Reviewerly/exHarmony huggingface dataset:
Enhancing Digital Transaction Security: A Comprehensive Study of Krypt Blockchain Technology

Abstract: In the ever-evolving digital landscape of our contemporary era, the imperative of securing and enhancing the integrity of digital transactions has never been more pronounced. With the increasing ubiquity of online financial interactions, the stakes are higher than ever, necessitating innovative technologies and strategies that play a pivotal role in fortifying the security and trustworthiness of these transactions. At the heart of this expansive research paper is a comprehensive exploration of such groundbreaking solutions. A central theme that permeates this comprehensive study is the profound examination of blockchain technology—an unassailable cornerstone of modern digital security. Blockchain, although not explicitly named here, emerges as a linchpin in the quest for secure, transparent, and tamper-resistant digital transactions
Characterizing Distributed Machine Learning Workloads on Apache Spark

Distributed machine learning (DML) environments are widely used in many application domains to build decision-making systems. However, the complexity of these environments is overwhelming for novice users. On the one hand, data scientists are more familiar with hyper-parameter tuning and typically lack an understanding of the trade-offs and challenges of parameterizing DML platforms to achieve good performance. On the other hand, system administrators focus on tuning distributed platforms, unaware of the possible implications of the platform on the quality of the learning models. To shed light on such parameter configuration interplay, we run multiple DML workloads on the widely used Apache Spark distributed platform, leveraging 13 popular learning methods and 6 real-world datasets on two distinct clusters. We collect and perform an in-depth analysis of workload execution traces to compare the efficiency of different configuration strategies. We consider tuning only hyper-parameters, tuning only platform parameters, and jointly tuning both hyper-parameters and platform parameters. We publicly release our collected traces and derive key takeaways on DML workloads. Counter-intuitively, platform parameters have a higher impact on the model quality than hyper-parameters. More generally, we show that multi-level parameter configuration can provide better results in terms of model quality and execution time while also optimizing resource costs.
Rancang Bangun Aplikasi E-Musrenbang Pada Desa Jungai Berbasis Web

The development of technology is getting faster and faster, especially information and communication technology. Information systems are no longer a foreign thing today. Utilization of this technology is also very useful and needed in producing information regarding musrenbang in Jungai Village. The problem that occurs in musrenbang activities in Jungai village is the relatively low level of community involvement and lack of community participation in musrenbang activities. In addition, the office has not been optimal in accommodating community proposals and aspirations in village development. In conducting this research using qualitative research whose purpose is to describe what is currently happening. So that the data will be obtained to support the preparation of the research report. While in this study the method used by the author is a qualitative descriptive method which is a research method that utilizes qualitative data and is described descriptively with the aim of describing or describing the results of existing research. The software development method that the author uses is the Waterfall model. While the analysis and design tools use UML (Unified Modeling Language) consisting of use case diagrams, class diagrams and activity diagrams. This application can make it easy for people with circumstances or conditions who are unable to come directly to the Jungai Village office to carry out musrenbang activities.
Penggunaan Bahasa Pemrograman Python Dalam Analisis Faktor Penyebab Kanker Paru-Paru

Python programming language is a popular programming language in the field of data analysis. In this research, researchers will analyze data on the cause of lung cancer, because lung cancer has the highest death case among other cancer types. The purpose of the research is to explore how dataset processing related to lung cancer-causing factors using the Python programming language and the datasets used are derived from Kaggle. The research method used is quantitative with a population of 462,000 people and a sample of 999 people. The analysis technique used is an explorative data analysis technique. From the results of the research, the highest cause of lung cancer was found in people aged 32-42 years, level 7 genetic factor, 7th level in dust allergies, level 7 obesity and male sex has higher risk than women.&#x0D;
Sistem Pakar Deteksi Dini Stunting pada Balita

Unmet nutritional needs can potentially affect the future growth of toddlers. One pf the consequences is stunting, which refers to failure in the growth of a child’s body and brain due to prolonged malnutrition. To assess the nutritional status of toddlers, parents usually consult nutrition experts. However, there are various factors that may prevent them from seeking such consultations, including a lack of experts or specialists, economic constraints, and busy schedules. This research aims to develop an early detection expert system for identifying stunting in toddlers, providing parents with the convenience of early detection at any time. The method employed in this study is forward chaining, a reasoning approach where users can select symptoms or characteristics, which will then be concluded as a diagnostic result. The outcame of this research is a web-based expert system for early detection of stunting in toddlers, which can be utilized by parents to identify stunting in their children at an early stage.
Enhancing Computer Literacy of Grade 11 Students through Digital Learning Resource – Project Basics (Beginners’ Aid from School that Imparts Computer Skills)

