Reviewerly/exHarmony huggingface dataset:
IMPACT OF COMMUNITY POLICING ON CRIME REDUCTION IN KADUNA CENTRAL SENATORIAL DISTRICT, NIGERIA

This study carefully examines the Effectiveness of Community Policing in Crime Reduction to the Community in Kaduna central senatorial district. Specifically the study intended to identify the community policing strategies used by the Police force to reduce crime, assessing the Impact of Community Policing in reducing Crime, identifying challenges facing Police Force in administering the Community Policing and evaluating the effectiveness of community policing on crime reduction. A case study research design was employed in which Kaduna south and Igabi Local governments were taken as a case study. The study used interview and questionnaires as data collecting tools. The study found that the main strategies used by police force to reduce crime includes; foot patrol, collaboration and community meetings, and public education programs etc. The show that, Community policing has little or no impact on reducing crimes as employed strategies have no effects in addressing the crime. Lack of training,poor implementation strategies, information gap and lack of involvement leads to unrealistic community expectations and present a challenge to the police force and the communities at large. In conclusion, the study recommends among others that community engagement should be the central focal point. Communities must participate in planning and choosing approaches as well as becoming equal owners of the process. Both the Police Force and communities need to have clearly defined roles and be given the skills and resources to carry it out. KEYWORDS: Community policing, Crime reduction.
The Effectiveness of Edmodo as E-learning in English Learning Practice Discussion at Indonesia Senior High School

This research aimed to find out the effectiveness of the use of social networking media Edmodo on class discussions on practical learning of English; and the relationship between the use of social networking media Edmodo and students’ participation in class discussions on practical learning of English. The sample of this research was 32 students of class XI in the 2022/2023 academic year of Kebangsaan senior high school Medan, North Sumatra, Indonesia. This research was an experimental research type using a factorial design, where the test variable (x1) was the use of Edmodo social media, the control variable (x2) was without the use of Edmodo social media, the dependent variable (y1) consisted of students’ participation in class discussion in the first simulation on practical English teaching materials (y1), and the second simulation on practical English teaching materials (y2). The results of the MANOVA test proved that the effectiveness of using discussion forums using Edmodo to increase students' English learning participation. As a result, there were differences in students’ participation in class discussions in the first simulation on English practice teaching material (y1), and the second simulation on English practice teaching material (y2) with those who did not take part in discussions on the social networking media Edmodo (x2).
Virtual quantum error detection

Quantum error correction and quantum error detection necessitate syndrome measurements to detect errors. Performing syndrome measurements for each stabilizer generator can be a significant overhead, considering the fact that the readout fidelity in the current quantum hardware is generally lower than gate fidelity. Here, by generalizing a quantum error mitigation method known as symmetry expansion, we propose a protocol called virtual quantum error detection (VQED). This method virtually allows for evaluating computation results corresponding to post-selected quantum states obtained through quantum error detection during circuit execution, without implementing syndrome measurements. Unlike conventional quantum error detection, which requires the implementation of Hadamard test circuits for each stabilizer generator, our VQED protocol can be performed with a constant depth shallow quantum circuit with an ancilla qubit, irrespective of the number of stabilizer generators. Furthermore, for some simple error models, the computation results obtained using VQED are robust against the noise that occurred during the operation of VQED, and our method is fully compatible with other error mitigation schemes, enabling further improvements in computation accuracy and facilitating high-fidelity quantum computing.
Development and Simulation of Operational Scenarios for Underwater Cable-driven Parallel Robot

A cable-driven parallel robot (CDPR) is a mechanism that controls the position and attitude of an object using multiple cables.We propose an underwater cable-driven parallel robot (UCDPR), which is a CDPR composed of multiple surface robots.A UCDPR is a type of mobile cabledriven parallel robot (MCDPR) that is composed of multiple mobile robots and is an underwater application of MCDPR, which has been used only on land and in the air.We describe the details of the operational scenario of the UCDPR, from landing on the water to executing a task.We also simulated numerically the stabilization phase required after landing on water and the trajectory tracking control phase required for underwater exploration and other tasks.In this numerical simulation, we used a high-gain feedback controller as the trajectory tracking controller to add robustness to the control system.As a result, a trajectory tracking control was realized within a tolerance tracking error range of 10 -2 m underwater in the presence of a maximum current velocity of 0.8 m/s (≅ 1.5 knots), which is a control requirement.
Theoretical backgrounds of learning at different levels of difficulty in learning and teaching

