Reviewerly/exHarmony huggingface dataset:
Random forest and decision tree algorithms for car price prediction

At this time in the era of cars that use renewable energy fuels such as electric cars which are highly supported by the government so that it has an impact on used cars based on these problems an analysis is needed. Determining whether or not the price of buying or selling a used car is appropriate is one of the obstacles faced by the community in making decisions when buying or selling a car or vehicle. Therefore, most people choose an alternative by buying a used car that is still good and usable. One way to make price predictions is to use the Machine Learning method. In this study the authors used random forest and decision tree methods to predict car prices. The results of the research on car price prediction analysis using the random forest and decision tree methods have different percentage results. Where using the random forest method there is an accuracy: 72.13% whereas with the analysis of the decision tree method accuracy: 67.21%. So it can be concluded that the Random Forest method has better analytical accuracy than the Decision Tree method.
Out-of-distribution 3D object generation for enhanced pose estimation

Contemporary object pose estimation algorithms predict transformation parameters of perspectives of objects from a reference pose. Learning these parameters often requires significantly more data than conventional sensors provide. Therefore, synthetic data is frequently used to increase the amount of data, number of object perspectives, and number of object classes, which is beneficial for improving the generalization of pose estimation algorithms. However, robust synthesis of objects from different perspectives requires manually setting precision describing increments between pose angles. Consequently, learning from arbitrarily small increments requires very precise sampling from existing sensor data, which increases time, complexity, and resources necessary for a larger sample size. Therefore, there is a need to minimize the amount of sampling and processing required for synthesis methods (e.g., generative) which have difficulty producing samples that lie outside of groups within the latent space resulting in modal collapse. While reducing the number of observed object perspectives directly addresses this problem, generative models have issues synthesizing out-of-distribution (OOD) data. We study the effects of synthesizing OOD data by exploiting orthogonality constraints to synthesize intermediate poses of 3D point cloud object representations that are not observed during training. Additionally, we perform an ablation study on each axial rotation for poses and the OOD generative capabilities between different model types. We test and evaluate our proposed method using objects from ShapeNet.
Sentiment analysis of the attorney general's office performance in handling corruption cases on twitter using naïve bayes classification algorithm

Corruption is defined as illegal activities such as bribery, fraud and forgery carried out through the abuse of power by public or private officials for personal, financial or other merits. In Indonesia, the Attorney General's Office is one of the institutions that has the authority to handle corruption cases. As the result of the overall business process, public perception is very important. One method to assess public perception is using data collected from social media. Among the many social media, Twitter is known for its high public interaction which then can be used to describe direct people's perceptions. This research aims to create a machine learning model using the Naïve Bayes Classification Algorithm based on Twitter data to determine public sentiment on the Attorney General's Office performance in handling corruption cases. As for the results, we managed to create a model with accuracy, recall, precision, and f-measure values of 74.34%, 71.80%, 73.09%, and 72.44% respectively. From the sentiment analysis result, it can be concluded that the public gives more positive sentiment to the Attorney General's Office in handling corruption cases carried out in the period January 2022 to December 2022 with a percentage of positive sentiment is 61.32% and 38.68% for the negative sentiment.
Modeling harmony biases in learning exceptions to vowel harmony

Artificial language learning experiments typically show non-categorical results after training on categorical data. This is generally due to incomplete learning, but these results can also reveal biases. One example is that participants trained on a vowel harmony language with alternating and non-alternating affixes prefer the non-alternating affix in harmonic contexts (Finley 2021). In this paper, I show that (i) the preference for harmonic items in non-alternating affixes replicates for remote (online) data collection, and (ii) that this effect can be modeled with MaxEnt Harmonic Grammar. In Harmonic Grammar, the harmony score of each candidate determines its grammaticality, and the probability of surfacing. Because non-alternating affixes that satisfy vowel harmony have higher harmony scores than non-alternating affixes that violate harmony, harmonic candidates will be more likely to surface than disharmonic candidates, even when both types of items surface at levels greater than expected by chance. The theoretical and methodological implications for these results are discussed
Supply Chain Management Model in Digital for Supervision Management in Cooperative Educationin the Universities

