Reviewerly/exHarmony huggingface dataset:
Instance Segmentation on Distributed Deep Learning Big Data Cluster

Abstract This paper presents workflow in deep learning inference models in a distributed manner. This is often required in production environments where real-time predictions need to be made on a massive scale., optimization approaches were applied to the instance segmentation YOLACT model to improve the inference throughput. , and the experiments show that the execution time taken by the OpenVino (Open Visual Inference and Neural Network Optimization) model with 16 FP is the least compared to ONNX (Open Neural Network Exchange) and the original PyTorch model. ONNX and OpenVINO provide a set of tools for optimizing deep learning models for deployment on a range of edge devices such as smartphones, laptops, tablets, IoT devices, sensors, and other devices that collect, process, and transmit data between the end user and the cloud or datacenter. The model optimizer in these frameworks plays a critical role in preparing deployment models, by reducing their size and increasing their performance.YOLACT in OpenVino format we run it on a big data cluster using the BigDL framework to apply inference on spark stand-alone and yarn clusters. where BigDL is a distributed deep learning library for Apache Spark that provides a high-level programming interface for defining and training deep neural networks. It supports a wide range of deep learning models and is designed to be scalable, making it ideal for large-scale deep learning applications. In distributed deep learning reference, the input data is partitioned and sent to multiple machines in parallel for processing. Each machine runs its portion and produces a partial output, which is then combined with the outputs of other machines to produce a final result. Spark stand alone perform better depending on the obtaining results under different conditions whereas significant speedups can be achieved by increasing the number of executors across the cluster.
XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters

In recent years, pre-trained language models have undergone rapid development with the emergence of large-scale models. However, there is a lack of open-sourced chat models specifically designed for the Chinese language, especially in the field of Chinese finance, at the scale of hundreds of billions. To address this gap, we introduce XuanYuan 2.0, the largest Chinese chat model to date, built upon the BLOOM-176B architecture. Additionally, we propose a novel training method called hybrid-tuning to mitigate catastrophic forgetting. By combining general-domain with domain-specific knowledge and integrating the stages of pre-training and fine-tuning, XuanYuan 2.0 is capable of providing accurate and contextually appropriate responses in the Chinese financial domain.
Deep Learning Approaches to Lexical Simplification: A Survey

Lexical Simplification (LS) is the task of replacing complex for simpler words in a sentence whilst preserving the sentence's original meaning. LS is the lexical component of Text Simplification (TS) with the aim of making texts more accessible to various target populations. A past survey (Paetzold and Specia, 2017) has provided a detailed overview of LS. Since this survey, however, the AI/NLP community has been taken by storm by recent advances in deep learning, particularly with the introduction of large language models (LLM) and prompt learning. The high performance of these models sparked renewed interest in LS. To reflect these recent advances, we present a comprehensive survey of papers published between 2017 and 2023 on LS and its sub-tasks with a special focus on deep learning. We also present benchmark datasets for the future development of LS systems.
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees

There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust counterfactuals, existing literature often assumes that the original model $m$ and the new model $M$ are bounded in the parameter space, i.e., $\|\text{Params}(M){-}\text{Params}(m)\|{<}\Delta$. However, models can often change significantly in the parameter space with little to no change in their predictions or accuracy on the given dataset. In this work, we introduce a mathematical abstraction termed $\textit{naturally-occurring}$ model change, which allows for arbitrary changes in the parameter space such that the change in predictions on points that lie on the data manifold is limited. Next, we propose a measure -- that we call $\textit{Stability}$ -- to quantify the robustness of counterfactuals to potential model changes for differentiable models, e.g., neural networks. Our main contribution is to show that counterfactuals with sufficiently high value of $\textit{Stability}$ as defined by our measure will remain valid after potential $\textit{naturally-occurring}$ model changes with high probability (leveraging concentration bounds for Lipschitz function of independent Gaussians). Since our quantification depends on the local Lipschitz constant around a data point which is not always available, we also examine practical relaxations of our proposed measure and demonstrate experimentally how they can be incorporated to find robust counterfactuals for neural networks that are close, realistic, and remain valid after potential model changes. This work also has interesting connections with model multiplicity, also known as, the Rashomon effect.
Improving the database on land use planning in Vung Tau city, Ba Ria - Vung Tau province

