of the artifacts generated by the Telegram Messenger application on Android OS which provides secure communications between individuals, groups, and channels. Since the past few years, the application went through major changes and updates and the latest version’s artifacts varied from the previous ones. Our methodology is based on the set of experiments designed to generate the artifacts from various use cases on the virtualized environment. The acquired artifacts such as messages, their location, and data structure how they relate to one another were studied and were then compared to the older versions. By correlating the artifacts of newer version with the older ones, it shows how the application have been upgraded behind the scenes and by incorporating those results can provide investigators better understanding and insight for the certain evidence in a potential cybercrime case.
Study about Rule Mining for Multiple Tables with Fuzzy Data

The research takes the track of mining association rules in databases with many tables and fuzzy data along with its taxonomy. Many data mining algorithms have been developed to deal with databases that are made up of a single table with fuzzy taxonomic structures constructed on top of it. This study uses fuzzy data from several tables, which were created using ER models, and each entity table maintains information on all attributes connected with a certain object, while the relationship table represents relationships between distinct entities. The study's major goal is to handle many tables at multiple levels. The study's objective is to create a new algorithm by combining the previously published algorithms Extended Apriori and Apriori star. The research will aid in the identification of relevant outcomes from database tables containing ambiguous data.
A Conditional Autoencoder for Galaxy Photometric Parameter Estimation

Abstract Astronomical photometric surveys routinely image billions of galaxies, and traditionally infer the parameters of a parametric model for each galaxy. This approach has served us well, but the computational expense of deriving a full posterior probability distribution function is a challenge for increasingly ambitious surveys. In this paper, we use deep learning methods to characterize galaxy images, training a conditional autoencoder on mock data. The autoencoder can reconstruct and denoise galaxy images via a latent space engineered to include semantically meaningful parameters, such as brightness, location, size, and shape. Our model recovers galaxy fluxes and shapes on mock data with a lower variance than the Hyper Suprime-Cam photometry pipeline, and returns reasonable answers even for inputs outside the range of its training data. When applied to data in the training range, the regression errors on all extracted parameters are nearly unbiased with a variance near the Cramr-Rao bound.
Human Cognition Detection In Location-Based Social Recommender System

Location based social recommender system helps the people to find the appropriate recommendations in the corresponding location. The recommendations are given in the form of text by the reviewers in the publicly available social networking platform. Social networking helps to find the people who shared the common recommendations which will in-turn may help to form further network. The reviews may be given by the reviewers when they are in respective mood. The moment at which the reviewer has given the review is highly tied with the mood of the individual. In order to find the same, this research tried to discover the role of human emotions plays in the review text of a location-based recommender system. It is being discovered that the sentiment polarity of the recommendations and the intensity scores of the recommendations are greatly varies. This research is focused on the review comments of restaurants to the eight different human emotions and also to assess the positivity and negativity of the comments in terms of its intensity scores.
EDUCATION SYSTEM DEVELOPMENT IN THE FIELD OF FOLK ARTISTIC CRAFTS AND DECORATIVE-APPLIED ARTS IN THE UDMURT REPUBLIC

The subject of the article is a historical process of teaching folk artistic crafts and decorative-applied arts in the Vyatka province and the modern Udmurt Republic. The research source was archive documents, reports of craftsman houses and centers, reports of educational institutions, published editions. The article studies the influence of the first exhibition events of the mentioned zemstvo and the first handicraft museum on the improvement of skills of artists from the people; describes the opening of training workshops, schools and classes for craftsmen teaching. The article indicates the reasons for the decay of traditional art craftwork during the period from 1930 to 1990, and the circumstances of the formation of new conditions for their development during the post-Soviet period. In the article, modern forms of specialist training in this field are represented based on the example of interaction between professional educational organizations, industrial enterprises, cultural institution. The growth factors of skills level among the specialists of craftwork centers and houses in the Udmurt Republic have been determined. Methodological cooperation and promotion of folk masters' art have been studied in the system of supplementary education. The article analyzes the interaction between scholars and specialists of craftwork centers while creating programs for teaching traditional types of decorative-applied art, enhancing the population engagement with national folk crafts as a cultural heritage of ancestors.
Distribution of the number of scientific and pedagogical staff of universities by work experience

