Reviewerly/exHarmony huggingface dataset:
Integrating blockchain-based access control model TBAAC for privacy and accountability for water science project

This research came from the need for a client application where researchers want to store and share their large-sized research data among their community. This application necessitates data exchange while respecting the media content owner’s privacy and sharing rights. To develop this client decentralized application, each issue related to storage, propagation capacity, or access permissions needs to be addressed along with the network structure and execution flow to work as swiftly as possible. To mitigate these challenges, we introduce the TBAAC (Token-based attributed access control) management, a modified attribute-based access control (ABAC) scheme to explore the full potential of the used schemas and strategies in a blockchain-supported client application. The presented tokenbased attributed access control has been validated through regress simulations and specified use cases of the DWS client application.
Rapid Face Mask Detection and Person Identification Model Based on Deep Neural Networks

As COVID-19 has been constantly getting mutated and in three or four months a new variant gets introduced to us and it comes with more deadly problems. The things that prevent us from getting Covid are getting vaccinated and wearing a face mask. In this paper, we have implemented a new Face Mask Detection and Person Recognition model named Insight face which is based on SoftMax loss classification algorithm Arc Face loss and named it as Rapid Face Detection and Peron Identification Model based on Deep Neural Networks (RFMPI-DNN) to detect face mask and person identity rapidly as compared to other models available. To compare our new model, we have used previous MobileNet_V2 model and face recognition module for effective comparison on the basis of time. The proposed model implemented in the system has outperformed the model compared in this paper in every aspect.
Recurring Payments on EVM-based Platforms

This article proposes a concept of building a payment system for subscription-based services in cases of fixed prices and with recurring frequency. The proposed solution offers the user the opportunity for ease-of-use and transaction fee cost savings. The proposed method of calculating balances for crypto-wallets can be applied to other financial services that provide traditional banking products based on decentralized public platforms. The concept is deployed in the form of a smart contract for issuing tokens on Ethereum, which is backward compatible with ERC-20 and ERC-777, and can form the basis for a new EVM-based decentralized network standard.
On coexistence of decentralized system (blockchain) and central management in Internet-of-Things

Network is composed of logical nodes and edges for communications. Atomistic component of things connected to the network is a memory chip. Accordingly, the unique linkage of a memory chip and a logical node can be a promising to resolve the root-of-trust problem on the Internet-of-Things. For this aim, we propose a protocol of challenge-response using a memory chip.For the central management, a central node controls the entry of electronic appliances with a memory chip into the network, and excludes a fake node (e.g., the spoofing entity) from the network that the central node manages. For the decentralized communications, Merkle’s tree turns out being composed of memory chips to which the logical nodes are uniquely linked, respectively. The root of Merkle turns out being the memory chip that stores the latest record of data transaction. We can register this memory chip as a new block by satisfying the requirement of the proof-of-consensus. After blocks are chained, it gets harder for even the central node to manipulate transaction record among memory chips. By this way, the decentralized system (e.g., blockchain) and the central management can coexist. A new idea of security state is also discussed briefly.
A Survey in Virtual Image Generation Based on Generative Adversarial Networks

With the rapid development of the metaverse, this is the inevitable trend content form from 2D to 3D, from real to virtual to virtual-real combination. The demand for virtual scenes will surge in the coming period. How to quickly generate a large number of high-quality scene graphs has sparked extensive academic attention. GAN being the most popular generation algorithm, and we discuss the prevalent architectures to solve the problems of training collapse and uncontrollable images during training. Besides, this paper focuses on the development of GAN and its variants in the field of image synthesis. A comprehensive review of recent advances in virtual image generation in the last two years is presented, with a summary of the classification of algorithms and an introduction to techniques related to multimodal generation. Finally, some suggestions for future research directions are proposed.
Proof-by-Location as a Socially Responsible Financial Infrastructure

The Proof-of-Work algorithm that underlies Bitcoin and many other cryptocurrencies is well known for its energy-intensive requirements. The Proof-of-Stake algorithm that underlies Ethereum2 and various other cryptocurrencies is less impactful environmentally, but it has a second, looming issue: the problem of wealth inequality. We have developed an alternative to Proof-of-Work and Proof-of-Stake, called Proof-by-Location, that has the potential to address both of these issues. This paper describes Proof-by-Location and a financial platform called Xylem that is based on it. This platform seeks to distribute transaction fees to billions of cryptocurrency "Notaries" around the world (essentially, anyone with a smartphone), who work together to establish a distributed consensus about financial transactions. Using Xylem as a global financial infrastructure could lead to significantly better social and environmental outcomes than existing financial platforms.
Supplier Selection in Healthcare using Blockchain

