Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Event Recognition in Aerial Imagery

In this paper, we investigate event recognition for aerial surveillance. This is a significant task especially when we consider the growing popularity of UAVs. The main purpose of the paper is to detect events both at the clip level in aerial videos and also at the frame level in aerial images. To achieve this goal, novel deep learning models and training techniques are used. In this work, we propose new model architectures to detect events in both image and video domains. The developed models are tested on the ERA dataset. Results show that the proposed models achieve state-of-the-art performance on both single images and aerial video clips of the ERA dataset.
Cross-Market Recommendation with Two-Stage Graph Learning

Cross-market suggestion is a technique used by social media, e-commerce platforms, and other online platforms to suggest to users' goods or services from several markets or domains. However, user engagement data (clicks, sales, and reviews) with products reveals various biases specific to certain markets, making recommendations more difficult. On the other hand, the lack of data in other markets can make it challenging to train models. Recently, the FOREC model that applies market adaptation has shown good performance on cross market recommendation problem. In this paper we propose a combined framework that employs the Light Graph Convolution Network (LGCN) algorithm, which has both market-agnostic and market-specific models in learning cycle like FOREC but has a less complex architecture than it. The experimental results reveal that our two-stage strategy outperforms FOREC's all findings with improvements ranging from 5 to 8 percentage points with the help of an enhanced 1 to 2 percent of the market-agnostic phase in terms of nDCG@10 evaluation.
Futures of Care: Care Technologies and Graphic Medicine

abstract: Assistive care technologies, developed to replace, support, or extend human capabilities and to address the surging demands of care, have been gaining prominence recently. The current trend summons a posthuman approach through decentering the privileged role of humans in several spaces of caregiving, such as hospitals and eldercare homes. The existence of these cutting-edge assistive technologies, exciting as they are, hints at a possible future when the distinction between humans and technology will be blurred, thus transforming care relations. However, these technological advances carry equal promises and dangers. While care robots may reduce the burden of caregiving, they also threaten to minimize human contact with vulnerable populations. This critical assessment reviews technological advances in care and close-reads several single-panel cartoons to theorize the impact of technologies on caring relations. The article also examines the neoliberal underpinnings of such technologies and the moral dangers of their unreflective use.
Digitality and Political Theory

Abstract Digitality is increasingly central to individuals’ existence, which has political implications. The article maps the political implications of digitalisation, focusing on African political thought. The latter is marked by Afro-communitarianism ideas, which foster solidarity, relationality, and communalism as foundational values of the polity. However, African communitarianism has granted little attention to contemporary phenomena such as digitalisation. Also, political theory discussions on digitality have looked mainly at (neo)liberal contexts. How the digital age is reshaping the tenets of communitarian political theories represents an underdiscussed issue. This article outlines a research agenda on digitality and African political thought. New digitality-enabled relational modes change human and political interactions. The issue at stake is how these new modes challenge or strengthen the Afro-communitarian political outlook. This article recognises digital-humanism, political community, relations of power as central matters of inquiry. The analysis relies on bibliographic sources from African philosophy and comparative political theory.
Insights Into Reconstructing the History of the Making of Ivan Franko’s Collected Works in 25 Volumes

The article outlines the stages of the history preparing an edition of Ivan Franko’s works in 25 volumes by systematizing literary studies and studying archive sources. The events are described in organic connection with historical and socio-political circumstances, which made it possible to show the meanders of Franko’s legacy reaching his readers. Some of the unknown documents are introduced into scientific circulation. The research methods of choice were textual and source analysis and synthesis. The use of these tools made it possible to establish little-known facts about the history of the collected works, trace the announcement of the contents of the individual volumes in the press, analyze the obstacles encountered, and find out the reasons for the sudden collapse of this publishing initiative.
Patologi Sosial dalam Novel Kado Terbaik Karya J.S. Khairen dan Relevansinya dengan Kejahatan di Indonesia

