Reviewerly/exHarmony huggingface dataset:
Speech Recognition Using Artificial Neural Network

Speech is a verbal communication used by humans through language. Likewise speech recognition is a process of converting speech to text. This paper provides a study of use of artificial neural networks(ANN) in speech recognition. Hidden Markov models (HMM) is a traditional statistical techniques for performing speech recognition. In speech detection software, Mel frequency cepstral coefficients (MFCCs) are frequently used. With different approaches evolving, we deal with the features used to recognize the speech pattern and implementation of speech recognition in the efficient types of artificial neural network (ANN).
Students’ Perception of Infographics: A Visualization Tool on Strengthening Critical Thinking Skills

As previous studies showed the effectiveness of infographic, it is important to find out students’ perception the use of infographic especially on its relation to critical thinking as on of the 21st century skills students require. Therefore, this study purposed to analyze students’ perception towards the use of infographics as a visualization tool to strengthen critical thinking. The study employed a descriptive quantitative research method by using questionnaire and semi-structured interview to collect the data, which included ten statements for the questionnaire and five questions for the interview. The participants in this study were 34 students of elevent grade students. The findings showed that the majority of students had a favorable opinion of using Infographics as a visualization tool in the learning process. Most students said learning with infographics provided them with a new learning environment. Moreover, they felt interested when they studied with infographics as it gave clear information they needed. Futhermore, the students stated that by using infographics, they can enhance their critical thinking skills compared to when they were not using infographics as a visualization tool during the learning process.
Sistem Pengukuran Kualitas Media pada Larva BSF (Black Soldier Fly) Berbasis Internet of Things Menggunakan Metode Naive Bayes

Piles of waste increase in line with population growth and consumption patterns. The concept of bioconversion using black soldier fly larvae can solve the problem of organic waste management. From these problems, an application of Internet of Things technology is needed. The system implemented aims to allow the system to find out how much accuracy, precision, and recall are in making decisions on media quality values using the Naive Bayes method. The main feature of this Naive Bayes Classifier is the very strong assumption of the independence of each condition or event. From the research results, the system has been successfully built according to the research design, as well as the goals that have been fulfilled in completing the development of the smart maggot. Several sensors used in this study were tested so that sensor performance could be determined by finding the average error value. Three parameters are measured; namely, the temperature obtained an average error of 1.6%, air humidity obtained an average error of 2.03%, and soil moisture obtained an average error of 2.7%. By measuring using Python, the Confusion Matrix is obtained so that the test results from the calculation of the Naive Bayes method can find the data in the form of accuracy, precision, and recall. Accuracy percentage results obtained 92%, precision percentage average results obtained 93%, and recall percentage average results obtained 92%. The conclusion shows the results of the system's accuracy obtained have worked well.
Linear Algebraic Systems Neural Network Solution. Part 1

In recent years, neural networks have become increasingly popular due to their versatility in solving complex problems. One area of interest is their application in solving linear algebraic systems, especially those that are ill-conditioned. The solutions of such systems are highly sensitive to small changes in their coefficients, leading to unstable solutions. Therefore, solving these types of systems can be challenging and require specialized techniques. This article explores the use of neural network methodologies for solving linear algebraic systems, focusing on ill-conditioned systems. The primary goal is to develop a model capable of directly solving linear equations and to evaluate its performance on a range of linear equation sets, including ill-conditioned systems. To tackle this problem, neural network implementing iterative algorithm was built. Error function of linear algebraic system is minimized using stochastic gradient descent. This model doesn’t require extensive training other than tweaking learning rate for particularly large systems. The analysis shows that the suggested model can handle well-conditioned systems of varying sizes, although for systems with large coefficients some normalization is required. Improvements are necessary for effectively solving ill-conditioned systems, since researched algorithm is shown to be not numerically stable. This research contributes to the understanding and application of neural network techniques for solving linear algebraic systems. It provides a foundation for future advances in this field and opens up new possibilities for solving complex problems. With further research and development, neural network models can become a powerful tool for solving ill-conditioned linear systems and other related problems.
Hate Raids on Twitch: Understanding Real-Time Human-Bot Coordinated Attacks in Live Streaming Communities