Vung Tau city is an area where management of land use planning (LUP) and urban planning is quite complicated and has been successful in building LUP database. However, this database still has limitations that must be addressed, including (1) lack of data layer on current land use for building LUP database, (2) incompleted LUP attribute database, (3) lack of metadata, (4) no connection between LUP database and digital legal materials and (5) no overlay cadastral database to data layer of current land use and LUP database. To solve these problems, this research used many methods such as: documents and data collection, inheritance, expert interview, data processing and analysis, mapping, GIS application, and IT applications. The results of this study showed that the structure of cadastral database of 8 wards was succesfully converted from existing structure (according to Circular 17/2010/TT-BTNMT) to the standard structure (according to Circular 75/2015/TT-BTNMT). The 2019 land inventory data was standardized and data layer of current land use for whole city was built. In addition, the LUP database (period 2010 - 2020) for Vung Tau was improved, including: supplementing attribute information, completing metadata, connecting LUP database with relevant digital legal records, overlaying cadastral database with LUP database and data layer of current land use. Briefly, our results could be a foundation to help Vung Tau manage land sustainably, contribute to successfully building national land database and meet the exploitation requirements of LUP information in accordance with the orientation of Ministry of Natural Resources and Environment for study area.
Volcanic ash threatens widespread disruption in Mexico

Headline MEXICO: Volcanic ash threatens widespread disruption
The Impact of Youtube on Iraqi EFL Learners’ Academic Achievements at Sawa Private University: A Critical Essay

The present essay aims at investigating the effects of the educational videos on the college students’ academic achievements. The present paper came after checking the students’ responses to the lecture’s questions submitted by the instructor to the students to see how far the students go with their responses following their tracking YouTube videos. The present essay was based on the quantitative method and data had been collected through a sample survey. The study has revealed that that watching educational videos affected the academic activities and performance of the respondents positively. It also has confirmed that most of the students preferred short animated to summarize the lectures that they were requested to be presented in the upcoming lecture.
Deployment of Co-operative Farming Ecosystems Using Blockchain

Blockchain has helped us in designing and developing decentralised distributed systems. This, in turn, has proved to be quite beneficial for various industries grappling with problems regarding a centralised system. So, we thought of exploring blockchain’s feasibility in the agricultural industry. India is a country where a large part of the population is still dependent on agriculture, however, there’s no proper system in use as yet that can help the farmers get the right price for their farm products and help the consumers get an affordable price for their needs.Thus, we propose a blockchain based decentralized marketplace where we will implement a collaborative model between farmers and consumers. This model will allow the farmers to record their potential crops and the expected output on decentralised ledger besides, enabling them to showcase their integrity and credibility to consumers. The consumers, on the other hand, can actually check everything about the farmers with the help of their information based on the previous supplies. This open and full proof digital market framework will thus reduce black marketing, hoarding, adulteration, etc. In this research paper, we have explored one possible blockchain model by creating a breach proof ledger of records. We have used solidity and ethereum frameworks for the working of the model.
Text Mining-Based Author Profiling: Literature Review, Trends and Challenges

Author profiling (AP) is a very interesting research field that can be involved in many application, such as, Information Retrieval, social network security, Recommender System, etc. This paper presents an in-depth literature review on Author Profiling (AP) techniques, concentrating on text mining approaches. Text Mining-based APs techniques can be categorized into three main classes: Linguistic-based AP, Statistical-based AP and a hybrid approach that combines both linguistic and statistic methods. Also, literature review shows the extensive use of classical Machine Learning and Deep Learning in this field. Besides, we perform in this paper a discussion of the presented models and the main challenges and trends in the AP domain.
Pengembangan Literasi Menulis Siswa MIS Darul Ulum Genengan: Pelatihan Penulisan Kalimat Efektif dan Penggunaan Tanda Baca

