Reviewerly/exHarmony huggingface dataset:
SMART INTRUSION DETECTION IN INDUSTRIAL DEVICES USING DEEP BELIEF NETWORK

Utilization of smart systems everywhere through mobile devices, laptops and home pc are now becoming flexible.The increase in web usage also increases the web application cyber threats to be happening in most of the thirdparty connectivity websites.A robust approach on detecting the threats present in the IoT applications are discussed here.In the proposed architecture the collection of number of possible attacks is collected from KAGGLE NIDS dataset.The system detects the similar occurrence of intrusion creating task and triggers the model to prevent through immediate notification.In the existing system IDP-IOT is based on agent technology to support mobility, rigidness, and self-started attributes.Due to IoT limitations, the proposed solution is implemented in the middle, between IoT devices and the router that can be installed in a gateway.In the proposed research work cloud based advanced intrusion detection model is developed.The robust architecture provides the collection of number of possible attacks in the massive internet of things network.The collection of intrusion models we call bags of attacks.The proposed machine learning algorithm creates a robust prediction system for detection of feasible intrusions in the IoT network, the vulnerability of the IoT attacks act as a key for detecting the intrusion present in the network.The proposed design focuses on creating a Novel architecture though Adaptive convolution neural network for improving accuracy and increased security.
Statistical Analysis of Counter-Hate Speech on Voice-based Social Media

With the high prevalence of offensive language against minorities on social media, counter-hate speech generation is considered an automatic way to tackle this challenge. The counter-hate speech is supposed to appear as a third voice to educate people and keep the social red lines bold without limiting the freedom of speech principles. The counter-hate speech generation is based on the optimistic assumption that any attempt to intervene the hate speech on social media can play a positive role in this context. Beyond that, previous works were ignored to investigate the sequence of comments before and after the counter-hate speech. To the best of our knowledge, no attempt has been made to measure the counter-hate speech impact on voice-based social media from a statistical point of view. In this paper, we take the first step in this direction by measuring the counter-hate speech impact on the following comments in terms of Google Perspective Scores. Furthermore, our experiments show that counter-hate speech can cause negative impacts, a phenomenon called psychological reactance.
Yoga Asanas Pose Detection using Feature Level Fusion with Deep Learning-Based Model

Yoga is a healthful activity that started in India and has spread throughout the world, rejuvenating a man's physical, mental, and spiritual well-being. However, Yoga Image analysis is recent research based on Position identification. So Pose discovery procedures can recognize yoga postures and help people practise Yoga more precisely. But increasing dimensionality and segmentation problems lead to posture recognition is a complex problem. To resolve this, we propose a Deep learning algorithm that can be used to reliably identify numerous yoga asanas, as demonstrated in this paper. Since all the video data is raw, pre-processing is necessary before the model can be used. Objects are recognized using the YOLOv5 network in the pre-processed frames, and skeleton pose estimation is performed. Based on the identified objects, an algorithm makes a skeleton of a human in real-time. The skeletal position skeleton and objects detected by YOLOv5 are fused using feature-level fusion to derive the angles of the linkages in the human body. Based on Generative Adversarial Network (GAN) is implemented to predict the yoga postures, and these outputs are fed into the learning process. About 80% of the dataset was for training, while the residual 20% was used for testing purposes. The proposed system achieves high performance in identifying the Yoga Pose gestures accurately compared to other systems.
EDGE-Net: Efficient Deep-Learning Gradients Extraction Network

Deep Convolutional Neural Networks (CNNs) have achieved impressive performance in edge detection tasks, but their large number of parameters often leads to high memory and energy costs for implementation on lightweight devices. In this paper, we propose a new architecture, called Efficient Deep-learning Gradients Extraction Network (EDGE-Net), that integrates the advantages of Depthwise Separable Convolutions and deformable convolutional networks (DeformableConvNet) to address these inefficiencies. By carefully selecting proper components and utilizing network pruning techniques, our proposed EDGE-Net achieves state-of-the-art accuracy in edge detection while significantly reducing complexity. Experimental results on BSDS500 and NYUDv2 datasets demonstrate that EDGE-Net outperforms current lightweight edge detectors with only 500k parameters, without relying on pre-trained weights.
A Study on Trade Damage and Trade Adjustment Assistance(TAA)

