Reviewerly/exHarmony huggingface dataset:
The Analysis of Research Review for the Protection of Children from Sexual Offences Act (POCSO)

Children are the nation's greatest human resource, and their well-being health, education, safety, happiness, and access to opportunities is a barometer of the socioeconomic progress of the nation. The article begins by outlining the POCSO Act and its goals, which was introduced in 2012 in response to an increase in crimes against children. Following this is a critical examination of whether the new POCSO rule, which now mandates the death penalty for raping children under the age of 12, will serve as a deterrence to rape against children. The Protection of Children from Sexual Offences (POCSO) legislation was passed by the Union Cabinet on April 21, 2018, in response to a spate of horrible child rapes. This law stipulates that anyone found guilty of raping a minor who is 12 years old or younger will be executed. Without a doubt, POCSO 2012 has significantly helped to address the child sexual abuse (CSA) issue in India. It has recognized and made illegal several inappropriate sexual activities that endanger children. The law has significantly contributed to educating the public, sensitizing the criminal judicial system, and making the reporting of CSA acceptable and mandatory, as seen by the fast-rising number of reported cases. The law is highly detailed and contains some unusual aspects. However, three key concerns that were noted in the letter and spirit of the law could make its implementation in the Indian setting problematic. The problems are rigidity in the age of consent for sex with those under the age of 18, mandatory reporting requirements, and the impreciseness of age determination. Additionally, POCSO 2012 and the Prohibition of Child Weddings Act 20065,6, which expresses the government of India's desire to outlaw child weddings and protect vulnerable children, should deter underage marriages. However, given the issues mentioned above and, in a setting, where social and cultural norms continue to tolerate, if not actively promote, child marriages, the possibility of resource waste and loss cannot be discounted. This article will critically analyze the law to ascertain whether the death penalty will result in a reduction in child rapes.
Conquering FIRE via Boundedly Rational HANK

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IMPLEMENTASI PENGENDALI LAYAR PROYEKTOR DENGAN ANDROID

The use of Liquid Crystal Display (LCD) in daily activities is often hampered because you still have to use a laptop or computer for features to deliver presentations, therefore this research designed an LCD projector control system using a smartphone, where the LCD projector can be controlled using a smartphone, either it turns on, turns off, or whoever the source of information or video that wants to be displayed on the LCD projector can only use a smartphone. This system uses a Raspberry Pi as the link between the LCD projector and the smartphone, the screen display from the smartphone will be sent to the Raspberry Pi via a wifi network, which will then be displayed on the LCD projector. As well as in tests that try to measure the strength of the WIFI signal against the distance obtained if this system can run properly with a maximum distance between the smartphone and raspberry pi 18 meters. And the ideal distance is 10 meters. As well as for the Quality of Service (QoS) test, the average delay value resulting from measurements from various points is 359ms. And the average throughput value is 169.3KBps.&#x0D;
Perbandingan Berbeda Alat Keamanan Untuk Mendeteksi Risiko Dalam Jaringan

Today, maintaining network security is a significant challenge. Data that has crossed a network is not regarded as secure. There are many risks, including snipping, phishing, spyware, hacking, and spoofing. Various network threats were covered in this article. There are numerous open-source technologies available to defend against these attacks. This study has examined tools like Acunetix and Intrusion Prevention System (IPS).
Perangkat Lunak Kriptografi Metode Gost

Indonesia is holding a democratic party, namely the general elections in 2019. The plurality of parties has represented the many interests of the Indonesian people, who are known to consist of many ethnicities, religions and races. Thus, the interests of the people here are the main thing that must be considered in the 2019 BEM Board Election. The problem that usually arises during campaign momentum is the many hoax (fake) issues circulating that are used to manipulate 2019 campaign news. Spinning this issue is very dangerous, especially for Panca Dharma Vocational School students who are new to political science. Research activities are expected to contribute to opening political insight from an early age among students of Panca Dharma Vocational School
3D Point Cloud Adversarial Sample Classification Algorithm Based on Self-Supervised Learning and Information Gain

