Reviewerly/exHarmony huggingface dataset:
Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption

Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.
Methods for explainable artificial intelligence

As AI models are becoming increasingly regulated by governments, it has become crucial to provide explanations for their decisions. The emergence of XAI has helped us to better understand AI systems and move towards models that can offer human-friendly explanations. However, it remains unclear whether the growing range of XAI methodologies and tools is enough to provide practical support in the risky scenarios that regulatory stakeholders are concerned about. For instance, can an intelligent model be used for a medical diagnosis simply because of the availability of score-CAM or GradCAM? The answer is a resounding "NO" because there are no established risk-aware scenarios that can guide the research community on the requirements for implementing XAI-supported AI models in real-world contexts. Therefore, society needs approaches that recognize XAI tools as necessary but insufficient steps toward assessing the trustworthiness of AI-based systems for specific tasks.
Government Policy as a Variable to Control the Influence of Exchange Rates, Interest Rates, and Inflation on ICI

This research explores how inflation, exchange rates and interest rates impacted the Jakarta Composite Index between 2016 and 2022. It also aims to determine the changes in the impact of exchange rates, inflation and interest rates on the Index before and after the implementation of government policies. Key policies examined include the enforcement of large-scale social restrictions (PSBB) in April 2020 due to the pandemic of covid 19, and a reduction of the BI7DRR interest rate by 25 basis points in October 2019. The study employs a quantitative experimental hypothesis and gathers data from www.bps.go.id, www.bi.go.id, and www.kemendag.go.id. Using purposive sampling for analysis, and SPSS version 26 for data processing, the study reveals that in the absence of government intervention, exchange rates, interest rates, and inflation significantly influence the Jakarta Composite Index. Specifically, while this exchange rate positively affects the Index, interest rates have a negative impact, and inflation also contributes positively. Conversely, following the PSBB policy in April 2019, both the exchange rate and interest rates negatively affected the Index, with exchange rate showing a significant negative impact, interest rate continuing to affect negatively, and inflation still having a positive influence.
Research on knn algorithm based on kmeans clustering and collaborative filtering hybrid algorithm in AI teaching

With the development of higher education in recent years, curriculum teaching is advocated to gradually change from experiential teaching to precision teaching, so it is increasingly urgent to analyze and apply the results of teaching process. The AI teaching platform makes detailed analysis of students' performance and excavates its deep application, and gives full play to the advantages of artificial intelligence through systematic classroom teaching to achieve precision and personalized teaching. This paper discusses the application principle of recommendation algorithm and collaborative filtering hybrid algorithm based on Knn-kmeans model in AI teaching platform. This paper analyzes the model method of analyzing teaching process results, and analyzes the personalized learning recommendation processing process based on Knn-kmeans model and collaborative filtering hybrid recommendation algorithm in detail.
Design and Development of Virtual Reality Science Laboratory on Science Education: An Analysis of Presences during Learning

The global spread of COVID-19 has resulted in the international shutdown of educational institutions, especially for science students. Education is a crucial industry that demands innovation to accomplish learning objectives and goals. Modern technology-based education has been globalized and interactive platforms allow students and educators to access, utilize, and benefit from all the educational content. The first research objective of this study is to develop a virtual reality science laboratory by utilizing the ADDIE Methodology, a popular instructional design model for instructional learning. The second objective is to analyze the presence experiences during learning sciences by using the Virtual Reality Science Laboratory by distributing a set of pretest and post-test questions to two different groups of 37 science students. Group 1, which is 20 students will learn about science through 2D videos while Group 2 consists of 17 students who will experience learning science through Virtual Reality Science Laboratory. The result shows that there is a significant level of presence between the two groups, and the VR group performs better. This study can help educators and developers design effective VR tools to learn science. For future research, we suggest including cognitive load as another variable to test with VR learning tools. This work contributes to the studies on how immersive learning can significantly affect learning towards positive longitudinal effects.
Improving The Efficiency of Semantic Image Retrieval using A Combined Graph and SOM Model