Supplier selection is a topic widely studied in both academics and industry. Even though it has extensive literature, there is not enough research on healthcare supplier selection (HSS). Healthcare is a critical sector. Any decision made for it needs to be done scrupulously and transparently. Because if later something goes wrong, only responsible individuals should be held accountable. However, the existing system for HSS is not competent enough. Therefore, we need a decentralised system that extensively supports automation, transparency, traceability, data immutability and security. This paper proposes a blockchain-based decentralised platform for HSS. The features of a blockchain system, like immutability, transparency, traceability, blockchain-backed smart contract, etc., are leveraged for HSS. Smart contact is employed for safe, intermediary-free & hassle-free contractual work between the buyers and suppliers. Supplier selection is done using a Multi-Criteria Decision Making (MCDM) method because multiple criteria must be considered to assess a supplier.
Towards Deanonymization of Mixing Services in Bitcoin

Bitcoin transactions are pseudonymous, which means that even when addresses or addresses can be connected one to another, it is really hard to connect them with outside entities. On top of that there have been a proliferation of bitcoins mixing sites in recent years. These sites operate mostly on the dark web and their main mission is launder bitcoins by making vast amounts of complex transactions with them and to make their association with a single owner even harder. Our mission is to make that distinction easier. In this paper we plan to introduce two novel heuristics. Our OI heuristic is designed to parse blockchain data in a way where we only receive information we deem is of interest. We introduce HOLO which aims to taint bitcoin addresses in reference to a fixed output. We also visualize the blockchain and our heuristic in an easily digestible manner.
Scale_it: Converting time series data into musical scales

This paper introduces open-source sonification software that represents a real-time stream of time-series data as notes on a musical scale. The software comprises a data processing unit and a signal-to-sound conversion unit. Before being expressed as a note on a musical scale of predefined length and character, each chunk of the incoming data stream is transformed into a parameter representing its relative position in the original population.
A Neural Network Based Approach for Combining Ultrasound and MRI Data of 2-D Dynamic Records of Human Speech

We aim at exploring the geometrical information that can be extracted from dynamic two-dimensional audiovisual records created by magnetic resonance imaging (MRI) and ultrasound (US) techniques during human speech. We connect US and MRI data by machine learning using tongue contours fitted automatically to the MRI and US images. We create different system configurations depending on the type and number of the input and output parameters of the network and the number of the hidden layers and neurons. We perform qualitative and quantitative analyses for all settings. The main benefit of this approach is to better understand the role of the geometric parameters of the vocal tract in speech production and to create a possible way to harmonize MRI and US sources.
Deep Learning for Self-learning in Yoga and Fitness: A Literature Review

With the rising levels of stress in the hustle and bustle of modern life, physical exercise has become imperative in order to maintain a sound body and soul. Yoga is one such art form that has received a lot of approval and has become well sought-after in recent times because of its physical, mental and spiritual benefits. Sports and other gym exercises also provide similar benefits. The paper analyzes the significance of modern deep learning techniques in making cost-effective self-learning aids for the aforementioned activities.
Cloud Computing Application in Medical Imaging: Challenges and Opportunities

In healthcare, critical systems are used to provide vital information and services 24x7x365. Recent years have brought important advancements in medical imaging and information technology (IT) which have changed significantly the way we operate and interact with each other. The use of cloud computing has centralized the infrastructure of radiology, software imaging, database storage or network configuration, that could then be provisioned as service based on user demand to any Internet computer. The present article provides an overview regarding the basic concept of cloud computing technology and discusses its application for medical imaging.
Arabic Sentiment Analysis based on Deep Reinforcement Learning

In this work, we handle the problem of Arabic sentiment analysis by combining the Arabic language understanding transformer-based model AraBERT and an LSTM-CNN deep learning model. We propose a new training objective function based on deep reinforcement learning that combines cross-entropy loss from maximum likelihood estimation and rewards from policy gradient algorithm. We evaluate our proposed system on the LABR book reviews dataset. Experimental results show that the proposed model outperforms the state-of-the-art models and provides an accuracy of 87.58%.
Analysis of Agricultural Product Package Recommendations Using the FP-Growth Algorithm

