Reviewerly/exHarmony huggingface dataset:
RECOGNITION OF HUMAN ACTIONS IN VIDEO SEQUENCES USING THE LSTM NETWORK

The development of computer vision and the wide applicability of its applied components determine the relevance of research in this field of science. One of the most interesting tasks of computer vision is to monitor the behavior of people, which includes the analysis of their actions and carried out for various purposes. Examples of use are systems for monitoring compliance with safety regulations and the wearing of personal protective equipment by workers in factories, systems such as “smart home”, which track actions, systems for monitoring the condition of people in medical or social institutions, home systems for monitoring the condition of the elderly, which are able to notify relatives in cases of emergency situations. There is no comprehensive program that can solve the described problem and its variations, regardless of the scope of application. Therefore, the development of its prototype, which is a module that solves the human action recognition problem in the video, is an important problem. The article describes the creation of a software module that solves the human action recognition problem in a video. An overview of existing data sets suitable for training a neural network is provided, and data collection and processing for a custom dataset for actions of four different classes is described. The key features of the stages of creating, training and testing a neural network with the LSTM (Long Short-Term Memory) architecture, as well as options for its practical application, are described below. The developed module is quite flexible, there is a possibility to increase the number of classes of recognized actions depending on the scope of its application, as well as the possibility of integration with other modules for monitoring the behavior of people who have a similar device.
The development of supra-professional competencies of future pedagogues in the practice-oriented environment of the university

The subject of this research is supra-professional competencies that contribute to successful pedagogical activity. The author examines the impact of supra-professional competencies upon the success of practical pedagogical activity. Having specialized knowledge, skills and professional competencies is not always sufficient to meet the requirements of modern educational environment. Developed social and emotional intelligence, ability to work both in a team and independently, balance between subordination and leadership qualities, organized thinking, and other qualities are becoming increasingly relevant in pedagogical profession. The scientific novelty consists in determination of the key supra-professional qualities that contribute to successful pedagogical activity. The experienced pedagogues believe that emotional intelligence, ability to work independently, and critical thinking are considered supra-professional qualities, while young pedagogues believe that such qualities are social intelligence, proclivity for leadership and design thinking. Despite the fact that depending on age category, the pedagogues have different representations of supra-professional qualities, they are not contradictory. In the context of practice-oriented educational environment of the pedagogical university, the students should be taught not only specialized knowledge and skills, but also develop supra-professional competencies.
A New Exploitation Scheme in the Context of Bipolar Classifiers

Villarino, Guillemo Gómez, Daniel Rodríguez, J. Tinguaro Fernández, Alberto From the basis of the combination of supervised classification and bipolar knowledge representation, we explore in this proposal a new approach for the exploitation of the bipolar classification scores. The proposed approach is based on fitting a rule-based classifier to determine the decision rule that produces the final class assignments on the basis of the bipolar scores provided by any soft classifier. Therefore, the proposed method can be seen as a generalization of ROC-curve based decision rules, that takes advantage of the extra information introduced through the bipolar knowledge representation. The presented experimental results shows the feasibility of the proposed approach.
Credit Card Fraud Detection: An Exploration of Different Sampling Methods to Solve the Class Imbalance Problem

Credit card fraud is a plague that has been affecting the financial organizations and businesses since the time of its inception. Businesses incur loss of millions of dollars for such fraudulent transactions and at times customers also bear the brunt by losing their hard-earned money in disputes. Hence, the importance of detection of credit card fraud cannot be undermined. Credit card fraud is usually a problem of classification in the machine learning parlance, and the focus remains not only to identify fraud but also not to penalize the genuine customers, i.e., reduce the misclassification rate. The problem is rendered difficult because though the impact of the issue is paramount the occurrence is in-fact not that frequent. This poses the challenge of the detection of fraud through samples that are often imbalanced. This paper explores the various detection methods of predicting fraud correctly through data driven machine learning techniques, while focusing on the data imbalance problem and providing solutions on how to resolve it.
An Automated Flow for Configuration and Generation of CNN based AI accelerators for HW Emulation &amp; FPGA Prototyping

Machine learning (ML) algorithms have proven to be a concrete component in various fields that aim to be fully automated. Therefore, many researchers have shed the light on the modifications of ML algorithms to be fully automated for more complicated tasks. However, the acceleration of such algorithms is extremely hard due to the high computations and memory required. This paper implements automated flow using Perl scripts and generated LeNet-5 (A Convolutional Neural Network Model). Our target is high throughput, configurable and scalable RTL design that is generated by Perl scripts. Our flow is designing and verifying using Veloce emulator.
A Study on Transliteration Techniques and Conventional Transliteration Schemes for Indian Languages