Extracting similarity and semantic images from images is a hot topic that is used in various semantic retrieval systems. The GP-Tree, a hierarchical clustering tree, is used in the article to retrieve semantic images. The GP-Tree is then used to create a graph of neighboring leaf nodes, resulting in a smaller search list and a reduced chance of missing related similar images. In addition, the neighbor graph is used to build a Self-Organizing Map (SOM) to improve clustering efficiency and image retrieval performance. To improve high-level semantic retrieval efficacy, we also propose extending the current ontology structure. The ontology framework with rich domains represents most of the basic objects. With this ontology, the sets of images are added for the enrichment and can be used for many different sets of images. The SPARQL query is automatically generated from visual words for querying on the ontology. The query result on the ontology is a set of similar images and the definition of its semantics. The proposed method is tested on the WANG and ImageCLEF datasets, and the results are compared to previous publications on the same dataset, demonstrating its efficacy.
Composing a Syllabus via Educational Program Maker: A Web Tool for Academic Documents Creation

This paper raises the question of the importance of syllabus structure. The research conducted on syllabus components allowed for a thorough analysis of the learning analytics literature, leading to the identification of the main components of the course model and the interrelationship between them. Based on these findings, a web service called the Educational Program Maker was developed. This tool has proven to be notably useful at ITMO University in Saint Petersburg, Russia since its launch in March 2021. It has gradually become the primary instrument for creating academic courses at the university, playing a crucial role in effective communication among educational process participants during the design, development, coordination, and approval of the syllabus. The Educational Program Maker follows a course model that is presented and discussed in this study. Feedback from faculty members who have used the tool has been evaluated, and the results are promising. The Educational Program Maker is not only making the process of creating syllabi and academic documents easier, but it is also improving the quality of these documents and fostering collaboration and communication among educators.
What Healthcare Leaders Need to Know About Information Blocking Enforcement

On June 27, 2023, the Office of Inspector General (OIG) for the U.S. Department of Health and Human Services (HHS) released its final rule implementing penalties for information blocking.
Efficient entry point encoding and decoding algorithms on 2D Hilbert space filling curve

&lt;abstract&gt; &lt;p&gt;The Hilbert curve is an important method for mapping high-dimensional spatial information into one-dimensional spatial information while preserving the locality in the high-dimensional space. Entry points of a Hilbert curve can be used for image compression, dimensionality reduction, corrupted image detection and many other applications. As far as we know, there is no specific algorithms developed for entry points. To address this issue, in this paper we present an efficient entry point encoding algorithm (EP-HE) and a corresponding decoding algorithm (EP-HD). These two algorithms are efficient by exploiting the &lt;italic&gt;m&lt;/italic&gt; consecutive 0s in the rear part of an entry point. We further found that the outputs of these two algorithms are a certain multiple of a certain bit of &lt;italic&gt;s&lt;/italic&gt;, where &lt;italic&gt;s&lt;/italic&gt; is the starting state of these &lt;italic&gt;m&lt;/italic&gt; levels. Therefore, the results of these &lt;italic&gt;m&lt;/italic&gt; levels can be directly calculated without iteratively encoding and decoding. The experimental results show that these two algorithms outperform their counterparts in terms of processing entry points.&lt;/p&gt; &lt;/abstract&gt;
Design and Build an Asset Management System at PT. Fajar Solusi Elektronik

PT. Fajar Solusi Elektronik is one of the medium to lower scale cooperatives based in the city of Jakarta. The large number of goods in the company will certainly be difficult to manage, care for, and maintain the assets of the goods owned by the company. Goods assets that exist in PT. Fajar Solusi Elektronik includes computers, laptops, tablets, printers, speakers and other supporting tools. Currently, the management of the asset lending process still uses paper manuals and individual memories, while there are many assets, there is no notification feature if there is a return of goods, so that errors in calculating goods often occur, the data obtained has not been managed properly, so data loss often occurs. Still manually using paper and goods data, data on the distribution of goods assets to reports have not been stored and organized properly. For analysis and design methods use the Unfield Modeling Language (UML). This asset management information system uses the PHP programming language and MySQL as its database. With this web-based asset management information system, the processes related to asset management are easier to use, accurate and integrated.
Website Optimization and Analysis on XYZ Website using the Web Core Vital Method

