Reviewerly/exHarmony huggingface dataset:
Exploring predictors of academic underachievement of college students based on learning analytics: Focusing on cumulative academic variables before the start of the semester

Objectives The purpose of this study is to explore the possibility of preventing academic underachievers by utilizing the data of enrolled students accumulated in the school academic system from the perspective of learning analytics. In particular, by exploring predictive factors for academic underachievement based on academic data before the start of the semester, we intend to predict and select risk groups for academic underachievement early before the start of the semester. The results of early prediction and screening are intended to be used to prepare a preemptive support plan for teaching and learning for those at risk of academic underachievement.&#x0D; Methods 670 students of 4-year A university located in the metropolitan area were selected as the research subjects and the academic data accumulated in the school academic system was analyzed. The independent variables are the previous semester's GPA, total credits completed, total number of leave of absence, total number of academic warnings, total number of F credit courses, and major course registration credits and liberal arts course registration credits for which course registration has been confirmed for the current semester. Dependent variables are GPA and low academic achievement in the current semester. For data analysis, SPSS 28.0 statistical program was used, linear regression analysis was performed to confirm the explanatory power of independent variables for grades, and logistic regression analysis was performed to derive predictive factors for low academic achievement.&#x0D; Results As variables predicting grades according to Research Question 1, the GPA of the previous semester, total credits completed, credits for major course registration in the current semester, and credits for general course registration in the current semester had a positive effect on grades. And the total number of F-credit courses and the total number of leave of absence were confirmed as predictors that negatively affected grades. According to Research Question 2, the variables predicting low academic achievement are GPA of the previous semester, total credits completed, credits for major course registration in the current semester, and credits for general course registration in the current semester have a negative effect on low academic achievement. And the total number of F grade courses, the total number of leave of absence, and the total number of academic warnings were confirmed as predictive variables that had a positive effect on academic underachievement.&#x0D; Conclusions This study has great educational significance in that it derives predictive factors for grades and academic underachievement based on the academic data of students enrolled in the school and prepares a method for teaching-learning support. The results of this study will be available as practical examples and reference materials for early prediction and support of grade and academic underachievers.
Analogy-based classifiers: An improved algorithm exploiting competent data pairs

Analogy-based classifiers, first proposed in 2007, have been applied with success to Boolean, nominal and numerical data. They rely on an extrapolation mechanism that exploits analogical proportions, which are statements of the form "a is to b as c is to d". There are usually a great number of triplets (a, b, c) in the data set that lead to a prediction for the class of d. The paper shows that, using a preprocessing step for assessing the "competence" of pairs, it is possible to drastically diminish the number of triplets used for the prediction, with an obvious computational benefit, while preserving, and sometimes improving the accuracy of the results.
Traffic flow privacy protection with performance guarantee for classification in large networks (minor revision of INS_D_21_805R3)

Privacy-preserving traffic flow classification has attracted a significant amount of research interest because of its increasing importance to both network management and privacy protection. In this paper, we propose novel methods for effectively protecting network flow identifiers and attributes against privacy inference attacks in port-based and payload-based classifications respectively, and analyze their performance guarantee on data utility for flow classification and privacy (security). For protection of flow identifiers, we propose a partial identifier protection approach applying randomization and anonymization respectively on desired bit positions to conceal sensitive information, and show their expected-case performance guarantee. For protection of flow attributes, we propose a perturbation-based scheme that first selects the representative attributes by deploying an entropy-based attribute selection method to filter out redundant and insignificant attributes and reduce the problem space, then partitions the attribute domains into either equal-depth or equal-width intervals and perturbs attribute values in these intervals by swapping them with those in adjacent intervals and intervals with same value distribution respectively. We analyze the performance guarantee of the proposed methods and show the experiment results of classification accuracy obtained by implementing popular machine-learning based benchmark classifiers on our selected attributes against that on raw attributes, and on our perturbed attribute values against that on raw values, respectively. The experiment results show that our proposed methods for attribute selection and perturbation retain a high degree of data utility under the desired privacy guarantee for network traffic classification.
Research on photovoltaic MPPT algorithm based on improved perturbation observation method