This community service aims to develop students' writing literacy in the form of training. The main focus of this community service is to train students to write effective sentences and apply punctuations. This training involved 15 grade VI students of MIS Darul Ulum Genengan who had taken the Minimum Competency Assessment (AKM). They were selected as training participants to follow up on the results of the AKM. The training was held for 6 meetings in the period from August 1–31, 2022. The training activities include: pretest (writing text); vocabulary identification; sentence analysis; punctuation analysis; vocabulary, sentence, and punctuation exercises; and posttest (writing text). These activities were carried out in groups and individually by applying attractive learning strategies and media. After participating in the training, 80% of students were able to reach a proficient level in writing. This result far exceeds the pre-training achievement; that is, 73% of students were only able to reach the basic level. With these results, it is highly recommended for the school to intensify literacy activities, especially writing. Those who are talented can continue to hone their skills, and those who still need special intervention can continue to develop their abilities.
Some problems of using the network form of educational programs’ implementation in educational institutions of higher education

Currently, to improve the quality of training of students, educational institutions of higher education use a network form of educational programs’ implementation. The use of the considered form by universities in practical activities led to the emergence of new problems and issues related to the process of educational activities within the framework of network interaction. This study aims to examine the existing problems in the legal regulation of network forms of educational programs’ implementation and propose ways to solve them in order to create advanced educational programs that allow training highly professional specialists through the use of the resources of several organizations. To reach this aim, the author analyzed the advantages and disadvantages of using a network form of education. On the one hand, there are numerous administrative barriers that prevent the use of a network form of educational programs; on the other hand, various practices of a successful implementation of network programs in universities are cited as an example. Particular attention is paid to the impact of the use of educational programs by universities in a network form on the development of regions. The author examines issues related to the licensing of educational activities of implemented network programs. The absence of a license to carry out educational activities by a participant organization that is a subject of economic activity does not allow it to implement part of the network program, share the skills and experience in the competence being mastered with students, but determines their participation in network training only through the provision of resources. As a way out of this situation, the author proposes to consider the possibility of implementing part of the network program without a license and make appropriate changes to the legislation. The author also touches upon problems in the field of international cooperation, considers the question of whether a foreign educational organization can be a basic organization, correlates these concepts with each other, emphasizes the need to integrate Russian higher education into the international educational space in terms of using online forms of implementing educational programs. The author also dwells on the problem of using state property in the implementation of network programs. She notes that, for the effective introduction of network programs into the activities of educational organizations, special attention should be paid to the issues of free use of property, to a detailed elaboration of the procedure for its use in regulatory legal acts. The study uses general scientific methods of cognition: a comparative legal method, methods of deduction, induction, analysis and synthesis. The result of the work is proposals for improving the current legislation regulating the issues of the network form of implementation of educational programs.
Pembuatan Web Unit Produksi Coffee Santri di Aziz Paradise

Along with the development of technology, the need for information can trigger services that are easier, faster and more practical, especially in doing business. Entrepreneurs today are competing with each other to innovate, almost all businesses, both medium and small businesses use technology to increase promotion of places and products. To attract buyers, an attractive website appearance is needed so that it can increase the attractiveness of buyers. In this study, we propose an attractive website display design using the Laravel framework and with a drink menu recommendation feature based on certain diseases and based on mood with the KNN Algorithm with the prototype method. To ensure that the website can be accepted by users, a system assessment is carried out from a user point of view using User Acceptance Testing (UAT). It was found that the system received an "Agree" rating after the UAT average score, meaning that the web-based application of the Coffee Santri Production Unit in Aziz Paradise succeeded in meeting the system standards desired by the user.
APLIKASI KASIR BERBASIS WEB UNTUK UMKM BAKARIN ROTI

The research was conducted to find out the cashier services for UMKM Bakarin Roti which were still done manually when inputting data, causing customer queues to pile up. In addition, the manual system has many weaknesses, including most errors in recording, and data search which is difficult because every time a seller's data is searched, he must search the ledger. There is no specific information that informs about the amount of stock, so it is not uncommon when the stock runs out, the shop owner will only know when a transaction process has taken place, thus disappointing the customer. Given these weaknesses, we need a web-based cashier application that can facilitate cashier data management. The method used is waterfall, in making applications starting from the analysis, design, coding, testing, evaluation, and maintenance stages.&#x0D;
Pengolahan Presensi Siswa pada Sistem Akademik SMK Alma Ata Majenang