Firstly, in the determination of this paper, it is very important to note that so far nothing has been done in this regard, as it is known that a large number of different factors have been influenced. By defining this paper we will explore and illuminate the factors but also the stagnation that directly affects learning and teaching.Also, this paper will analyze the possibilities for implementing and applying an advanced learning format that directly affects this content learnable lessons comprehension by students, in our schools.We will continue to recognize those factors that are effective and have impacted on this area of learning.Students with such problems have a variety of difficulties, focusing on attention, having difficulties in performing tasks and passing quickly from one activity to another etc.These learners also show other behaviors such as: they are often confused, forget things, do not care, do not repeat, develop excessive conversations, or move often in the classroom etc.In addition paper also has analyzed the theoretical and practical aspect of students in learning and teaching.
Mobile Robotic Arm for Opening Doors Using Proximal Policy Optimization

The traditional robotic arm control method has strong dependence on the application scenario. To improve the reliability of the mobile robotic arm control when the scene is disturbed, this paper proposes a control method based on an improved proximal policy optimization algorithm. This study researches mobile robotic arms for opening doors. At first, the door handle position is obtained through an image-recognition method based on YOLOv5. Second, the simulation platform CoppeliaSim is used to realize the interaction between the robotic arm and the environment. Third, a control strategy based on a reward function is designed to train the robotic arm and applied to the opening-door task in the real environment. In this paper PPO algorithm is used to solve the result. The experimental results show that the proposed method can accelerate the convergence of the training process. Besides, our method can effectively reduce the jitter of the robotic arm and improve the stability of control.
A Systematic Review of 3D Metaphoric Information Visualization

Today, large volumes of complex data are collected in many application domains such as health, finance and business.However, using traditional data visualization techniques, it is challenging to visualize abstract information to gain valuable insights into complex multidimensional datasets.One major challenge is the higher cognitive load in interpreting information.In this context, 3D metaphor-based information visualization has become a key research area in helping to gain useful insight into abstract data.Therefore, it has become critical to investigate the evolution of 3D metaphors with HCI techniques to minimize the cognitive load on the human brain.However, there are only a few recent reviews can be found for 3D metaphor-based data visualization.Therefore, this paper provides a comprehensive review of multidimensional data visualization by investigating the evolution of 3D metaphoric data visualization and interaction techniques to minimize the cognitive load on the human brain.Complying with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines this paper performs a systematic review of 3D metaphor-based data visualizations.This paper contributes to advancing the present state of knowledge in 3D metaphoric data visualization by critically analyzing the evolution of interactive 3D metaphors for information visualization.Further, this review identifies six main 3D metaphor categories and ten cognitive load minimizing techniques used in modern data visualization.In addition, this paper contributes three taxonomies by synthesizing the literature with a critical review of the strengths and weaknesses of metaphors.Finally, the paper discusses potential exploration paths for future research improvements.
Virtual tour-based digital batik village as a place for promotion of Bekasi city batik in the industry 4.0 era: Study on batik tiara

The development of digital technology and virtual reality has also given birth to virtual tourism innovations that impact improving the community's economy through training. This program is carried out to create virtual tourist villages, mainly based on virtual batik tourism so that batik artisans have more time to promote their products and the tourist aspect. The virtual tour-based batik village aims to make the batik artisan community more active in utilizing digital application platforms to market their products and produce practical marketing applications. This digital application makes it easier for buyers to find out the promotions being carried out by Bekasi batik by utilizing gadgets and taking advantage of the digital world. The training method uses a case study approach, discussion, and lecture and ends with a training evaluation. The results of the study are based on the review of the user experience of Bekasi city batik artisans; out of 50 respondents, it is found that 55% strongly agree that training materials and virtual tour systems that are socialized are easy to access, can develop digital village web-based virtual tours as well as a forum for urban batik promotion Bekasi. In the aspect of the desired goals of the Bekasi city batik artisans participants, out of 50 respondents, the result was that 65% strongly agreed that the learning objectives of the training and virtual tour system met the expectations of the respondents. It is the hope of the training participants that the problem of marketing the virtual tour system will be a solution to expand the marketing of batik in the City of Bekasi.
Construction of a Smart City Network Information Security Evaluation Model Based on GRA-BPNN