Abstract The connection of new energy, mainly solar energy, is closely related to the output of photovoltaic power supply. When the PV curve of photovoltaic output is optimally solved, it is easy to take the local optimal solution as the global optimal solution. Aiming at the shortcoming that the traditional perturbation observation method is only suitable for single peak tracking in maximum power tracking, the characteristics of photovoltaic output power are studied and analyzed, and the perturbation method is used to track the maximum power according to the distribution characteristics of each extreme point. Through the simulation analysis in MATLAB/Simulink environment, the results show that the improved algorithm has high accuracy and is easy to realize, and it has a good effect on solving the global optimal solution of photovoltaic output power.
Summarization of Video Clips using Subtitles

Due to the ever growing world of high speed internet, videos have become a common medium for information on the web. When we want to gain information about anything from educational topics to entertainment we prefer watching videos instead of reading long paragraphs. With the vast diversity of videos available on the internet today on every single topic possible it gets confusing to find the right content for our needs. People end up wasting time on trying to find a good video instead of on the actual work the video is needed for. Video content being such a big part of our information source today it is necessary to have a system that will enable users to understand a gist of the video instead of having to sit through hours of content just to find nothing useful. The primary objective of this given paper is to propose a method to create a video summary in a way that it contains only the necessary and important information in a concise format by using various NLP algorithms such as Textrank, LexRank and LSA(Latent Semantic Analysis).
B-LNN: Inference-time linear model for secure neural network inference

Machine Learning as a Service (MLaaS) provides clients with well-trained neural networks for predicting private data. Conventional prediction processes of MLaaS require clients to send sensitive inputs to the server, or proprietary models must be stored on the client-side device. The former reveals client privacy, while the latter harms the interests of model providers. Existing works on privacy-preserving MLaaS introduce cryptographic primitives to allow two parties to perform neural network inference without revealing either party's data. However, nonlinear activation functions bring high computational overhead and response delays to the inference process of these schemes. In this paper, we analyze the mechanism by which activation functions enhance model expressivity, and design an activation function S-cos that is friendly to secure neural network inference. Our proposed S-cos can be re-parameterized into a linear layer during the inference phase. Further, we propose an inference-time linear model called Beyond Linear Neural Network (B-LNN) equipped with S-cos, which exhibits promising performance on several benchmark datasets.
Character Segmentation of Devnagari Script in Printed Document Images using Projection Profiles

An efficient segmentation module plays an important role in the complete OCR system as errors in the segmentation module hampers the recognition rate of OCR systems. Compared to basic characters, segmentation of modified and conjunct characters is a difficult task because of the presence of modifiers and half characters. In this paper, a novel technique based on projection profile, which also utilizes different inherent features possessed by these characters for finding an accurate segmentation path is proposed. The proposed system accurately segments basic as well as modified and conjunct characters with segmentation accuracy ranging from 91.84% to 99.11%.
Malicious User Detection using Honeywords

Malicious users can steal user credentials by launching various attacks. In most of such scenarios, honeywords are proven to be the best way to detect failure and unauthorized access. However, there are some flaws in honeyword based malicious user detection systems such as lack of integrity handling and robust confidentiality mechanism. We have proposed hybrid approach for honeyword generation using chaffing by tweaking digit and take a tail method. We also proposed modified BLAST algorithm to detect malicious users. If a fraudulent user is detected, an email is sent to the administrator. Additionally, QR Code is being used to strengthen overall security of login process. The proposed approach reduces risk of data theft from users. The hybrid model is performing better compared with all other honeyword generation techniques. In addition, user password hashes are stored in the database, reducing the risk of password cracking.
P2P Negotiation Framework for trading Carbon Credits

The act of bargaining between two parties over the allocation of a resource whose supply is constrained by the laws of nature is known as negotiation. One goal of the digital revolution as we move closer to the digital era has been to replicate, simulate, and automate processes that need higher level human cognition, such as negotiation. The introduction of e-negotiation is the main force behind the automation of negotiation. Our goal is to present a P2P negotiating framework in this study that may be broadly applied in a range of scenarios and domains. Our proposed, domain-specific solution is primarily driven by fuzzy controllers.
Significance of Cyber Security of IoT devices in the Healthcare Sector

