Reviewerly/exHarmony huggingface dataset:
Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm

Abstract Evacuation routes are necessary to guide people to avoid hazardous and dangerous zones and to prevent the loss of human lives, especially in the event of volcanic eruptions. This article attempts to examine the evacuation routes of the sister village program by using the ant colony optimization (ACO) algorithm. Model simulations and calculations of the ACO algorithm were done by aggregation of the five determined parameters including distance, speed, hurdle, density, and secure point. The validation of the model was carried out by the examination of the five most prone disaster villages located in Mount Merapi that are interconnected as sister villages in Sleman Regency, Yogyakarta Province, and Indonesia. This research is important to ensure that the sister village evacuation system is effective in reducing the impact of the risks posed by the eruption of Mount Merapi. Based on the results, sister village evacuation systems are proven to be the fastest and safest routes by the ACO examination
A High-Efficiency Modular Multiplication Digital Signal Processing for Lattice-Based Post-Quantum Cryptography

The Number Theoretic Transform (NTT) has been widely used to speed up polynomial multiplication in lattice-based post-quantum algorithms. All NTT operands use modular arithmetic, especially modular multiplication, which significantly influences NTT hardware implementation efficiency. Until now, most hardware implementations used Digital Signal Processing (DSP) to multiply two integers and optimally perform modulo computations from the multiplication product. This paper presents a customized Lattice-DSP (L-DSP) for modular multiplication based on the Karatsuba algorithm, Vedic multiplier, and modular reduction methods. The proposed L-DSP performs both integer multiplication and modular reduction simultaneously for lattice-based cryptography. As a result, the speed and area efficiency of the L-DSPs are 283 MHz for 77 SLICEs, 272 MHz for 87 SLICEs, and 256 MHz for 101 SLICEs with the parameters q of 3329, 7681, and 12,289, respectively. In addition, the N−1 multiplier in the Inverse-NTT (INTT) calculation is also eliminated, reducing the size of the Butterfly Unit (BU) in CRYSTAL-Kyber to about 104 SLICEs, equivalent to a conventional multiplication in the other studies. Based on the proposed DSP, a Point-Wise Matrix Multiplication (PWMM) architecture for CRYSTAL-Kyber is designed on a hardware footprint equivalent to 386 SLICEs. Furthermore, this research is the first DSP designed for lattice-based Post-quantum Cryptography (PQC) modular multiplication.
Ensemble-Based Short Text Similarity: An Easy Approach for Multilingual Datasets Using Transformers and WordNet in Real-World Scenarios

When integrating data from different sources, there are problems of synonymy, different languages, and concepts of different granularity. This paper proposes a simple yet effective approach to evaluate the semantic similarity of short texts, especially keywords. The method is capable of matching keywords from different sources and languages by exploiting transformers and WordNet-based methods. Key features of the approach include its unsupervised pipeline, mitigation of the lack of context in keywords, scalability for large archives, support for multiple languages and real-world scenarios adaptation capabilities. The work aims to provide a versatile tool for different cultural heritage archives without requiring complex customization. The paper aims to explore different approaches to identifying similarities in 1- or n-gram tags, evaluate and compare different pre-trained language models, and define integrated methods to overcome limitations. Tests to validate the approach have been conducted using the QueryLab portal, a search engine for cultural heritage archives, to evaluate the proposed pipeline.
Resourcing Small Indigenous Languages in the Field

Small indigenous languages of the world are often spoken in regions where literacy rates are low and access to digital services is limited. Whereas voice information services could be useful for low literate speakers of these languages, written and audio data from these languages are hardly available. This paper presents a method for resourcing indigenous languages, with the aim to build targeted voice services based on an automatic speech recognition system. The proposed method for data collection has been co-developed with native speakers of an indigenous language, dagbanli and validated in the field, through a case study in northern Ghana.
Secure and Explainable Artificial Intelligence (XAI) in Cloud Ecosystems: Challenges and Opportunities

The widespread adoption of artificial intelligence (AI) in cloud ecosystems has revolutionized various domains, offering significant advantages such as improved efficiency, scalability, and cost-effectiveness. However, the use of AI also poses significant security risks, and the need for more transparency in AI models makes it difficult to understand how decisions are made. This paper reviews recent research efforts toward developing secure and explainable AI (XAI) for cloud ecosystems, highlights the critical need for secure XAI in cloud ecosystems, and explores the challenges and potential solutions. The paper also discusses a case study involving the security of cloud-based health care with intrusion detection systems (IDS). Finally, the paper concludes with open research directions in this domain and their potential impact on cloud ecosystems.
Probing the Role of Information and Communication Technology (ICT) in Enhancing Research: An Epilogue of Accessible Research Tools