In this study, we propose an optimized network information security evaluation GRA-BPNN model based on gray correlation analysis method combined with BP neural network model, and make corresponding optimization for network information security evaluation index. Simulation experiments are conducted to analyze the experimental model, and the simulation results show that the test sample values reach the best training performance at the 7th iteration after 13 iterations, and the R-values in the regression of training results all reach above 0.99, and the data are well-fitted. When the number of training iterations reaches 13, the training gradient is 0.00067928, the value of Mu is 0.001, and the validity test value is 6. The GRA-BPNN model scores 0.028 higher than the GRA method, which is in line with the expected error, and the higher score also proves that the GRA-BPNN model is more comprehensive and specific in its scoring consideration.
THE ROLE OF INNOVATIVE PLATFORMS FOR THE IMPLEMENTATION OF EDUCATIONAL PROJECTS

The formation of innovative platforms in the regions is focused on improving the educational system through the development and implementation of innovative ideas aimed at optimizing the mechanisms and effectiveness of the educational process, expanding educational technologies, taking into account the specifics of the main and additional educational programs and projects being implemented.
Toward Better Analysis of Breast Cancer Diagnosis: Interpretable AI for Breast Cancer Classification

Recently, some countries have been distressing with the increasing number of breast cancer cases. Those cases were extremely increased in every year. Practicaly, the increasing number of patients was caused by the manual examination. Recently, some researchers have been done in the development of AI method for solving this problem. However, AI itself still has limitation since it worked in the black-box approach which was difficult to be trusted. Thus, to overcome those problems, we proposed a method that was able to classify breast ultrasound images into two classes (benign and malignant) and able to explain how the prediction was made. Our proposed method consisted of four processes i.e., pre-processing step, development of CNN model, interpretable step and evaluation. In this research work, our proposed method performed into 780 breast ultrasound images divided into three classes (133 normal, 210 malignant, and 437 benign). In the training process, our proposed method obtained training accuracy of 0.9795, training loss of 0.0675. The validation process obtained validation accuracy of 0.8000 and validation loss of 0.5096. While, in the testing process, our proposed method achieved accuracy of 0.7923. In the interpretable process using LIME, the LIME result is covered by doctor visualization. It was indicated that LIME was suitable enough in visualizing the important features of breast cancer severity. Regarding to the results, our proposed method has a potensial to be implemented as an early detection method for classifying malignancy of breast cancer in order to help the doctor in the screening process
DjunkGo: A Mobile Application for Trash Classification with VGG16 Algorithm

Garbage is one of the big problems in many countries including Indonesia. A bad waste management and low awareness of people participating in sorting the trash are 2 obstacles that we face in daily life. However, if we can ask them to sort the trash properly, they will not only help the waste collector, but also improve the waste management in the country. That encourages us to develop a mobile application that helps people to identify the type of the trash they have so that they can sort it by themselves. This application applies image processing and VGG16 algorithm to identify the trash with accuracy 90%. Furthermore, this application also links them to an appropriate agency that can recycle their trash based on its type. Therefore, the waste sorting process will be easier and recycling is also faster.
Native gold from the Crocoite pit of the Berezovsky ore field (Middle Urals)

The aim of the research is to study the features of the morphology and chemical composition of native gold from the Crocoite pit at the Uspenskaya mountain of the Berezovsky gold ore field. Research methodology. Analytical studies were carried out in the laboratory of the PCRM IGG UB RAS, Ekaterinburg. The chemical composition of native gold and hypergene minerals was analyzed using a JSM-6390LV scanning electron microscope by Jeol with an INCA Energy 450 X-Max 80 energy dispersive attachment by Oxford Instruments. The same device was used to take photographs of minerals in the backscattered electron mode. Results. It has been established that gold forms two generations, which differ from each other both in morphology and size, and in chemical composition. The average standard of gold of the first generation is 789‰, the second generation is 9530 / 00. In addition to native gold, we found golden silver among the samples. Native gold associates with goethite and beudantite, filling space or forming complex shapes in these minerals. The first generation of gold is represented by thin curved plates with uneven edges; branching wires and thicker, irregularly shaped segregations similar to small nuggets. The size of the first generation segregations is approximately 500 μm. The second generation of gold has a predominant grain size of about 10 μm and is present on almost all grains of the first generation, forming dendrites, thin wires, small, flat flakes, elongated grains of irregular shape. Conclusions. Most of the studied noble metal of the first generation from the samples from the Crocoite pit most likely refers to native gold of the gold-polymetallic-quartz association. The second generation of gold was formed as a result of hypergenesis processes.
Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis

Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain individuals or communities, this could instigate animosity towards them. Widespread condemnation of obesity (fatness) has led to much fat stigmatizing content being posted online. A methodological framework that uses a novel mixed-method approach for unearthing hate speech patterns from large text-based corpora gathered from social media is proposed. We explain the use of computer-mediated quantitative methods comprising natural language processing techniques such as sentiment analysis, emotion analysis and topic modelling, along with qualitative discourse analysis. Next, we have applied the framework to a corpus of texts on gendered and weight-based data that have been extracted from Twitter and Reddit. This assisted in the detection of different emotions being expressed, the composition of word frequency patterns and the broader fat-based themes underpinning the hateful content posted online. The framework has provided a synthesis of quantitative and qualitative methods that draw on social science and data mining techniques to build real-world knowledge in hate speech detection. Current information systems research is limited in its use of mixed analytic approaches for studying hate speech in social media. Our study therefore contributes to future research by establishing a roadmap for conducting mixed-method analyses for better comprehension and understanding of hate speech patterns.
A Performance Perspective of Live Migration of Virtual Machine in Cloud Data Center with Future Directions

Live virtual machine migration is a valuable feature for the virtualized data center or cloud environment.This is the process to migrate running virtual machines from one physical host to another host.Live virtual machine migration can be used to provide various benefits such as server consolidation, energy-saving, and maintenance.It is a valuable feature for the virtualized data center or cloud environment.Cloud computing provides IT capabilities as a service and its key technology is virtualization.The key benefit of virtualization is to offer better resource employment by executing various VMs on the same physical system.In this research, we analyze the performance of the various hypervisors based on their migration features and compares the migration feature.Hypervisors are Xen, VMWare, KVM, and their migration feature are XenMotion, vMotion, and KVM migration, respectively.According to our study, we find that there are many factors that affect the performance of the live virtual machine migration such as a long downtime, the large amount of data that is sent in an iteration manner so with a higher dirtying rate the total migration time extends.In our comparison, we show VMWare has the least downtime.We also identify and discuss the various research challenges in detail to stimulate the researchers to work in this direction.
Working with Apache Spark

When it comes to working with Spark or building Spark applications, there are many options. This chapter describes the three common options, including using Spark shell, submitting a Spark application from the command line, and using a hosted cloud platform called Databricks. The last part of this chapter is geared toward software engineers who want to set up Apache Spark source code on a local machine to study Spark source code and learn how certain features were implemented.
Applying Data Mining in Surveillance

In the current times where human safety is threatened by man-made and natural calamities, surveillance systems have gained immense importance. But, even in presence of high definition (HD) security cameras and manpower to monitor the live feed 24/7, room for missing important information due to human error exists. In addition to that, employing an adequate number of people for the job is not always feasible either. The solution lies in a system that allows automated surveillance through classification and other data mining techniques that can be used for extraction of useful information out of these inputs. In this research, a data mining-based framework has been proposed for surveillance. The research includes interpretation of data from different networks using hybrid data mining technique. In order to show the validity of the proposed hybrid data mining technique, an online data set containing network of a suspicious group has been utilized and main leaders of network has been identified.
Current Multi-factor of Authentication: Approaches, Requirements, Attacks and Challenges

Now-a-days, with the rapid and broad emergence of local or remote access to services on the internet.Authentication represents an important security control requirement and the MFA is recommended to mitigate the weaknesses in the SFA.MFA techniques can be classified into two main approaches: based biometric and non-biometric approaches.However, there is a problem to maintain the tradeoff between security and accuracy.The studies that have been reviewed on both authentication mechanisms are found contradictory in the direction of others.In the direction of authentication-based biometrics the researchers tended to increase the recognition accuracy, while in the other direction, the researchers proposed to combine many authentication factors to increase the security layers.The main contribution of this survey is to review and spotlight on the current state of the arts in both authentication mechanisms to achieve a secure user identity.This paper provides a review of authentication protocols and security requirements.In addition to a detailed review with a comparison of secure one-time passcode generation and distribution.Furthermore, a comprehensive review of cancelable biometrics techniques, attacks, and requirements.Finally, providing a summary of key challenges and future research directions.
Analysis of the Network Architecture and Typical Protection Techniques of Wind Power Monitoring System

Because of the lacking professional and systematic security planning layout and relevant technical post setting, the overwhelming wind farms are in a state of “under protection” and “over protection” problems. Based on lots of research and practice, this paper analyzes the overall functional architecture and communication characteristics of the wind farm power monitoring system, clarifies the overall protection needs, anatomizes the mechanisms and key points of various security protection techniques, and then puts forward feasible security protection strategies and suggestions.