Nair, Jayashree Ahammed, Riyaz Shaji, AnakhaTransliteration is the process of transcribing text scripted in one system of writing into another. It tries to script the same word in a language into another, thereby enabling reader to rebuild the original spelling of the unknown transliterated word. The primary objective of transliteration is to produce a different way of representing texts using a different language or writing system. India, being a multilingual nation, transliterators have played a major role in various modes of communication. Apart from the readability of unknown languages, transliterated texts or words are also used in Computational Linguistics Tools such as Stemmers, Morphological Generators, Dictionaries, etc., for easier processing and handling. Forward transliteration means transliterating the source language to its prescribed targeted language and the conversion of the transliterated word to its original form is known as back-transliteration. The motivation of the proposed work is to provide a forward and backward transliteration on different Indian language and on Roman script. This paper presents a study on the existing transliteration schemes and the various attempts made in building transliterations and back-transliterations for Indian languages from a computational linguistics perspective.
Development of an Algorithm for Energy Efficient Resource Scheduling of a Multi-cloud Platform for Big Data Processing

The problem of optimal distribution of virtual resources in multi-cloud systems for big data processing is relevant to this day. End users want to access virtual resources as soon as possible using one of the existing content distribution models: virtual desktop as a service, specific software as a service or even infrastructure as a service. The cloud system scheduler can handle incoming requests from users in physical constraints, taking into account the reduction of energy consumption and limited resources of cloud system. Violation of the Service Level Agreement in terms of exceeding the response time can lead to penalty for the service owner. This paper formalized the optimization problem of close to optimal energy efficient scheduling and provided general architecture of cloud platform under consideration. On the basis of the evolutionary annealing simulation algorithm, a cloud system simulator is implemented. Conducted experimental studies have shown the effectiveness of the proposed method in comparison with the greedy algorithm.
Optimal Quantum Ranging — Hypothesis Testing for an Unknown Return Signal

We use quantum hypothesis testing for unknown coherent-state return signals, deriving the error-probability limits of single-shot ranging experiments. We engineer optimal measurements, independent of the range, contributing to better rangefinders and LIDARS.
Using Amazon Mturk to Reach Public Assistance Recipients

Means-tested assistance programs in the United States are long criticized for their paternalism, insufficiency, and inability to move recipients’ to long-term financial independence. However, there is a limited empirical understanding of why these programs have been unsuccessful from the perspective of participants. This may be because low-income households and those receiving public assistance are often described as a “hard to reach” population for researchers. This survey of Amazon Mturkers who are self-identified current or former recipients of major means-tested assistance programs explores the effects these programs have on recipient financial planning and goal setting. We then compare these recipients and their responses to the existent welfare state knowledge base in order to assess the opportunities and challenges presented by using the MTurk platform as a method of reaching such individuals.
Video Summary Based on Visual and Mid-level Semantic Features

The continuous creation of digital media mainly video has caused a tremendous growth of digital content. To make accessible of such a large volume of videos, a number of techniques have been proposed. To summarize video of the different domain, researchers have targeted visual information contained in frames. But visual features only are not enough to create a semantic summary. In this work, we proposed a methodology for generating semantic based static summary of original videos. This method considers visual features like color, shape, textures etc.; and optical flow motion as middle level semantic feature. Along with these features, flipped scale invariant feature transform (F-SIFT) has been applied because of its invariance to scale, rotation or lighting changes in images. In the real world, it is common to observe flip like transformation in images, it is because of symmetric view of images, opposite capture viewpoint or it may be by artificial flipping. Another benefit of proposed application is that, along with semantic video summary user may have different length of summary according to their interest. Technically length of the summary is varying just by changing threshold in a different iteration of the key-frames extraction process. Proposed technique has been tested on different datasets, mainly videos available on YACVID dataset and UCF_Sports dataset. Interestingness about the proposed technique is that, users have the choice to select short length, medium length or longer length video_summary.
Comparing Speed Reduction of Adversarial Defense Systems on Deep Neural Networks

