Reviewerly/exHarmony huggingface dataset:
Deep network for underwater image enhancement inspired by physical imaging model

Underwater images often suffer from problems such as low contrast, color distortion, and blurred details, which have a negative impact on subsequent image processing tasks. To mitigate such problems, we propose an algorithm that combines an underwater physical imaging model with a convolutional neural network. The physical imaging model has two important types of parameters: background scattering parameters and direct transmission parameters. For the background scattering parameters, we divide them into three levels, which are primary information, secondary information, and advanced information, and design three subnetworks for feature extraction to represent them. For the direct transmission parameters, we decompose them into two levels, which are shallow transmission information and deep transmission information, and also design two subnetworks to indicate them. Experimental results show that, compared with other enhancement algorithms, the proposed algorithm can not only effectively correct the color deviation and enhance the object edges and texture details but also can obtain superior values of objective evaluation metrics in terms of peak signal-to-noise ratio, structural similarity, underwater color image quality evaluation, and underwater image quality measure.
Blockchain Technology Adoption in Small and Medium Enterprises: Indian Perspective

The purpose of this research is to understand the possible linkage between variables that constitute blockchain technology adoption in small and medium enterprises (SMEs). In the study, the authors tried to decode the complex relationship among variables which is missing in extant literature. The authors employed interpretative structural modeling (ISM) to detect the complicated equations among variables after identifying the variables through a review of the literature. The ISM model helps the industrialist to understand the various process involved in the adoption of blockchain technology (BCT) in SMEs. The results of the study identified that a three-level approach is needed for the adoption of BCT in SMEs. First, the company needs to have a sophisticated usage of information technology. IT project management, innovation complexity, benefit compared with other technological choices, compatibility with the organization, and trialability are the level 2 variables in which the small-scale industries have to focus before the implementation of BCT. Once the company is equipped with first and second-level variables, SME should focus on variables such as the implementation cost of BCT, software revision of BCT, the maintenance cost of BCT, and observability.
CRYPTOLIGATION: An Offbeat Blueprint of Crypto Contract in the Decentralized Administration

Building models for improved decision-making using mobile, IoT, social media, cloud, and other technologies is driving higher efficiency, digital products, and deeper consumer relationships in the digital era. During negotiation and execution, the terms of an underlying legal contract are digitally facilitated, verified, or enforced through smart contracts, a sort of electronic transaction protocol to meet typical contractual requirements. In order to lower transaction costs, including arbitration and enforcement expenses, this article intends to create an unusual intelligent contract architecture. In this case, it is done by implementing trackable and irreversible transactions using blockchain technology for distributed databases. By fostering the entrepreneurial collaboration of cross-organizational business activities typical of intelligent supply chains, ‘CRYPTOLIGATION’ has the potential to go much beyond cost savings. This approach concerns that are covered in this article include how and to what degree blockchain technology and smart contracts might help design cooperative transaction structures for long-term P2P activities in intelligent supply chains. The goal of ‘CRYPTOLIGATION’ is to look at the challenges, difficulties, and advantages and by using blockchain technology in many fields of the present research work. The obstacles to smart contracts in a real-world setting in future are discussed in this paper's conclusion.
Passion-Net: a robust precise and explainable predictor for hate speech detection in Roman Urdu text

Abstract With an aim to eliminate or reduce the spread of hate content across social media platforms, the development of artificial intelligence supported computational predictors is an active area of research. However, diversity of languages hinders development of generic predictors that can precisely identify hate content. Several language-specific hate speech detection predictors have been developed for most common languages including English, Chinese and German. Specifically, for Urdu language a few predictors have been developed and these predictors lack in predictive performance. The paper in hand presents a precise and explainable deep learning predictor which makes use of advanced language modelling strategies for the extraction of semantic and discriminative patterns. Extracted patterns are utilized to train an attention-based novel classifier that is competent in precisely identifying hate content. Over coarse-grained benchmark dataset, the proposed predictor significantly outperforms state-of-the-art predictor by 8.7% in terms of accuracy, precision and F1-score. Similarly, over fine-grained dataset, in comparison with state-of-the-art predictor, it achieves performance gain of 10.6%, 17.6%, 18.6% and 17.6% in terms of accuracy, precision, recall and F1-score.
Secured Covert Communication Through Blockchain Technology

