Reviewerly/exHarmony huggingface dataset:
The Effect Of Parents' Social Economic Status and Learning Motivation On Economic Learning Achievement Of Students Of Class X Sma Negeri 1 Arse Academic Year 2017/2018

The problem in this research is the low of student economic learning Outcomes.This study aims to determine the influence of socio economics of parents and student achievement, intereset to learning motivation with student achievement and determine the influence of teacher creativity and learning motivation with student achievement X SMA Negeri 1 Arse school Year2017/2018.This research was conducted in senior high school 1 Arse in Simangambat street, North Tapanuli. There is all of the class X numbered 112 students as population in this research. The sample in this study using 56 people. Instrument that used to collecting data is questionnaire with using likert scale that firstly tested the validity and reliability of the questionnaire before conducting research. The analysis used in this research is testing variable partially performed by t test while testing for both variables togetger with F test.Based on the analysis and discussion of this study concluded that (1) There is a positive significant influence between socio economics of parents and student achievement with tcount (4,407) &gt; ttable ( 2,004). (2) There is apositive significant influence between learning motivation with tcount (4,962) &gt; ttable (2,004). (3) There is a significant influence between socio economics of parent and learning motivation together with Fcount&gt; Ftable namely ( 97,108 &gt; 3,17).Based on the analysis, the conclusions from this study is there a positive and significant influence between teacher creativity and learning motivation with student achievement in class X SMA Negeri 1 Arse and hipotesis in this research can accepted
The Influence of Promotion, Brand Image and Facilities on the Decision to Choose Prime One School

This study aims to determine the Effect of Promotion, Brand Image, and Facilities Toward Decision To Choose Prime One School. The population in this study were all parents of students in Prime One School in 2019 totaling 1.699 people. The sampling technique used in this study was the Slovin formula and amounted to 94 respondents. The results of the analysis of the study showed that the Promotion and Brand Image partially have a positive and significant effect on the decision to choose a Prime One School school while Facility have no effect. The results showed that the Promotion, Brand Image and Facilities simultaneously have a significant effect on The decision to choose Prime One School. The results of this study are supported by the value of Adjusted R Square R2 which means that Promotion, Brand Image and Facilities can explain the decision to choose and the remaining is influenced by other variables outside of this study such as service quality, psychological factors.
Sistem Pengukur Berat Badan Dan Tinggi Badan Dengan Pencatatan Otomatis Berbasis Internet Of Things

The weight and height measurement tools used at the Tarowang Jeneponto Health Center do not get accurate data, because they are still conventional or manual. this has an impact on inefficient use because it is still carried out by humans who have inaccuracies and usually human error occurs. This study aims to design an accurate and automatic body weight and height measurement system at the Tarowang Jeneponto Public Health Center. This data was obtained through field research, library research, interviews and documentation. The method used in this research is the blackbox method and UAT (User Acceptance Test). The results of this study indicate that the System for Measuring Weight and Height with Internet Of Things-Based Automatic Recording was successfully implemented and very well used with a questionnaire value of 86.4%.
Digital Evidence Nature:its Role in enhancing judge's Convincing

The rate of impunity for perpetrators the information's crimes to be brought to the courts for trial is very large because of the inability of the judicial authorities to reach the proof of the commission of this type of crime. As well as, identifying the perpetrators of the perpetrators, because of not dealing with evidence of proof of this type of crimes on As it is characterized by its specificity. This prompted us to research the merits of the digital evidence and identify the characteristics that must be paid attention to, as well as the defects that distinguish it from the evidence in traditional crimes.
PENGENALAN AKTIFITAS CYBER THREAT HUNTING : STUDI KASUS HUNTING MALWARE DENGAN METODE MEMORY FORENSIC

Cyber Threat Hunting is one of the activities in the cyber security sphere that is currently developing. Threat Hunting shows the process of proactively searching for vulnerabilities and cybercrime actors through network media to anticipate cybersecurity incidents that will occur in a related infrastructure. Of course with this threat hunting, it will complement passive monitoring activities that only rely on alerts on SIEM (Security Information and Event Management), and the case study that I will raise is the threat hunting method at low level memory so that if there are cybercriminal actors who escape the system SIEM and possibly already on internal systems can be detected and can perform forensic and related system recovery activities
Recommender System Frontier Topics

This chapter introduces the hotspots of recommender system research, the key challenges of recommender system application, and how to achieve responsible recommendation technically. These contents may become the key of recommender system research and application in the future, so they need the continuous attention of researchers and developers.
Overview of Recommender Systems