The advancement of computing power has facilitated the development of three-dimensional point cloud neural networks, the issue of adversarial examples in point clouds has garnered increasing attention. However, Current adversarial training methods often result in reduced classification effectiveness of the model. Additionally, the scarcity of point cloud data poses challenges in acquiring sufficient training samples. This paper proposes a novel algorithm for classifying 3D point cloud adversarial samples, employing a combination of self-supervision and information gain. The algorithm first utilizes a self-supervised learning model to extract potential information from the data. Then, it employs information gain to screen the features of the adversarial samples and concentrate on the beneficial classification information. The selected features are fed back to the classification model through joint training, aiming to further enhance classification accuracy. In comparison to the standard training approaches of Pointnet, Pointnet++, and Dgcnn on the Modelnet40 and ScanObjectNN datasets, the proposed algorithm exhibits significant improvements in classification effectiveness. Specifically, on the ModelNet40 dataset, the algorithm achieves improvements of 0.70, 0.88, and 0.07 in classification effectiveness over PointNet, PointNet++, and DGCNN, respectively. Similarly, on the ScanObjectNN dataset, the algorithm demonstrates improvements of 0.06, 0.54, and 1.72 in classification effectiveness for the three networks, respectively. These results demonstrate that the algorithm is capable of overcoming the limitations of traditional adversarial training, which tends to reduce the generalization performance of the model, and further enhances the classification accuracy.
The use of online interactive teaching mode in university music teaching under the background of informationization

Abstract This research method uses vocal recognition, classroom interaction analysis and social network analysis theory to cluster audio data through the process of data collection and feature extraction, active speech detection and speaker change detection, and finally quantifies and visualizes classroom data based on S-T analysis and social network analysis methods, and calculates corresponding indicators for qualitative analysis. After reforming the teaching mode, 80% of students expressed great satisfaction. The information-based teaching tools resulted in an overall upward trend in the grades of the three classes. In the middle and high score range of 80-89, Class 1 increased by 34%, Class 2 by 37%, and Class 3 by 28%, and all three classes showed a more significant increase. Regarding classroom interaction behaviors, teachers' verbal behavior rates decreased from 55.86% and 44.01% to 27.48%, while students' verbal behavior rates increased from 21.92% and 21.23% to 39.73%.
Application of Bp neural network model based on modern popular music and traditional music culture fusion teaching in colleges and universities

Abstract This paper analyzes the characteristics of music teaching development, constructs a variety of music integration teaching methods in the background of the “Internet+” era, and evaluates the quality of this teaching method based on the BP neural network algorithm. Since the traditional BP neural network cannot consider modeling the critical point of adjacent teaching quality levels, a Markov chain Monte Carlo simulation with adaptive sampling and BP network is used to study the teaching evaluation problem. Simulation through the constructed evaluation system yields that the prediction accuracy of the model constructed in this paper reaches more than 94%, and the absolute value of the relative error between the predicted and expected results of the model in this paper is controlled within 1.5%. According to this study, teaching the integration of modern pop music and traditional music culture on the Internet is feasible.
Construction of a Digital Shared Management Platform for Enterprise Finance in the Information Age

Abstract Based on the construction objectives of the enterprise financial sharing management platform, this paper focuses on the design concept and implementation process of each system module based on the overall system design under the framework of the financial sharing platform. The BP neural network algorithm is used to evaluate the financial information risk, the particle swarm optimization algorithm is used to optimize the database, the big data visualization technology is used for digitizing the financial information, and the metadata sharing technology is used to transfer the financial sharing data, to complete the financial information sharing. Through testing the platform, it is concluded that the real-time packet loss rate of financial sharing data is kept below 20%, and the average response time of multi-user clicks is within 2S. Therefore, the sharing function, the function of each module, and the performance of the platform in this paper can meet the needs of enterprises.
Analysis of Fuzzy Semantic Translation in English Language and Literature Based on Bayesian Nonparametric Model

Abstract The development of machine learning provides a new path for translating fuzzy semantics into English and literature. In this paper, we propose translation points for fuzzy semantics based on the fuzziness of language by studying the linguistic features in English literature. The hierarchical PY process under the Bayesian nonparametric model is adopted to achieve word alignment in English translation, and the clustering process of English words is controlled by introducing discount and intensity parameters. Based on this, the parameter estimation of the word probability model is completed using Gibbs sampling, and the Moses decoder extracts the translation features. Based on monolingual word classification, this model reduces 6.85% in the average time consumed per sentence compared to the Beam algorithm and 9.57% compared to the A* algorithm.
A study on the construction of an online interactive teaching platform for English in colleges and universities based on the fusion of multi-source and multi-objective statistical information