XYZ website is a business website that operates in the field of e-commerce which is implemented through websites and applications, for several years the website has had a percentage of users' usage speed which has decreased quite a bit and has become old, due to lack of maintenance of some of the features contained in the website application which have an impact on the lack of customer interest in buying goods on the XYZ website and more influential in terms of access from searching e-commerce notifications from Google that if the percentage of websites decreases over a long period of time, this will result in websites not being allowed to publish advertisements. In this study, we analyze the problem to understand the problem starting from small things, namely from the use of programming languages, the data provided, the use of writing code, third party or vendor support, filling out website content, and websites using vital core web architecture. So that the website used has good comfort, accelerates the use of the website which can be affected by the large number of customer visitors, and can facilitate the development team's performance in management and maintenance and provide many positive things from customers so that business runs fast and provides convenience for customers and the XYZ website can received by Google.
Prof. Nevzat Tarhan: “Brain does not help to one who is hopeless and pessimist”

The event, which was hosted by the Türkiye Turing and Automobile Institutions with students studying at different universities in Istanbul, was moderated by Journalist Şaban Özdemir.
Comparing Chatbots Trained in Different Languages

The Communications website, https://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications , we'll publish selected posts or excerpts. twitter Follow us on Twitter at http://twitter.com/blogCACM https://cacm.acm.org/blogs/blog-cacm Antony Chayka and Andrei Sukhov examine how training chatbots in English or Russian affects their responses.
Federated Learning with Privacy-Preserving Incentives for Aerial Computing Networks

With the help of artificial intelligence (AI) model, aerial computing can help analyze and predict the network dynamics and support intelligent decision-making to improve the performance of 6G space-air-ground integrated networks. Federated learning has been proposed to tackle the challenges of limited energy and data shortage for the application of AI models in aerial computing networks. A critical problem of FL for aerial computing is the lack of incentives due to privacy concerns. On the one hand, the information needed to measure users' learning quality may be eavesdropped. On the other hand, users' real costs for determining payments may also undertake inference attacks. In this paper, we design a privacy-preserving and learning quality-aware incentive mechanism for federated learning in aerial computing networks. We propose differential privacy based scheme to protect the privacy of the real cost. In addition, utilize Combinatorial Multi-Armed Bandit (CMAB) algorithm to evaluate the user learning quality without any participant information. Simulation results demonstrate that our scheme can significantly motivate high-quality participants with guaranteed privacy preservation and achieve effective federated learning under the constraint of the limited budget.
Strive to Build a Smart Education Public Service System with “Six Standards”

In February 2021, Shenzhen was selected for the list of regions created by the Ministry of Education's 2020 “Smart Education Demonstration Zone”. According to the overall deployment and work requirements of the Ministry of Education on the construction of “Smart Education Demonstration Zone” and the actual situation of our city, the implementation plan of “Smart Education Demonstration Zone in Shenzhen” was formulated, and the work of smart education demonstration zone was fully started.
Holistic Governance of Education in Subliminaria from the Perspective of Artificial Intelligence

Exploring the inherent coupling of intelligent technology and regional education holistic governance has important theoretical and practical significance for eliminating increasingly complex regional education problems, avoiding cross-departmental policy conflicts, activating cohesion in governance network structure, and providing seamless public services for the public.
Refinement and Depth

In this chapter, we will shape up visual elements and implement visual structure together with code structure. Visual elements can evolve best when the idea is still under construction, and we can follow new inspirations from working with code. We will also dig a bit deeper into working with data structures and unleash new powers of Processing. To achieve this, we introduce four refinements here: “Randomness and Noise,” “MemoryDot,” “Using Computed Values,” and “Interaction.” Using these refinements, we can make big steps from the simple visual elements of the previous step toward solutions that match our idea even better.
Enhancing Multi-Label Vulnerability Detection of Smart Contract Using Language Model

Smart contracts are decentralized applications that play an important role in blockchain-based applications. Smart contracts are written by programming languages (e.g., Solidity, Python, etc.), so it is error-prone and suffers from vulnerabilities, leading to a huge amount of economic loss for the blockchain ecosystem. In the past, there were many existing vulnerability detection tools such as MythX, Oyente, Slither, and so on. However, these tools contain several limitations related to low accuracy and high execution time. Therefore, many studies focus on vulnerability detection mechanisms using Deep Learning which takes into account the bytecode of smart contracts to detect its vulnerabilities. Despite achieving good accuracy, these studies make an assumption that there is only one vulnerability in a smart contract. When there is more than one vulnerability in a smart contract, these studies can not obtain good performance. Therefore, in this paper, we propose a multi-label vulnerability detection of smart contracts using a language model. Concretely, the proposal takes into account the bytecode by using the SecBERT pre-trained model to extract the implicit features and analyzes it using the Multi-Layer Perceptron algorithm to identify multiple vulnerabilities in a smart contract. The experimental results show that the proposal outperforms benchmarks and obtains 92.55 percent accuracy.
A Competitive Segmentation of the Human Brain using Artificial Neural Network Approach Towards MRI