Purpose: This paper tries to evaluate Korea's Trade Adjustment Assistance (TAA) system and proposes measures to more faithfully implement amendments to the TAA legislation from 2022.&#x0D; Research design, data, and methodology: This paper focuses on the regulations related to the TAA system, and on the problems that may arise from the regulations.&#x0D; Results: TAA support is allowed only when damage caused by the opening of trade is proven and confirmed. But it is very difficult for most small and medium-sized companies to prove trade damage.&#x0D; Conclusions: While the TAA system is designed to support trade-damaged companies, it should also include policies that focus on restructuring the business of a damaged company.
3. Materials and Data . Digitised Sources and a Lexicon

The computational analysis of emotion in historical texts undertaken in this study relies on an external digital resource: the NRC Word-Emotion Association Lexicon (EmoLex).This emotion lexicon is the pre-eminent external (re)source used in this investigation and its use is explained in this chapter.The lexicon is used in a computer-assisted analysis of digitised historical sources, primarily the Handelingen der Staten-Generaal dataset, which is comprised of parliamentary proceedings.Historians already work with digitised collections, digital search tools, or explorative digital user interfaces.Indeed, websites that give access to digitised source collections with user-friendly (search) interfaces are already popular with those conducting research. 1Many historians incorporate tools and websites based on these materials in their analytical processes, although they often do not even consider themselves to be 'working computationally' or 'digitally'. 2 Historians today seem open to experimentation with the possibilities that the mass digitisation of historical sources has to offer.With a single keyword, such as 'war', 'Holocaust', or 'genocide', hundreds of possibly relevant historical sources now appear on our screens.However, it is important to remember that the digitisation processes underlying these datasets not only has much to offer, but also alters our relationship and interactions with the sources. 3 In what follows, the underlying digitised historical sources and machinereadable data are therefore addressed in more detail. 4 They are an inevitable part of the computer-assisted methodology discussed in Chapter 4.
On Domain Generalization for Batched Prediction: the Benefit of Contextual Adversarial Training

This paper considers the domain generalization problem in which the labels of the query batch may be predicted at once and the model is allowed to assign a label to a query example utilizing all information contained in the query batch. In this setting, we identify two problems in the standard adaptive risk minimization (ARM) framework, namely, mismatched context and overfitted context, which are most severely manifested respectively at small and large support batch sizes. The existence of these problems signifies a fundamental tradeoff between within-domain learning and cross-domain generalization. We also propose two context adversarial training approaches to alleviate these problems so as to achieve a better tradeoff. We demonstrate experimentally that the proposed approaches uniformly outperform the standard ARM training for all choices of support batch sizes.
Computer Vision based Activity Recognition: Studying and Chit chatting

In a tech-driven and automated world, it is no surprise that innovation and research are reaching new heights and one such progress that has been researched, falls in the domain of image recognition, that is, Human Activity Recognition (HAR). Recent studies have shown their interest in human activity recognition systems that use computer vision and various machine learning algorithms to classify an image into different activities over which the model has been trained. A detailed review of many existing similar literature works that follow the CV-based projects for recognition purposes was also referred. This paper mainly focuses on activity recognition of studying and chitchat activities. The proposed method for HAR is purely CV based which uses a dataset of 2400 images, equally divided into two different activities, containing 1200 per activity. This work will be beneficial in recognizing human behavior, in surveillance and assisted living, elder care, and healthcare monitoring systems along with trending research areas such as human-robot interactions as well as gaming and entertainment. The best results were showcased by the KNN classifier after using the BRISK feature detector achieving an overall accuracy of 78 percent.
Basic Network Construction and Network Security Design Analysis of Cloud Computing

The basic network construction and network security design and analysis of cloud computing is the process of designing, planning and implementing computer or communication networks. The purpose of this study is to study the basic network construction in cloud computing, analyze the security design problems in cloud computing, and propose solutions to these problems. In addition, we also conducted an experiment to test whether our proposed solution can effectively solve these problems. In this paper, The design and implementation of the network architecture and network security of the cloud platform improve the stability of the platform, solve the problem of large concurrent server access, shorten the response time of user access, and improve the overall security of the platform.
On Shaky Ground: Reconsidering the Justifications for First Amendment Protection of Hate Speech