The size of the agricultural sector in Indonesia provides opportunities for small businesses managed by local residents to provide various needs to support agricultural activities. The rise of agricultural shops has made business competition in the agricultural sector quite competitive. Therefore, a marketing strategy is needed that can increase the sales of various marketed agricultural products. The method applied in this research is Association Rule Mining using the FP-Growth algorithm. This method is applied to obtain association rules that are used as a reference in making recommendations for agricultural product packages at agricultural shops to increase sales. The system workflow starts from preprocessing data to form an itemset which is then processed using the FP-Growth algorithm. The next step is to determine the minimum value of support and minimum confidence as a limit in calculating the FP-Growth algorithm. The system will eliminate a number of itemsets that do not meet the specified threshold to produce frequent itemsets which are then mined into rules as a reference to form the most recommended package of agricultural products. From the research conducted it can be seen that there are 3 itemets that almost always appear and the most recommended agricultural product package is Prowl 250 ml (Herbicide) which is associated with Antracol 70wp 1 kg (fungicide) with a support value of 6.38% and a confidence value of 85, 71% and the lift ratio is 8.95.
Synonym-based Text Generation in Restructuring Imbalanced Dataset for Deep Learning Models

One of which machine learning data processing problems is imbalanced classes. Imbalanced classes could potentially cause bias towards the majority classes due to the nature of machine learning algorithms that presume that the object cardinality in classes is around similar number. Oversampling or generating new objects in minority class are common approaches for balancing the dataset. In text oversampling method, semantic meaning loses often occur when deep learning algorithms are used. We propose synonym-based text generation for restructuring the imbalanced COVID-19 online-news dataset. Three deep learning models (MLP, CNN, and LSTM) using TF/IDF and word embedding (WE) feature are tested with the original and balanced dataset. The results indicate that the balance condition of the dataset and the use of text representative features affect the performance of the deep learning model. Using balanced data and deep learning models with WE greatly affect the classification significantly higher performances as high as 4%, 5%, and 6% in accuracy, precision, recall, and f1-score, respectively.
The Investigation into Deep Learning Classifiers Towards Imbalanced Text Data

Class imbalance is an important classification problem where failure to identify events can be hazardous due to failure of solution preparation or opportune handling. Minorities are mostly more consequential in such cases. It is necessary to know a reliable classifier for imbalanced classes. This study examines several conventional machine learning and deep learning methods to compare the performance of each method on dataset with imbalanced classes. We use COVID-19 online news titles to simulate different class imbalance ratios. The results of our study demonstrate the superiority of the CNN with embedding layer method on a news titles dataset of 16,844 data points towards imbalance ratios of 37%, 30%, 20%, 10%, and 1%. However, CNN with embedding layer showed a noticeable performance degradation at an imbalance ratio of 1%.
Shadows as Ambient Displays - A Design Space

In this paper, we present a design space for using shadows in interactive systems. The main elements of the design space are optical properties, dynamicity, reality, temporality, and projection surface. The use and applicability of the design space are evaluated through three concept designs. The paper contributes a systematic approach to designing interactive systems with shadows, adding to the prior art focused on case studies.
Improving Efficiency on BFV-based Encrypted Watermarking using Hadamard Product Decomposition

Fully homomorphic encryption (FHE) enables arithmetic computation over encrypted data to preserve data privacy in the untrusted computing domain. Nevertheless, the BFV-based FHE computation is memory expensive that does not scale for high-resolution image computation such as image watermarking. This paper proposed a vector decomposition approach based on the Hadamard product to enable memory-efficient encrypted watermarking on huge-size images. The method uses singular value decomposition (SVD)-based watermarking by splitting the watermark embedding into row-wise of Hadamard products. The evaluation shows that the proposed method reduces the memory requirements to N times, where N refers to the image dimension in pixels. In addition, the proposed method allows parallel computation for faster computation, from 4-times up to 24-times faster, by utilizing the available memory.
Utilizing AR for 3D Geometry Object Visualization

Employing a learning medium can support learning and teaching activities. This learning medium can be one way to help students in understanding particular lessons, e.g., mathematics, art, and science. Although due to some reasons that understanding such specific lessons might be very difficult, using a learning tool can help students in understanding them easily. Thus, this study aims to develop such a learning medium in helping students to learn mathematics using visualization. In particular, we utilize augmented reality (AR) technology to visualize cube and beam objects. The visualized cube and beam objects are in the form of models and nets. The models and nets visualization is shown as a basic understanding to learn the geometry of three-dimensional (3D) flat-sided shapes. For this purpose, we use Research and Development (R&D) method consisting of define, design, development, and dissemination steps in this study. Our experimental results show that the developed AR application could work well to visualize the models, nets, and volume of cube and beam objects in 3D.
PREVENTION OF IDENTITY THEFT: AN ANALYSIS

