Reviewerly/exHarmony huggingface dataset:
Results of Experimental Research on Phase Data of Processed Signals in Voice Authentication Systems

The number of cybercrimes in infocommunications area is growing, and they are becoming more sophisticated. According to the Cisco 2018 Annual Cybersecurity Report, spreading malicious software (in particular, ransomware), the volume of encrypted web traffic transmitted by cybercriminals, and the number of e-mail threats have increased. To protect financial and information resources pin codes, passwords, identification cards are used; however, they can be lost or counterfeited. Biometric authentication is now a solution these problems. Initially, static biometric features (fingerprints, face shape and size, iris and retina patterns) were mostly used, especially in forensic science. Due to simple forgery and limited amount of analyzed data in access systems, preference is given to behavioral biometric features, especially to the user voice signal. Voice systems are preferred by the efficiency/cost criterion. Moreover, voice systems have additional advantages: simplicity, ease of use, complexity of counterfeiting, remote use through communication channels, unlimited operational increase in password phrases, and the availability of digital data processing achievements. Unfortunately, the qualitative characteristics of voice authentication systems yield to systems which use static biometric features because the formation of a user's template is based on amplitude and frequency information. One of the directions for improving the quality indicators of voice authentication systems, according to the authors, is to use phase information of registration materials, which has not been sufficiently considered in the scientific literature. The object of research is the process of digital processing of speech signals in voice authentication systems. Research methods include analysis, observation, measurement, modeling and experiment. The paper considers the procedures for the formation and direction of voice signal phase data use in authentication systems. The feasibility of phase data use, which allows to improve qualitative indicators of the considered systems, is essentially proved on the example of processing experimental voice signals.
Rating Urban Transport Services Quality Using a Sentiment Analysis Approach

Urban transport systems are recognized as one of the most relevant pillars in the operation of a city, as well as in the quality of service they provide to its inhabitants and visitors. Today, transport network management and control systems play a key role in ensuring their operation, and planning new services in accordance with some specific requirements. Some of these services provide means to users for expressing their opinions about the services they used. When properly treated and analyzed, the opinions reveal very relevant information, both positive and negative, about the operation of public transport companies, which can be used to identify risk situations, malfunctions, or emerging threats, among other things. From the identification of these situations, transport companies can improve their image, attenuating or eliminating such critical and undesirable situations and, consequently, increasing the quality of the service they use and their earnings. In this paper, we present and discuss a system for analyzing complaints about services provided by a public transport company, in order to identify sentiments expressed in complaint texts and establish a complaint-index reflecting the negativity level of the complaint.
Toward More Robust Classifier: Negative Log-Likelihood Aware Curriculum Learning

The curriculum learning has shown immense potential in improving computer vision tasks. However, the drawback still exists when it comes to the multiclass classification problem, because of the nature of both data and model uncertainties. In this paper, we introduce a novel curriculum sampling strategy that takes into consideration uncertainty, confidence, score, and negative log-likelihood. We also suggest a novel method of grading the samples that have already been shown to be very successful. During the training period, curriculum learning is put into practice. After the preliminary training is finished, we use curriculum learning in our experimental setting. For this experiment, we used the CIFAR-10 dataset, and we were able to demonstrate the effectiveness of our approach by showing faster convergence, more accurate findings, and a strong deep learning model for image classification. We have demonstrated the use of NLL-based CL post-training on the same model to accomplish the indicated results, in contrast to the state of the art where curriculum learning is utilized before the model training. Problem statements involving multiclass object detection and segmentation can be addressed using the technique.
Extractive Summarization Approaches for Biomedical Literature: A Comparative Analysis

Text summarization is one of the key applications of Natural Language Processing that has become more prominent for information condensation. Text summarization minimizes the content of a source document to generate a coherent summary while maintaining vital information from the source. Automatic text summarization has emerged as a key area of research in information extraction, notably in the biomedical field. Keeping pace with the recent research findings on certain issues has become an incredibly difficult challenge for medical specialists and researchers as the number of publications in medical research increases over time. Automatic text summarization approaches for biomedical literature may aid researchers in quickly reviewing research findings by extracting significant information from recent publications. Typical techniques for medical text summarization necessitate domain knowledge. Such systems’ effectiveness depends on resource-intensive clinical domain-specific knowledge bases and preprocessing mechanisms for generating relevant information. This research compares three text summarization approaches word frequency, cosine similarity, and the Luhn algorithm. The comparative results are generated for 20 biomedical articles and evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric; the outcome demonstrates the best-suited technique for summarization of biomedical literature.
SMS Spam Detection Using Federated Learning