In the United States, hate speech is protected by the First Amendment. This approach differs from most other Western democracies, many of which have enacted criminal or civil laws to punish those who publicly incite hatred toward groups based on their fixed identity characteristics such as race or ethnicity. Traditionally, U.S. jurisprudence and legal scholarship have relied on several discrete theories to justify this approach. These include the marketplace of ideas, political self-governance, personal liberty, the bellwether argument, the safety valve argument, and the reverse enforcement argument. For decades, these theories have been cited as the reasons why the United States allows hate speech. But do they still hold water? Social norms are changing. More Americans are beginning to understand how systems of oppression have shaped our laws and institutions. Young people are more willing now than they've been in the past to accept limits on our right to free expression to promote an inclusive society that welcomes diverse groups. Given these shifting norms, this article asks whether and to what extent each of these theories remains a viable justification for protecting hate speech. The result of this analysis is a comprehensive picture of how these ideas operate in our modern social, political, and media environments. With this clear-eyed view, we can more accurately weigh the potential benefits and drawbacks of our collective decision to protect hate speech in the United States.
Self-Operated and Efficient Recruitment Procedure Using Natural Language Toolkit

Self-Operated and Efficient Recruitment Procedure using Natural Language Toolkit (NLTK) can be exercised in companies to enhance the quality of resume evaluation and to reduce human errors. The main motive of this application is to automate the entire evaluation process and let the company know the candidates with the appropriate quality based on the job requirements. The project is a Web-based application into which all the resume of candidates along with the job requirements will be uploaded for each job individually. The PDF resume is extracted into digital text using Object Char-acter Recognition (OCR) and tokenized using NLTK. Candidates will be shortlisted accordingly. Finally, the shortlisted candidates are sent an inter-view invite through email. At the back end, the application uses Redis for the cache storage and Postgres database for secondary data storage for future use. Google Calendar API and GitHub API is used for sending invites and in-depth analysis of the candidates respectively.
Blockchain Based Inter-Organizational Secure File Sharing System

A consortium of organizations collaborates and exchanges information to create synergies in their operations. Centralized systems of file-sharing cannot provide distributed trust and transparency. Blockchain technology can be used to share files securely and transparently.This paper proposes a blockchain-based inter-organizational secure file-sharing system. It can be used by a consortium of organizations to securely exchange files in a distributed fashion. Hyperledger Fabric, an enterprise blockchain framework, is used for blockchain network setup and the development of smart contracts. The Inter Planetary File System (IPFS) is used for storing files in a distributed way. The paper provides the workflow for identity management and file-sharing processes. The proposed system allows a consortium of organizations to share files with confidentiality, integrity, and availability using blockchain.
Anomaly Detection for Highly Imbalanced Data–an Empirical Analysis

An event or an observation that is statistically different from the others is termed an anomaly. Anomaly detection is the process of identifying such anomalies. Anomaly detection is an effective tool for risk mitigation, fraud detection, and improving the system's robustness. It is also an active research area, with numerous algorithms being proposed. In this paper, we compare the performance of various anomaly detection algorithms on mul-tivariate as well as univariate datasets. The assessment measures generated are important and can be beneficial for predicting anomalies in a timely and accurate manner. Experimental results demonstrate that on a univariate dataset, the auto-regressive moving average (ARMA), performs better than the local outlier factor (LOF), while on a multivariate dataset, the LOF model performs better. The prototype developed has been extensively tested on publicly available datasets and can be evaluated on larger, more comprehensive datasets for deployment in the real-time anomaly detection setup.
An Improved Method of Calculating Electricity for Household Consumption and Storing Excess Electricity

Energy efficiency is essential for home and country. It has been suffering from power shortage for the past few years. If the fan doesn't spin at night or the AC stops, our sleep goes with it. On the other hand, the electricity bill has also gone up. So here are some ways to use electricity sparingly without wasting it. Although many people know this, it is better to follow it. In this paper, an improved method has proposed to calculating electricity for household consumption and storing excess electricity. Reducing the use of appliances is one way to conserve electricity, while installing electrical appliances properly is another key way. Accordingly proper instructions should be followed during wiring work. All electrical appliances have a definition. There is a theory that this amount runs on electricity. It should be set accordingly.
A Sensor-Based Unit for Diagnostics and Optimization of Solar Panel Installations

To solve the problem of relatively low efficiency in most of nowadays solar panels, this paper proposes a new solution by diagnosing and optimizing solar panel installations. Dr. Solar, a sensor-based device, acquires information on solar radiation, geographical position, inclination, roll, panel temperature, and ambient temperature and humidity. The sensor-based device is mounted to the solar panel for a few days while collecting data. The cloud-based service gathers information from the inverter while the device is moved to other installations. Combined with the data of power generation from the inverter, a cloud service is offered for processing and analyzing the data to maximize power production by adjusting the panel installation. During several short-term experiments in different places, Dr. Solar provided reliable data for analysis on the cloud platform, and a web interface was developed to calculate power production. The solution has increased photovoltaic panels’ efficiency by optimizing adjustments to the panel's physical position (tilt and angle). The paper describes the sensor-based device, the cloud service, and the algorithms used.
Innovating Primary Education of Promoting Students’ Language Competencies Through Mobile Assisted Language Learning Approach: Selection Framework of Innovative Digital Technologies