Online harassment and content moderation have been well-documented in online communities. However, new contexts and systems always bring new ways of harassment and need new moderation mechanisms. This study focuses on hate raids, a form of group attack in real-time in live streaming communities. Through a qualitative analysis of hate raids discussion in the Twitch subreddit (r/Twitch), we found that (1) hate raids as a human-bot coordinated group attack leverages the live stream system to attack marginalized streamers and other potential groups with(out) breaking the rules, (2) marginalized streamers suffer compound harms with insufficient support from the platform, (3) moderation strategies are overwhelmingly technical, but streamers still struggle to balance moderation and participation considering their marginalization status and needs. We use affordances as a lens to explain how hate raids happens in live streaming systems and propose moderation-by-design as a lens when developing new features or systems to mitigate the potential abuse of such designs.
Petrographic and Geochemical Characterisation of Organic Matter of Precambrian Black Shales from Ossa-Morena Zone, South of Portugal

Black shale (BS) lithologies are represented in Precambrian (Ediacaran) of Ossa-Morena Zone, SW of Iberian Massif, through the Série Negra lithological succession. The organic matter (OM) investigation in BS is relevant to understanding the mechanisms and depositional paleoenvironments related to that genesis. This study aims to characterise the OM in BS from the Série Negra succession through petrographic and geochemical methodologies. The petrographic study allowed the identification of organic particles with variable abundance, size and shape in all samples. The maximum reflectance indicates that the OM corresponds to anthracite to semi-graphite. OM maturation variation in the Série Negra succession is related to variable burial evolution and metamorphic/metasomatic processes in the Ossa-Morena Zone sectors.
Constant-Round Multiparty Private Function Evaluation with (Quasi-)Linear Complexities

Private function evaluation (PFE) is a special case of secure multiparty computation. In multiparty PFE, the party $$P_1$$ holds its private n-variable function $$f$$ and private input $$x_1$$ , while other parties $$P_i~(n\ge i\ge 2)$$ hold their private input $$x_i$$ . All n participants can jointly evaluate the function $$f$$ , and learn nothing from the interactions except the result $$f(x_1,...,x_n)$$ (known to a subset or all of the parties). The existing multiparty PFE protocols (e.g., Mohassel et al. at Eurocrypt’13 and Asiacrypt’14) are with round complexity $$O(g)$$ ( $$g$$ is the circuit size) which makes them extremely unpractical. In this work, we propose for the first time constant-round multiparty PFE protocols that are secure against any number of corrupted parties under the semi-honest security model. We design our first construction from oblivious evaluation of switching network (OSN) protocol (Mohassel et al. at Eurocrypt’13), which only needs 9 rounds of interaction and can achieve quasi-linear communication and computation complexities (i.e., $$O(ng\log (g))$$ ). Our second construction is based on singly homomorphic encryption, which only needs 8 rounds of interaction and can achieve linear complexities. The OSN-based construction also benefits from the design trick that it only relies on symmetric operations (which makes it really efficient in actual executions). We further optimize our constructions by half-gate technology.
BeamAttack: Generating High-quality Textual Adversarial Examples Through Beam Search and Mixed Semantic Spaces

Natural language processing models based on neural networks are vulnerable to adversarial examples. These adversarial examples are imperceptible to human readers but can mislead models to make the wrong predictions. In a black-box setting, attacker can fool the model without knowing model’s parameters and architecture. Previous works on word-level attacks widely use single semantic space and greedy search as a search strategy. However, these methods fail to balance the attack success rate, quality of adversarial examples and time consumption. In this paper, we propose BeamAttack, a textual attack algorithm that makes use of mixed semantic spaces and improved beam search to craft high-quality adversarial examples. Extensive experiments demonstrate that BeamAttack can improve attack success rate while saving numerous queries and time, e.g., improving at most 7% attack success rate than greedy search when attacking the examples from MR dataset. Compared with heuristic search, BeamAttack can save at most 85% model queries and achieve a competitive attack success rate. The adversarial examples crafted by BeamAttack are highly transferable and can effectively improve model’s robustness during adversarial training. Code is available at https://github.com/zhuhai-ustc/beamattack/tree/master
International Student Mobility and Covid-19 Pandemic: Challenges and Opportunities in Southeast Asia

