Reviewerly/exHarmony huggingface dataset:
Sistem Informasi Manajemen Data Surat Berbasis Web di Dinas Penanaman Modal Dan Pelayanan Terpadu Satu Pintu Provinsi Sumatera Barat

This research intends to create a web-based mail data management information system at Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Provinsi Sumatera Barat which can facilitate archivists in managing correspondence to speed up correspondence processing, make it easier for archivists to find evidence of correspondence, can store, change, print correspondence data and reduce the accumulation of archives in the workspace in the Secretariat. This research method is carried out by collecting data through internships, observations, and interviews. The method used in developing this system uses the SDLC concept. At the analysis stage, the system modeling uses the Unified Modeling Language (UML) as a design tool. The implementation process is carried out using the MySQL database and the PHP programming language. The process of testing this system is carried out using the Black Box testing method. The results of the trial of the designed system have been in accordance with the needs expected by the agency. This research has produced a mail data management information system at Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu Provinsi Sumatera Barat
Analisis Keamanan Jaringan Wi-Fi Terhadap Serangan Packet Sniffing di Universitas PGRI Sumatera Barat

Universitas PGRI Sumatera Barat has wi-fi which does not rule out attacks on wi-fi networks carried out by hackers. Network Security is a protector in a network system in the process of preventing unauthorized and irresponsible network users. This research includes descriptive research. Descriptive research method is a research method that aims to explain an event. This research was conducted by testing and analyzing the test results on network security using Wireshark and Ettercap applications (Kali Linux OS). The results of this study in the form of a wi-fi network security system in building A1, building B1, building D1 at University of PGRI West Sumatra is good. During the scan for hosts, the IP of the laptop victim of the packet snffing attack was not found.
Are Existing Tort Theories Ready for AI?

The rapid development of robotics and intelligent systems raises the issue of how to adapt the legal framework to accidents arising from devices based on artificial intelligence (AI) and machine learning. In the light of the numerous legal studies published in recent years, it is clear that ‘tort law and AI’ has become one of the hot topics of legal scholarship both in national and comparative contexts.
IMPLEMENTATION OF MARKOV CHAINS BY SOFT COMPUTING FOR ESTIMATING OPERATIONAL RELIABILITY UNDER UNCERTAINTY OF ORGANIZATIONS' PRODUCTION ASSETS

Soft computing makes it possible to solve problems that are difficult to formalize. Such tasks include the issues of assessing the operational reliability of production assets by the organization in the course of their operation in various conditions. The estimation solution itself is implemented using Markov chains with discrete states and continuous time, which gives probabilistic estimates. Based on the combination of these two approaches, we will show the implementation of Markov chains described by fuzzy differential equations of the Kolmogorov system using triangular fuzzy numbers and Zadeh and OFN extensions. The convergence of the obtained results is assessed and conclusions are drawn on the evaluation of the operational reliability of production assets.
Average Image Hashing-Based Tamper Detection and Localization Using Fragile Watermarking

Fragile watermark technique is highly sensitive to slight change in watermark image, and hence, it is widely used for image authentication and verification process. In this paper, we are using both the cover image and the watermark image of same size. Before we proceed to the embedding process of the watermark onto the cover image, we encrypt the watermark by XOR-ing it with the chaotic image, which is generated using the logistic map. After that, the least significant bits (LSBs) of the pixels of the cover image are used for the embedding process, where we replace these LSBs by the encrypted watermark. Authentication process is performed by extracting the watermark from the sent file and performing image hashing on it to generate an extracted hash watermarked image. Then, we compare the hash watermarked image with the extracted watermark. If the hashes match, then the image is not tampered, and if they do not match, the image is tampered and tile-based tamper localization is performed. This step can be performed manually by inspecting the extracted watermark or automate it by taking the help of image hashing. We tested the performance and the output of our proposed method by performing a range of attacks to the watermarked images. Experimental results imply that our proposed method is not only able to detect attacks in images but also to localize the region of attack. Also, the proposed method can be used to localize the source of tampering.
Differentially Private, Federated Learning on Gene Expression Data for Tumour Classification