A plethora of literature and studies recognized the positive impact of computer literacy on learners’ academic performance. It is considered a backbone of sound academic performance. As one of the skills required in the 21st century, every learner should have equal access to resources and skills development. Thus, this study aimed to provide equal opportunity to learners to access digital learning resources and skills development relevant to computer literacy. Thirty students participated in the conduct of Project BASICS – Beginners’ Aid from School that Imparts Computer Skills. However, only 28 were recognized as subjects in this experimental study through simple random sampling, while the remaining two were considered blind respondents. To determine the effect of the computer literacy program, the computer knowledge application was diagnosed, and a pretest and posttest were administered before and after the implementation. McNemar test results determine the difference in the computer knowledge application of students before and after the conduct of the program. Paired samples T-test rules out the difference in the pretest and post-test scores of the subjects. The significant difference in the computer knowledge applications before and after became notable. Moreover, the pretest and posttest scores differed significantly at an alpha value of &lt;.001. Thus, the aim of enhancing the computer literacy of grade 11 students was attained. Using BASICS is highly recommended across grade levels to develop further students’ computer literacy, which can leverage their academic performance. Keywords: Computer Literacy, Academic Performance, 21st Century Skills
DeepMind AI is a games master

System that beats humans at chess and poker could be a step towards general intelligence
Does Information Make Us Safer or More Secure?

On October 19th, 2023, Dr. Patrick Neal, Chief Social Scientist at AQ-IQ, presented Does Information Make Us Safer or More Secure? The presentation was followed by a question-and-answer period with questions from the audience and CASIS Vancouver executives. The key points discussed were data protection responsibilities and obligations, and management of excessive data for future-proofing projects and security.
Penggunaan Bahasa Pemrograman Python Untuk Memvisualisasikan Data Peluang Selamat Dari Kecelakaan Titanic

Titanic is a cruise known for its grandeur during the past and its tragedy that caused lots of people to die. Until now, its tragedy has drawn the interest of many people towards the ship. As a result, there are many films, documentaries, and books that discuss the tragedy. Titanic is still a research object in many different study fields, including data science. In this article, we analyze a dataset containing samples from the passengers of Titanic to determine the probability to survive the tragedy generally and to test our hypothesis that there are some factors that enlarge the probability to survive. In this research, we use Python programming language to visualize the Titanic dataset. The population of this research is 2.240 people and the sample is 890 people. We use quantitative and qualitative methods to analyze the data. From this research, a person’s chance to survive the Titanic tragedy in general is 0.38, and there are some factors such as age, gender, and cabin class that can enlarge the chance of survival.&#x0D; &#x0D;
PERANCANGAN SISTEM PENUNJANG KEPUTUSAN PEMBERIAN KREDIT PADA KOPERASI BERKAH MAKASSAR

System design support lending decisions on the cooperative Berkah Makassar.. the cooperative is one of the non-bank financial institutions in charge of providing public services in the form of loans and storage for the community. The aim of this study Designing and building decision support system application program lending cooperative Makassar blessing. Test program data processing applications using black box testing approach. These results indicate that this application can help the staff cooperative in lending to the applicant
PERANCANGAN APLIKASI ABSENSI PESERTA MAGANG BERBASIS ANDROID MENGGUNAKAN JARINGAN INTRANET

Attendance is an activity in making data to find out the number of attendance at an activity. Data collection on attendance of apprentices at the Manpower Office of South Sulawesi Province still uses the manual method, namely in paper form by using a signature. It is found that there are many shortcomings, namely attendance data will be easily damaged and lost, so that data processing becomes less effective. The purpose of the research is to design an Androidbased internship attendance application using an intranet network to monitor the attendance, permits and daily activities of interns during working hours. This research is qualitative in nature by digging information on interns and intranet networks under investigation by making direct observations to the research location, can be tested and produce an expected application. The method of testing the black-box system, namely checking the feasibility and quality of using the application, has been functioning properly or not. The results of application testing, the user logs in with username and password verification to enter the main menu. The user (supervisor) inputs permission, sees the history of attendance and activities, while the user (internship participant) accesses the entry and exit attendance display. The conclusion of the application shows that the input and output functionalities generally run well, providing easy access to the attendance recap compared to the manual system currently used.
SISTEM PELELANGAN IKAN KOI BERBASIS WEBSITE