Identity theft is the crime of acquiring other person's private or financial data for the purpose of assuming the name or identification of the victim and commit crimes. Such data can be used in the name of that victim to commit crimes in the name of the victim, to acquire credit or loans, products and services and even provide fake credentials to government authorities. The crime has advanced with the advancement of technology over the past few years in India. To prevent cybercrimes such as identity theft and other computerrelated/internet-based crimes, in the year of 2000, Indian Government enacted The Information Technology Act, 2000, amended in 2008. Identity theft is also governed by few provisions (Section 147A, Section 419, Section 463) of Indian Penal code, 1860. This research paper will analyze the implementation of the Act in India and other preventive measures taken by the Government of India relating to the matter of securing personal data and preventing the theft of identity. Key words: Identity, Identity theft, Data Technology, Cyber-crime, Phishing/vishing, Skimming, Hacking, Shoulder surfing, Dumpster diving, Mail theft, Pretexting.
Research on the Text2SQL Method Based on Schema Linking Enhanced

In recent years, natural language generation of SQL sentences (Text2SQL) has received a lot of attention as an important research direction natural language processing (NLP). Text2SQL makes it easier for users to query complex databases without learning SQL sentences and the underlying database schema. The current mainstream Text2SQL method is the grammar-based IRNet, which attempts to present an efficient method with explanations from the perspective of constructing reasonable grammars and provides a good solution for solving complex nested queries. Still, it makes simple use of external database ontology knowledge, resulting in natural language problems in which words do not correspond well to tables and columns in the database. To address this problem, a new method that considers the entity relationships between natural language problems and data in the database - SLESQL is proposed, which extends some of the functionality of IRNet by using schema linking enhanced Experiments show that SLESQL achieves 6.8% improvement in accuracy over IRNet on the publicly available dataset Spider.
Review of Generative Adversarial Networks in Object Detection

Generative Adversarial Network (GAN) has become a research focus in the field of deep learning, and its research output has grown exponentially. This brand-new technology provides new ideas and methods for object detection, and has achieved remarkable success. Firstly, this paper introduces the basic GAN model and its derivative models in the field of object detection. Then analyzes the application status of GAN from object detection fields, such as industrial defect detection, medical image detection, remote sensing image detection, and face detection. Finally, summarize and prospect the technology development of generative adversarial networks.
Semi-Supervised Event Predictions with Graph Networks

This paper describes how we envision classifying events into the United Nations Sustainable Development Goals (SDGs) by utilizing machine learning techniques on global news data. We propose extracting data from a media intelligence platform using an ontology and a classifier to assign each event to its corresponding SDG. To minimize the labeling effort, a few-shot classification approach is employed. Additionally, a labeling tool is developed to facilitate event analysis and assign labels accurately. We envision this approach could be used for analyzing media events at a large scale and track progress towards the SDGs.
Graph-Scrutinizer: Towards Massive Graph Analytics and Reasoning

Graphs can represent various phenomena and are increasingly used to tackle complex problems. Among the challenges associated with graph processing is the ability to analyze and mine massive-scale graphs. While the massive scale is usually associated with distributed systems, the complex nature of graphs makes them an exception to the rule. Currently, most graph processing is performed within a single computer. In this research, we describe a solution at a conceptual level in the context of the Graph-Massivizer architecture. We use two approaches to provide graph analytics and querying functionalities at scale. First, we leverage graph sampling techniques to obtain relevant samples and avoid processing the whole graph. Second, we support heuristic and neural query execution engines. We envision an interface that will decide which queries to execute with a given engine, given constraints (e.g., execution time boundaries, exactness of results, energy saving requirements).
How Do ML Jobs Fail in Datacenters? Analysis of a Long-Term Dataset from an HPC Cluster

Reliable job execution is important in High Performance Computing clusters. Understanding the failure distribution and failure pattern of jobs helps HPC cluster managers design better systems, and users design fault tolerant systems. Machine learning is an increasingly popular workload for HPC clusters are used for. But, there is little information on machine learning job failure characteristics on HPC clusters, and how they differ from the previous workload such clusters were used for. The goal of our work is to improve the understanding of machine learning job failures in HPC clusters. We collect and analyze job data spanning the whole of 2022, and over 2~million jobs. We analyze basic statistical characteristics, the time pattern of failures, resource waste caused by failures, and their autocorrelation. Some of our findings are that machine learning jobs fail at a higher rate than non-ML jobs, and waste much more CPU-time per job when they fail.