Literary works are a result of the creativity of a literati, literary works can be in the form of novels, poems, and so on. This study examines social pathology in the novel Best Gift by J.S. Khairen. This study uses the study of literary sociology. Sociology discusses how a science in which is discussed how people's lives. Literary sociology is a science that links literary works with sociology. In research conducted in the novel Best Gift by J.S Khairen, the social symptoms studied are bad social behavior. In the novel Best Gift by J.S Khairen, it describes how the social dynamics experienced by the main character. This study aims to describe the form of social pathology and the factors that influence the occurrence of social pathology in the novel Best Gift by J.S. Khairen. This research data is in the form of sentence quotes which are forms or factors of social pathology in the Best Gift novel by J.S. Khairen. Data collection techniques use reading techniques and recording techniques. Data analysis techniques use data reduction, data presentation, and drawing conclusions. This research data validity technique uses theory triangulation. From this study found the form of social pathology and the factors affecting it. Forms of social pathology in J.S. Khairen's novel Best Gift include child exploitation, child abuse, fraud, drug sales, theft, kidnapping, and drug sales. The factors that influence social pathology in the novel Best Gift by J.S. Khairen are family, social environment, economic and educational factors. Keywords:novel best gift, literary sociology, social pathology.
A Secure Cardless Automated Teller Machine

Automated Teller Machines (ATM) are critical to banking operations due to their convenient and widespread use. Apart from queues reduced in banking halls, bank clients can still access services outside the regular operational hours. However, a rise in ATM fraud cases has led to banks improving ATM security by adding security ATM keypad covers, surveillance cameras, and even ATM security booths, measures that were not there at ATMs' inception. The academia is also replete with literature on various ATM designs to enhance the security of the facility and the user. However, proposed solutions rarely build on existing infrastructure and technologies, making them difficult to implement as they require additional equipment. For IoT-based solutions, testing is relegated to the communication of components without evaluating the practicability of the proposed solution. In an effort to correct these challenges and secure an ATM user, this paper introduces a conceptual design of a secure cardless ATM system that leverages existing technologies and infrastructure with little additional costs for implementation. A workshop was then used to identify improvements for future implementation. Recommendations from stakeholders and lessons learned from the workshop are discussed.
An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication

The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future.
An Efficient Security Solution for IoT and Cloud Security Using Lattice-Based Cryptography

This study introduces lattice-based cryptography to enhance security in IoT and cloud systems. It focuses on two fundamental challenges in lattice-based encryption. The paper compares the advantages and disadvantages of lattice-based encryption with traditional RSA and elliptic curve cryptography. Furthermore, it presents a novel approach called “Lattice-based secure data aggregation,” specifically designed for IoT and cloud systems, ensuring confidentiality and integrity during data aggregation. The paper covers various aspects, such as data encryption, aggregation, decryption, and implementation on resource-constrained devices. Performance and security evaluations compare the proposed algorithm with other data aggregation techniques, demonstrating its effectiveness against attacks, computational efficiency, and low memory requirements. The paper also addresses the approach's limitations and proposes potential areas for future research. Overall, the proposed lattice-based cryptography offers a promising solution for privacy and security-sensitive IoT and cloud systems, making it a viable option for widespread adoption.
GasOptiScan: Unveiling Gas-Inefficient Smart Contracts via Loop Fusible Pattern Detection for Enhanced Cost Efficiency

Gas inefficiency in smart contracts deployed on the Ethereum blockchain can lead to unnecessary expenses due to high gas fees. In this article, we address this issue by proposing GasOptiScan, an innovative web application tool to automatically detect all the possible Fusible Loop Optimization (FLO) patterns considering multiple loops at the same time within gas-inefficient code present in smart contracts. The findings revealed that a range of operations, such as comparisons and conditional jumps, can be minimized to reduce the number of opcodes and the associated gas fees. GasOptiScan harnesses cutting-edge static code analysis techniques to equip developers with valuable fusible loop pattern recommendations to optimize gas usage and minimize transaction costs in smart contract execution. With GasOptiScan's insightful analysis, developers can make informed optimizations that result in potential cost savings, ultimately enhancing the overall performance and affordability of executing smart contracts on the Ethereum blockchain. By leveraging techniques such as hexadecimal conversion, graph analysis, block detection, and variable identification, the tool identifies fusible blocks with varying upper bounds and lower bounds to minimize gas fees during contract execution. The GasOptiScan tool is designed with user-friendliness in mind, ensuring a seamless and intuitive experience for developers and enabling them to optimize their smart contracts and maximize efficiency on the Ethereum blockchain.
Identifying Sexual Predators in Chats Using SVM and Feature Ensemble