The purpose of our study was to assess the proportion of teachers with different work experience in relevant positions in higher education institutions in the total number of university teachers in each of the regions of Russia. In the course of the work, the indicators characterizing the proportion of teachers belonging to five groups by work experience of up to five years, from five to ten years, from ten to fifteen years, from fifteen to twenty years and more than twenty years in the total number of teachers working in higher educational institutions were evaluated. The study used official statistical information for 82 regions of Russia. We used the density functions of the normal distribution as models. The results of the study allowed us to draw conclusions: approximately every fourteenth teacher has worked in a teaching position for less than five years, every ninth teacher has worked in this position for five to ten years, every seventh teacher has worked in this position for ten to fifteen years, every fifth teacher has worked in this position for fifteen to twenty years. It is proved that the number of teachers with work experience up to twenty years was 52% and was slightly more than the number of teachers with work experience of more than twenty years (48%). The analysis showed the presence of a certain differentiation of the values of the considered indicators by region. The proposed methodological approach and the results obtained have originality and scientific novelty.
Dispeech: A Synthetic Toy Dataset for Speech Disentangling

Recently, a growing interest in unsupervised learning of disentangled representations has been observed, with successful applications to both synthetic and real data. In speech processing, such methods have been able to disentangle speakers’ attributes from verbal content. To have a better understanding of disentanglement, synthetic data is necessary, as it provides a controllable framework to train models and evaluate disentanglement. Thus, we introduce diSpeech, a corpus of speech synthesized with the Klatt synthesizer. Its first version is constrained to vowels synthesized with 5 generative factors relying on pitch and formants. Experiments show the ability of variational autoencoders to disentangle these generative factors and assess the reliability of disentanglement metrics. In addition to provide a support to benchmark speech disentanglement methods, diSpeech also enables the objective evaluation of disentanglement on real speech, which is to our knowledge unprecedented. To illustrate this methodology, we apply it to TIMIT’s isolated vowels.
VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization

Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been the major approach in improving the robustness against common corruptions. However, the samples produced by popular augmentation strategies deviate significantly from the underlying data manifold. As a result, performance is skewed toward certain types of corruption. To address this issue, we propose a multi-source vicinal transfer augmentation (VITA) method for generating diverse on-manifold samples. The proposed VITA consists of two complementary parts: tangent transfer and integration of multi-source vicinal samples. The tangent transfer creates initial augmented samples for improving corruption robustness. The integration employs a generative model to characterize the underlying manifold built by vicinal samples, facilitating the generation of on-manifold samples. Our proposed VITA significantly outperforms the current state-of-the-art augmentation methods, demonstrated in extensive experiments on corruption benchmarks.
A Contrastive Evaluation Method for Discretion in Administrative Penalty

Discretion, namely discretionary power, indicates that administrative agencies could make modifiable decisions under personal judgment when facing situations defined in the law. It plays an essential part in an administrative practice that existing laws and regulations could hardly cover all cases. However, this may also cause the abuse of enforcement power. The rapid development of the Internet of Things (IoT) and databases has provided a powerful tool to measure discretionary power, such as judging if a given administrative punishment is appropriate, and recommending similar cases for a new law-violation record. In this paper, we develop a multi-task framework to extract contrastive patterns from historical records and recommend unprocessed penalties. There is massive ambiguity in collected records, where the limited samples of specific penalties and a large number of whole records make it hard to distinguish factors in individual administrative enforcement actions. We propose an automatic data-labeling method based on data pattern discovery, clustering, and statistical analysis to replace manual labeling under potential personal prejudice. We estimate the distribution of collected penalty records to distinguish deviated and reasonable ones, then produce contrastive samples, which are fed into different network branches. We build a complete IoT platform and collect three-year administrative penalty records nationwide as an empirical evaluation. Experiments show that our proposed methods can learn reasonable discretion by measuring the objectiveness in samples and combining it with a joint training strategy. The final results of penalty amount forecasting and penalty reasonableness judging tasks reach ready-to-use performance.
Face Detection Based Emotion Recognition Using Deep Learning

