Reviewerly/exHarmony huggingface dataset:
Opt2Ada: an universal method for single-image low-light enhancement

This paper proposes that the task of single-image low-light enhancement can be accomplished by a straightforward method named Opt2Ada. It contains a series of pixel-level operations, including an optimized illuminance channel decomposition, an adaptive illumination enhancement, and an adaptive global scaling. Opt2Ada is traditional and it does not rely on architecture engineering, super-parameter tuning, or specific training dataset. Its parameters are generic and it has better generalization capability than existing data-driven methods. For evaluation, both the full-reference, non-reference, and semantic metrics are calculated. Extensive experiments on real-world low-light images demonstrate the superiority of Opt2Ada over recent traditional and deep learning algorithms. Due to its flexibility and effectiveness, Opt2Ada can be deployed as a pre-processing subroutine for high-level computer vision applications.
A multimodal human-robot sign language interaction framework applied in social robots

Deaf-mutes face many difficulties in daily interactions with hearing people through spoken language. Sign language is an important way of expression and communication for deaf-mutes. Therefore, breaking the communication barrier between the deaf-mute and hearing communities is significant for facilitating their integration into society. To help them integrate into social life better, we propose a multimodal Chinese sign language (CSL) gesture interaction framework based on social robots. The CSL gesture information including both static and dynamic gestures is captured from two different modal sensors. A wearable Myo armband and a Leap Motion sensor are used to collect human arm surface electromyography (sEMG) signals and hand 3D vectors, respectively. Two modalities of gesture datasets are preprocessed and fused to improve the recognition accuracy and to reduce the processing time cost of the network before sending it to the classifier. Since the input datasets of the proposed framework are temporal sequence gestures, the long-short term memory recurrent neural network is used to classify these input sequences. Comparative experiments are performed on an NAO robot to test our method. Moreover, our method can effectively improve CSL gesture recognition accuracy, which has potential applications in a variety of gesture interaction scenarios not only in social robots.
D2DLive: Iterative live video streaming algorithm for D2D networks

Smart healthcare system incorporates new technologies making healthcare more efficient and more convenient. Virtual consultations provide interactions with healthcare professionals via video technology, which lessens the number of patients visiting health facilities and consequently reduces the risk of infections. Recently, device to device (D2D) communication showed extraordinary abilities to save the available resources and improve the network quality of service (QoS). Some of the existing algorithms for multimedia services over D2D networks consider only the signal to noise ratio (SNR) and ignore temporal requirements, which do not provide optimum performances. So, more efficient transmission mechanisms that improve the users’ QoS are needed to respond quickly to the ever-changing application demands. In response to these challenges, the research community began exploring novel solutions for video streaming, namely: Quantile-based Carrier-sense multiple access (CSMA), Flexible video transmission (Flexi) and Distributed Random approach (DR). However, these solutions introduce higher throughput, and jitter, especially in the case of live streaming. Other solutions are also available in the literature, but most of them cannot efficiently scale to large multi videos, multi-hops multi-user live video streaming. In particular, in most of these techniques, the chunk number, node transmission capacity or specific video delay, and time requirements are not taken into consideration in the path optimization.To overcome these shortcomings, we propose a novel D2DLive video streaming algorithm as a new solution for a higher QoS to improve medical data delivery. For each requested video, the proposed algorithm divides its content into small playable units called chunks that are transmitted using a new network selection algorithm. The proposed algorithm computes iteratively a suboptimal set of paths to be used to forward all the video chunks cooperatively to their destinations. Furthermore, we develop a new weight algorithm to evaluate the performance of the proposed algorithm in adhering to the upload and download capacities for each device while minimizing the transmission delay. Simulation results show that the proposed algorithm outperforms other established methods in terms of full delay, average upload, and download capacities.
<scp>VulGraB</scp>: <scp>Graph</scp>‐embedding‐based code vulnerability detection with bi‐directional gated graph neural network

Abstract Code vulnerabilities can have serious consequences such as system attacks and data leakage, making it crucial to perform code vulnerability detection during the software development phase. Deep learning is an emerging approach for vulnerability detection tasks. Existing deep learning‐based code vulnerability detection methods are usually based on word2vec embedding of linear sequences of source code, followed by code vulnerability detection through RNNs network. However, such methods can only capture the superficial structural or syntactic information of the source code text, which is not suitable for modeling the complex control flow and data flow and miss edge information in the graph structure constructed by the source code, with limited effect of neural network model. To solve the above problems, this article proposes a code vulnerability detection method, named VulGraB, which is based on graph embedding and bidirectional gated graph neural networks. VulGraB uses node2vec to convert the program‐dependent graphs into graph embeddings of the code, which contain rich structure information of the source code, improving the ability of features to express nonlinear information to a certain extent. Then the BiGGNN is used for training, and finally the accuracy of the detection results is evaluated using target program. The bi‐directional gated neural network utilizes a bi‐directional recurrent structure, which is beneficial to global information aggregation. The experimental results show that the accuracy of VulGraB is significantly improved over the baseline models on two datasets, with F1 scores of 85.89% and 97.24% being the highest, demonstrating that VulGraB consistently outperforms other effective vulnerability detection models.
Transformation of Financial Trading With Blockchain Technology in India