In the field of Adversarial Machine Learning, several defense systems are employed to protect machine learning systems from data poisoning attacks, where the training or testing data is altered to invalidate the prediction model created by the system. Although many defense systems have been proposed to combat poisoning attacks, comparisons between the effectiveness and performance cost of these defense systems are few. To address this issue, the NULL label and Adversarial Training methods were implemented to protect a machine learning system against various amounts of data poisoning. The NULL Label proved to not only provide better defense on average, but also did not affect performance as much as Adversarial Training. Although no trade-off between performance and accuracy was detected in the experiment, this work will hopefully provide a framework for future experimentation into this matter.
Comparison of Gurumukhi and Latin scripts in handwriting identification: A forensic perspective

The present study has been carried out to study the possibility of handwriting comparison in cases where document under question is inscribed in different language and the available standards are in other language. The aspect is prevalent in the field of languages since people around the globe are being attracted to learn new languages, but their native language always has some impact on the next learned languages. Punjabi is the first language taught in the schools of Punjab and other languages afterward. Punjabi is the mother tongue of Punjab area and in contemporary times most of the population of Punjab leaning towards learning English language. For this study, 1200 samples in Punjabi and English from 100 individuals who studied English as well as Punjabi and using these languages on a daily basis were collected and analyzed. Class characteristics like alignment, slant, size, the arrangement of a piece of handwriting, connections, fluency line quality of handwriting along with the characters of letter form and formation were considered and the results were further analyzed statistically using Chi-square test. The results of the present study revealed the handwriting written in distinct scripts is similar and comparable with each other. Additionally, the study will help correlate a person’s handwriting with ethnicity, nationality, and region based on the characters present in the handwriting samples.
Challenges of the Albanian Language in the Internet Era

Abstract Language is a live organism and as every other living being develops and is enriched with new words and terms, which enter the life of society together with the new tool, i.e. they enter in and mix with the order of Albanian words. Such a thing is inevitable and in some cases even useful, but every word that is lined up in the order of Albanian words must be well filtered. “The introduction of new words and exclusion of old ones is a natural process, and it happens in any language. This is what happened with the Greek borrowings in Latin, with the Arabic borrowings in Greek, with the Latin borrowings in many European languages, with the Persian borrowings in Turkish, with the Turkish borrowings in Albanian, etc. ” - Prof. Hajri Shehu emphasizes in a scientific interview (Shehu, 2017). In recent decades, with the expansion of the Internet, many foreign words began to enter and be used in the Albanian language. English took up more space than expected; in addition to penetrating through various tool notions, it also replaced centuries-old native words of Albanian. The paper has been divided into three parts and in each part an issue has been dealt with: the first part talks about the use of foreign words and the existing words in Albanian; the second part deals with the use of emoticons used instead of words and the third part deals with writing errors in the Albanian language. These are some of the issues that have created a great concern for the Albanian language in this century and they certainly need to be addressed seriously to prevent the dangers that may threaten it.
Abnormality Diagnosis in NPP Using Artificial Intelligence Based on Image Data

Accidents in Nuclear Power Plants (NPPs) can occur for a variety of causes. However, among these, the scale of accidents due to human error can be greater than expected. Accordingly, researches are being actively conducted using artificial intelligence to reduce human error. Most of the research shows high performance based on the numerical data on NPPs, but the expandability of researches using only numerical data is limited. Therefore, in this study, abnormal diagnosis was performed using artificial intelligence based on image data. The methods applied to abnormal diagnosis are the deep neural network, convolution neural network, and convolution recurrent neural network. Consequently, in nuclear power plants, it is expected that the application of more methodologies can be expanded not only in numerical data but also in image-based data.
Privacy Concerns Due to the Platformization of Secondary Education in Bulgaria in the Time of a Global Pandemic

This paper discusses the platformization of education in the time of a global pandemic in order to better understand the potential privacy implications for the teachers who had first-hand experience with this rapid digital transition. We have developed a case study of Bulgarian secondary education including eleven in-depth interviews aiming to grasp the main context-specific concerns, values, and norms related to privacy in the sector. On the basis of this data, we argue that educational technology (edtech) should be governed with the communities it serves in mind while respecting the appropriateness and the integrity of the information flows. Potential disruptions should be renegotiated with all affected parties before settling for a certain digital solution to be used in class.
Classification of Anomalies in Multivariate Streaming Phasor Measurement Unit Data Using Supervised and Clustering Ensemble Techniques