Information and Communication Technology (ICT) has revolutionized the way researchers conduct their work. It has enabled them to access a wealth of information through online databases, collaborate with colleagues across the globe, and analyze vast amounts of data quickly and accurately. This paper explores the role of ICT in enhancing research tools, highlighting the benefits it provides to researchers in terms of increased efficiency, improved accuracy, and greater access to resources. It also discusses some of the challenges associated with using ICT in research, such as data security and privacy concerns, and offers potential solutions. Overall, the paper concludes that ICT is an essential tool for researchers and will continue to play an increasingly important role in advancing scientific knowledge and innovation.
A Cross-sectional Analytical Model for Cloud-Based ERP

This is well-known fact that to improve business processes, upsurge efficiency, and improve the decision-making capabilities, there is necessity to design and implement a cross-analytical model. Similarly, in the present context the role of cloud computing is increased to utilizing ERP with cloud support. This implicates opinion from stakeholders and users, identifying business processes that require improvement, data sources for integration, and analytical tools for data analysis. After that, next step is to customize the ERP system to meet organizational needs, including workflows, user access, and permission configurations. To standardize the integrated approaches into the ERP system, there is need of development of a model. An analytical model is developed using appropriate analytical tools, designing the model, and developing algorithms and statistical models. The analytical model is then tested and validated by running test scenarios, validating results, and making necessary adjustments. The present study tries to give a cross-analytical model to manage the cloud-based ERP system.
Autograder: A Feature-Based Quantitative Essay Grading System Using BERT

Automated essay grading has become an important area of research in natural language processing. In this paper, we present a new approach for essay grading using BERT language model with Convolutional Neural Networks and Long Short-Term Memory networks on the Automated Student Prize Assessment dataset. The proposed essay grader evaluates essays based on several writing traits such as grammatical mistakes, semantics, coherence, prompt relevance, and others and provide a score for each trait. We evaluate the performance of our model on a fairly large dataset of essays and compare its performance with existing state-of-the-art models. Our results demonstrate the effectiveness of our proposed approach for automated essay grading, achieving high accuracy and improving on the performance of existing models.
Data Security in a Cloud Environment Using Cryptographic Mechanisms

Internet-based host service delivery is referred to as “cloud computing”. The essential necessity of such applications, where data are being exchanged or transmitted between the servers and their users, is the secure data storage on cloud environments. In this research, we use cloud-based cryptography, one of the best methods for secure communication. Writing or generating codes to allow for information confidentiality is known as cryptography. This research proposed a method of plain text and key encryption to protect data security in cloud computing. The suggested system offers data concealing and information-theoretic security. Due to its benefits, including cost-savings, resource sharing, widespread network access and simplicity of management, cloud computing has become quite popular among various enterprises. By maximising shared use, it improves the capabilities of physical resources. In a shared environment where several customers are joined together, the valuable objects of the clients (data and applications) are transferred outside of the purview of regulatory oversight. Unfortunately, this procedure raises security issues like the theft of confidential information and the disclosure of personally identifying information. By creating a range of technologies to safeguard cloud data, including encryption, several academics have helped to lessen the issue of data security in cloud computing.
Survey on Implementation of Machine Learning in Cloud Security

The need to access data quickly without increasing the physical memory in devices like computers and mobile made big IT firms, IT persons and individuals move to cloud computing, but the confidentiality of data is the main concern. There are various stages of data travel while stored onto the cloud and many breaches between these stages of data storage. Many advanced technologies can secure data on the cloud; machine learning technique is one of them. The machine itself will manage the security with less human involvement. This paper is focused on cloud computing and the implementation of machine learning (ML) techniques and models in the field of cloud security to protect data from threats and malware.
Recent Advances in Semantic Segmentation for Sports Analytics