Despite all technological advancements, the biggest issue tech giants face is mining data while keeping user privacy intact.According to an article in quartz, which says Google spent “hundreds of years of human time complying with Europe’s privacy rules”. A lot of important data cannot be accessed because of these privacy rules. A lot of methods have been under research to use the power of ML and keep privacy intact at the same time. In this work, we implemented one such method called federated learning. In the federated learning paradigm, the data is not moved out of the device, instead, the model is moved to the device where the data is trained and only the parameters are shared with the main server thus keeping the data privacy intact as well as using it to train the models. This could prove to be an innovative technology keeping in mind the current scenarios where most of the tech giants are driven by data and because of the privacy policy, they are unable to make the best out of the data being collected.
Solar Power Forecasting to Solve the Duck Curve Problem

Solar power is widely regarded as the power of a green future. However, excessive generation of solar energy can cause damage to the existing power sources, if it’s not balanced properly. Herein comes the concept of the duck curve, a load curve that shows how much power is required to be produced by non-solar sources when solar power generation is in full swing. In order to calculate the duck curve, it is necessary to calculate the solar power generated. There are several methods to achieve this, and this project focuses on the machine learning side of it. Using weather data with parameters like temperature, humidity and wind temperature of a region, the solar output is predicted. This allows easier allocation of power demand to the non-solar sources. As expected, solar power peaks during the daytime and this results in a sharp drop in demand for non-solar sources. This is followed by a steady increase as the sunsets. This load curve essentially shows the impact of solar power on the load demand and gives valuable information on how the other sources have to be adjusted for efficient power generation. Using the dataset obtained from NSRDB, we were able to predict the per hour GHI of VIT Chennai and corroborate with official sources. We were able to plot the duck curve of VIT and then use the model to observe the GHI of other locations in India.
A Comparative Analysis of Transformer-Based Models for Document Visual Question Answering

Visual question answering (VQA) is one of the most exciting problems of computer vision and natural language processing tasks. It requires understanding and reasoning of the image to answer a human query. Text Visual Question Answering (Text-VQA) and Document Visual Question Answering (DocVQA) are the two sub problems of the VQA, which require extracting the text from the usual scene and document images. Since answering questions about documents requires an understanding of the layout and writing patterns, the models that perform well on the Text-VQA task perform poorly on the DocVQA task. As the transformer-based models achieve state-of-the-art results in deep learning fields, we train and fine-tune various transformer-based models (such as BERT, ALBERT, RoBERTa, ELECTRA, and Distil-BERT) to examine their validation accuracy. This paper provides a detailed analysis of various transformer models and compares their accuracies on the DocVQA task.
An Approach to Estimate Body Mass Index Using Facial Features

People must have a healthy daily routine in order to stay away from health-related problems. They should keep their body measures in check. A simple measure like body mass index (BMI) can be considered as an indicator for the ratio of weight and height. Generally, health risk is directly proportional to the BMI value, so it is advised that one should keep check on it. But, due to the busy routine, normally people are reluctant to measure weight and height regularly and then observe the BMI value. This motivated us to come up with a system that aims to calculate the BMI by just using a face image of an individual. From the face image, the facial features are extracted using three methods. These are then used to estimate the BMI of the person using machine learning algorithms. In regression, seven algorithms and in classification, nine algorithms were tested. The multiple linear regression technique outperformed the other methods. The main objective of this study is to estimate the BMI from face image accurately in a hassle-free manner.
Data Extraction and Visualization of Form-Like Documents

While the world moves toward digitization and most vital information is getting stored, processed, and understood in computers, there are still organizations, businesses, and companies that are serving employees and audiences that prefer physical forms to online ones. Along with them, there are also forms and documents that are filled online in a Word Document or PDF, like NDAs, resumes, etc. Manually going through each physical or digital copy of the form, and then typing in the data into an Excel sheet or database leads to the investment of a lot of man-hours, energy, and computer resources. Our solution, Form Analyzer, aims to solve this problem by being a one-stop platform where a bulk number of forms will be accepted, segmented, parsed and data will be stored and visualized. The system considers physical forms which are filled manually and soft copy (or digital) forms which are filled by typing and then printed. Data visualization dashboard will help the organization to get insights about the available information. This will lead to lower time and resource consumption. Also, the system analyzes the sentiments of the comments or information provided in the form which can be helpful for further strategic planning of the organization. The paper discusses the architecture, working, and performance evaluation of the system on handwritten as well as typed forms.
Experimental Evaluation of Reinforcement Learning Algorithms

