Reviewerly/exHarmony huggingface dataset:
Assimilation of Blockchain with Internet of Things (IoT) with Possible Issues and Solutions for Better Connectivity and Proper Security

The Internet of Things (IoT) has an amazing role in the development of numerous fields like health care, smart grids, disaster management, supply chain management, etc. It also simplifies the publics' day-to-day lives and improves their collaboration with one another, environment as well as neighbors in a wide area. IoT achieves this by using devices, tools, fixed sensors, and carryable sensors/devices for computerized systems. Conversely, IoT systems are developing in dimension, intricacy, and the total number of linked devices due to this, several experiments and difficulties arise like safety, legitimacy, consistency, and scalability, it is very important to preserve and rise assurance in IoT systems by addressing the problems mentioned above. Blockchain's features like safety, limpidity, consistency, as well as tracking mark it a flawless applicant to develop IoT systems, by resolving their complications and maintaining their forthcoming growth. The chapter outlines the main issues faced by IoT organizations and recommends the role of blockchain to resolve those issues. Likewise, it examines the status of recent research in integrating blockchain with IoT systems and the updated phases of implementation. Moreover, it deliberates numerous problems interlinked to the integration of IoT and blockchain. Furthermore, the paper highlights many benefits and needs of integration of both technologies.
Statistical Effective Fault Attacks: The Other Side of the Coin

The introduction of Statistical Ineffective Fault Attacks (SIFA) has led to a renewed interest in fault attacks. SIFA requires minimal knowledge of the concrete implementation and is effective even in the presence of common fault or power analysis countermeasures. However, further investigations reveal that undesired and frequent ineffective events, which we refer to as the noise phenomenon, are the bottleneck of SIFA that can considerably diminish its strength. This includes noise associated with the attack’s setup and caused by the countermeasures utilized in the implementation. This research aims to address this significant drawback. We present two novel statistical fault attack variants that are far more successful in dealing with these noisy conditions. The first variant is the Statistical Effective Fault Attack (SEFA), which exploits the non-uniform distribution of intermediate variables in circumstances when the induced faults are effective. The idea behind the second proposed method, dubbed Statistical Hybrid Fault Attacks (SHFA), is to take advantage of the biased distributions of both effective and ineffective cases simultaneously. Our experimental results in various case studies, including noise-free and noisy setups, back up our reasoning that SEFA surpasses SIFA in several instances and that SHFA outperforms both or is at least as efficient as the best of them. For example, in the case of a 4-bits random-AND fault injected into the AES with a 35% missed fault rate, utilizing SEFA reduces the number of needed ciphertexts by 50%. In the same case study, SHFA can yield 10% and 55% reductions compared to SEFA and SIFA.
DSIM: Distributed Sequence Matching on Near-DRAM Accelerator for Genome Assembly

Matching nucleic acid sequences has long become the performance bottleneck in genome assembly which aims to connect enormous partial genome reads without prior knowledge of the reference sequence. The intensive and random data accesses of querying sequences using the widely adopted FM-Index data structure have caused in-efficient usage and long runtime of the memory system. Existing software FM-Index tools are limited on algorithmic inefficiency and poor processing parallelism. Solutions on GPU, FPGA and ASIC focus mainly on computational acceleration while still bottlenecked at the memory-bound nature of querying FM-Index. This paper proposes DSIM, a scalable FM-Index querying on near-DRAM accelerators. DSIM supports highly parallel multi-step query processing by distributing partial FM-Index table to different DRAM chips. Each genome sequence is partitioned into shorter queries and dispatched to the corresponding DRAM chip for string lookup. The optimized data layout and execution control on DRAM enables high row-data reuse and minimizes CPU-DRAM data transfers. The light-weight mapping scheme on the host CPU facilitates effective query distribution to DRAM chips and further supports scalability to multiple DIMMs (Dual-Inline Memory Modules). An in-DRAM arbiter is implemented to control the intra-chip data processing without affecting the external memory controller and DDR protocol. Experiments on 128-chip DRAM system showed that DSIM achieves up to 231 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> and 8.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> overall speedup compared to the software FM-Index tool and the state-of-the-art near-DRAM solution respectively.
Object Interaction-Based Localization and Description of Road Accident Events Using Deep Learning