Recent advances on deep learning has enabled significant progress in the field of Sports Analysis. Semantic segmentation is a technique that involves dividing an image or video into multiple segments or regions and labeling each segment with a class or category. In sports, semantic segmentation can be used for action recognition, pose estimation, and athlete tracking during competitions or training sessions. This has the potential to revolutionize how we analyze and understand athlete performance, leading to better training strategies, injury prevention, and performance optimization. This paper provides a survey of image segmentation techniques applied in various sports, including their applications and limitations. It also proposes a model that addresses the concerns discussed above by using a framework that takes advantage of the full range of information available, a PSPNet model for precise localization, metamorphic testing for evaluating model behavior, and explainability to understand the factors influencing model predictions. By exploring current applications of semantic segmentation, we hope to encourage further research in this area and facilitate the adoption of these techniques in the sports industry words.
Meta-Analysis of Popular Encryption and Hashing Algorithms

Data is any information that is useful, and we have a variety of resources to protect it. In order to stop illegal access to computers, personal databases, and websites, data security entails upholding digital privacy protections. Confidentiality, authenticity, integrity, and nonrepudiation are all elements of the modern information security system scenario. Cryptography, which offers the capability to safeguard users using various kinds of encryption and decryption methods, is the answer to this specific challenge. We employ compression to protect the data so that it uses fewer bits and takes up less disc space, ultimately costing less money. One of the most essential procedures is encryption, which enables us to conceal the true nature of data so that only the decryption procedure can reveal the original data. Encryption helps to protect data from unauthorized access. The data is encrypted and decrypted using a variety of symmetric and asymmetric key-based techniques. There is another way that has been popularized in recent times which consists of the use of hashing. Hashing offers better security and reliability than encryption-decryption algorithms.
Bluetooth Automated Wheelchair

The wheelchairs may all move inside and around manually. But a smart wheelchair gives a person independence and ease. A smart wheelchair is a mechanically controlled vehicle that can move on its own when a simple head or hand command is given by the user. As a result, using the wheelchair’s wheels requires less effort from the user. Moreover, this enables people who are physically or visually handicapped to move between two locations. Even if a person’s body is completely or partially paralyzed, only the movement of his head or hands will allow a wheelchair to go forward, backward, left, and right. The wheelchair and the human are in wireless communication. He keeps the apparatus that detects human movement on his head or in his palm. The chair will automatically move forward if the device detects a head swing. The process is comparable to another such as left, right, and backward. If the head is stationary and not moving, it is regarded as having stopped. The experimental version of our product works by pairing the mobile Bluetooth with the Bluetooth module installed in the wheelchair. The Arduino Uno supports Bluetooth and wheel movement and detects human movement.
Blockchain-Based Secure Cloud Data Management: A Novel Approach for Data Privacy and Integrity

Due to its potential to increase trust, security, and transparency across numerous industries, decentralized apps and smart contracts are growing in popularity. A network of peer-to-peer computers called Inter Planetary File System (IPFS) enables decentralized content exchange and storage. As smart contracts allow for the automated and safe execution of established rules and logic, they can improve IPFS functionality when implemented on blockchain networks. This study compares the support for smart contracts on IPFS provided by various blockchain networks. The comparison comprises Polkadot, Binance Smart Chain, Solana, and Ethereum. When utilized to deploy and carry out smart contracts over IPFS, these networks’ scalability, security, and transaction costs are the main subjects of the study. A thorough literature examination of the various blockchain networks and the elements connected to them, including as consensus methods, gas costs, and IPFS compatibility, forms the basis of the research technique employed in this study. The study’s conclusions show that each blockchain network has different strengths and shortcomings when it comes to enabling smart contracts on IPFS. Despite having higher transaction costs, more established networks like Ethereum and Solana provide greater degrees of security and scalability. Although it has cheaper transaction costs than Ethereum and Solana, Binance Smart Chain is less secure. Although less mature and more sophisticated than Polkadot, it can handle numerous parallel chains and offers greater compatibility with other the use of blockchains.
NICE: Navagraha Iconography Classification Engine

India is a country with a very rich cultural legacy. Its religion plays a significant role in shaping its customs and traditions. Navagraha gods are a group of nine celestial bodies that are revered in Hinduism. They are believed to have a significant impact on human destiny and are worshiped to seek their blessings and mitigate their negative effects. Throughout recent years, numerous architectural sculptures, figurines, statues, and inscriptions of these Navagraha gods have been unearthed and discovered in India. Recognizing entities in these sculptures presents a highly challenging and complex task in the domain of image recognition and classification. Our idea is based on a manually compiled database of images of the nine Navagraha gods in separate folders that are to be classified. Each image has a unique feature that sets it apart from others, and its orientation, angle, size, and color are crucial in the model's processing. The images were captured from different angles, sizes, colors, and orientations. The model will be trained on the image dataset. Deep learning plays a very important role in image classification and recognition. The effectiveness and accuracy of the suggested model can be evaluated using Resnet50 which is a convolutional neural network architecture. Additionally, many other important libraries such as NumPy, Matplotlib, Keras have also been used in this process of image recognition and classification. The model which this project has helped trained has propelled an accuracy of 100% on similar images with the help of Resnet50 model.
Hardware Architecture of Reinforcement Learning for Edge Devices