Cyber grooming is a compelling problem worldwide nowadays since people spend most of their time online. All of the reports strongly suggested that it becomes very urgent to tackle the online child grooming problem in order to protect children from sexual exploitation. Automatic sexual predator identification can be a promising solution to this issue since the number of online conversations is too large to be monitored manually. In this work, a two-stage is proposed with a combination of several features. The first stage is for detecting the predatory conversations while the second step aims to distinguish the predator from the victim in the predatory conversations. The features ensemble used will be combining lexical and behavioral features. The lexical features used include BoW, POS-based, topical, and emotion-based. Meanwhile, the behavioral features used for this work include the number of messages, the average number of words, the number of exclamation marks, the number of questions, sentence complexity and readability, and the number of intentions. SVM was used as a classifier due to its good ability for many text classification tasks. The experiment result shows that BoW with tf - idf term weighting provided the best performance for both PCI and VPD tasks. BoW with tf- idf term weighting obtained an F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> .5 score of 0.9893 on PCI and 0.9798 on VPD. The features ensemble can exceed most of the individual features that form it but still cannot beat BoW.
Harnessing Advanced Classifiers for Sentiment Analysis on Augmented Tweets

The utilization of the internet has undergone rapid growth over the years. An increasing number of businesses find it imperative to establish an online presence to engage with a broader spectrum of potential customers. In tandem with this trend, the adoption of social media has undergone exponential expansion. Brands spanning various sectors must grasp their customers' perceptions to optimize their operations, enabling them to provide pertinent content and products to their clientele. Twitter emerges as an effective avenue for gathering insights from customers. This study curates a dataset from Twitter encompassing South African Mobile Network Operators. The acquired data is enhanced using the Valence Aware Dictionary and sEntiment Reasoner (VADER) lexicon-driven approach. The subsequent training leverages Support Vector Machine and Naïve Bayes techniques. Through the application of diverse vectorization methods, it becomes evident that both models exhibit heightened accuracy when utilizing the Term Frequency - Inverse Document Frequency (TF-IDF) technique. Among the two models, the Support Vector Machine notably achieves the highest accuracy rate, standing at 75%.
Blockchain Smart Contracts for Policy Compliance: A Healthcare Perspective

Access to an electronic health record (EHR) is very heavily regulated through many different types of policies: federal-level policies, state-level statutes, and even local and organizational-level policies, which may, for example, include procedures to follow at the organization level to ensure compliance with other policies. In addition, individual patients can also establish agreements with their healthcare providers to express their consent related to access to or sharing their protected health records. When such policies are adequately specified and implemented, they go a long way toward protecting EHR data. However, research has shown that there are often significant problems or gaps in policy compliance that are not identified until after a breach. Further, a recent study shows that subcultures within a healthcare organization influence whether employees violate policies, perhaps unintentionally. These observations motivate us to revisit the compliance and provenance aspects of policies. This paper proposes a blockchain-powered contract-based access control model for enforcing patient-provider agreements (PPAs) and other applicable policies and attributes for policy compliance and provenance in the healthcare sector. Blockchain smart contracts appear to be a promising new technology that can potentially ensure that policies are enforced. In addition, the immutable storage property of blockchains and strong integrity guarantees give hope that an adequate trail for policy compliance (or non-compliance) can be maintained, thus facilitating provenance. The average PPA integrity writing cost for the deployed smart contract is 0.00059 Ether (1.06666 USD). The average reading time is 0.152864 seconds to verify PPA integrity on the blockchain network. Writing gas consumption and reading time are feasible for the proposed approach.
“Hastabhashya”-Interpretation of Indian Sign Language using SqueezeNet and YoloV3

The primary means of communication for persons who are hearing and voice-impaired is sign language, which is sadly not understood by a significant section of the public. To solve this problem, a naïve method is offered that converts sign language symbols into comparable plain text alphabets almost instantly using image processing and deep learning principles. Beyond providing healthcare for the deaf and dumb community, this technology has the potential to evolve into a general-purpose symbol-to-text converter with a variety of uses. The difficulties with character classification for Indian Sign Language (ISL) is tried to solve in this paper and work. Even though American Sign Language (ASL) has been the subject of much research, this cannot be said of Indian Sign Language (ISL) due to the absence of standardized datasets, obscured aspects, and regional variances in the language. ISL research has been hampered by these issues, but our initiative aims to fix them.
Exploration of Algorithms for the Hardware Acceleration of Multi-Scalar Multiplication on FPGA