Blockchain is a public open ledger that provides data integrity in a distributed manner. It is the underlying technology of cryptocurrencies and an increasing number of related applications, such as smart contracts. Secure covert communication can be implemented as a combination of cryptography and steganography. Cryptography ensures that the communicated message stays private, while steganography is used to hide the fact that the encrypted communication provide. The key idea of this investigation is to automate the covert communication on blockchain technology. Covert communication channels are designed to protect the relationship between the transmitter and the receiver by hiding the fact that secret communication is taking place. The proposed scheme provides higher embedding capacities compared to the existing schemes. The motivation behind the development of this research work is an attempt which create a practical application that will help in improving the emerging use of IT technologies in a developing country like India today.
From lab to industry: lessons learned from the evaluation of augmented and virtual reality use cases in the Austrian manufacturing industry

For evaluation of industrial augmented and virtual reality (AR/VR) applications, a comprehensive knowledge of their impact on productivity, process metrics, and human factors is critical to facilitate the transition from lab-scale research to industrial settings. While laboratory-scale evaluations have been extensively researched, successful implementation in industrial settings has fallen short. This paper presents a multi-criteria evaluation model using a mixed methods approach to study thirteen industrial use cases of AR/VR applications in manufacturing over two years. Semi-structured interviews were conducted with companies about the impact of the application. A human factors questionnaire was completed by 69 participants, including in-house users, industry experts, and inexperienced novices. The results of the use cases show an average reduction of 23.61% in process and 25% in cycle time. Process quality indicators improved significantly compared to baseline. The evaluation found no significant differences in workload and ease of use between age groups.
Securing Farm Insurance Using a Private-Permissioned Blockchain Driven by Hyperledger Fabric and IPFS

Bangladesh is a nation that mainly relies on agriculture for its resources. And any farm could experience a mishap that would result in irreparable losses and near bankruptcy for the owner. When livestock is injured or killed as a result of an accident or disease, farms, ranches, and other businesses that raise or care for livestock, such as livestock farms, can suffer financial losses. By purchasing farm insurance, farm owners can protect themselves against the loss of priceless animals and farm equipment. However, an effective structure is needed to ensure insurance which is easy to administer and tailored to farm owners, helping insurers propose appropriate insurance and identify fake insurance claims. In this study, we developed a system that assists insurance companies in conducting farm insurance, utilizing the hyperledger fabric-based blockchain in the safest way imaginable. We opted to use this technology due to its private nature, which offers access control and ensures the protection of sensitive data, which is the key priority for any insurance company. In an experimental test, our system outperformed Ethereum and Bitcoin with a scalability of 0.021 s for each transaction.
A Novel Approach of Network Security Using Genetic Algorithm

This article focuses on providing security of sensitive information and message transmission using symmetric key cryptosystem based on genetic algorithm and combined with mathematical and bitwise operators to provide confidentiality, authenticity, integrity, and nonrepudiation of the messages. A plain text is encrypted using a private key and resizer parameter taken as input from the user to produce an intermediate cipher which is further encrypted using genetic algorithm and mathematical operations to obtain the final cipher. The level of complexity of encryption and the size of the final cipher text can be regulated using the resizer parameter. The final cipher is decrypted first to obtain an intermediate cipher, which in turn is decrypted using the private key to get back the plain text.
Detection of Deepfakes in Financial Transactions Using Algorand Blockchain Consensus Mechanism

Algorand is a new cryptocurrency confirming transactions in a few minutes and scales to a large number of users. Even if some users are bad and the network is momentarily partitioned, Algorand assures that users never have contradictory views of confirmed transactions. Contrarily, current cryptocurrencies support momentary forks and demand a long time to confirm transactions with great confidence. Algorand uses a unique Byzantine Agreement (BA) technique based on Verifiable Random Functions (VRFs) to scale consensus to many users in order to reach an agreement among users on the upcoming round of transactions. In order to agree on the following set of transactions, this technique enables users to secretly verify if they have been chosen to engage in the BA and to include evidence of their selection in the network messages. In BA used in Algorand, other than their private keys, no other private values are stored, so users can be instantaneously replaced after sending the message. This protects selected individuals from targeted attacks after their identities are known and also helps to detect deepfakes. Experimental results have proven that the confirmed transactions are achieved within a minute and detect deepfake users in minimum time.
IEMS3: An Image Encryption Scheme Using Modified SNOW 3G Algorithm