This chapter first introduces the history of the recommender system and the revolutionary changes in the field of recommender systems. Then, this chapter introduces the basic principles of recommender systems, including introducing the basic assumptions of recommendation algorithms from the perspective of machine learning, introducing how to define the recommendation problem in the form of a machine learning problem, and emphatically introducing the deep learning-based paradigm to solve the recommendation problem—"representation learning + interaction function learning". This chapter also gives an overview of the technical architecture of recommender systems, including the differences between small- and medium-scale recommender systems and large-scale recommender systems. Finally, this chapter introduces the main application areas of recommender systems, such as e-commerce, content platforms, etc., and the actual business value brought by recommender systems to these application areas and compares the three main applications in the Internet field—search, advertising, and recommendation, by the differences and connections among them. Starting from industry problems, this chapter summarizes the differences in the application of recommender systems in different industries and outlines the solutions to different types of problems.
Foundations of Deep Learning

This chapter introduces the basics of deep learning, including feedforward computation and backpropagation algorithms for deep neural networks, as well as various classic neural network models. As readers learn, they can combine the content of other chapters in this book to understand and design different types of neural network models for recommendation scenarios, taking into account the data characteristics and task properties, in order to improve recommendation performance.
Classic Recommendation Algorithms

This chapter introduces four types of classic recommendation algorithms, including content-based recommendation algorithms, classic collaborative filtering algorithms, matrix factorization methods, and factorization machines. Before the emergence of deep learning, these methods were the most mainstream techniques for recommender systems, widely recognized by both academia and industry. Although after the emergence of deep learning, these technologies are no longer the first choice of the industry, but the basic ideas and practical experience extracted from these technologies still affect the follow-up research. Therefore, in many deep learning-based recommendation algorithms, we can often see the reflections of the above approaches.
EVALUASI OTOMATIS PRAKTEK PEMBERIAN INJEKSI DARI INPUT FOTO

Service to patients is an important factor in the medical world. The problem of limited medical personnel in developing countries as well as in war zones and refugee areas is the reason many researchers try to conduct research that is able to assist patient care and improve competence, especially in providing medical treatment to patients. Based on these problems, this study aims to develop applications that can assist in learning for prospective medical personnel, especially in the practice of giving injections. This study uses the CNN model using a training system to determine the parameters used to classify the type of injection while SVM predicts subcutaneous class in the data testing in this study. This study aims to identify the type of injection from photo input data to be evaluated as subcutaneous and non-subcutaneous class with an accuracy rate of 96%, recall of 89% and precision of 100%.
The Evolution of ELT Methods: A Comparative Analysis of Pre and Post-COVID-19 Pedagogical Practices in Pakistan

The present research was designed to investigate the effect of the pandemic on the teaching methods of the English language that are used in Pakistan and also to analyze the adaptations and innovations that occurred in English Language Teaching after COVID-19. A literature review of 5 research studies was conducted to acquire deeper knowledge about the evolution of the ELT methods in Pakistan. Interviews and questionnaires assisted in quantitative and qualitative data collection from teachers. During the research, the researchers discovered that the pandemic has accelerated the changes in pedagogical practices so quickly that online and remote teaching modalities have become compulsory. Teachers were focused on the dynamic characteristics of flexibility, creativity, and continuous professional Development for maneuvering the complexities of distance education and learning. This study concludes that the ELT method is very active, and it involves a lot of digital tools, online platforms, and blended learning approaches, which means that the teaching and learning methodologies could be more stable, but they are adaptable and diversified. Therefore, educators and policymakers should prioritize integrating digital tools and online resources into that curriculum to improve accessibility and engagement.
Understanding the Influence of Trust as Knowledge Management Enabler on Organizational Effectiveness Through Organizational Culture: A Mediation Analysis

The current study examines the influence of trust as knowledge management enabler on organizational effectiveness via organizational culture. The study collected quantitative data from the 487 teaching and non- teaching faculty working in public sector universities in Punjab, Pakistan. Analysing the data using structural equation modeling in Mpus (8.10), the study found that all the proposed hypotheses were supported. The study extends literature on different streams such as knowledge management enablers, organizational culture and organizational effectiveness and offers valuable practical insights for policy makers and university management to effectively create, disseminate and manage knowledge.
Deep Learning-Based Recommendation Algorithms

