Reviewerly/exHarmony huggingface dataset:
Blockchain based Efficient and Secure Peer-to-Peer Distributed IoT Network for Non-Trusting Device-to-Device Communication

The security and privacy issues in the Internet of Things (IoT) is a mandatory process and also a challenging task for researchers. Blockchain technology enhanced and motivated the recent security parameters and it has been validating various technical sectors since its inception. In this paper, a peer-to-peer distributed IoT network is presented where non-trusting devices can interact with the other devices without a trustworthy inter-mediator using blockchain technique, in automated veriable mode. Major implementation issues to deploy blockchain in the IoT network is pointed out in this paper. The model presents modern blockchain technique to surpass the traditional security system in ecient and secure IoT deployment at various conditions. Finally, to validate the signication of blockchain in IoT network, the avalanche effect is calculated and compared with Triple-DES, AES and Blowsh cryptographic algorithms for non-trusting device-to-device communications and transactions. The result presents signicant output changes in hash for blockchain-IoT integrated model as compared to other cryptographic algorithms. Conclusively, blockchain in IoT network can make a remarkable impact across various industrial and business applications.
Live Audio Steganography Using LSBWAB to Covertly Communicate Secret Data

Conventional steganographic approaches make use of stored audio and are hence susceptible to steganalysis as the cover audio is available easily. The proposed work considers live audio as cover and therefore cover audio is not available for attackers as live audio like telephone conversations will not be kept recorded normally, the LSB (least significant bit) technique is modified to achieve high security for the secret data in this approach. The technique is called “LSB with Any Bit” (LSBWAB). The strength of the proposed work can be optionally increased by scrambling the binary form of the secret data through the XNOR (exclusive-NOR) operation with the specified binary form of the key.
Enhancing Object Detection and Tracking from Surveillance Video Camera Using YOLOv8

In the rapidly evolving landscape of object tracking and detection techniques, video surveillance systems have seen significant advancements, boosting their ability to discern threats and adversarial activities. However, as technology progresses, malicious actors continually adapt their strategies to evade detection. Adversarial attacks, along with incidents like fire outbreaks, violent actions, intrusions, and video tampering, pose substantial risks to the integrity and effectiveness of these systems. Manipulating images and videos can lead to compromised tracking results, either through alteration or deletion of key frames. To address these evolving challenges, researchers and developers are tirelessly working to create more flexible, robust, and resilient object tracking and detection systems. While video object detection is paramount for in-depth scene exploration, it has remained relatively underexplored due to the scarcity of labelled video datasets. The YOLOv8 algorithm harnesses the strengths of the YOLOv8 architecture to elevate object detection performance. This study focuses on real-time analysis of surveillance camera-generated video data, presenting an automated detection approach employing smart networks and algorithms.
A Comparative Study of Feature Extraction Methods in Image Classification Using Convolution Neural Network Model

Image classification is the supervised classification in which one assigns labels to images based on its characteristics or features. The term Outlier which is also referred as anomaly are points, patterns or sequences of data that do not fall into the normal behaviour of the system under consideration. Outlier detection refers to the study of identifying the genuine outlier within a given data set. This paper aims to present a deep learning convolution neural network which is based on outlier detection technique with applied gabor, LBP and SIFT filters for feature extraction that can detect outliers by assigning weights to selected features such as to prioritise its significance. A comparison is made with the traditional CNN and applied filters on two image data-sets. The first dataset is the publicly available malaria parasites blood smear sample dataset and the the second dataset is the publicly available facial expressions dataset. Classification related evaluation metrics were applied to the results for analysis. Our experimental performance indicate that the applied filters combined with CNN performs considerable better in detecting existing anomalies with good accuracy but the training time to create features exceeds the traditional approach.
Development Of Android-Based Application For English Learning System Using Drill and Practice Method

English, being an international language, plays a significant part in daily life worldwide. Many nations utilize English as their primary language of communication. The lack of a novel approach to English learning—such as the use of contemporary technology—means that kindergarteners at Al-Barkah Kindergarten only learn the language through books. In light of these issues, Al-Barkah Kindergarten developed an Android-based application to teach English via the drill and practice method, hence creating a variance in teaching methods. Because this method applies a learning system in the form of visual, audio, and animation that can attract students' interest in learning language, the application design results show that the learning method is effective and that a solution, especially for Al-Barkah Kindergarten students, can easily understand the learning material. English.
Optimizing Invoice Management: A Case Study of PT. Madina Mitra Teknik's Transition to a Laravel-Based Information System