Abstract This paper firstly combines English teaching in colleges and universities and big data technology and proposes the information technology of English teaching in colleges and universities based on the fusion of multi-source and multi-objective statistical information and conducts research on the traditional teaching optimization algorithm and multi-task teaching optimization algorithm to promote English teaching in colleges and universities. Secondly, in the “teaching” phase, teachers and teaching assistants teach according to the difference between their average level and the class average to increase the value of each student’s factor and increase individual exploration and group diversity in the “learning” phase. Then, the English teaching platform was designed and studied regarding system structure and platform function, simulated and analyzed. The results show that the efficiency of the multitasking optimization algorithm is increased by 21.85% compared with the traditional teaching optimization algorithm for the number of concurrent users from 10 to 100 and 16.46% when the number of concurrent users is 100, which has a positive effect on students’ English learning efficiency.
5g web service based on AI speech recognition platform in blended teaching application of English in higher education

Abstract This paper first investigates English teaching using an AI speech recognition platform. In the feature value analysis module, using the hidden Markov model is beneficial to improve the accuracy of MFCC features for parameter speech recognition. Therefore, the hidden Markov model is proposed for English audio recognition. Secondly, according to the recognition platform pronunciation error detection, the speech pronunciation scoring algorithm is proposed, and an example analysis of English blended teaching based on AI speech recognition is conducted. The results show that the blended teaching mode is 0.33 points higher than the traditional teaching mode in the entrance examination, 2.96 points higher in the total score of Grade 4, 2.10 points higher in listening, 0.57 points higher in reading, and 0.31 points higher in writing, and the effect of blended teaching is positively correlated with the students’ entrance English level. This study proposes a blended English teaching strategy that utilizes a speech recognition platform to achieve innovation and diversification of English teaching efforts.
Research on the Innovation Path of English Culture Teaching Methodology in Universities Based on Cluster Analysis

Abstract In this paper, firstly, based on cluster analysis in data mining technology, we propose to build an English online learning platform based on the system of B/S structure and realize the research on the innovation path of the English culture teaching method through the online learning platform. Secondly, the clustering analysis is carried out by taking a certain number of students’ level test data from the database of the studied English culture online learning platform, which is divided into four modules: basic English, professional English, business English and English listening, and the process of grouping or classifying the given set of data samples by using K-means algorithm, while the data attributes can be calculated according to the objective function correlation. Then, three stages of English culture teaching platform before, during and after class were selected as the research objects, and the K-means algorithm was used to analyze the correlation of English teaching platform satisfaction. The results show that there is a positive correlation between satisfaction of the English culture teaching platform before, during and after the lesson and the correlation strength is | r s | &gt; 0.79, P = 0.000 &lt; 0.01, which is highly correlated. Among them, the correlation coefficient between pre-class and in-class reached 0.871. This study creates an information-based teaching environment and innovates English teaching methods to improve students’ comprehensive English literacy.
A study on the integrated teaching model of core literacy in college physical education and health courses using multiple data fusion

Abstract This paper introduces an experiential teaching model for physical education and health courses. Secondly, the parameters of the multiple linear regression model were estimated and tested for significance based on the least squares method, and the integration of multiple data was carried out by using rough set simplification. Finally, an empirical analysis of the core literacy of physical education and health curriculum was conducted through three aspects: physical activity ability, health behavior and physical morality. The results showed that the p-values of physical exercise awareness, physical exercise habit, and health knowledge mastery and application were 0.015, 0.024, and 0.021, respectively, thus indicating that the experiential teaching model can effectively improve students’ core literacy in physical education and health curriculum.
Enhancing Workflow Security in Multi-cloud Environments Through Monitoring and Adaptation upon Cloud Service and Network Security Violations

Cloud computing has emerged as a crucial solution for handling data- and compute-intensive workflows, offering scalability to address dynamic demands. However, ensuring the secure execution of workflows in the untrusted multi-cloud environment poses significant challenges, given the sensitive nature of the involved data and tasks. The lack of comprehensive approaches for detecting attacks during workflow execution, coupled with inadequate measures for reacting to security and privacy breaches has been identified in the literature. To close this gap, in this work, we propose an approach that focuses on monitoring cloud services and networks to detect security violations during workflow executions. Upon detection, our approach selects the optimal adaptation action to minimize the impact on the workflow. To mitigate the uncertain cost associated with such adaptations and their potential impact on other tasks in the workflow, we employ adaptive learning to determine the most suitable adaptation action. Our approach is evaluated based on the performance of the detection procedure and the impact of the selected adaptations on the workflows.
Multiscale Template Matching for Multimodal Remote Sensing Image