Music aids in the reduction of stress, the development of self-esteem, and the improvement of health, among other things. It can be broken down into a number of different genres. People tend to choose a particular music genre based on their mood and interests. As a result, there is a clear need for a platform that can automatically recommend music based on an individual's feelings. Facial expressions can be seen of as a type of nonverbal communication that can transmit information about a person's moods. As a result, Egophony is primarily concerned with the development of an application that would recommend songs to the end user based on their emotions and interests by capturing their facial expressions. We've developed a system that can recognize emotions based on facial expressions. Once the mood has been identified, the system will recommend a playlist of music from a specific genre based on the emotion. It will ultimately save a significant amount of time that would otherwise be spent manually searching, selecting, and playing songs.
Research on target detection method based on CNN

Abstract Traditional target detection methods have poor tracking effect for targets with excessive displacement or occlusion between adjacent frames, and it is difficult to deal with high-speed moving targets. In view of the low detection rate of the current system in detecting long-distance weak and small targets, false targets and occlusion in the air, the working mode of convolutional neural network (CNN) and its advantages in target detection are analyzed, and a target detection method based on CNN is proposed, which can ensure the target detection accuracy and meet the real-time requirements of combat scenes, Finally, compared with other algorithms, the proposed method achieves the best results on four types of data. While the center error is low, it has high tracking success rate and low time complexity, which meets the requirements of high precision and real-time of the system.
Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews

Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews and comments before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, to make a decision. This study attempts to bridge this gap and proposes a hybrid approach of topic modeling that combines supervised and unsupervised learning. In particular, the study collected a massive amount of Amazon user reviews, analyzed the reviews' texts, and combined two approaches of topic modeling, which are unsupervised and supervised learning, i.e., semi-supervised learning. Besides, the study makes classification on reviews based on sentiment analysis. The resulting reviews' topics and their sentiment classifications are displayed on a visual dashboard. The proposed hybrid approach showed better performance in terms of text analysis and clearer representation of review topics. The outcome of this study helps customers make their decision on purchase products in a more effortless and clearer way.
An Effective Query Response using Chatbot

A Chatbot is an artificial intelligence computer program which performs communication using audio and video system. A person can ask any questions and chatbot will answer accordingly. Nowadays a chatbot is highly popular and takes speed as a computer communication application. Chatbot system is in trend, thus it is being used on many websites. With the chatbot, one doesn’t have to wait to talk to the customer helpline, they don’t even have to search for shopping through Websites. A chatbot is used in many areas like order food, product suggestions, customer support, weather, personal finance assistance, scheduled a meeting, search and track flights, send money, and many more. The main objective that we will discuss in this paper is about creating a web API, and also about sample web and text messaging interfaces that demonstrate the use of API. A bot is trained on and according to the training, based on some rules on which it is trained, it answers questions. It is called ruled based approach. Using these ruled based approach, creation of these bots becomes relatively straight forward. But it is not sufficient for the bot to answer questions whose pattern does not match with the rules on which it is trained. The language by which these bots can be created are Artificial Intelligence Markup Language (AIML). It is a language based on XML which allows the developer to write the rules which bot will follow. In this Project we learn about the working of these chatbots and their outputs.
Facial Expression Recognition using Robust Algorithm based on Modern Machine Learning Technique

Recently facial expression recognition has turned out to be an interesting field in research because of more demand for security and the advancement of mobile devices. Due to many serious incidents like terrorists’ attack, there arises more concern to develop the security systems mainly in certain places like airports and border crossings where identification and verification are mandatory. On the other hand, these surveillance systems aid to identify the missing person, even though it is based on robust facial expression recognition algorithms and on the developed database for facial expression recognition. However, the human faces are complex and multidimensional which make the facial gesture extraction to be very challenging. Obviously, in high secured applications facial expression recognition (FER) systems are mandatory to avoid incidents. In this paper, the automatic facial expression recognition system is developed based on the machine learning algorithms for classification. This research reveals the identification of FER for the ease of communication. Hybridization of Adaptive Kernel function based Extreme Learning Machine with Chicken Swarm Optimization (HAKELM-CSO) algorithm is introduced for identifying the accurate facial expression among the large database. In this work, an approach is developed by applying the machine learning techniques for the automated classification on the image region. The major purpose of this research work is to overcome the flaws of traditional algorithms and to improve the process of facial expression recognition which could be used in various applications.
The Influence of Game Components on Motivation and Immersion of Middle-Aged People: Focusing on Pokemon Go