Reinforcement learning is an active field of machine learning that deals with developing agents that take actions in an environment with the end goal of maximizing the total reward. The field of reinforcement learning has gained increasing interest in recent years, and efforts to improve the algorithms have grown substantially. To aid in the development of better algorithms, this paper tries to evaluate the state-of-the-art reinforcement learning algorithms for solving the task of learning with raw pixels of an image as input to the algorithm by testing their performance on several benchmarks from the OpenAI Gym suite of games. This paper compares their learning capabilities and consistency throughout the multiple runs and analyzes the results of testing these algorithms to provide insights into the flaws of certain algorithms.
JSON Document Clustering Based on Structural Similarity and Semantic Fusion

The emerging drift toward real-time applications generates massive amounts of JSON data exponentially over the web. Dealing with the heterogeneous structures of JSON document collections is challenging for efficient data management and knowledge discovery. Clustering JSON documents has become a significant issue in organizing large data collections. Existing research has focused on clustering JSON documents using structural or semantic similarity measures. However, differently annotated JSON structures are also related by the context of the JSON attributes. As a result, existing research work is unable to identify the context hidden in the schemas, emphasizing the importance of leveraging the syntactic, semantic, and contextual properties of heterogeneous JSON schemas. To address the specific research gap, this work proposes JSON Similarity (JSim), a novel approach for clustering JSON documents by combining the structural and semantic similarity scores of JSON schemas. In order to capture more semantics, the semantic fusion method is proposed, which correlates schemas using semantic as well as contextual similarity measures. The JSON documents are clustered based on the weighted similarity matrix. The results and findings show that the proposed approach outperforms the current approaches significantly.
Blockchain: A Tool to Create a Sustainable Future

In recent years, the concept of sustainability has gained significant traction across various industries. As the world grapples with the challenges of climate change and resource depletion, innovative technologies are being explored to contribute to a more sustainable future. One such technology that holds immense potential is blockchain. While commonly associated with cryptocurrencies like Bitcoin, blockchain's applications extend far beyond digital currencies. This comprehensive research paper aims to explore how blockchain can contribute to building a sustainable future, focusing on its role in environmental, social, and economic aspects.
Recognition of Static American Sign Language Hand Gestures Using Convolutional Neural Networks

" It's not what happens to us, but it's how we respond to it that matters the most." That is what motivated us to undertake this project. People with disabilities are not considered to be impaired. They simply communicate in a way that normal humans like us are unable to understand. The primary form of communication for those who face difficulty in hearing or who cannot speak is sign language. Without a translation, it can be difficult for people with hearing and speech impairments to interact with other average people. This paper would like to present a Convolutional Neural Network (CNN) model for predicting American Sign Language. We utilize a Modified National Institute of Standards and Technology dataset, as well as the Grassknoted ASL datasets in order to apply transfer learning to this task. The proposed model significantly raises the ASL recognition accuracy reported by various existing methods that are already studied. The successful development of this model can be used as the foundation for the creation of a complex sign language translator.
Investigating the Corporate Governance and Sustainability Relationship A Bibliometric Analysis Using Keyword-Ensemble Community Detection

Sustainability is a business strategy combining economic, social, and environmental issues. This paper examines the corporate governance and sustainability literature. So we consider a new bibliometric database focusing on the network of keywords appearing in the literature. The quantitative approach is also new: we combine the information from different community detection algorithms to find the most important results and relationships in the literature. The final results show that the literature on corporate governance and sustainability raises an essential strategic question: for long-term sustainability if there needs to be a strong link between stakeholders and corporate social responsibility (CSR). So, considering a company's actions' social, economic, and environmental effects can help figure out how much corporate responsibility is needed. Also, companies that consider CSR and sustainability in their businesses find it easier to keep long-term relationships with customers, employees, and other stakeholders, which can be considered vital. Last, a strategic view of corporate governance should emphasize the importance of intellectual capital and the Triple-Bottom-Line approach to sustainable growth in a strategic view of corporate governance. In this sense, a more wholesome view of value creation aims to provide companies with better financial results while also serving society's environment and social well-being. By addressing these issues, governments and other groups can make the business world more sustainable and responsible.
THE ANALYSIS OF DEIXIS IN STUDENTS’ TALK IN LEARNING ENGLISH SPEAKING