In recent times, there has been a sudden spike in demand for IoT devices. Humans depend on IoT devices to ease their day-to-day activities, and hence their demand is increasing. But there is growing concern about the vulnerabilities of these devices with their booming growth. IoT devices are prone to cyber-attacks Even the healthcare sector doesn’t shy from the use of IoT devices. But, with the current vulnerabilities that they face, their liability is under question. The healthcare sector has been using IoT devices recently and hence there is a dearth of academic literature with respect to the study of cyber threats and their probable solutions for mitigation. Keeping this research gap in mind, this paper provides an overview of the cyber threats faced by IoT devices in the healthcare sector. More precisely, the paper discusses foreseeable threats and vulnerabilities and provides measures to mitigate or minimize them. This study hopes to provide healthcare providers with a view of the various types of cyber threats occurring due to the usage of IoT devices and how to curtail such risks. It has implications for the IoT infrastructure providers to understand probable frauds and help implement checks while deploying the infrastructure.
State Vector’s Fusion for Passive Underwater Tracking Using Two Sensor Arrays

The issue of real-time state estimation in passive target tracking using only bearing measurements is addressed in this research. An algorithm has been developed to detect multiple targets and fuse state vectors when a single target is detected. Target detection is carried out considering the state vectors from two different sensor arrays with various noise variances. The algorithm is evaluated against targets having unique and identical parameters such as range, speed and course. The state vectors are determined using three different filtering techniques, namely, extended Kalman filter (EKF), modified gain bearings-only EKF and unscented Kalman filter. Using the MATLAB software environment, Monte-Carlo simulations are conducted to more precisely assess algorithm performance.
Face Manipulation Detection in Images using Inception Layers and Graph Convolutional Networks

Recently, there has been an upsurge in the availability and advancement of technologies that can morph images. With the advancement in machine learning and deep learning methods, it is now increasingly difficult to detect which image is real or manipulated generated by the latest technologies. When done nonconsensually, images of manipulated faces can harm people's public image and warrant the development of a method that can contrast authentic images from morphed ones. Therefore, image manipulation detection is paramount for regulating online video frame/image data and protecting their authenticity. This study has generated the latest tampered image dataset and subsequently pioneered an efficient model of a two-fold contribution based on Inception Layers with a Graph Convolutional Network (GCN). The Inception layers consist of several convolutional layers of incrementally increasing kernel sizes that help capture morphological information, which GCN processes to capture the relationship between features, followed by a dense layer module. Further, this study conduct experiments on the proposed dataset and compare the results of the proposed method with existing techniques. The result shows that the proposed approach yields better accuracy than the existing state-of-the-art methods.
Support for Children’s Reading in Different Countries of the World. Review of the New Edition

One of the disturbing trends of our time is the decline in interest in reading all over the world, despite its obvious importance and irreplaceability. The reasons for this negative trend have not yet been fully investigated. The presented review gives a high assessment of V.P. Chudinova’s book “Development of ‘Nations of Readersʼ in Different Countries of the World: Studies, Strategies, Projects, Practices” (2022). The publication contains the analysis of the situation with the support of childrenʼs reading in different countries of the world, as well as characterizes international programs of this profile. V.P. Chudinovaʼs work is based on the reliable materials of large-scale studies, including comparative ones that greatly increases the objectivity of the book. Much attention is paid to the support of childrenʼs and family reading by major international organizations: UNESCO, IFLA and the International Literacy Association. In separate chapters, the author considers promotion of reader literacy in the advanced countries of the world — Japan, Singapore, China and Great Britain. Along with the previously published textbook “Library and Information Services for Children and Youth Abroad” (2012), the new book by V.P. Chudinova has made a big step in highlighting the problem of supporting childrenʼs reading at the level of international organizations and the most successful countries in this regard. The considered subject may be linked to the “Sustainable Development Goals” adopted in 2015 by the UN General Assembly as a plan for achieving a better and more sustainable future for all.
Approach for Blockchain Technology Implementation in Indian Courts for Speedy Disposal of Cases