The lack of optimal koi auction results and the lack of participants in koi auction activities are &#x0D; thea main reasons for creating this system. Fish auctions that are less than optimal can even resukt in big &#x0D; losses. The auction system is made so that the result of selling fish are maximized. Waterfall was chosen to &#x0D; build an auction system to facilitate the fish aucyion process and also to increase the interest of bidders &#x0D; because this system is easy to use on all platform. This system has been tested through the black box test &#x0D; which states all the system functions run well and efficiently. External and internal validators also agreed &#x0D; to use the system by 89,87% and 94%. The practicality of the system was also agreed by respondents ay &#x0D; 86,07%.
Jargon used by mechanic hold baggage screening in i gusti ngurah rai international airport

Jargon is a language variety containing a set of unique vocabulary that is used by the people who have the same interest, class (social status), or same position in the same area, for distance in the working area. The purpose of this study is to identify the form and the meaning of jargon used by Mechanic Hold Baggage Screening in I Gusti Ngurah Rai International Airport. This study uses qualitative methods by using field research. Descriptive study in textual analysis is applied in this study to analyze the form and meaning of jargon through the record of interview with the mechanics. It was analyzed by using the theory from Halingga (2004) that divided the form of jargon and the theory from Leech (1981) about the theory of meaning. The study concluded that there were 12 data (60%) for the word form, 3 data (15%) for the phrase form and 5 data (25%) for the abbreviated form. Acronyms are not used in this community. This percentage is proven if words, phrases and abbreviations are widely used in the data source.
PERANCANGAN SISTEM ANALISIS KARAKTER SISWA-SISWI SMAN 79 JAKARTA BERBASIS JAVA

In conducting the character test of the students, SMAN 79 Jakarta uses the FSQ Personalitree concept which is still done manually. FSQ Personalitree is a personality character analysis test through the existing structure in the tree includes roots, fruits, branches and leaves. This character analysis system is designed to assist the school in conducting character tests so that it can determine the character of the students taking the test and present the processing of participant data and examiner data quickly and accurately accordance with the needs. The research method used in design this character analysis system is grounded research and carry out data collection methods with literature study methods, observations and interviews. After the author conducted the grounded research method, then the writer can draw the conclusion that there is a need for a character analysis system for SMAN 79 Jakarta students that can provide information on the results of character analysis and is stored properly in the system database. So that it can be accessed easily by guidance and counseling teachers, SMAN 79 Jakarta. This character analysis system was successfully implemented to be application used Java programming language and MySQL database.
PENGATURAN BILLING HOTSPOT PADA SISTEM JARINGAN RT/RW NET DENGAN MIKROTIK ROUTER OS

This research is oriented to the Design of RT/RW Net Hotspot System With Mikrotik RouterOS as Billing Management, where previously Delta Net was a cable-based internet cafe where users could only enjoy internet access after visiting Delta Net. With the hotspot system, it is hoped that users will be facilitated in terms of getting internet access without having to leave their homes and Delta Net can survive in the midst of the growing number of internet cafe businesses.&#x0D; From the problems that exist in Delta Net in terms of limited internet access for users who want to be able to enjoy easy internet access without having to leave home and the growing internet cafe business, the use of a hotspot system in the RT / RW environment as a means to facilitate users who want to get access easy internet without having to leave the house and as a means to remain competitive in the midst of the development of the cafe business, so that both users and cafe owners can benefit from the hotspot system
Smart Regime with IoT application using AI

The Internet of Things (IoT) has made it possible for previously unconnected items, such as vehicle engines, to be connected to the network, leading to the emergence of numerous active data streams. The IoT and big data analytics have made considerable strides, opening up intriguing new possibilities for medical and healthcare solutions. Many organisations still struggle with the usage of AI and ML technology when attempting to expand their digital transformation programmes and utilise IoT data. The most current trends involve modifying IoT data for smart applications using artificial intelligence techniques. Numerous apps use data science and analytics to extract conclusions from gigabytes of data. However, these applications do not deal with the issue of constantly identifying patterns in IoT data. The introduction of the IoT and the cloud has further enhanced things by offering smart business recommendations as well as insights into how people operate and how lives are changing. We discuss a variety of AI capabilities and how to apply them to IoT devices in Hands-On AI for IoT. The logic-based substrate provides low energy footprints and higher cognitive accuracy during training and inference, which is a crucial requirement for effective AI with long operating life. The use of AI in the industrial sector has enormous potential. However, it frequently necessitates expensive and resource-intensive machine learning professionals as well as in-depth knowledge of complex statistics and how they are implemented in practical use cases.
A Hyperparameter Tuned Ensemble Learning Classification of Transactions over Ethereum Blockchain