In today's world enormous data is generated continuously due to tremendous growth in industry. Many applications namely, traffic surveillance, energy management, health care and target tracking, industrial machineries, medical equipment's, mobiles and web applications use cameras and sensors which produce massive streaming data. Data mining on this data can lead to very valuable patterns and information that can prove extremely beneficial to that particular industry. Phasor Measurement Unit (PMU) is a sensor installed in smart electrical grid which produce huge streaming data. The remote centers depend on this huge data in decision making process. But data stream classification provides a lot of challenges compared to traditional data mining like concept drift, huge volume of data and high velocity data. This paper has focused to classify anomalies in streaming PMU data by considering all the features using supervised and clustering ensemble techniques. Here we are using random forest algorithm which is a supervised ensemble-based algorithm. In this algorithm, there are individual decision trees, each of which produces a single output. The average or mode of all the outputs of all the individual trees is then declared as the final output for the particular data. A novel framework is proposed for detecting and classifying the anomalies in the streaming PMU dataset using the clustering ensemble algorithm. The benefit of the proposed work is the labelling of streaming PMU data in addition to detection of anomalies. The ensemble members are created using the clustering methods namely K means, hierarchical method and Gaussian Mixture Model (GMM). The consensus functions used to integrate the result of individual base clustering method are Cluster-based Similarity Partitioning Algorithm, Hyper Graph Partitioning Algorithm and Meta-Clustering Algorithm. The clustering model is evaluated using adjusted rand index. Experiments are carried out in Python platform and promising results are obtained which demonstrates the effectiveness of the proposed method for classifying anomalies in real time PMU data.
Analysis of the Effectiveness on SIAK (Population Administration Information System) Use at Melawi Regency

Effectiveness is the main element in achieving the goals and targets that have been targeted in every organization or agency. Something is said to be effective if it achieves the objectives that have been set. During the use of SIAK in Department of Population and Civil Registration of Melawi Regency, there were still many shortcomings and obstacles. Therefore, analysis about this system’s effectiveness should be done. The method used in this research was the DeLone and McLean Information System Success Model. The population in this study were employees who had the authority to use SIAK at the Department of Population and Civil Registration of Melawi Regency. Data collection techniques were carried out by distributing questionnaires and conducting interviews. This study obtained the results that there were deficiencies in SIAK, especially in system quality, information quality and service quality which have no significant effect on the usage. This shows that the three variables still need to be improved and optimized so that the use of SIAK can be more effective.
Deep Listening

Abstract Throughout the history of the recording industry, the creative practices of mixing and mastering engineers were viewed simultaneously as a dark art and a highly specialized skill set requiring years of training in critical listening. In the 2010s, the implementation of AI algorithms and deep learning techniques in mixing and mastering plug-ins made these techniques available to casual users with a single button click. This chapter surveys the emergence of automation in the creative workflows of music producers, from AI drum machines and composition algorithms to the mixing and mastering “assistants” available in iZotope’s suite of plug-ins. How have automated tools for music composition, postproduction, and distribution affected divisions of labor in the recording industry? Further, how might the ocular-centric design of software plug-ins shape the nature of musical listening, especially amid twenty-first-century debates about automation and digital surveillance?
Opinion Mining Algorithm Based on the Evaluation of Online Mathematics Course with Python

Opinion mining algorithm based on the evaluation of the online mathematics course with Python is designed and implemented in this study. Natural language is the language used by humans to communicate. When using natural language, it can more or less always leave the user's subjective view of people, things and things. Therefore, texts with subjective content are widely present in our lives. Hence, in this paper, the data mining and the pattern recognition models are used to construct the model. The Python is used and optimized for the systematic design. The framework is simulated on different datasets, the performance is well validated.
Multimodal Product Matching and Category Mapping: Text+Image based Deep Neural Network

In the expanding world of online retail, there exists an extensive catalog of products in the current retail markets. Different retailers and e-commerce sites have millions of product images and text descriptions. Matching the products across the universe proves to be an important and challenging task to determine if specific products exist in our catalog. Expanding upon our previous work on product matching and category mapping from textual descriptions using transformer based models, we propose a weighted multi-modal approach for product matching by using both images and text within the training and matching process. We integrate both transformer and ResNet architectures into the siamese network to generate fine-tuned product embedding. Extensive experiments are conducted to evaluate our proposed weighted multi-modal approach comparing with single-modal approaches. The experiments show our proposed approach outperforms single-modal approaches.