Blockchain as a technology if implemented judiciously will prove to be effective and efficient for both private as well public sector enterprises. The use cases in Blockchain provide ample demonstration of improvement in the processes. The paper intends to provide insight into the application of Blockchain in the Indian Judiciary System. Some of the benefits as envisaged by the Blockchain implementation are time-efficient, cost-effective, greater security, and transparency resulting enhancement of trust in the overall working of the legal system. India’s global counterparts such as Estonia, China, the UK, Ghana, Ukraine, Canada, and Sweden have already integrated the Blockchain into their legal system. The judicial system in India is under tremendous pressure. According to the data from National Judicial Data Grid, 4.7 Crore cases are pending as on May 2022 at different levels of the judiciary, out of which 1.82 Lac cases have been pending for at least 30 years. For many years courts have been following the traditional working system, but during the Covid-19 pandemic, the courts have adopted the e-justice paradigm via online case hearings and video conferencing. Since, the system has already experienced the use of digital platforms, the experimentation if continued post-pandemic will yield a faster and better result. The technological advancements should complement the traditional working systems for the greater benefit of the entire system. The overall objective of the paper is to explore the application of Blockchain that will help improve the efficiency and transparency in operations in the Indian Judicial system.
Theoretical Guidance in the Field of Health and Healing Using NLP

The majority of ailments are now treatable because of advances in medical science, and discoveries are paving the path for a more advanced future. Smart technology is already being applied to healthcare to enhance patient care. In this paper, natural language processing (NLP) is employed. To understand the content of text documents, machine learning (ML) for natural language processing (NLP) and text analytics requires the employment of machine learning algorithms and “narrow” artificial intelligence (AI). Facebook displays a vehicle ad on your screen if you post a status update about buying a car. This is not a dark spell! Facebook is using this to offer you more relevant advertising. The attempt to provide the correct advertisement in the commercial above certainly failed. Recognizing the context in which a term has been used is much more crucial. This is a frequent issue in Natural Language Processing (NLP) activities. Homonyms are words that have the same spelling and sound but are used in different context, and calculating word representations might be a solution to the aforementioned issue. For word embedding and text categorization learning, Facebook's AI Research (FAIR) created the Fast Text package. This research work uses a model that enables the development of supervised or unsupervised word vector representation learning algorithms. For 294 languages, Facebook offers pre- trained models. Fast Text embeds words using a neural network. To locate the word vectors for this research work, this study focuses on the semantic terms with the standard treatment guidelines. This study has created a vector representation by visualizing the word embedding in the vector space after converting the word vectors into word embedding.
Drug Tracing In Healthcare Supply Chain Using Distributed Ledger Technology

The distributed ledger technology, which is fundamentally a form of blockchain technology, is an example of an emergent technology in and of itself. In the context of cryptographic systems, it was conceived of simultaneously with the bitcoin application. The significance of the proposed system's role in the healthcare sector is implied by the fact that it aims to improve efficiency in the healthcare supply chain by incorporating distributed ledger technology. The fact that the chain of distribution of medical supplies involves multiple businesses makes it a complex system. In the context of the medical system, these kinds of issues can be familiar with the creation of fake drugs. Tracing pharmaceuticals is established utilizing technology known as distributed ledgers in order to combat the issues that arise during the manufacturing of drugs and to make the manufacturing process more reliable and transparent. Tracing will be done across the whole healthcare supply chain utilizing distributed ledger technology, also known as blockchain, and all of the players involved will have the ability to readily trace those transactions of pharmaceuticals in order to authenticate the medicine.
Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes.
Predicting Tags of Stack Overflow Questions: A Deep Learning Approach

Software developers and programmers will have questions about code syntax, coding best practices, and solutions based on specific error types, learn through discussions in various forums to improve their knowledge. These forums contain an abundant number of questions and answer discussions on a broad range of topics. To get the exact match based on the specific search on the internet, these posts must contain tags related to that specific post. These tags allow the post to gain more exposure and easier discovery of the post for both solutions as well as answers on the forums. The objective of this paper is to predict tags of Stack Overflow posts using Long Short Term Memory, a special type of Recurrent Neural Network algorithm. The proposed system is examined with contrast to Multi-Layer Perceptron and Gated Recurrent Unit (GRU). The tag prediction system is assessed on test accuracy, hamming loss, subset accuracy, jaccard score, precision, recall and f1-score.