This research reports the types and the dominant types of deixis found in students’ talk in learning English speaking. The design of this study used a descriptive qualitative. The present study was carried out at one of Senior High School of the tenth grade, as participants, in Sodonghilir. The data were collected from the classroom observation. The result indicated that the total of deixis used in English speaking was 201 data types of deixis from 114 data in utterances. The most type of deixis obtained was person deixis with 183 words in data utterances, the second type was temporal deixis with 11 words in utterances and the least types of deixis was spatial deixis with 7 words in utterances. While the most frequent used of person deixis types during classroom activities were the word "I" and "my". In conclusion, person deixis purposed by Yule (1996) was mostly found and it was the dominant one in students’talks. Thus, it is expected to the next writers who are interested in deixis pragmatics and students’ talk to carry out another research in online class. Keywords: pragmatic, deixis, students' talk, speaking, yule’s theory
A data-driven method for SSCI identification using synchrophasor measurements

In recent years, subsynchronous control interaction (SSCI) frequently occurred due to the rapid development of inverter based resources. Online monitoring and identifying SSCI are thus of great importance for the safe operation of power systems. This paper proposes a data-driven method for SSCI identification using synchrophasors generated by phasor measurement units (PMUs). The challenges of this work mainly result from two factors: 1) SSCI may simultaneously involve a sub-synchronous oscillation (Sub-SO) mode and a super-synchronous oscillation (Sup-SO) mode. The spectra of these two modes would be aliased due to the limited reporting rate of PMUs. 2) The reporting filter in PMUs significantly weakens the oscillation signal as the frequency of SSCI is generally far away from the nominal frequency. To tackle these challenges, the influence of Sub-SO and Sup-SO on spectra of synchrophasors is analyzed first. It is revealed that the positive and negative spectra are linearly correlated with Sub-SO and Sup-SO modes. Several features are then extracted via the analysis of the spectra which can reflect the SSCI features and a data-driven identification method is further proposed through a fast reduced kernel extreme learning machine (RKELM) model. Compared with the traditional classification techniques, the RKELM algorithm based on kernel function has stronger generalization performance and higher accuracy while ensuring shorter training time. This significantly improves the accuracy and efficiency of SSCI identification, especially under noise conditions. Both simulations and field tests demonstrate the effectiveness and usefulness of the proposed method.
Collaborative Poetry through Ecological Messaging and Authentic Audience

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Picturing Writing: Differentiating Secondary Writing Instruction with Picture Books

Conversations about how picture book authors use language led to growth in sentence fluency for a group of high school writers.
New Voices: Your Words, Your Voice

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Feature: In the Palm of My Hand: The Efficacy of Mobile Devices in a Community College Developmental Writing Class

This study explores two community college developmental writing courses that made use of mobile devices and apps, specifically iPads and iPhones, iTunes U, and Apple Books as primary learning materials and devices.
RANCANG BANGUN SISTEM INFORMASI PERPUSTAKAAN BERBASIS ANDROID DAN IOT

Advances in technology today are very helpful for humans in completing work. Radio Frequency Identification (RFID) is one of the new technologies that has developed rapidly. By using RFID and entering an RFID tag in every book in the library, the process of borrowing and returning books will be easier by scanning the books with an RFID reader and entering them into the database. The ULI (UNSIKA Library) application is an application which is designed as a liaison between the user and the admin (library staff) in conducting borrowing transactions or returning books in the library. This application can be used by users as a medium for ordering books (booking), checking a list of borrowed books and returning books. ULI scanner tool is used as a confirmation medium for borrowing and returning books to be borrowed or returned by the user through the ULI application. The RFID (Radio Frequency Identification) sensor test shows that the sensor can detect RFID tags well. The overall tool system testing shows that the tool can carry out its mission, namely detecting book IDs using RFID. Based on the measurement results, Telkomsel's data access speed is superior with the fastest data access reading of 1.85 seconds while Indosat is 3.50 seconds and the average data access reading on Telkomsel is 2.2 seconds and Indosat is 6.7 seconds.