One business that engages in trading goods and services is PT. Madina Mitra Teknik. During the observations, Microsoft Excel was still used for manual invoice recording, even though the collection division had access to computer tools that may facilitate collection efforts. The goal of this research is to design an invoice-recording information system that can help manage existing invoices at PT. Madina Mitra Teknik in the collection section with development using the Laravel framework. Currently, no information system helps record invoices and their progress as they are issued and incoming payments, so the process of making reports takes time. The research process includes analysis, design, coding, and testing. The waterfall development method is employed. The output of this study is an invoice recording system, and the results of the black box testing show that every function has operated as intended. To ensure that the invoice recording information system is appropriate for use and implemented to support the invoice management process in the PT collection section, Madina Mitra Teknik conducted a portability test using three different browsers and found that all aspects of the system could be accessed without any issues.
Do Bad Smells Lead to Defects?

Code that disregards fundamental sound design practices is said to “smell.” While a code smell is not normally a bug, conventional wisdom suggests that it could lead to bugs when the software is later modified. This makes logical sense, but is there empirical evidence that supports this argument? If code smells can be shown to metastasize over time, are there some code smells that more virulent than others? We set out to address these questions by examining five popular Python open-source software projects to determine if recorded defects traced back to segments of code containing a smell. Our study focused on eight code smells: Large Class, Long Parameter List, Feature Envy, Parallel Inheritance Hierarchy, Data Class, Lazy Class, Refused Bequest, and Long Method. We analyzed GitHub change and defect logs for defects, then identified how the code as repaired. A smell was assumed to be the cause of the defect if changes resulted in removal of the smell. We counted the number of code smells in the project and used the ratio of the count of smells to the count of defects to quantify the influence of smells on defects. Our results indicate that the Long Parameter List code smell has the highest positive correlation, the Long Method and Large Class smells have a moderate correlation, and the Parallel Inheritance Hierarchy, Data Class, Lazy Class, Feature Envy and Refused Bequest smells have no significant correlation on the presence of defects.
Efficient-YOLOv8: Multi-Scale ConvNet for Underwater Object Detection

The field of underwater object detection has gained significant attention recently, yet it remains challenged by issues like turbid water conditions and limited lighting, despite the rapid development in deep learning algorithms. In this paper, we try to provide a novel hybrid underwater object detection framework, named Efficient-YOLOv8, designed to address confusion-scale, low-resolution and small targets. In particular, our focus lies on the detection of cuttlefish, an underwater mollusc species known for its irregular postures. In our self-collected dataset, our proposed approach demonstrates remarkable results, achieving a precision of 94.18%, recall of 97.40%, and mAP of 98.18%.
Emotional Intelligence And The Tendency To Behave Aggressively In Junior High School Students

This study aims to determine the relationship between emotional intelligence and the tendency to behave aggressively in senior high school students. The research method used in this study is the quantitative correlational method. The sample of this study consists of 190 8th grade junior high school students in Blitar, West Java. The data collection process is carried out by providing a Google Form containing an emotional intelligence scale and a tendency scale to behave aggressively. The analysis technique used to analyze the data is the product-moment Pearson correlation test. The result of this study shows a significant negative correlation between emotional intelligence and the tendency to behave aggressively among junior high school students (r = -0.373; p &lt; 0.05). The hypothesis proposed is accepted, meaning that there is a significant negative relationship between emotional intelligence and the tendency to behave aggressively in junior high school students in Blitar, West Java. This means that the higher the emotional intelligence, the lower the tendency to behave aggressively in students.
Analysing the implications of cybersecurity breaches on firm leadership

The object of this research is the implications of cybersecurity breaches on the leaders of accounting firms in KwaZulu-Natal, South Africa. The research employed a qualitative approach with interviews as the primary data collection technique. The researcher adopted a rigorous analytical framework, utilising different scholarly sources to analyse and explain the intricate experiences of firm leaders. The study revealed that leaders of accounting firms experience psychological, financial, and social consequences due to cybersecurity breaches. It highlights the emotional impact, including anxiety and increased stress. The fear of potential job losses was found to be one issue leaders were worried about after the data breach. The stress from dealing with the aftermath of data breaches affected their family relationships. In addition, leaders experienced low productivity and increased pressure dealing with the media and organization stakeholders and the stigma associated with data breaches. Given the critical role that accounting firms play in the financial ecosystem and the sensitive nature of the data they handle, it is imperative that cybersecurity is prioritised. However, studies have focused on the financial implications of cybersecurity breaches on businesses, but less attention has been paid to the psychological, social, and financial implications of breaches on firm leaders. The findings are significant for academic discourse but also provide leaders with strategies to mitigate the adverse effects of breaches, while also offering a framework for other researchers and practitioners in different regions and sectors to understand and study the phenomenon further.
Accelerating Decision-Tree-Based Inference Through Adaptive Parallelization