The diagnosis of brain abnormalities, prognosis monitoring, and treatment evaluation all rely heavily on the magnetic resonance scan of brain tissue segmentation. Although numerous automated or semi-automatic methods have been proposed in the literature to reduce the need for human intervention, the degree of accuracy is frequently still significantly lower than that of manual segmentation. We give a clever technique for fragmenting the cerebrum utilizing a managed counterfeit brain organization system called artificial neural network (ANN) and volumetric shape models. In the beginning, in addition to the usual spatial-based and intensity-based image features, a level-set oriented brain boundary fitting technique is used to accomplish this. This is controlled by the picture intensity. The ANN is then informed of the number of important structures. Additionally, rather than directly applying standard guidelines to local appearances, this ANN learns local adaptive feature classification conditions. The outcomes demonstrate that the proposed strategy achieves competitive results in a relatively shorter time spent training.
Multilingual Code-Mixed Sentiment Analysis in Hate Speech

Sentiment analysis discovers the emotion expressed in a text. It helps in analyzing the product reviews, customer feedback and survey responses. Researchers have developed various algorithms for this purpose, however, they have majorly focused only on the sentiment analysis in English language. Although, few works are available for Hindi and multilingual sentiment analysis, however, these works are not efficient enough to perform sentiment analysis in code-mixed languages. To overcome the limitation of the existing works, this paper presents a multilingual code-mixed language model which identifies the sentiments of the hate speech dataset extracted from Twitter. As the hate speech dataset with sentiment labels are not available, we first collect the data from Twitter. After that we label the data using a transformer-based pretrained sentiment analysis model trained on a large corpus of tweets in multiple languages. We pass our collected data as test data to this model and predict the sentiment labels. Now, we train six different machine learning models to perform our own task i.e sentiment analysis for multilingual code-mixed hate speech dataset. The machine learning models perform well across multiple languages and also code-mixed languages. In future, it can be easily adapted to different classification tasks based on code-mixed languages. The results yield that hate speech invokes negative sentiment whereas non-hate speech reflects either positive or neutral sentiment.
Current state of knowledge of the gold mineralization at Imonga-Saramabila, Maniema (DR Congo): a petrographic and mineralogical study of the mineralized vein system

The Great Lakes area in Central Africa forms a large metallogenic province that hosts important deposits of gold mineralization. We present a petrographic, mineralogical and geochemical study of unique borehole samples from the Imonga-Saramabila gold deposit, a historical mine site located in the Maniema Province (DR Congo) in the Karagwe-Ankole belt (KAB) in the Great Lakes area and one of the only places in the Maniema province with accessible boreholes allowing to study the mineralization. The samples are metasedimentary rocks belonging to the Mesoproterozoic Kivu Supergroup, with bedding-parallel meta-igneous rocks. These rocks have undergone upper greenschist to lower amphibolite facies metamorphism, based on the presence of andalusite and chiastolite porphyroblasts, and are affected by hydrothermal alteration. The porphyroblasts formed during peak metamorphism and posterior to a first vein generation. Three additional vein generations were identified at Imonga based on crosscutting relationships, with the second and third events overprinting the porphyroblasts by intense chloritization, and associated with sulfide mineralization. The fourth vein generation is again barren. The first veining event formed pre-folding and the three subsequent generations postdate folding, as concluded based on the relationship of the veins with the cleavage. Only one important folding event is proposed based on the development of only one cleavage. Gold occurs as free gold or is included in pyrite in the second (and maybe third) vein generation. Based on the paragenesis, structural characteristics, and the link between veining and metamorphic minerals, the gold mineralization at Imonga is interpreted to be linked to the early Neoproterozoic (~980 Ma) compressional deformation event, associated with the amalgamation of the Rodinia supercontinent.