Over recent years, machine learning (ML) methods have enabled considerable progress to be made within a variety of data-rich research domains.Genomics is one prominent field, with ML-based approaches achieving exciting results in a range of historically difficult tasks.One such area where ML approaches have achieved particular success is the classification of cancerous tissues using gene expression data.Despite this success, recent advances share a common issue with many other areas of ML researchstate-of-the-art performance relies upon the aggregation of large training datasets.Many have raised concerns over the implications such large-scale data aggregation practice has for data privacy, and the nature of genomic data makes such concerns all the more pertinent within a cancer classification setting.Because of the sensitivity of such data, strict policies are enacted to protect patient privacy.Whilst currently unavoidable, such policies are a key roadblock to advances in ML research in this domain.Federated learning (FL) aims to solve this issue by allowing ML models to be trained on decentralized data.Despite the promise held by this approach, recent work has demonstrated that FL is not sufficient for fully private training of ML models.The incorporation of differential privacy (DP) into an FL framework has previously been shown to enable decentralized model training in a privacy-preserving manner.We introduce a method to incorporate custom differentially private algorithms directly into federated learning workflows that are not currently implemented by privacy libraries such as Opacus.
Klasifikasi Penyakit Tanaman Jagung Menggunakan Metode Convolutional Neural Network (CNN)

Corn is one of the cereal crops and an important food ingredient because it contains a source of carbohydrates. It is the third largest food crop in the world after rice and wheat. In Indonesia, corn is one of the second largest food crops after rice. This plant has high yields and wide uses. However, in terms of disease, there are still many farmers who still have difficulty in distinguishing diseases, therefore digital assistance is needed to distinguish the characteristics of these plant diseases. Diseases used in this study were: blight and leaf rust. The dataset used has 2 types of corn plant diseases and a total of 2000 images of corn diseases. Convolutional Neural Network (CNN) algorithm is used for classification of corn plant diseases, which is included in the Deep Learning method which has good ability to recognize and classify a digital image object. This study uses the python programming language and the Tensorflow framework to conduct training and data testing. In this study, the classification of corn plant diseases using the Convolutional Neural Network (CNN) method was obtained with a total training accuracy of 97.5%, on data validation it got 100% accuracy and the level of accuracy in testing data using new data was 94%.
Digital technology and privacy attitudes in times of COVID-19: formal legality versus legal reality in Ireland

The adoption of digital technologies to counteract the spread of COVID-19 has resulted in a major exposure of our rights to privacy and data protection. An empirical study conducted in Ireland by the Science Foundation Ireland-funded project PRIVATT demonstrates that privacy attitudes have shifted, resulting in a greater willingness to share personal data in order to combat the pandemic, while, at the same time, upholding a persistent mistrust in the public and private institutions overseeing this global health crisis. This article interprets these findings from a socio-legal perspective, arguing that people tend to overlook the inalienable nature of the essence of their rights to privacy and data protection, the compression of which is not admissible under EU law. Moreover, the widespread mistrust of public and private actors evidences a divergence between the formal legality of the technological solutions adopted and the legal reality that brings about the Irish public’s perception of government measures as potentially infringing their fundamental rights. These considerations will prompt recommendations in pursuit of enhancing transparency, involvement in decision-making processes and data protection literacy amongst the population.
Foreword — Special Issue on Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications

International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 30, No. 03, pp. v-vii (2022) No AccessForeword — Special Issue on Deep Neural Networks for Graphs: Theory, Models, Algorithms and ApplicationsTu N. Nguyen, Warren Huang-Chen Lee, and Nam P. NguyenTu N. NguyenDepartment of Computer Science, Kennesaw State University, Marietta, GA 30060, USA, Warren Huang-Chen LeeDepartment of Communications Engineering & Electrical Engineering, National Chung Cheng University, Taiwan, and Nam P. NguyenDepartment of Information and Computer Sciences, Towson University, USAhttps://doi.org/10.1142/S0218488522020020Cited by:0 Next This article is part of the issue: Special Issue on Deep Neural Networks for Graphs: Theory, Models, Algorithms and Applications AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetails Recommended Vol. 30, No. 03 Metrics History PDF download
GPS and Barcode of Underground Natural Gas Assets

Citizens Energy Group, a natural gas pipeline operator, piloted a GIS (geographic information system) based solution to collect barcode information, create barcode/QR codes, and capture high accuracy GPS locations of underground assets. This paper will provide details on the solution, share lessons learned, and present the next steps. Compliance with the natural gas federal rules has driven part of the solution. This paper will, at a high level, touch on those rules. Then, the paper will discuss the approach taken and how the solution meets those requirements for both distribution and transmission pipelines. Once there is a basic understanding of what approach was taken, this paper will then jump into some of the details of the solution. That includes the GIS based mobile solution, and hardware for the mobile solution to include tablet, GPS unit, barcode reader, and QR code creation. This paper will continue to follow the workflow and discuss field data collection and the approach to crew roll out and training. Now that the data has been collected, this paper will then move into the details of document storage, retrieval, and GIS editing. At the end of this paper, we will discuss lessons learned as it relates to the GIS solution development, hardware selection, data storage, and field crew feedback. The paper will then conclude with next steps for the solution.
Multipurpose Linux Tool for Wi-Fi Based Attack, Information Gathering and Web Vulnerability Scanning Automations