Detection and localization of road accidents in real-time is an integral part of the Intelligent Transportation System (ITS). Even though the existing road accident detection methods show promising results, the process suffers from some drawbacks. For example, existing methods require a large number of sample videos for feature learning. Moreover, features such as temporal gradients or flow fields are time-consuming. To address these issues, we introduce a new method that uses objects and their positions to detect accidents in real-time. Apart from localization of the accident events in videos, we perform a high-level post processing to describe the severity and context of an accident. Firstly, we divide an input video into pre-accident, accident and post-accident stages to extract object interactions. These interaction proposals are then filtered using a refinement algorithm. We then adopt an iterative training procedure to classify normal and accident interactions. We also highlight the damaged zone using heat maps. Finally, we generate high-level textual descriptions to quantify the context and severity of an accident. We have trained the proposed model using offline setups. However, it can be deployed online to detect road accident events in real-time by taking the video inputs directly from the CCTV camera. Moreover, with a minimal supervision, the model can be retrained for online surveillance. Extensive experiments carried out on UCF Crime and CADP datasets reveal that the proposed framework achieves state-of-the-art performance when compared with the recently proposed accident event detection methods in terms of AUC (UCF Crime: 69.70% and CADP: 72.59%) and FAR (UCF Crime: 0.8 and CADP: 2.2). The high-level description of the accident is an added advantage that will certainly help the traffic police to react in a timely manner.
Construction and Evaluation of Sentiment Datasets for Low-Resource Languages: The Case of Uzbek

To our knowledge, the majority of human language processing technologies for low-resource languages don’t have well-established linguistic resources for the development of sentiment analysis applications. Therefore, it is in dire need of such tools and resources to overcome the NLP barriers, so that, low-resource languages can deliver more benefits. In this paper, we fill that gap by providing its first annotated corpora for Uzbek language polarity classification. Our methodology considers collecting a medium-size manually annotated dataset and a larger-size dataset automatically translated from existing resources. Then, we use these datasets to train what, to our knowledge, are the first sentiment analysis models on the Uzbek language, using both traditional machine learning techniques and recent deep learning models. Both sets of techniques achieve similar accuracy (the best model on the manually annotated test set is a convolutional neural network with 88.89% accuracy, and on the translated set, a logistic regression with 89.56% accuracy); with the accuracy of the deep learning models being limited by the quality of available pre-trained word embeddings.
Edge Computing-Aided Coded Vertical Federated Linear Regression

For the training process of federated linear regression (FLR), which is the simplest form of federated learning, the integrated computation at each company is slowed down either by huge volume data or by time-consuming homomorphic encryption. Targetted at accelerating the training process of FLR, through the incorporation of edge computing aided coded distributed computing (CDC) into intensive computation (matrix multiplication), a novel coded FLR framework is proposed where several edge nodes aid the computing of one company. Two schemes, including linear combination (LC)-based vertical FLR and Matdot-based vertical FLR, are proposed and designed, which enjoy in-parallel computation and homomorphic encryption at the edge nodes. Since workload at each edge node is reduced significantly, the training runtime of these two schemes may be reduced significantly. Numerical studies show that our proposed coded schemes outperform traditional uncoded schemes significantly in terms of overall runtime (sum of encoding, computing, and decoding phases) of the training process. Besides, among the two proposed coded schemes, LC-based scheme and Matdot-based scheme each has its own advantage scenarios which conforms with the analysis.
Non-Fungible Tokens (Nfts): A Bibliometric and Systematic Review, Current Streams, Developments, and Directions for Future Research

As bibliometric analysis has become very popular in various academic fields, this paper reviews the literature on Non-Fungible Tokens (NFTs). NFTs are tradeable rights that can be used to acquire ownership of digital assets such as music, videos, and images (Dowling, 2022b). They became the first blockchain technology application to achieve clear public prominence in early 2021. In this study, 12 clusters were identified. In addition, the results indicate that NFTs have become more widely used in blockchain research because of their significant contributions. The findings of this study present future research directions in the field of NFTs.
Use of AI Voice Authentication Technology Instead of Traditional Keypads in Security Devices

Traditional keypads and text-based passwords are vulnerable to scams and hacks, leading to enormous levels of embezzlement and frauds apart from various other threats to data security.AI based voice authentication holds unparalleled value for data protection, security, and privacy, by providing an effective alternative to traditional password-based protection.This paper reports the findings of a limited literature review that forms the basis for further research towards enhancing the reliability and security of AI voice authentication.Based on the findings of the review of existing literature, this paper proposes that integration of the blockchain technology with the AI voice authentication can significantly enhance the data security, starting from mobile devices to the security of big agencies and banks.The key processes in implementing an AI voice authentication system are proposed as a conceptual model, to facilitate further research for implementation.
On Succinct Non-interactive Arguments in Relativized Worlds