Supply chain management model in digital for supervision management in a cooperative education in the universities is composed of six elements namely main elements suppliers, manufacturer, Education customers, Consumers Satisfaction and Return. The objective of this research is to develop and to evaluate an appropriate about Supply chain management model in digital for supervision management in a cooperative education in the universities A sample consisted of ten experts in supply chain. Data analysis was the average mean and standard deviation. The results from ten experts agreement of about Supply chain management model in digital for supervision management in a cooperative education in the universities was a high level which means that model can develop digital for supervision management in a cooperative education.
Representation of NCLB Implementation Program and Its Impact on Hispanic Students: A Critical Discourse Analysis on Media Reports

No Child Left Behind (NCLB) aims to improve US education system. Every student in the US must have the same rights to get a quality education. The state is responsible for providing fair, equitable and quality educational facilities to the community. In reality, however, the NCLB was not entirely successful. While the law stipulates equality for underprivileged students, in reality, the richer students get better facilities and the best teachers. In addition, not all school teachers meet the standards. Based on these problems, this study aims to examine the challenges NCLB faced. It also intends to investigate the ideal implementation of NCLB and its effectiveness for students in the United States. This research also explores minority groups, like Hispanics. They are the largest minorities in the US. This research seeks to answer (1) How is the NCLB implementation reported in the media? and (2) What are the impacts of the NCLB implementation on Hispanic students?. This study uses the theory of Critical Discourse analysis from Norman Fairclough and Jager with news articles as data sources. The news articles were taken from the New York Times, usnews.com, dailynews.com, fivethirtyeight.com, and CBS News. The results found that the NCLB is still problematic that still there are achievement gaps, protests, and low performance. In addition, there are still wrong beliefs, discrimination, and dishonesty regarding the implementation of NCLB. Through some possible ways to solve the problem, like supervising and revising NCLB.
English Language Training And Guiding For The Community-Based Tourism Group in Perean Village, Tabanan

The English and guiding training conducted in Perean Village aims to improve the language and guiding skills of the members of the Pokdarwis group. This is expected to enhance the quality and services of the village as a tourist destination that advances in the future. The community engagement activity was carried out by the community service team from the Language Lab Unit at Politeknik Negeri Bali for 5 months, consisting of 5 stages of activities from proposal to evaluation. In its implementation, the community service team provided various methods of English and guiding training and learning to the Pokdarwis participants, including interactive games, speaking, listening, and writing. Interactive games provide an opportunity for participants to learn English without feeling pressured, which positively impacts their motivation to learn consistently. Furthermore, the combination of speaking, listening, and writing training also has the potential to efficiently develop participants' skills, which is essential for tour guides who require good English skills. However, the weakness of participants in recognizing English vocabulary provides an opportunity for the community service team to develop a more specific approach in improving the vocabulary of the participants. This approach is expected to have a positive and significant impact in improving the English skills of tour guides in Perean Village, Tabanan, Bali.
Exploring Deepfake Generation and Detection: A Comparative Study

Deep learning has been effectively used to address a variety of challenging issues, from computer vision to big data analytics.Advances in deep learning have also been used to develop software that poses risks to national security, privacy, and democracy.Deepfake is one of those recently developed deep learning-based applications.The recent entry of deepfakes has marked a turning point in the development of fake material, even if manipulations of visual and auditory media are as old as media itself.Deepfakes offer automated methods for producing fake content that is getting more difficult for human observers to spot thanks to the most recent developments in artificial intelligence and machine learning.Therefore, the development of systems that can instantly identify and evaluate the integrity of digital visual media is essential.The discipline of computer vision, a branch of computer science, has developed methods for producing and identifying deepfakes.Humanities and social science approaches have focused on the social and ethical ramifications of deepfakes.This study examines the algorithms used to produce deep fakes.
Identifying Fraudsters and Fraudulent Strategies in Mobile Social Network

Modern communication technologies have developed quickly, especially communications through (mobile) phones, which has greatly aided in the sharing of information and social connections between people. Nonetheless, the rise of telemarketing scams has the potential to seriously deplete communal and private wealth, slowing down or harming the economy. With an emphasis on exposing the "precise fraud" phenomena and the techniques employed by fraudsters to precisely choose targets, we propose to identify telemarketing scams in this study. We utilise a one-month comprehensive dataset of telecommunication information from Shanghai, which includes 698 million call logs and 54 million customers, to explore this issue. During our research, we have discovered that user information may have been substantially compromised, and that fraudsters prefer to target users who are younger and more active on mobile networks. To further separate fraudsters from non-fraudsters, we provide a unique semi-supervised learning approach. Our technique beats various cutting-edge algorithms in terms of accuracy of identifying fraudsters, according to experimental findings on real-world data. We think that our research may help governments and mobile service providers make better policy decisions.
Real Time Face Attendance System Using ML