Manual processes of various attacks or scanning are huge time killers for penetration testers and students. Penetration testers, Bug bounty hunters have their own way of doing reconnaissance and penetration testing, for these phases the crowd has to surf through lots and lots of tools and applications in order to deliver a fully-fledged bug report or to identify a bug. Providing a tool for automating those processes would be a great thing for the users. The uniqueness of this tool is accessibility, Accessibility in the sense even though CLI has no attractive element to make the user more engaged to the interface; This Linux Command Line Interface (CLI) tool would satisfy the users by providing automation techniques used for Wi-Fi based attacks and Web application vulnerability scanning. Also, this tool has Open Source Intelligence (OSINT) for information gathering.
A Systematic and Novel Ensemble Construction Method for Handling Data Stream Challenges

Data stream mining is a difficult job because it must deal with its speed, variety, and ever-changing nature. As the industry’s current demand is for data processing at high speed and accuracy, it has become an important task for developers and researchers. One solution to this problem is to use an ensemble approach. Along with speed, concept change is a data stream issue that drift detectors can handle. Similarly, there could be imbalances in the data. Taking these three issues into account, this paper proposes a novel approach to resolving the problem by combining a data stream balancer with an ensemble and a drift detector. The proposed solution is tested using a cutting-edge data balancer, a boosting-based ensemble, and various drift detectors. The experiments are carried out on cutting-edge real-world imbalanced datasets, and the results are discussed in terms of performance metrics.
Sentiment Analysis and Sentence Classification in Long Book-Search Queries

Handling long queries can involve either reducing its size by retaining only useful sentences, or decomposing the long query into several short queries based on their content. A proper sentence classifi- cation improves the utility of these procedures. Can Sentiment Analysis has a role in sentence classification? This paper analysis the correla- tion between sentiment analysis and sentence classification in long book- search queries. Also, it studies the similarity in writing style between book reviews and sentences in book-search queries. To accomplish this study, a semi-supervised method for sentiment intensity prediction, and a language model based on book reviews are presented. In addition to graphical illustrations reflecting the feedback of this study, followed by interpretations and conclusions.
Journal Unique Visitors Forecasting Based on Multivariate Attributes Using CNN

Forecasting is needed in various problems, one of which is forecasting electronic journals unique visitors. Although forecasting cannot produce very accurate predictions, using the proper method can reduce forecasting errors. In this research, forecasting is done using the Deep Learning method, which is often used to process two-dimensional data, namely convolutional neural network (CNN). One-dimensional CNN comes with 1D feature extraction suitable for forecasting 1D time-series problems. This study aims to determine the best architecture and increase the number of hidden layers and neurons on CNN forecasting results. In various architectural scenarios, CNN performance was measured using the root mean squared error (RMSE). Based on the study results, the best results were obtained with an RMSE value of 2.314 using an architecture of 2 hidden layers and 64 neurons in Model 1. Meanwhile, the significant effect of increasing the number of hidden layers on the RMSE value was only found in Model 1 using 64 or 256 neurons.
Festschrift to honor the lifetime achievements of Prof. Marco Ajmone Marsan

We formulate offloading of computational tasks from a dynamic group of mobile agents (e.g., cars) as decentralized decision making among autonomous agents. We design an interaction mechanism that incentivizes such agents to align private and system goals by balancing between competition and cooperation. In the static case, the mechanism provably has Nash equilibria with optimal resource allocation. In a dynamic environment, this mechanism’s requirement of complete information is impossible to achieve. For such environments, we propose a novel multi-agent online learning algorithm that learns with partial, delayed and noisy state information, thus greatly reducing information need. Our algorithm is also capable of learning from long-term and sparse reward signals with varying delay. Empirical results from the simulation of a V2X application confirm that through learning, agents with the learning algorithm significantly improve both system and individual performance, reducing up to 30% of offloading failure rate, communication overhead and load variation, increasing computation resource utilization and fairness. Results also confirm the algorithm’s good convergence and generalization property in different environments.
Corrigendum to: Qualitative analysis of post-consumer and post-industrial waste via near-infrared, visual and induction identification with experimental sensor-based sorting setup

[This corrects the article DOI: 10.1016/j.mex.2022.101686.].
Pengamanan Mnemonic Phrase Menggunakan Modified Advanced Encryption Standart