Q learning algorithm is a part of classic reinforcement learning algorithm. The Q leaning algorithm has a wide range of applications in the fields of robotics, traffic control, industry automation, etc. In this paper, a Q learning algorithm is implemented for an environment having nine states and four actions. This algorithm is used to determine best path that agent can take in an environment by maximizing reward value for each iteration of state and action pair. The process of maximizing reward function makes use of Markov decision process equation with Bellman equation. Using this equation Q table is updated for each iteration, rewards are awarded for each state action pair. Based on the updated Q table, agent determine the best path that the agent can take.
A Study and Overview on Current Trends and Technology in Mobile Applications and Its Development

Nowadays, everyone uses a mobile phones. Everything is embedded in the mobile phones. Day by day numbers of features are introduce in the mobile phone as well as in mobile applications too. Peoples are using mobile phone and mobile application to simplify their daily life task. It has been discovered that smartphone apps are becoming an extremely crucial aspect of people’s lives. As we can see, mobile applications are used in almost every industry. Different technologies are used in different mobile applications. In this paper we are focusing on currently available of different types of mobile applications in the market. In this paper we discussing currently which kind mobile applications and technologies are widely are used in the market? We also talk about how much effort and money will be spent developing mobile apps. The objectives of this review paper are to understand the usage of smartphones and mobile data, understand current trends in mobile applications, understand mobile application development life cycle, understand mobile application development time and cost, and understand revenue generated from mobile application. According to the survey, India has the most smartphone usage, and mobile data rates are quite low, the Android and iOS platforms are used to create the majority of mobile applications. In mobile applications, technologies such as geo-based location, augmented reality, beacons, and the Internet of Things (IoT) are commonly used.
Strengthening the Privacy of Blockchain with Zero Knowledge Proof Case Study: Online Exam Student Verification

Blockchains provides a decentralized, distributed and immutable ledger which is verifiable and digitally available over a network, bringing in a technological revolution in this fast-forwarding digital world which helps in building smart supplychains, eGovernance, eHospital, etc. Nevertheless, blockchains are bound by divergent issues such as scalability, security and privacy. Privacy issues are of great concern these days, when every Nation is fighting against various potential privacy hackers and safe guarding the data from various types of heists. Despite numerous endeavors to concoct and merge new privacy techniques, present day blockchain methods are quite farfetched to survive the privacy issues. This situation undermines rights of the user, similar as the option to become anonymous in specific cases, the option to edit or delete data, thus decreasing the consummation of a rightful privacy preserving system on the framework of blockchain. Retaliatory action to overcome multitudes of crime causing havoc to the blockchain privacy and to work on the existing privacy mechanisms is the main focus for blockchain developers and users. The Zero Knowledge Proof (ZKP) is a method of cryptography which has the prospects to change how data is processed. It provisions to prove the users' validity without sharing any other private information, thereby opening possibilities in preserving the privacy of blockchain applications. Further we discuss a use-case which is studied and evaluated with ZKP model to facilitate user verification without gathering any other private information. The Hyperledger Fabric platform is used and Hyperledger Caliper to analyze the use-case and to calibrate the results.
Real-Time Face Recognition and Counting

Facial recognition is an important task in computer vision and is used in security, surveillance, human–computer interaction, etc. It has many applications in fields. Siamese neural networks have emerged as a good method for detection as they can learn good representations of faces and resolve the difference between illumination, illumination and instruction. In this article, we propose a new face detection method based on Siamese neural networks. Our method uses a pair of identical subnets to assign weights and learn to extract features from two face images. The results of the two-web links are then compared with distance measurements to determine if the faces in the input images are the same. Evaluation of proposed method is compared on several datasets and exhibits a good performance in terms of accuracy and speed. Results of the proposed method demonstrate the performance of Siamese neural networks in face recognition and their potential for practical applications.