In this paper, we present the exploration of algorithms for the hardware acceleration of multi-scalar multiplication (MSM) on field programmable gate arrays (FPGAs). We have aggregated Verilog and System Verilog implementations of popular algorithms for each component in the MSM processing stack, including large integer multiplication, modular reduction, and elliptic curve point addition, doubling, and scalar multiplication. Additionally, we have compared these algorithms in the context of MSM and evaluated their performance. Our results highlight the efficiency of application specific hardware over general purpose processors for computationally intensive operations. Our contribution provides a valuable resource for those interested in using hardware acceleration to improve the efficiency of zero knowledge proof systems.
An Analysis of the Implementation of Blockchain Technology in Smart Grid Edge IoT Devices

A smart grid provides a reliable power supply with fewer and fleeting outages and self-recovery power systems, using digital information, automated control, and autonomous systems. Smart grids are more efficient, providing reduced total energy use, reduce peak demand, reduced energy losses, and the ability to induce end-user use reduction instead of new generation in power system operations. The smart grid offers several benefits such as reduced costs. However, with the increase of cyber-attacks worldwide smart grids are becoming key targets. The degree of risks from cybersecurity threats that smart grids are facing has grown rapidly in recent years. The increasing threats place sensitive data and organizational security at risk. This is exacerbated by using IoT IoT-based grid. The smart grid contains sensitive data, and power system data which are confidential. If intruders manipulate the smart grid, it can result in catastrophic damage and bring disaster to society. Sensitive data and organizational security are exposed to danger because of the growing threats. Therefore, the main objective of this paper is to analyze the implementation of a blockchain layer of security to address various security problems in IoT devices on smart grid edge computing. A Desktop review was employed in collecting, organizing, and evaluating available information to gain a deeper insight into the subject matter from various relevant sources. The findings of the research show that although blockchain technology has the potential to manage a variety of security issues in smart grid edge IoT devices, total security cannot be achieved by the implementation of blockchain technology alone; policies regulations, and skills development should also be put in place.
Strategies for Developing Acoustic Model to Pronounce Names in Low-Resourced Languages

Names in Namibian culture are deeply rooted and carry profound meaning, reflecting a person's language, culture, and identity. Incorrect pronunciation distorts the meaning and can be offensive to native speakers. The preservation of indigenous knowledge and the pride associated with family names motivate the development of tools for accurate pronunciation. Namibian names are rooted in indigenous languages, hold cultural significance, convey identity and history. Incorrect pronunciation of names is a widespread issue, and the lack of tools to aid in correct articulation poses challenges. This study aims to describe the strategies involving review, methodology, implementation and evaluation guidelines for developing an acoustic model to pronounce low-resourced Namibian names in Oshiwambo, Otjiherero and Khoekhoe gowab languages. The study utilizes literature survey methodology to focus on Bantu Namibian names, including those with click sounds from the Khoekhoegowab language. In the implementation guidelines, it was recommended that ccommonly mispronounced names, including those with click sounds, should be collected through an online survey, thereafter, users will generate a list of phonemes and record the pronunciations for each name. These names, phonemes and recordings would then be used to construct an acoustic model, which is a key component of the pronunciation tool. This research contributes to providing guidelines associated with providing tools for proper name pronunciation in a multilingual society, with a specific focus on Namibian names. In conclusion, the development of an acoustic model for accurate Namibian name pronunciation holds significant implications for the Namibian community. It promotes cultural appreciation, intercultural understanding, linguistic preservation, individual and institutional benefits, and the broader society.
A Survey on Automatic Discover Approach by Using Static Analysis for Smart Contract Vulnerability

Smart contract runs on blockchain platforms and plays a critical role in decentralized applications. Unfortunately, since smart contracts manage valuable digital assets, attacks against them can result in substantial economic losses. Especially, most of the attack are carried out by exploiting the vulnerabilities in smart contracts. Aiming to perform efficient and comprehensive identification for contract vulnerabilities, there emerges a number of research on smart contract security and vulnerability detection. This paper provides a comprehensive surveys on various smart contract vulnerabilities and corresponding detection methods proposed in recent years. Meanwhile, this paper implements an automatic discover approach by using static analysis for smart contract vulnerability, which can effectively identify and localize vulnerabilities within contracts. Experimental results show this method can precisely locate and classify contract vulnerabilities.