Gradient-boosted trees and random forests are among the most popular machine learning algorithms. They are applied to a broad spectrum of applications as diverse as search engine ranking, credit card fraud detection, customer relationship management, fault diagnosis in machinery, and geological studies, to name a few. Inference of these models is supported by many widely-used libraries and frameworks including Scikit-Learn, XGBoost, and ONNX Runtime. Many of the inference algorithms integrated in these libraries are optimized for performing fast predictions for large input batches, often targeted at ensemble models containing relatively shallow decision trees. This does not necessarily match well with the requirements of new emerging applications that depend on real-time predictions for individual samples or small input batches. Also, cloud-based inference services put more emphasis on small memory footprints. This paper aims to fill this gap by proposing a novel inference scheme for decision-tree ensembles that efficiently handles a wider range of application requirements and model characteristics. Performance is maximized for an extensive number of parameter combinations through a new concept in which a predict function is selected dynamically at runtime. This is done in a way that the processing resources, including the use of SIMD vector processing units and multithreading, are optimally exploited. Experiments have shown substantial performance improvements over state-of-the-art algorithms for common models.
HugeGPT: Storing Guest Page Tables on Host Huge Pages to Accelerate Address Translation

Expensive page table walks triggered by frequent TLB misses have incurred major performance bottlenecks for data-intensive workloads that are dominated by memory accesses with weak locality. Since it is hard to reduce TLB misses for such workloads, reducing page table walk overhead (i.e., the overhead of each TLB miss) is an increasingly important direction for improving application performance. The direction is more compelling for workloads running in virtual machines (VMs). In virtualized environments, each TLB miss triggers a two-dimensional page table walk, which has a significantly higher overhead than that on native systems. This paper presents HugeGPT, a software approach to reducing two-dimensional page table walk overhead in virtualized environments. HugeGPT ensures that page tables used in guest systems are physically held in the huge pages formed in the host. This brings two-fold benefits: 1) the number of steps walking down the host page table is reduced; 2) the misses of page walk caches incurred by accessing the leaf nodes on host page tables can be eliminated. Extensive evaluation based on the prototype implementation and diverse real-world applications shows that HugeGPT can efficiently reduce address translation overhead and improve application performance in virtualized clouds.
Drishyam: An Image is Worth a Data Prefetcher

Hardware prefetching is a latency-hiding technique that hides the costly off-chip DRAM accesses. Although hardware prefetching is an extensively researched topic with many state-of-the-art data prefetchers pushing the performance limits, prefetching for irregular applications with hard-to-predict access patterns is still a challenging problem to solve. The usage of neural networks for hardware prefetching is a promising direction, especially for predicting irregular memory access patterns. This paper presents Drishyam, a novel hardware prefetcher based on computer vision algorithms that use images to learn memory access patterns and predict future memory accesses with high accuracy and coverage. For hardware prefetching, an image is a graphical representation of memory accesses observed over time. For a sequence of memory addresses, Drishyam creates images that predict the future addresses by predicting the future OS page and a cache line offset within the OS page. Drishyam outperforms Voyager, the state-of-the-art machine learning (ML) based prefetcher, for a set of irregular benchmarks by an average of 4.7% with an average prefetch accuracy and prefetch coverage of 89.5% and 66.6%, respectively. In terms of training time, Drishyam outperforms Voyager by 225.5%.
Probability Distribution for the Backward-Evolving Quantum State Concerning a Monitored Qubit