The emergence of the big data era and the economic significance of facial recognition technology have greatly increased the prospects and market demand for this technology. The purpose of this essay is to design a real-time video-based facial recognition attendance system. This article primarily sets four directions to consider the issues: the face recognition attendance system's truancy rate with real-time video processing, the stability of the system with real-time video processing, the accuracy rate of the system during actual check-in, and the interface settings of the system with real-time video processing. A PYTHON script and a SQL database are used to support the proposed system, which was developed on the Miniconda platform. The algorithm used by the system is based on image comparison, comparing the picture recorded by the system while it is running with the encoded values of the face from the image from the database..
Joint Microservices Caching and Task Offloading Framework in VEC Based on Deep Reinforcement Learning

Vehicle capacities and intelligence are rapidly increasing, which will likely support a wide range of novel and interesting applications. However, these resources are not effectively utilized. To take advantage of these invaluable capabilities in smart vehicles, they can be used in the cloud environment and can be operated through distributed computing platforms in order to benefit from their combined processing power, storage capacity, and memory resources. Vehicular edge computing (VEC) is a promising field that allows computing tasks to be transferred from cloud servers to vehicular edge servers for processing, allowing data and apps to be placed closer to vehicles (users).&#x0D; This paper proposes a framework that combines two modules, the first one for managing microservice caching in vehicle-mounted edge networks, such that we use cluster-based caching technique to deal with the case where similar microservices are frequently requested in VEC. The second one integrates the computational capabilities of the edge servers with the capabilities of vehicles to perform task offloading in a collaborative manner.&#x0D; Our solution addresses the limitations of existing edge computing platforms during peak times by combining microservices caching with computational task offloading to improve overall system performance.&#x0D;
Research on Copyright Protection of Film and TV Series in the Era of Short Video Deluge in China

Film and television works refer to audio files with or without words and video files that can be recorded, which also enjoy copyright. In this era of short video fast food, movies and TV series have become the benchmark in the cultural field with their mature scripts, careful shooting and large investment of funds and personnel. The copyright protection of movies and TV series is the foundation for the long-term development of China’s film and television industry, and has a great impact on the development of China’s cultural industry. With film and television dramas becoming increasingly popular subjects for short-form video creation on the Internet, short-form video users and film and television drama creators have formed a fierce collision of rights. However, in practice, the copyright protection of film and television series is faced with such problems as high creation costs and low transport costs, high difficulty and high cost for platforms to identify infringing videos, and no clear standards for judicial identification of infringement issues. Therefore, based on the concept of the haven principle and the principle of proportion, this paper uses the method of empirical research to analyze the current situation of film and television play copyright protection in our country by combining the Copyright Law and the short video infringement case of the popular the film “Nagatsuko”. Specifically, this includes legislation to clarify the limits and scope of judicial determination, refine the legal responsibilities of the three main parties, establish a legal risk prevention and control mechanism, and establish a benefit sharing mechanism between creators and short video users, thus promoting coordinated coexistence and win-win cooperation between users of short online videos and creators of TV dramas.
MCQ Question Generation using Natural Language Processing Techniques

This paper is about creation of Multiple Choice Questions (MCQ) using Natural Language Processing Techniques. In natural language processing we used T5 text transformer model for creating question and WordNet or Sense2vec are used to generate distractors. The result should be displayed to the user using Gradio user interface. This research will be very useful in educational sectors for conducting Computer Based Test Examinations(CBTE).
A Study to Assess Anemia Level among Hearing Impaired School Students in Special School, Palakkad

A descriptive study was conducted to assess anemia among hearing impaired school children in a special school in Palakkad. The objective of the study is to assess hemoglobin level among hearing impaired children. The sample size is 30 and purposive sampling technique is used. In data collection procedure, hemoglobin level of school children was assessed and grade of anemia was analyzed. The study findings is that normal haemoglobin leve1 is 6 (53.33%), mild anemia includes 12 (40%), moderate anemia zero and severe anemia includes 2 (6.67%). After the research and analysis, the study subjects were administered with iron supplementation.
The Current Perceptions About Instructional Tools in Educational Towards Adoption of Virtual Reality Among Undergraduate Students