The transactions of Ethereum are recorded on a decentralized ledger that is open to everyone. Behind a false identity, i.e., a pseudonym called an address, the true identity of the Ethereum Blockchain user is hidden. Ethereum is commonly utilized in criminal activities including gambling and ransomware threats. The activities and addresses of the numerous malevolent cybercriminal users must therefore be categorized. The Blockchain’s open data allow for a thorough investigation. This paper classifies Ethereum Blockchain addresses and users’ activities through the application of ensemble machine learning (ML) models. Randomized search on the hyperparameters is performed to improve the classifications, enhancing the models’ accuracy. In this research, the classification models are evaluated based on cross-validation (CV) accuracy, Precision, F1-score, and Recall. Random Forest has emerged as the best classification model with a CV accuracy of 59.14%, while Adaptive Boosting as the worst classification model with a CV accuracy of 54.7%. The employment of randomized search has improved the CV accuracy of the classification models. The outcomes demonstrate that it is practically possible to recognize malevolent users’ addresses and their activities. Additionally, the CV accuracy of the classification models can be optimized by the use of hyperparameter techniques.
Nearest Memory Augmented Feature Reconstruction for Unified Anomaly Detection

Reconstruction-based anomaly detection methods expect to reconstruct normality well but fail for abnormality. Memory modules have been exploited to avoid reconstructing anomalies, but they may overgeneralize by using memory in a weighted manner. Additionally, existing methods often require separate models for different objects. In this work, we propose nearest memory augmented feature reconstruction for unified anomaly detection. Specifically, the novel nearest memory addressing (NMA) module enables memory items to record normal prototypical patterns individually. In this way, the risk of over-generalization is mitigated while the capacity of the memory item is fully exploited. To overcome the constraint of training caused by NMA that has no real gradient defined, we perform end-to-end training with straight-through gradient estimation and exponential moving average. Moreover, we introduce the feature reconstruction paradigm to avoid the reconstruction challenge in the image space caused by information loss of the memory mechanism. As a result, our method can unify anomaly detection for multiple categories. Extensive experiments show that our method achieves state-of-the-art performance on MVTecAD dataset under the unified setting. Remarkably, it achieves comparable or better performance than other algorithms under the separate setting.
Graph Attention Network Knowledge Graph Completion Model Based on Relational Aggregation

Knowledge Graph Completion (KGC) refers to inferring missing links based on existing triples. Research has found that graph neural networks perform well in this task. By using the topology of the graph and the characteristics of the nodes to learn, the feature representation of the nodes can be updated, and more semantic information can be obtained from the surrounding entities and relationships. This paper aims to propose an end-to-end structured Graph Attention Network Enhanced Relationship Aggregation (GANERA) knowledge graph completion model. Firstly, entity aggregation is performed on the central entity by adding an entity attention mechanism. The addition of entity attention can distinguish the importance of different neighbor entities and screen more important entity embeddings. At the same time, the expression ability of the message function is enhanced through specific relational parameters, so that the model can extract richer relational information. Finally, the decoder chooses the convolutional network ConvR. We conduct experiments on standard datasets such as FB15k-237 and WN18RR, and the experimental results confirm the effectiveness of the model, while also achieving relative improvements in Hits@N and MRR values compared to other classical models.
Unleash the Capabilities of the Vision-Language Pre-training Model in Gaze Object Prediction

In a retail environment, it is valuable to evaluate the products of interest to perform accurate recommendations. However, the existing method (gaze following) only predicts the gaze area, and the prediction problem of gaze objects has not been fully explored. To this end, this paper proposes a new visual language model based on pre-trained large language models for the gaze object prediction framework, named EdgeCLIP. Primarily, we employ a set of adaptable and instructive cues to judiciously infuse instructional cues into the extensive language model, while proficiently retaining its pre-training knowledge. Secondly, we introduce a multi-head pooled attention block, MPATB, to achieve semantic enhancement and extract the joint representation of multimodal components, thereby mitigating the discrepancy in fixation points and subsequently reducing inaccurate predictions of gaze objects. Furthermore, we introduce a regulatory loss function that effectively governs the gaze heatmap within the stared box. A large number of experiments have proved that our model outperforms previous models. The code will be available in: https://github.com/fadaishaitaiyang/EdgeCLIP.