Succinct non-interactive arguments of knowledge (SNARKs) are cryptographic proofs with strong efficiency properties. Applications of SNARKs often involve proving computations that include the SNARK verifier, a technique called recursive composition. Unfortunately, SNARKs with desirable features such as a transparent (public-coin) setup are known only in the random oracle model (ROM). In applications this oracle must be heuristically instantiated and used in a non-black-box way. In this paper we identify a natural oracle model, the low-degree random oracle model, in which there exist transparent SNARKs for all NP computations relative to this oracle. Informally, letting $$\mathcal {O}$$ be a low-degree encoding of a random oracle, and assuming the existence of (standard-model) collision-resistant hash functions, there exist SNARKs relative to $$\mathcal {O}$$ for all languages in $$\mathsf {NP}^{\mathcal {O}}$$ . Such a SNARK can directly prove a computation about its own verifier. To analyze this model, we introduce a more general framework, the linear code random oracle model (LCROM). We show how to obtain SNARKs in the LCROM for computations that query the oracle, given an accumulation scheme for oracle queries. Then we construct such an accumulation scheme for the special case of a low degree random oracle.
Eye Movement in User Experience and Human–Computer Interaction Research

Eye-movement measures are popular in user experience (UX) and human–computer interaction (HCI) research as a way to provide insights into the factors that hinder the usability of computer-based technologies. In this chapter, we overview the various eye-movement metrics employed in UX and HCI research. In addition, we summarize studies that have used these metrics, focusing on examples of both pioneering research involving relatively simple and static interfaces as well as recent research involving more complex and dynamic interfaces (e.g., as arise in mobile computing). We also examine ongoing trends in eye-movement research in relation to UX and HCI, including the application of eye movements as an input mechanism, the use of eye-movement traces to elicit retrospective reports of interface problems, and the drive toward the automated identification and categorization of eye-movement patterns that are diagnostic of interaction difficulties.
A Systematic Review of Perception of E-Learning Users in Formal Education during COVID-19 Pandemic

Education as we know has shifted its paradigm in which the results are in the apparent changes in communication, especially in a formal education environment.When COVID-19 Pandemic emerged back in 2020, the education we knew faced massive turn of event worldwide.This has called for the implementation of E-learning into the education system around the world.This systematic literature review provided insights into education system around the world in coping with the Pandemic by looking into the perspective of E-learning users in formal education.Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) were used when conducting this review.There are 48 articles ranging taken from sources such as SCORPUS and Google scholar.The study looked into four aspects in an article whereby challenges, impacts, and preference of teaching and learning were examined.The result of the study shows that impacts were the most discussed and examined aspect of studies regarding E-learning in formal education during the COVID-19 pandemic.This systematic review also inspected the overall perceptions of the COVID-19 outbreak towards the education system globally.The results showed an incline towards positivity despite some challenges.
“Ghost” and Attention in Binary Neural Network

As the memory footprint requirement and computational scale concerned, the light-weighted Binary Neural Networks (BNNs) have great advantages in limited-resources platforms, such as AIoT (Artificial Intelligence in Internet of Things) edge terminals, wearable and portable devices, etc. However, the binarization process naturally brings considerable information losses and further deteriorates the accuracy. In this article, three aspects are introduced to better the binarized ReActNet accuracy performance with a more low-complex computation. Firstly, an improved Binarized Ghost Module (BGM) for the ReActNet is proposed to increase the feature maps information. At the same time, the computational scale of this structure is still kept at a very low level. Secondly, we propose a new Label-aware Loss Function (LLF) in the penultimate layer as a supervisor which takes the label information into consideration. This auxiliary loss function makes each category&#x2019;s feature vectors more separate, and improve the final fully-connected layer&#x2019;s classification accuracy accordingly. Thirdly, the Normalization-based Attention Module (NAM) method is adopted to regulate the activation flow. The module helps to avoid the gradient saturation problem. With these three approaches, our improved binarized network outperforms the other state-of-the-art methods. It can achieve 71.4% Top-1 accuracy on the ImageNet and 86.45% accuracy on the CIFAR-10 respectively. Meanwhile, its computational scale OPs is the least 0.86&#x00D7;10<sup>8</sup> compared with the other mainstream BNN models. The experimental results prove the effectiveness of our proposals, and the study is very helpful and promising for the future low-power hardware implementations.
Linear Lag Models and Measurements of the Lag Correction Factors