The influence of Corona Virus (COVID-19) pandemic is significant in international higher education, especially student mobility. Due to the travel restrictions and campus closure, many students changed or cancelled their plan of studying abroad. Thus this study was therefore conducted primarily to determine the opportunities and challenges of the international mobility of students during the Covid-19 pandemic to draw prospects and recommendations to further enhance the progress of this educational endeavour and as well as, sustainable collaborations in Southeast Asia. The study used descriptive method because the study was associated with large sample sizes, respondents asked the same questions, and concentrating to quantity responses, which ensures that the entire data collection can be analyzed fairly. 35 respondents from institutions in the SEAMEO Network participating in the SEA-TVET Consortium within Southeast Asia were surveyed in this paper, and a questionnaire was used as the main data-gathering instrument for the study. Descriptive method of research and statistical tools were considered. The finding showed that the institution’s program for international student mobility was affected by the COVID-19 worldwide pandemic and greater part of the respondents encounter Travel and Government restrictions since the COVID-19 pandemic had disrupted most facets of lives including education. This had resulted in many schools and institutions of higher learning (IHE) being forced to cancel physical classes, and controlled international travel, with many nations keeping their borders closed to non-residents. Nevertheless, International Student Mobility in South East Asia was complemented using technological advancement for students such as online language or internet education and webinars in this pandemic. The researchers concluded based on the findings that the challenges and opportunities were both adapted by the institutions for International Student Mobility during pandemic in South East Asia.
INTELLIGENT APPROACH TO LOCAL AND GENERAL PHYSIOTHERAPY DEVICES

This paper discusses the usage of intelligent approach to a physiotherapy. Physiotherapy as a branch of medicine has existed for a long time, but its methods of application remain about the same as they were a hundred years ago. This causes a number of problems, in particular a sceptical attitude towards physiotherapy itself. The use of traditional techniques in this field has a major influence on its attitude. Even though even traditional methods of treating patients with physiotherapy show good results, people are increasingly turning to medication. However, these methods are not directed at the individual patient, with his or her personal problems, and can be detrimental to the patient, let alone the treatment. Every patient, irrespective of gender, race or age, has his or her own individuality. Physiotherapists now prescribe treatment to patients on the basis of their findings and personal experience. But this is not always the right approach, especially if we're talking about the severely or chronically ill patients.But we are lucky to live in the era of computer progress, when machines can not only help in determining the correct diagnosis, but also autonomously make decisions about the patient's treatment. With this modern approach to health care, it is even possible to automate certain areas. A global collection of data on patients, with their different illnesses and experiences, will help. By processing this data, we can create knowledge bases, with different patterns, so that the patient's treatment plan will be as appropriate and accurate as possible. This approach is already being used in other areas of medicine, for instance for the recognition of X-ray images. Using fuzzy logic, machine learning and artificial intelligence algorithms, data from databases can be used to create predefined patient treatment patterns. This can help in determining the diagnosis and prescribing the right treatment in cases where doctors take a long time to decide on the right approach. Keywords: Physiotherapy devices, Fuzzy logic, Machine learning, Artificial Intelligence, Data bases, Biofeedback.
DETECTİON OF HAND MOVEMENTS BY ANALYZING EEG SIGNALS USING CNN

In this paper a method for detecting six specific hand movement events in a 32-component brain EEG signal by using an ensemble of convolutional neural networks (CN) as a multiclass classifier is considered. The paper proposes and empirically evaluates several options for the architecture of convolutional neural networks, as well as an ensemble that combines the proposed options for the architecture of convolutional neural networks, using a blending algorithm and a final classifier based on logistic regression. The advantages of the chosen classification method for the problem being solved are shown. The results obtained make it possible to say that the use of a classifier in the form of an ensemble of several models of convolutional neural networks allows one to effectively identify characteristic features in the initial EEG signals, and at the output of the classifier to obtain the probabilities that the input signal belongs to one of the given classes of hand movements. The use of the blending algorithm makes it possible to obtain optimal classification results by integrating the best estimates of several models, which individually on the entire test set may give a non-optimal result.
Optimization of Deep Neural Networks with Particle Swarm Optimization Algorithm for Liver Disease Classification

Liver disease has affected more than one million new patients in the world. which is where the liver organ has an important role function for the body's metabolism in channeling several vital functions. Liver disease has symptoms including jaundice, abdominal pain, fatigue, nausea, vomiting, back pain, abdominal swelling, weight loss, enlarged spleen and gallbladder and has abnormalities that are very difficult to detect because the liver works as usual even though some liver functions have been damaged. Diagnosis of liver disease through Deep Neural Network classification, optimizing the weight value of neural networks with the Particle Swarm Optimization algorithm. The results of optimizing the PSO weight value get the best accuracy of 92.97% of the Hepatitis dataset, 79.21%, Hepatitis 91.89%, and Hepatocellular 92.97% which is greater than just using a Deep Neural Network.
TamilEmo: Fine-grained Emotion Detection Dataset for Tamil