Blockchain technology is creating triumph all over the world with its presence, and the combination of artificial intelligence and blockchain technology will prove to be more impactful in the coming decades. The various countries of the world are assimilating blockchain technology in the financial trading system because it is robust and secure. It has been observed that after the pandemic, Indian consumers have overcome the fear of using online transaction systems. The usage of smart gadgets, speedy networks, increased product services, and the flood of startups has created a conducive ecosystem for online financial transactions. This study attempts to assess the use of blockchain in financial sectors by various countries. Blockchain is the next generation of technology for any field that needs security, privacy, speed, and accuracy. Financial systems is a field facing problems like financial fraud, data theft, late settlement, and data manipulation. The use of blockchain technology on a large scale will create a secure financial ecosystem in India.
Stacked graph bone region U-net with bone representation for hand pose estimation and semi-supervised training

3D hand estimation from 2D joint information is an essential task in human-machine interaction, which has achieved great progress as an application of deep learning. However, regression-based methods do not perform well because the structural information is not effectively exploited, and the joint coordinates are variable. To address these issues, the hand pose is represented with bone vectors instead of joint coordinates in this study, which are stabler to learn and allow for easier encoding of the hand geometric structure and joint dependency. A novel graph bone region U-Net is specifically designed for bone representation to learn multiscale structural features, where the proposed novel elements (graph convolution, pooling and unpooling) incorporate hand structural knowledge. Under the introduced “finger-to-hand” framework, the network gradually decreases the scale from bone to finger to hand for learning more meaningful multiscale features. Moreover, the unit network is stacked repeatedly to extract multilevel features. Based on the above network, a simple but effective semi-supervised approach is introduced to address the lack of 3D hand pose labels. Many experiments are conducted to evaluate the proposed approach on two challenging datasets. The experimental results show that the proposed supervised approach outperforms the state-of-the-art methods, and the proposed semi-supervised approach can still achieve favorable performance when the labeled data are scarce.
A systematic comparison and evaluation of building ontologies for deploying data-driven analytics in smart buildings

Ontologies play a critical role in data exchange, information integration, and knowledge sharing across diverse smart building applications. Yet, semantic differences between the prevailing building ontologies hamper their purpose of bringing data interoperability and restrict the ability to reuse building ontologies in real-world applications. In this paper, we propose and adopt a framework to conduct a systematic comparison and evaluation of four popular building ontologies (Brick Schema, RealEstateCore, Project Haystack, and Digital Buildings) from both axiomatic design and assertions in a use case, namely the Terminological Box (TBox) evaluation and the Assertion Box (ABox) evaluation. In the TBox evaluation, we use the SQuaRE-based Ontology Quality Evaluation (OQuaRE) framework and concede that Project Haystack and Brick Schema are more compact with respect to the ontology axiomatic design. In the ABox evaluation, we apply an empirical study with sample building data that suggests Brick Schema and RealEstateCore have greater completeness and expressiveness in capturing the main concepts and relations within the building domain. The results indicate that there is no universal building ontology for integrating Linked Building Data (LBD). We also discuss ontology compatibility and investigate building ontology design patterns (ODPs) to support ontology matching, alignment, and harmonisation.
On the Construction of Quantum and LCD Codes from Cyclic Codes over the Finite Commutative Rings

Let Fq be a field of order q, where q is a power of an odd prime p, and α and β are two non-zero elements of Fq. The primary goal of this article is to study the structural properties of cyclic codes over a finite ring R=Fq[u1,u2]/⟨u12−α2,u22−β2,u1u2−u2u1⟩. We decompose the ring R by using orthogonal idempotents Δ1,Δ2,Δ3, and Δ4 as R=Δ1R⊕Δ2R⊕Δ3R⊕Δ4R, and to construct quantum-error-correcting (QEC) codes over R. As an application, we construct some optimal LCD codes.
PDAS: Improving network pruning based on Progressive Differentiable Architecture Search for DNNs