This chapter introduces the relationship between collaborative filtering and deep learning and then presented various deep learning-based collaborative filtering algorithms. Leveraging cutting-edge methods from deep learning, these algorithms can significantly improve the accuracy, scalability, diversity, and interpretability of recommendation systems, offering richer technological choices for recommendation system design. However, most of these algorithms are optimized for specific problems, and there are often limitations in practical applications. Therefore, at the system design level, algorithm integration or fusion needs to be considered.
An IoT-Based Health Care Solution: A Smart Medical Box

IoT in healthcare is intended to keep people safe and healthy, with the primary goal of lowering healthcare costs and reducing the involvement of caregivers in the coming years. This research proposes a smart IoT-based health- care system, which includes an intelligent medical box connected to a mobile app for continuous health monitoring. Patients can get regular health care with the help of this medical box with a dedicated app and web server. The proposed system helps the patients to check their heart rate, temperature, and oxygen level, all using just one device. It provides an easy way for the user to do this checkup by themselves without any help from caregivers or even doctors. Moreover, the proposed system assists the patients in taking the proper medicinal drugs in due time with an SMS and buzzer alarm. It additionally creates a clean verbal exchange between medical doctors and patients rather than bodily contact. The proposed system can be accessed through an app that stores all the detailed health reports about the patient. Doctors can easily check the patient's report and can change the prescriptions based on the condition of the reports, if necessary. This box also consists of an emergency button that the user can use in case of any emergency to notify their respective caregivers immediately.
Exploring an Approach on Learning Feedback Strategy in the Metaverse: A Perspective of the Fusion of Multi-source Data

Feedback is widely recognized as a crucial element in promoting the overall quality of education. However, the approach of feedback within the emerging Metaverse remains unclear. This study aims to address this gap by conducting an exploratory investigation into the feedback approach in the Metaverse. By utilizing supplementary log data and eye-tracking data, a feedback framework driven by multiple data sources is proposed. The findings of this study have significant implications for both theoretical and practical research in virtual learning, shedding light on the intricate feedback processes within the Metaverse and its potential to enhance educational practices.
Application and Limitations of Digital Signature Schemes for Implementing Non-Fungible Token (NFT)

The ERC721 standard defines a Non-Fungible Token (NFT) as an identifier that uniquely identifies digital data recorded on a blockchain. The NFT currently in use claim to ensure the uniqueness of the contents associated with the NFT by taking advantage of the tamper-resistant characteristics of the blockchain data. Besides, digital signatures based on public-key encryption is a representative technique to prevent digital data from being falsified, and its application to NFT is also discussed. In this study, we first consider an NFT implementation by applying a designated confirmer signature using an interactive verification method without using blockchain technology. We design an NFT issuance protocol that guarantees the uniqueness of data with a designated confirmer signature, and evaluate the security of the protocol. Moreover, we compare it with existing blockchain implementations. By analyzing the both methods, we aim to provide insights into the potential applications and performance in using designated confirmer signatures for NFT. Our findings contribute to the ongoing research on secure and efficient mechanisms for ensuring the integrity and uniqueness of digital assets in decentralized systems.
Deduplicating and Ranking Solution Programs for Suggesting Reference Solutions

Referring to solution programs written by other users is helpful for learners in programming education. However, current online judge systems just list all solution programs submitted by users for references, and the programs are sorted based on the submission date and time, execution time, or user rating, ignoring to what extent the programs can be helpful to be referenced. In addition, users struggle to refer to a variety of solution approaches since there are too many duplicated and near-duplicated programs.
VeriAnon: an Anonymous, Verifiable, and Tamper-proof Commenting System Based on Ring Signature and Clustering Merkle Tree for Decentralized Trading

As blockchain technology continues to evolve, decentralized markets have been progressively expanding, offering novel avenues for participation in economic activities. However, within this evolving landscape, the commenting systems of decentralized trading have not yet reached a state of comprehensive refinement. Such shortcomings have impeded participants' trust in the platforms and have constrained the further proliferation of the decentralized trading model. An integral feature within the commenting system is anonymous evaluation, which holds significance not only for safeguarding participants' privacy but also for playing a pivotal role in establishing reputation and credibility for trading platforms. Nonetheless, devising mechanisms to manage anonymous evaluations and establishing a trustworthy commenting system presents substantial challenges, owing to the inherent characteristics of decentralized systems that lack third-party intermediaries and regulatory bodies. Therefore, motivated by ensuring the authenticity, impartiality, and effectiveness of anonymous evaluations, along with preventing malicious evaluations and undesired behaviors like credit manipulation, we propose a robust anonymous comment management mechanism, leveraging ring signature and clustering Merkle tree for decentralized trading. Besides, we implement VeriAnon, a commenting system integrating our mechanism to enable privacy-preserving authentication of anonymous evaluators, cost-efficient management of anonymous comments, and effective verification of comment integrity and authenticity.
Clustering-Induced Generative Incomplete Image-Text Clustering (CIGIT-C)