Emotional Analysis from textual input has been considered both a challenging and interesting task in Natural Language Processing. However, due to the lack of datasets in low-resource languages (e.g. Tamil), it is difficult to conduct research of high standards in this area. Therefore we introduce a large manually annotated dataset of more than 42k Tamil YouTube comments, labeled for 31 emotions for emotion recognition. The goal of this dataset is to improve emotion detection in multiple downstream tasks in Tamil. We have also created three different groupings of our emotions namely 3-class, 7-class, and 31-class, and evaluated the models’ performance in each category of the grouping. We ran several baselines of different models and our MuRIL model has achieved the highest macro F1 score of 0.67 across our 3-class group dataset. In 7-class and 31-class groups, the MuRIL and Random Forest models performed well with a macro F1 score of 0.52 and 0.29 respectively.
Textual Entailment Recognition with Semantic Features from Empirical Text Representation

Textual entailment recognition is one of the basic natural language understanding (NLU) tasks. Understanding the meaning of sentences is a prerequisite before applying any natural language processing (NLP) techniques to automatically recognize the textual entailment. A text entails a hypothesis if and only if the true meaning and intent of the hypothesis follows the text. Classical approaches generally utilize the feature value of each word from word embedding to represent the sentences. In this paper, we propose a new framework to identify the textual entailment relationship between text and hypothesis, thereby introducing a new semantic feature focusing on empirical threshold-based semantic text representation. We employ an element-wise Manhattan distance vector-based feature that can identify the semantic entailment relationship between the text-hypothesis pair. We carried out several experiments on a benchmark entailment classification (SICK-RTE) dataset. We train several machine learning (ML) algorithms applying both semantic and lexical features to classify the text-hypothesis pair as entailment, neutral, or contradiction. Our empirical sentence representation technique enriches the semantic information of the texts and hypotheses found to be more efficient than the classical ones. In the end, our approach significantly outperforms known methods in understanding the meaning of the sentences for the textual entailment classification task.
Opinion Classification on Code-mixed Tamil Language

User Sentiment Analysis (SA) is an interesting application of Natural Language Processing (NLP) to analyze the opinions of an individual. The user's opinion is beneficial to the public, business organizations, movie producers etc. to take valid decisions and enhance it. Few sentiments are incorrectly interpreted due to context errors such as multi-polarity. People belonging to multilingual communities utilize multiple regional languages for communication and thus social media platform enabled the users to express their ideas in mixed languages. The user opinions posted as a mixture of two or more language is known as code-mixed data. It is quiet challenging to handle such code-mixed data as it contains colloquial vocabulary and is difficult to interpret the context in mixed languages. This proposed system focuses on this issue by analyzing the efficiency several word embedding techniques in the generation of contextual representation of words. To evaluate the performance of various embedding techniques, the representations generated are given as input to a standard machine learning technique for sentiment classification. The efficiency of several embedding algorithm is analyzed by classifying the code-mixed data based on its representation. This analysis is carried out on Dravidian Code-mixed FIRE 2020 Tamil dataset which contains review comments collected from YouTube. The evaluation proves that the transformer model generates effective representations and the positive labels are efficiently identified with the F1 score of 0.75. The representations generated by various embedding algorithms are fed as input to several classification algorithms and the accuracy of the models are estimated. From the result, it is derived that IndicBERT generates semantically efficient representations and thus facilitates in achieving greater classification accuracy.
Utilization of Big Data in the Financial Sector, Construction of Data Governance and Data Management

In recent years, efforts have been made in the financial sector to centrally aggregate the large daily amounts of data generated, manage data that contributes to marketing and analysis, and then utilize it for customer services. In the insurance industry, the principle products that have high customer needs are insurance products, for banks, it is loan products, and for securities companies it is investment trust products. These show trends such as converting customer financial transactions into data and encapsulating customer demand into products. The history of data management in the financial sector is long compared to that of other industries, but the current situation is that it is lacking in proper data utilization. Based on the background outlined herein, this research examines what kind of data management and data governance the financial sector should implement in order to utilize Big data, and how information system platforms and data infrastructures should be developed moving forward.
New Personalized Social Approach Based on Flexible Integration of Web Services

Dynamic design or coordination stays one of the significant difficulties of web administration innovation. This article gives an inventive way to deal with the robust making of web administrations given practical and non-utilitarian properties and individual inclinations. This approach utilizes long-range informal communication web administrations to help the connection between web administrations, choice, and formation of web benefits all the more firmly connected with client inclinations. We utilize the web administration local area idea of the web administration informal community to essentially lessen the inquiry space. This people group was made with the immediate interest of web specialist co-ops.