Virtual reality (VR) has emerged as a major tool in this field of research and education development. However, many challenges arise for students during the course of instruction and learning. The learning process, the placement of support assessment variables, and the behavioral intention to continue using it within the learning spectrum are crucial to the success of virtual reality in the educational sector. The goals of this study are to inquire into the degree to which VR is now being utilized in the field of education. In addition, Thematic analysis was used to analyze the data since it provided an applicable approach to use across the interviews, many methods combined into this study. The study polled 32 teachers and analyzed the results with advanced analysist tools. All of the proposed points were determined using the data analysis.&#x0D;
Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints

Ensuring the realism of computer-generated synthetic images is crucial to deep neural network (DNN) training. Due to different semantic distributions between synthetic and real-world captured datasets, there exists semantic mismatch between synthetic and refined images, which in turn results in the semantic distortion. Recently, contrastive learning (CL) has been successfully used to pull correlated patches together and push uncorrelated ones apart. In this work, we exploit semantic and structural consistency between synthetic and refined images and adopt CL to reduce the semantic distortion. Besides, we incorporate hard negative mining to improve the performance furthermore. We compare the performance of our method with several other benchmarking methods using qualitative and quantitative measures and show that our method offers the state-of-the-art performance.
Lyapunov-Stable Deep Equilibrium Models

Deep equilibrium (DEQ) models have emerged as a promising class of implicit layer models, which abandon traditional depth by solving for the fixed points of a single nonlinear layer. Despite their success, the stability of the fixed points for these models remains poorly understood. By considering DEQ models as nonlinear dynamic systems, we propose a robust DEQ model named LyaDEQ with guaranteed provable stability via Lyapunov theory. The crux of our method is ensuring the Lyapunov stability of the DEQ model's fixed points, which enables the proposed model to resist minor initial perturbations. To avoid poor adversarial defense due to Lyapunov-stable fixed points being located near each other, we orthogonalize the layers after the Lyapunov stability module to separate different fixed points. We evaluate LyaDEQ models under well-known adversarial attacks, and experimental results demonstrate significant improvement in robustness. Furthermore, we show that the LyaDEQ model can be combined with other defense methods, such as adversarial training, to achieve even better adversarial robustness.
Bridging graph data models: RDF, RDF-star, and property graphs as directed acyclic graphs

Graph database users today face a choice between two technology stacks: the Resource Description Framework (RDF), on one side, is a data model with built-in semantics that was originally developed by the W3C to exchange interconnected data on the Web; on the other side, Labeled Property Graphs (LPGs) are geared towards efficient graph processing and have strong roots in developer and engineering communities. The two models look at graphs from different abstraction layers (triples in RDF vs. edges connecting vertices with inlined properties in LPGs), expose - at least at the surface - distinct features, come with different query languages, and are embedded into their own software ecosystems. In this short paper, we introduce a novel unifying graph data model called Statement Graphs, which combines the traits of both RDF and LPG and achieves interoperability at different levels: it (a) provides the ability to manage RDF and LPG data as a single, interconnected graph, (b) supports querying over the integrated graph using any RDF or LPG query language, while (c) clearing the way for graph stack independent data exchange mechanisms and formats. We formalize our new model as directed acyclic graphs and sketch a system of bidirectional mappings between RDF, LPGs, and Statement Graphs. Our mappings implicitly define read query semantics for RDF and LPGs query languages over the unified data model, thus providing graph users with the flexibility to use the query language of their choice for their graph use cases. As a proof of concept for our ideas, we also present the 1G Playground; an in-memory DBMS built on the concepts of Statement Graphs, which facilitates storage of both RDF and LPG data, and allows for cross-model querying using both SPARQL and Gremlin.
Roll-Drop: accounting for observation noise with a single parameter

This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noise during deployment without explicitly modelling its distribution for each state. DRL is a promising approach to control robots for highly dynamic and feedback-based manoeuvres, and accurate simulators are crucial to providing cheap and abundant data to learn the desired behaviour. Nevertheless, the simulated data are noiseless and generally show a distributional shift that challenges the deployment on real machines where sensor readings are affected by noise. The standard solution is modelling the latter and injecting it during training; while this requires a thorough system identification, Roll-Drop enhances the robustness to sensor noise by tuning only a single parameter. We demonstrate an 80% success rate when up to 25% noise is injected in the observations, with twice higher robustness than the baselines. We deploy the controller trained in simulation on a Unitree A1 platform and assess this improved robustness on the physical system.