The target of incomplete image-text clustering (IITC) is to find correct clusters by integrating complementary and consistent information of multi-modalities for these unpaired heterogeneous samples. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Most existing methods hardly consider the distinct gap between heterogeneous feature domains. 2) For missing data, the representations generated by existing methods are rarely guaranteed to suit clustering tasks. 3) Existing methods do not tap into the latent connections both inter and intra modalities. In this paper, we propose a Clustering-Induced Generative Incomplete Image-Text Clustering(CIGIT-C) network to address the challenges above. More specifically, we first use modality-specific encoders to map original features to more distinctive subspaces. The latent connections between intra and inter-modalities are thoroughly explored by using the adversarial generating network to produce one modality conditional on the other modality. Finally, we update the corresponding modality-specific encoders using two KL divergence losses. Experiment results on public image-text datasets demonstrated that the suggested method outperforms the state-of-the-art baseline methods on 2 benchmark datasets with missing rate of 50% and 70%.
FEDesk: A Web and Mobile Document Management System for the University of Santo Tomas Office for Faculty Evaluation and Development

With the increasing number of individuals working from home and the development of information technology, more information is being disseminated over the Internet. Regardless of the industry, several aspects must be implemented for the workplace environment to be efficient and operate smoothly. Documents are the most essential assets of any organization, therefore understanding how to properly maintain a paper trail may have a significant impact on how efficiently things run daily.
A Decentralized Blockchain Powered Social Network for Secure and Transparent Online Interactions

The dominance of social media platforms has raised concerns regarding privacy, censorship, and the misuse of user data. To address these issues, the utilization of blockchain technology for decentralized social networks has gained considerable attention. In this study, we propose a novel social network built on blockchain, offering a secure and transparent environment for online interactions. The blockchain ensures the integrity of transactions and content through an immutable record, preventing unauthorized modifications. Smart contracts govern the system's behavior, enforcing rules, facilitating transactions, and rewarding participants. We discuss the advantages of our proposed system and compare it with existing social networks. We highlight its potential to create a more secure and transparent online environment for social interactions.
Collaborative Structure Learning Framework from Wikipedia Based on Heterogeneous Graph Attention Network

The development of internet technology has brought great opportunities to online education. The vast learning resources and complex relationships between these concepts on the internet have brought great difficulties to learners. The prerequisite relation between these concepts has great guiding significance for learners' learning order. Hence, automatic prerequisite relation learning has become a hot research topic. The rise of graph neural networks has driven the entire artificial intelligence field and also brought development to prerequisite relation learning. Therefore, this paper proposes a weak supervised learning method collaborative structured learning framework. This framework takes the advantage of a graph attention neural network and node2vec to learn a heterogeneous graph composed of concepts and learning objects and a concept graph based on Wikipedia. Then, it obtains the prerequisite relation between concepts through a classification network. Our experiments on the W-ML dataset of MOOC and the University Course dataset show that the proposed collaborative structured learning framework has better performance than the current baseline models.
ContraPonzi: Smart Ponzi Scheme Detection for Ethereum via Contrastive Learning

In recent years, blockchain technology has witnessed rapid development and received considerable attention. However, its decentralized and pseudonymous nature has also attracted many criminal activities. Among them, Ponzi schemes, a classic form of financial fraud, also hide their true face in smart contracts, causing huge losses to blockchain users. Although numerous methods have been proposed to detect Ponzi contracts, these methods still have limitations in terms of generalization and feature learning. To address this issue, we conduct research on Ethereum, the currently largest blockchain platform enabling smart contracts, and propose a novel contrastive learning-based smart Ponzi scheme detection method named ContraPonzi. This method first extracts control flow graph information from bytecodes and models it as attribute graphs that preserve both semantic and structural information. Next, by augmenting the bytecode data of multi-version compilers and maximizing the graph representation similarity of multi-version bytecodes of the same contract, a pre-training graph encoder is obtained and then can be used in Ponzi contract detection. Experimental results on real-world data demonstrate that ContraPonzi is significantly superior to the state-of-the-art in Ethereum Ponzi scheme detection.