Network pruning is an efficient approach to adapting large-scale deep neural networks (DNNs) to resource-constrained systems; the networks are pruned using the predefined pruning criteria or a flexible network structure is explored with the help of neural architecture search, (NAS). However, the former crucially relies on the human expert knowledge, while the latter usually requires one to make many simplifications to ensure the efficiency of the search, resulting in limited performance. This paper presents a new pruning approach called Progressive Differentiable Architecture Search (PDAS) that realizes a better balance between computation efficiency and model performance. First, a joint search-update scheme for search optimization is presented; it constantly refines the candidate number of channels in each layer by performing differentiable searching and evolutionary updating alternately. The latter can provide new high-probability candidates continuously to avoid local minimum point. Second, a two-stage constrained progressive search strategy is presented for some complex nonlinear networks (such as ResNet) that are more difficult to prune for existing approaches; it effectively avoids the over-fitting problem caused by the excessive search space and largely reduces the consumption of 1x1 convolution in the skip connections of the residual blocks with little accuracy loss. Extensive experiments on some representative datasets (such as CIFAR-10, CIFAR-100, and ImageNet) approve the superior performance of PDAS compared to most existing network pruning algorithms available. Notably, compared to the state-of-the-art LFPC, PDAS can even prune about 8% more FLOPs on ResNet-110 (on CIFAR-10) and ResNet-50 (on ImageNet), respectively, while coming with almost identical and ignorable accuracy losses.
Technologies Behind Crypto-Based Decentralized Finance

A new breed of consumer-facing financial applications called decentralized financial applications (DeFi) is structured using smart contracts and runs on permissionless blockchain technologies. In this chapter, the authors contextualize the DeFi idea within the theoretical framework of blockchain and related technologies and their potential real-world applications. In addition, the authors looked at the basic DeFi concepts and application scenarios for DeFi today and pinpointed distributed ledger technologies, artificial intelligence, machine learning, and zero-knowledge proofs as the three key technologies that help revolutionize DeFi. The authors concluded that blockchain is the foundation of DeFi, and AI and ML capabilities help organizations deliver crypto insights to their users to cope with the market volatility, and zero-knowledge proofs ensure the security of digital transactions in an unregulated, decentralized finance world.
Lightweight and Efficient Hardware Implementation for Saber Using NTT Multiplication

Although Saber is no longer a candidate in the fourth round of the National Institute of Standards and Technology (NIST) just-announced post-quantum cryptography standardization process, Saber's lightweight and high-speed hardware implementation is still research interest and could be applied to digital signatures. During the process of encryption and decryption, polynomial multiplication is the most time-consuming and resource-consuming unit. Most of the previous research used Toom-cook or Karatsuba multiplication. In this paper, we use the saber-unfriendly but fast NTT multiplication, and a switchable butterfly computing unit is used in the polynomial multiplication process to improve speed and reduce the number of registers. The proposed design provides a good balance between frequency and area consumption which only uses 2247 LUTs, 327 FFs, and 6 DSPs and is capable of high clock frequency on FPGAs by 327MHz.
Blockchain in Healthcare for Achieving Patients’ Privacy

Heath data are sensitive and valuable for individuals. The patients need to integrate and manage their medical data continuously. Personal Health Record (PHR) is introduced as a solution for managing their health information. It gives patients ownership over their medical data and provides physicians with realignment data. However, it does not achieve reliability, traceability, trust, nor security of patient control. Centralization of any data is vulnerable to the problem of hacking and single failure in addition to control from one organization. So, the centralization of data is the common problem that all current healthcare systems suffer from. Even in the Metaverse world, the application of Metaverse in healthcare services loses users’ privacy as one of its challenges. In this study, we suggest using blockchain in healthcare to improve security and privacy in medical records. The proposed system employs the advantages of blockchain technology to give patients full control over their data with low throughput, high overhead, and latency. We present a security analysis of our suggested architecture as well as blockchain issues in healthcare systems.
A Survey on BERT and Its Applications

A recently developed language representation model named Bidirectional Encoder Representation from Transformers (BERT) is based on an advanced trained deep learning approach that has achieved excellent results in many complex tasks, the same as classification, Natural Language Processing (NLP), prediction, etc. This survey paper mainly adopts the summary of BERT, its multiple types, and its latest developments and applications in various computer science and engineering fields. Furthermore, it puts forward BERT's problems and attractive future research trends in a different area with multiple datasets. From the findings, overall, the BERT and their recent types have achieved more accurate, fast, and optimal results in solving most complex problems than typical Machine and Deep Learning methods.
Solution to Web Scraping

Data retrieval from a website, frequently automatically and without the owner’s consent, is known as scraping. This information can also be utilized however the scraper sees fit. The action is considered criminal, but the legality has changed because hasn’t stopped others from following suit. anti-scraping instruments are not used though. Anti-scraping Solutions are offered as fairly expensive services that are both slow, and effective services. This essay list offers suggestions for reducing impediments. the development of an anti-scraping System as a Product app for small- to medium-sized websites. are expensive.
Indian Sign Language Recognition System for Emergency Words by Using Shape and Deep Features