Multimodal matching remains a difficult and pressing problem in the imaging processing community. Accurate and robust multimodal matching is important for the performance of applications such as registration and fusion. Traditional image matching algorithms cannot effectively handle multimodal images with severe nonlinear radiometric distortion (NRD). In this paper, a novel Multi-scale Template Matching Algorithm (MSTM) for multimodal image matching is proposed to address this problem. we propose a novel Frequency-domain Convolutional Map (FDCM) based on the wavelet transform and phase congruency (PC) to construct a feature description map that significantly reduces the NRD between multimodal images. The development of omnidirectional aggregated feature vectors with rotational invariance also helped to achieve robustness on rotated images. Finally, a multi-scale template matching strategy improved the matching performance on multimodal images with displacement and scale variations.To improve the time efficiency of the algorithm, most of the complex computations in this paper are performed in the frequency domain. According to the experimental findings on six multimodal image datasets, the method can obtain accurate and robust matching results between multimodal images. Through qualitative and quantitative evaluations, the method outperforms several mainstream multimodal image matching algorithms in terms of matching accuracy, success rate and time consumption.
Dash Sylvereye: A Python Library for Dashboard-Driven Visualization of Large Street Networks

State-of-the-art open network visualization tools like Gephi, KeyLines, and Cytoscape are not suitable for studying street networks with thousands of roads since they do not support simultaneously polylines for edges, navigable maps, GPU-accelerated rendering, interactivity, and the means for visualizing multivariate data. To fill this gap, the present paper presents Dash Sylvereye: a new Python library to produce interactive visualizations of primal street networks on top of tiled web maps. Thanks to its integration with the Dash framework, Dash Sylvereye can be used to develop web dashboards around temporal and multivariate street data by coordinating the various elements of a Dash Sylvereye visualization with other plotting and UI components provided by the Dash framework. Additionally, Dash Sylvereye provides convenient functions to easily import OpenStreetMap street topologies obtained with the OSMnx library. Moreover, Dash Sylvereye uses WebGL for GPU-accelerated rendering when redrawing the road network. We conduct experiments to assess the performance of Dash Sylvereye on a commodity computer when exploiting software acceleration in terms of frames per second, CPU time, and frame duration. We show that Dash Sylvereye can offer fast panning speeds, close to 60 FPS, and CPU times below 20 ms, for street networks with thousands of edges, and above 24 FPS, and CPU times below 40 ms, for networks with dozens of thousands of edges. Additionally, we conduct a performance comparison against two state-of-the-art street visualization tools. We found Dash Sylvereye to be competitive when compared to the state-of-the-art visualization libraries Kepler.gl and city-roads. Finally, we describe a web dashboard application that exploits Dash Sylvereye for the analysis of a SUMO vehicle traffic simulation.
Computational projects with the Landau–Zener problem in the quantum mechanics classroom

The Landau–Zener problem, where a minimum energy separation is passed with constant rate in a two-state quantum-mechanical system, is an excellent model quantum system for a computational project. It requires a low-level computational effort, but has a number of complex numerical and algorithmic issues that can be resolved through dedicated work. It can be used to teach computational concepts, such as accuracy, discretization, and extrapolation, and it reinforces quantum concepts of time-evolution via a time-ordered product and of extrapolation to infinite time via time-dependent perturbation theory. In addition, we discuss the concept of compression algorithms, which are employed in many advanced quantum computing strategies, and easy to illustrate with the Landau–Zener problem.
Lightweight Histological Tumor Classification Using a Joint Sparsity-Quantization Aware Training Framework

Cancer decision-making is a complex process that can be exacerbated by the limited availability of oncological expertise. This is particularly true in rural areas and settings with fewer resources. Recently, there has been an interest in the potential of artificial intelligence in reliable computer-aided diagnosis tools in such settings. Nevertheless, the majority of deep learning algorithms are resource hungry in terms of data and storage requirements. In this work, we propose a novel lightweight deep learning model for histological tumor classification through a Joint Sparsity-Quantization Aware Training framework. Extensive experiments were conducted to evaluate the proposed framework. This work aims at opening doors toward efficient point-of-care diagnostic devices suitable for environments with limited resources.