Abstract - Security is an action to prevent the occurrence of dangers, threats, and attacks, especially the security of cryptocurrency storage assets on the smartphone crypto mobile wallet application. Security is important to protect wallet data from asset theft attacks. The results of the observations made by many users store their wallet recovery words (mnemonic phrases) on cloud services such as Google Drive and One Drive, this is very vulnerable to data hacking if the account has been compromised then hackers take valuable data for their own benefit. Computer scientists created a cryptographic security algorithm to steal data from theft. The AES (Advanced Encryption Standard) algorithm is a symmetric block cipher cryptography algorithm where to obtain data that has been encrypted using a secret key or passkey user must enter the same key when encoding data (encryption). AES is divided into three based on key length, namely AES-128 has a key length of 128 bits, AES-192 has a key length of 192 bits, and AES-256 has a key length of 256 bits. The system that will be made in this research is to modify the rotation of the standard AES algorithm which has 10 rounds to 16 rounds. Modifications made by increasing the number of algorithm cycles make the system more secure and stronger from attacks and require longer computational time for hackers to crack encryption. The tests carried out are comparing the results of ciphertext, time and avalanche effects. The test results show differences in ciphertext output in the same file after encryption, then the time required for the modified AES algorithm in the encryption and decryption process is longer than the AES standard. While the test results on the avalanche effect showed a slight change that occurred in the standard AES by 48.6% and then in the modified AES by 55.6%. An avalanche effect has good results if there is a slight change of 45-60% (half or more). The more bits that change the more difficult the cryptographic algorithm to crack.
A Market Segmentation Aware Retail Itemset Placement Framework

It is a well-established fact in the retail industry that the placement of products on the shelves of the retail store has a significant impact on the revenue of the retailer. Given that customers tend to purchase sets of items together (i.e., itemsets) instead of individual items, it becomes a necessity to strategically place itemsets on the shelves of the retail store for improving retailer revenue. Furthermore, in practice, customers belong to different market segments based on factors such as purchasing power, demographics and customer behaviour. Existing research efforts do not address the issue of market segmentation w.r.t. itemset placement in retail stores. Consequently, they fail to efficiently index, retrieve and place high-utility itemsets in the retail slots in a market segmentation aware manner. In this work, we introduce the problem of market segmentation aware itemset placement for retail stores. Moreover, we propose a market segmentation aware retail itemset placement framework, which takes high-utility itemsets as input. Our performance evaluation with two real datasets demonstrates that our proposed framework is indeed effective in improving retailer revenue w.r.t. existing schemes.
TrustControl: Trusted Private Data Usage Control Based on Security Enhanced TrustZone

The past decade has seen the rapid development of data in many areas.Data has enormous commercial potential as a new strategic resource that may efficiently boost technical growth and service innovation.However, individuals are becoming increasingly concerned about data misuse and leaks.To address these issues, in this paper, we propose TrustControl, a trusted data usage control system to control, process, and protect data usage without revealing privacy.A trusted execution environment (TEE) is exploited to process confidential user data.First of all, we design a secure and reliable remote attestation mechanism for ARM TrustZone, which can verify the security of the TEE platform and function code, thus guaranteeing data processing security.Secondly, to address the security problem that the raw data may be misused, we design a remote dynamic code injection method to regulate that data can only be processed for the expected purpose.Our solution focuses on protecting the sensitive data of the data owner and the function code of the data user to prevent data misuse and leakage.Furthermore, we implement the prototype system of TrustControl on TrustZone-enabled hardware.Real-world experiment results demonstrate that the proposed Trust-Control is secure and the performance overhead of introducing our prototype system is very low.
Keyword-Based Model of Teaching English for Adult Learners

This study discusses the keyword-based model in learning English to for adult learners who in fact have little free time due to various activities but require the ability to communicate in English. Researchers are triggered to make this model because of their weakness in English, so they often have problems communicating with foreign partners. The research method adopted in this study is a qualitative method with descriptive analysis. "Descriptive analysis is an analysis used to analyze data by describing the data that has been collected as it is without intending to make conclusions that apply to the public or generalizations." (Sugiono. 2016). The instrument used for data collection in this study is a questionnaire. The model's effectiveness test is also carried out by looking at the increase or decrease in the respondents' English understanding of English language. The respondents of this study are lecturers and officers at the Islamic University of North Sumatra which are determined using purposive sampling. The findings of this study is an effective and efficient keyword-based model for adult learners because with a keyword-based model, learners do not need a long time to spend in learning English, so it does not cost too much considering they have little free time.