For fluoroscopic imaging, flat-panel dynamic detectors can acquire X-ray image sequences with frame rates higher than 300 frames per second. However, the sequentially acquired images have artifacts due to the lag signals, which are caused from trapping charges in the amorphous structure and incomplete reads. Furthermore, the lag signal lowers the noise power spectrum (NPS) of the detector; hence, the detector performance can be inflated. Conventional approaches for correcting the measured NPS are based on the lag correction factor (LCF). Various LCF measurement methods have been developed based on moving average and auto-regressive models. Current methods require high computational complexities with many images. In this paper, we first review the current methods and next propose three LCF measurement methods in simplified forms under an autoregressive model of order 1 based on the temporal periodogram mean and line means. Here, we suggest schemes that deal with several disturbances, such as nonuniform temporal gains and exposure leaks, to accurately measure LCF. A comparative review of the LCF measurement methods can establish a taxonomy of measurements. Through extensive experiments using X-ray images acquired from dynamic detectors, it is shown that the proposed methods yield comparable performances with lower computational complexities compared to the existing methods.
Deep Learning Techniques for Natural Language Processing

The article analyzes problems of deep learning (DL) approaches to natural language processing (NLP). The research covers various NLP tasks implemented with artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). These architectures allow solving a wide range of natural language processing problems that previously did not lend themselves to effective solutions: sentence modeling, semantic role marking, named entity recognition, answering questions, text categorization, machine translation. Along with the advantages of using CNN for solving NLP problems, there are problems associated with many variable network parameters and the choice of its architecture. Authors propose an evolutionary algorithm for optimizing the architectures of convolutional neural networks, which has the advantage of a small population size of networks. It turned out to be important because, due to the complexity of CNN, it takes a long time to train one network. The experimental results showed that the proposed neuroevolutionary approach could quickly find an optimized CNN architecture for a given dataset with acceptable accuracy.
Recommendation System For Anime Using Machine Learning Algorithms

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Data Literacy Practices of Students Conducting Undergraduate Research

Undergraduate research is considered to be a high-impact practice; however, research into the data literacy of students conducting undergraduate research is lacking. In addition, institutionwide assessments of data practices are challenging because of varied disciplinary approaches to data. This study investigates the data practices of undergraduate students who submitted research posters to a student research symposium in 2019 and found that students engage in a variety of data practices during undergraduate research regardless of their research method and approach for obtaining and working with data. This study identifies potential areas for data-related library instruction in support of undergraduate research.
Role of Banks and Other Financial Institutions in Enhancing Green Digital Finance

Led by investor action, policies, and products, sustainable debt issuance has seen traction in recent years, to reach a cumulative US $1.7 trillion as of 2020. However, the flows still pale in comparison to the annual investment gap of US $5–7 trillion to achieve the sustainable development goals. The challenge lies in devising ways to ensure that all the target beneficiaries can access capital at the right price and terms. In addition, the perceived risks need to be mitigated, and impact needs to be monitored using verified data. Inability to devise these ways leads to divergence between finance flows and impact, dissuading greater mobilization of sustainable finance. Financial product intermediation using technology platforms can be instrumental in bridging this gap to some extent to create an enabling ecosystem. This chapter looks at how banks and financial institutions can do this by linking finance, technology, and sustainability. This will promote green finance intermediation, enhance financial inclusion, and bring about green capital mobilization. The chapter identifies the challenges and opportunities that banks may face, both on the supply side and demand side, while working toward green fintech solutions. Apart from secondary research and leveraging the authors’ industry experience, it incorporates primary interview findings. As the green fintech space grows, banks need to position themselves favorably to ride the curve. This chapter seeks to find some answers to help them take those decisions.
A Deep Learning Based Approach to Measure Confidence for Virtual Interviews

Confidence means trust and a strong belief in something or someone. During an interview, demonstrating self-confidence, i.e., showing trust and faith in your own abilities can be the deciding factor for your success versus the other candidates. It is as important as showing your skills, as without self-confidence one cannot convince the interviewer to trust one’s skills, one’s ability to learn and the ability to get the work done. Demonstrating a positive body image and strong, confident personality can make a lasting impression on an interviewer, and in the future on other colleagues as well. In the age of coronavirus, the interviews have migrated to virtual platforms, so only a candidate’s face is used to gauge his/her self-confidence. Deep Learning can be helpful to gain best results on image classification for confidence detection. With the help of different types of facial expressions present in dataset we can classify and help in telling the measure of self-confidence. This work aims to develop a confident and unconfident image classifier which will help interviewees to practice confident expressions and improve their skills. The Deep Learning CNN model is trained with the help of two optimizers, namely, Adam and SGD.