The younger children are, the better they acquire language skills. That is why it is high time for parents who expect the best from their children and provide conditions for their personal development from their early childhood, including language acquisition. It is known that mobile phones as portable and accessible devices are being used for educational purposes, especially language education. The general aim of this study is to analyze innovative ways of promoting linguistic competencies of primary education school students through mobile-assisted language teaching and select appropriate technologies suitable to an ESL classroom of primary education in Uzbekistan. By using the analytical hierarchy process (AHP) approach and mobile application user survey, the study examines the prioritization of primary education teachers about using the selected technologies and shows students rating the usefulness and effectiveness of those technologies. The results show that the mobile applications used by students and the ranking status of those applications are the factors that contribute most to stimulating the academic achievement of language learning of primary education students.
Logical-mathematical thinking and creative didactics: study carried out in the eighth grade of the educational circuit 13D01_C07 Ecuador

In education, one of the most important pillars is learning mathematics, because it focuses on cognition and develops important skills such as logic, reasoning, problem solving, and critical thinking.For this reason, when a good learning environment is fostered and thestrategiesdidactics in teachingthe creative potential of students is developed.Reason why this article aims to analyze the influence of creative didactic strategies for the development of mathematical logical thinking in eighth-year students of upper basic school of district 13D01 of circuit C07 of Cantón Portoviejo, Republic of Ecuador.The work was carried out from a mixed descriptive level approach using in-depth interviews and the administration of the Logical Thinking Battery in the formal stage of the student body.The results showed that 56.7% of the students have a grade below 7.00, which means that they have not passed the required learning levels in problem solving.On the other hand, teachers use didactic strategies in the teaching processto solve everyday problems by linking learning with the environment, so that significant learning can be achieved from this contextualization.
Knowledge, Attitude, and Practices Regarding Infection Control Measures among Clinical Medical Students in South Eastern Nigeria

Introduction: HAIs are a major cause of morbidity, mortality and economic burden considering it affects millions of people each year. Evidence has shown that a great proportion of healthcare providers and patients had acquired infections within a healthcare facility. Methodology: A snowball sampling technique was utilized in the distribution of the online questionnaires sent in the form of a link through social media outlets such as WhatsApp, Face book and emails. This study was carried out among the clinical medical students of the Faculty of Medicine, in all colleges of medicine in south-eastern Nigeria. There was no restriction on the number of clinical medical student per school. An online survey was created using the free software Google form and distributed through social media networks (WhatsApp, Facebook, Emails). Data was collected within 4 weeks interval. Analysis was performed using the Statistical Software Package SPSS version 22.0. Descriptive statistics (including means and standard deviations) was calculated for the numerical variables. Approval to carry out this study was obtained from the Scientific and Ethical Review Boards of Nnamdi Azikiwe University Teaching Hospital Nnewi, Anambra State, South East Nigeria. Result: From the research conducted, out of 333 respondents, a whopping number of 138 medical students (41.4%) admitted to not having undergone any form of hand hygiene training within the past year. Only 78 students (23.4%) have been through frequent hand hygiene training frequently within this period. On universal precaution training within the same period, 150 students (45%) have not received any training while 72 students (21.6%) have frequently been exposed to the training. In addition, 21 students (6.3%) have undergone Needle stick injury training. Meanwhile, 258 students (77.5%) have not within the last 12 months. The same discrepancy is also seen in surgical scrubbing training where 192 students (57.7%) have not undergone any, having only 30 students (9%) admitting to such training. Conclusion: With clinical medical students having the most exposure to infectious agents during their training, and considering that they are still in their formative years as future clinicians, this study assessed their knowledge, attitude and practices regarding the four major components of Infection control measures (ICMs).
Context-Sensitive Meta-Constraint Systems for Explainable Program Analysis

Abstract We show how to generate a constraint system of symbolic expressions as part of an inter-procedural constraint-system–based program analysis such that any chosen slice of the intended analysis may be computed through the evaluation of the symbolic constraints. Thus, our method ensures that the computed expressions provide genuine explanations for the chosen analysis slice. The resulting system is then annotated with program location information, translated into closed-form expressions, and simplified to yield a human-readable justification for the analyzer’s verdict. Justifications are given using program locations, constants from the program, abstract lattice operations, loops in the analysis, and computed results.