The use of computer technology in data processing is necessary for the purposes of the learning process and administration in schools. Especially during a pandemic, every activity begins to depend on computer and network technology, namely the online system. In mid-2021, the government has permitted face-to-face learning (PTM) activities in schools by implementing health protocols. The attendance process at school still uses the handwritten method so it takes a lot of time, while the learning time is quite short. In addition, manual attendance still has various weaknesses including data that is piled up and data that is prone to loss. Therefore, a web-based student attendance application was created by utilizing the QR-Code scan feature to mark student attendance. The web-based Student Presence application using the QR-Code was developed using the Rational Unified Process (RUP) methodology with analysis and design tools using UML, and using HTML and PHP to code it. This research resulted in a web-based and QR-Code student attendance application, where this attendance application can be utilized in managing student attendance in face-to-face learning at school. The benefits of the Web-Based and QR-Code Student Presence application are to reduce physical contact and gathering time in the classroom during a pandemic so that it can reduce the risk of transmission of the Covid-19 virus and assist the process of reporting student attendance for the assessment process.
Encryption in India: Preserving the Online Engine of Privacy, Free Expression, Security, and Economic Growth

This article argues that the traceability mandate imposed in India by the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 undermines encryption and negatively impacts cybersecurity as well as the fundamental right to privacy. In doing so, it explains how the traceability requirement fails the necessity and proportionality test laid down by the Indian Supreme Court in the Puttaswamy judgment, wherein it held that the right to privacy is a fundamental right under the Constitution of India. Further, the article makes a case for why encryption is important for protecting privacy, free expression, and other human rights, and also for bulwarking the economy, preserving democracy, and ensuring national security. Part I of the article provides a background on how encryption works and the purpose it serves in the digital era. Part II analyzes the trajectory of encryption policy in India and the relevant legal frameworks. Thereafter, Part III explains the traceability mandate under the New Intermediary Guidelines and its effect on encryption, and consequently, the impact on cybersecurity and the right to privacy. It assesses whether it meets the requirement of necessity and proportionality as set out by the Supreme Court. Finally, Part IV explains that encryption should be protected and encouraged because it guards against unwarranted surveillance and preserves privacy and expression, is a crucial tool to protect human rights in the digital age, strengthens national security, and benefits the economy
GazeCast: Using Mobile Devices to Allow Gaze-based Interaction on Public Displays

Gaze is promising for natural and spontaneous interaction with public displays, but current gaze-enabled displays require movement-hindering stationary eye trackers or cumbersome head-mounted eye trackers. We propose and evaluate GazeCast – a novel system that leverages users' handheld mobile devices to allow gaze-based interaction with surrounding displays. In a user study (N = 20), we compared GazeCast to a standard webcam for gaze-based interaction using Pursuits. We found that while selection using GazeCast requires more time and physical demand, participants value GazeCast's high accuracy and flexible positioning. We conclude by discussing how mobile computing can facilitate the adoption of gaze interaction with pervasive displays.
Identification of Network Vulnerabilities through IISRA Framework

Abstract: As the globe transitions to complicated networks and as we move towards digitization, its worth is rising daily. Working in an organization across a network and the internet creates vulnerabilities. As is well known, data is a crucial component of every organization and must be safeguarded from threats. The attackers' job is to try and take advantage of the networks by using these weaknesses. When organizations use the Internet, intranets, and related technologies more frequently, system security becomes one of the key considerations. Network security protects computer systems from unauthorized threats and breaches, which lowers the likelihood that confidential information may be stolen. The organization will feel more secure if these vulnerabilities are closed up in the systems and network well in advance of an attack. The availability of numerous technologies for network vulnerability assessment enables organizations to thwart potential attacks. In this research paper, we have developed an Integrated Information Security Risk Assessment (IISRA) Framework for identification of Network Vulnerabilities. We have implemented this IISRA Framework on real computing environment of an organization.
AI-Based Sign Language Recognition System for Deaf and Hard-of-Hearing Individuals

Abstract: Sign language is a vital form of communication for the deaf and hard-of-hearing community. The development of sign language recognition systems plays a crucial role in bridging the communication gap between hearing and non-hearing populations. This paper presents a Sign Language Recognition System (SLRS) built using the MediaPipe Hands model and web technologies such as HTML, CSS, and JavaScript. The system utilizes real-time hand tracking and gesture recognition to interpret sign language gestures and provides corresponding textual output. The proposed system demonstrates the potential for accessible and inclusive communication through the integration of machine learning and web-based technologies.