The evolution of two-level quantum systems (qubits) in the presence of continuous-time weak measurements can be described by stochastic differential equations, which is dominated by the Hamiltonian and decoherence channels. In more concrete terms, the conventional density matrix evolves according to the stochastic master equation in the Schrödinger picture. Particularly, for a monitored qubit, the deterministic submanifolds concerning the dynamical evolution of the density matrix has been previously obtained. As a complement, We have recently developed deterministic submanifolds for the backward-evolving quantum state for a monitored qubit. In this paper, in order to better understand the behavior of a monitored qubit, by using the Bloch sphere representation and the properties of Pauli matrices, we provide a closed-form expression for probability distribution over the deterministic submanifolds of the backward evolving dynamics in different scenarios. We further analyze the backward-evolving trajectories of the quantum state by taking into account the analytic solutions and the corresponding submanifolds under heterodyne/homodyne detection associated with non-demolition or fluorescence measurements.
Multi-Scale Channel Attention Inspiring Multi-Task Network via Self-Supervised Learning for Violence Recognition

Generally, a large amount of training data is essential to train deep learning models for obtaining more accurate detection performance in the computer vision domain. However, collecting and annotating datasets will lead to extensive costs. In this paper, we propose multi-task network based on multi-scale channel attention to learn general video features without adding any human-annotated labels, aiming at improving the performance of violence recognition. Firstly, we propose a violence recognition method based on a convolutional neural network with the self-supervised auxiliary task, which can learn visual features for improving down-stream task (recognizing violence). Secondly, we establish a balance-weighting scheme to solve the crucial problem of balancing the self-supervised auxiliary task and violence recognition task. Thirdly, we develop a multi-scale channel attention module, indicating that the proper use of the channel attention mechanism can effectively reduce the weights of the feature channels and regions representing the background, further improving the semantically meaningful representation of the network. To evaluate the proposed method, two benchmark datasets have been used, and better performance can be shown by the experimental results compared with other state-of-the-art methods.
The Bitcoin Industry and the Principle of Subsidiarity

This article evaluates the Bitcoin Industry from the point of view of the principle of subsidiarity. Bitcoin is the first successful cryptocurrency because of its algorithm, distributed-ledger technology, use of anonymity, and cross-border nature. Anonymity and cross-border nature avoid control of monetary authorities. Bitcoin’s market share and market capitalization are the biggest among cryptocurrencies. But what is Bitcoin? Is it a currency, a payment system, a speculative asset, a commodity, a tax haven, an asset suitable for money laundering, all together controlled by “miners,” or simply a computer file? According to the principle of subsidiarity, all forms of collectivism are contrary to the formation of a harmonious society, and private initiatives should not be taken away by a higher power. On the other hand, individuals or groups of people cannot be equated with sovereign states or global institutions. Bitcoin enthusiasts are libertarians who glorify technology, despising any ethical or social control. They support an erroneous idea of subsidiarity that argues that subsidiarity is a matter of devolution of power or smallness of scale in favor of individual freedom and a utilitarian common good.
Recursive Filtering for Nonlinear Systems with Measurement Saturation and Correlated Noises

This paper is concerned with the recursive filtering problem for the nonlinear systems with measurement saturation and correlated noises. The process and the measurement noises are one-step autocorrelated and two-step cross-correlated, respectively. The objective of this paper is to design a Kalman-like filter, which would minimize the upper bound of the filtering error covariance by proper design of the filter gains at each time step. The suggested scheme provides recursive computing in online applications. A numerical simulation is utilized to show effectiveness of the developed filtering algorithm.
Sentiment Analysis of Public Opinion on the 2024 Presidential Election in Indonesia Using Twitter Data with the K-NN Method

Twitter is often used by the public as a platform to speak and express their opinions, especially in the context of the 2024 Presidential Election. Tweets related to the '2024 Presidential Election' can be used as a source of data for social media analysis to determine whether the expressed opinions tend to be positive or negative. The research process involves data collection of tweets, preprocessing, tokenization, class attribute determination, directory filling, sentiment analysis, and classification steps, including testing the value of k and testing the confusion matrix. The research and testing results show that the K-NN method successfully achieves a sentiment classification accuracy rate of 86.48%.
Applying Make a Match Technique to Improve the Seventh-Grade Of Students’ Vocabulary Mastery In SMP Dharma Wanita Medan

The design of this research is Classroom Action Research (CAR). This research will observe and study the problems that arise in the implementation of the teaching and learning process. This study aims to prove that the make a match technique can improve the students' vocabulary mastery and to prove that the students' motivation in learning vocabulary is improved by using the make a match technique. This study used tests, questionnaires, observation sheets, and field notes as data collection instruments. The research results can be seen that applying the Make a Match Technique can successfully improve the students' vocabulary mastery. The students' mean score from the pre-test to post test. The students' mean score was 40 in the pre-test, the formative test was 62, and the post test was 80.