Sign Language Recognition Systems (SLRS) are an advanced communication tool that provides a platform for the deaf community to communicate with each other or with the hearing community. In the past decade, SLRS has made significant advances; however, they are still far from realworld implications. Most existing techniques for Sign Language (SL) recognition using the Convolution Neural Network (CNN) based approach provide satisfactory results for isolated word recognition; however, the accuracy can be improved by incorporating handcrafted features with CNN features and eliminating redundant frames. Thus, this paper proposes an Expert System for Indian Sign Language Recognition (ESISLR) for accurately and efficiently predicting isolated sign words. The ESISLR incorporates a keyframe extraction technique for eliminating redundant frames from an extensive sequence of frames. Then the system uses a combination of CNN and handcrafted features, which are given to a stacked Bi Directional Long Short Term Memory (BiLSTM) network for sequence learning. The proposed model uses VGG-19 for CNN feature extraction and Hu Moments (HM) and Zernike Moments (ZM) for extracting handcrafted features. The proposed model achieved an average accuracy of 93.68% with HM and 94.17% with ZM.
A High Throughput and Configurable Pseudo-random Number Extension Generator for Lattice-based Post-quantum Cryptography

Pseudo-random number extension and hashing limit the time for encryption and decryption in multiple lattice-based post-quantum cryptography (PQC). Keccak is a crucial part in pseudo-random number extension and hashing, being the most restrictive module. With the requirement of high-performance, it is important to implement a configurable Keccak core with flexibility and high throughput. In this paper, a novel structure of high throughput pseudo-random number extension generator is proposed. The method utilizes two-stage series round function circuits to reduce cycles in half. And benefiting from combining the p, π, σ, and I steps into a single step in the Keccak, the logic resource overhead is reduced. It can be configured to support multiple sampling strategies including central binomial distribution and rejection. This work is implemented on ZYNQ UltraScale+ FPGA platform with the highest throughput of 11.7Gbps. Compared to related works, the high-throughput and configurability make the proposed pseudo-random number extension generator suitable for various lattice-based cryptographic schemes.
Design and Optimization of PID Controller based on Metaheuristic algorithms for Hybrid Robots

Metaheuristics optimization techniques are significant to search methods that are used to solve challenging Artificial intelligence (AI) problems. In hybrid robot control systems, Meta-heuristic optimization methods are widely applied. The major goal of this paper is to develop optimized PID control parameters to improve the performance of the hybrid robot control system. For that purpose, two optimization techniques followed by fine-tuning are proposed and simulated to get the optimized PID parameters. The first proposed optimization method applies the Satin Bowerbird (SB) optimization technique to optimize the PID parameters. The Crow Search Optimization (CSO) technique is applied to the SB results to improve the algorithm's performance and the PID parameters. The second proposed method applies the Emperor Penguin Optimization (EPO) technique for the optimization of the PID parameters. The results of both methods are fine-tuned. Moreover, a Kalman filter is used to improve the outcomes after and before tuning the PID parameters. Simulation results show that the proposed first method is more effective for the optimization methods of the PID controller, and its results outperform the results given by previously published research.
A Comprehensive Review on Access Control Systems amid Global Pandemic

In the contemporary time of technology, security is the utmost concern for every building automation system. Access Control Systems are the backbone of any security system being employed in any intelligent building, and can be operated in a biometric or non-biometric manner. There are various types of recognition systems available, depending upon the required level of safety and security. The ongoing pandemic has challenged and tested Access Control System in many aspects.This paper aims to review the various forms of access control systems and their viability in the context of COVID-19. It is found that some access control solutions fail to provide the required security during this global epidemic due to their contact-based operations. So, in the midst of the worldwide pandemic, a realistic integrated electronic access control system can be designed to meet the requirements of users.
A Dynamic JPEG CODEC with Adaptive Quantization Table for Frame Storage Compression

Joint Photographic Experts Group (JPEG) is one of the most advanced compression formats for still images. However, JPEG is still limited and inefficient in real-time image processing and hardware implementation. This paper presents a dynamic JPEG CODEC (the encoder and decoder) to compress the frame storage. Based on the pipeline structure, the system uses ping-pong buffers to support the circuit across two clock domains. In addition, through dynamically adjusting the quantization table, the frame storage compression can fluctuate around 80% with high quality. Also, the simplification of compression decreases the initialization time of the decoder. The proposed word can yield a reduction of the frame storage of 76.60% and an improvement of the latency of 77.10% compared to the baseline JPEG CODEC.