Pokemon Go is an augmented reality game that moves around the real world, catches monsters that appear in the game, and raises them.It has gained popularity around the world and spread the game culture.Pokemon Go gained popularity around the world and spread the game culture.This study was conducted to investigate how the Pokemon Go game composition affects the game motivation and immersion of middle-aged people.Previous studies that have discussed existing games focus on the problematic effects on teens and 20s as well as excessive immersion in adolescents, so there is insufficient research on the actual reactions of middle-aged users.Therefore, through in-depth interviews of users, AR(Augmented Reality) data are collected, and through this, implications are drawn so that the components of the game can lead to new research directions that combine the experiences of middle-aged players.This study focused on Pokemon Go, which can be said to be a representative case of recent AR( Augmented Reality) games.In order to identify the research problem from a specific point of view, in-depth interviews were conducted with middle-aged professionals who had played the Pokemon Go game for more than a year continuously, had a game level of 40 or higher, and had a high understanding of the game.As a result of the analysis of the interview, it was found that among the elements of Pokemon Go, achievement, position, and competition factors affect game motivation, and achievement and competition factors affect game immersion.As the continuity showed similar results to immersion, it could be confirmed that immersion and continuity were interconnected.It was found that the component of experiencing the joy of interacting with society through Pokemon Go and creating a community to build a bond regardless of age affects immersion and continuity, but the self-expression factor has a weak effect.
Content-Based Video Retrieval Based on Multifaceted Feature Extraction Using Statistical Methods

For content-dependent video recovery, this paper proposes a multifaceted feature extraction approach based on color string extraction, local texture characteristics, and amount of Absolute Difference, which extracts the features such as color components , texture components, and motion behavior. The color string function is derived using the string duration count, and the color histogram approach is used for detecting scene shifts. Based on scene shift recognition, the key-frames are retrieved. Based on the color string and local texture functions, the color and texture characteristics are derived from the key-frame. The Amount of Absolute Difference is measured, and the motion function of a video is derived using it. To compare the function vectors of the question and main frames of the video to be recovered, the Jeffrey's Divergence measure is implemented. The suggested approach has the greatest capture of both spatial and temporal aspects, and the recovery efficiency is increased by utilizing multifaceted features. The system suggested outperforms the current approaches.
Classification of Rupiah to Help Blind with The Convolutional Neural Network Method

Currency is an item humans require as a medium of exchange in transactions, including those with vision impairments. It can be challenging for certain blind people to identify currencies. This research aimed to help blind people identify nominal currency when in the transaction. Deep Learning with the CNN algorithm and preprocessing with a sequential model were used in this research. This algorithm is modeled as neurons in the human brain that communicate and learn patterns. Data collecting, preprocessing, testing, and evaluation are this research stage. Six hundred eighty-one datasets are used, consisting of IDR 50.000, IDR 75.000, and IDR 100.000. Model testing was carried out with different iterations of 5, 10, 15, and 20 epochs. Different epoch values will affect the time it takes the model to learn, but the length of the learning process will result in more accurate models. The highest result obtained from all epoch tests is 100%. The class prediction results for the 69 test data show that they can be predicted based on the actual class, indicating that the model is adequate. The results of this classification might be used to construct a smartphone app that would assist visually challenged people in recognizing the nominals.&#x0D;
A Systematic Review of Fault Management Framework in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have progressively appeared as a buzzword for the prevalent research areas of networking in the twenty-first century. The main application areas include threat detection, industrial monitoring, environment monitoring, military surveillance, weather forecasting. Since the sensor nodes are deployed in a hostile environment, they can develop a fault due to this environmental impact. The occurrence of these faults must not hamper the functioning of the entire network. Hence, Fault Management plays a vital role in improving fault tolerance. To improve the Quality of Service (QoS) of the overall network, a fault management scheme has to be incorporated. This scheme can be further enhanced using Machine Learning. Fault Management scheme includes the fault prevention mechanism which is applied at the design level. Fault Detection is performed at the operational level using Machine Learning techniques. The classification of faults occurring in the network helps in Fault Identification and Isolation. The last step is fault recovery, which provides a suggestive countermeasure. This article discusses the taxonomy of fault management framework categorized into central, distributed, hierarchal, hybrid, classification based on their performance.