This paper deals with an updated SNOW 3G key stream generator (KSG). The proposed KSG can be applied to encrypt images in the Internet of Things (IoT) applications. The KSG algorithm replaces the LFSR structure of SNOW 3G by a $$\sigma -$$ LFSR of delay blocks of $$64-$$ bit input–output and 8 gain matrices. It also incorporates a new feedback polynomial with 8, $$64 \times 64$$ coefficient matrices over GF(2). As a nonlinear component, 16, 8 input-8 output parallel S-BOXes are concatenated to produce the 64 input and 64 output S-BOXes S1 and S2. The algorithm generates a 64 bit pseudorandom number in each clock pulse, making the image encryption faster than some existing techniques. We practically justify the algorithms with various security parameter analyses.
SVD-Based Watermarking Scheme for Medical Image Authentication

In the current pandemic COVID-19 situation, many patient records are exposed without their knowledge, so, a novel technique is needed to protect the authenticity of patient information by incorporating a new watermark to the medical images. So checking the authenticity of any digital copy becomes very much important aspects of digital communications. In this proposed work, a novel SVD based watermarking method has been introduced for medical image authentication in various medical applications. This method examined against various types of threads and attacks. The results, show that the proposed scheme achieved 73.36 dB PSNR and SSIM is near about 0.99 with 64,980 bit embedding capacity. Compared to the traditional methods, proposed method effectively increases the accuracy of tamper localization as well as PSNR value of the self-recovered image are both significantly increased.
Secured Information Communication Exploiting Fuzzy Weight Strategy

This work uses the fuzzy weight strategy to construct a novel image interpolation method. By taking into account the fuzzy weight value of each pair of pixels in a chosen block, the interpolated pixel values are created. Each input pixel pair’s fuzzy membership values have been taken to represent the range between the block’s minimum and maximum value. The input membership value is fed into the fuzzy output function, which calculates the fuzzy rule’s strength using the Max–Min composite principle. Then, through a defuzzification process, interpolated pixel values are calculated from the fuzzy output function dependent on the strength of the fuzzy rule. In actuality, fuzzy weight based interpolation algorithms create virtual pixels, which are superior to the interpolation techniques now in use. The results of the experiments show that the suggested method almost always produced images with the highest PSNR. So, in the suggested technique, the FWS was used to generate the improved cover image.
InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images

Power line maintenance and inspection are essential to avoid power supply interruptions, reducing its high social and financial impacts yearly.Automating power line visual inspections remains a relevant open problem for the industry due to the lack of public real-world datasets of power line components and their various defects to foster new research.This paper introduces InsPLAD, a Power Line Asset Inspection Dataset and Benchmark containing 10,607 high-resolution Unmanned Aerial Vehicles colour images.The dataset contains seventeen unique power line assets captured from real-world operating power lines.Additionally, five of those assets present six defects: four of which are corrosion, one is a broken component, and one is a bird's nest presence.All assets were labelled according to their condition, whether normal or the defect name found on an image level.We thoroughly evaluate state-of-the-art and popular methods for three image-level computer vision tasks covered by InsPLAD: object detection, through the AP metric; defect classification, through Balanced Accuracy; and anomaly detection, through the AUROC metric.InsPLAD offers various vision challenges from uncontrolled environments, such as multi-scale objects, multi-size class instances, multiple objects per image, intra-class variation, cluttered background, distinct point-of-views, perspective distortion, occlusion, and varied lighting conditions.To the best of our knowledge, InsPLAD is the first large real-world dataset and benchmark for power line asset inspection with multiple components and defects for various computer vision tasks, with a potential impact to improve state-of-the-art methods in the field.It will be publicly available in its integrity on a repository with a thorough description.It can be found at https://github.com/andreluizbvs/InsPLAD/or https://drive.google.com/drive/folders/1psHiRyl7501YolnCcB8k55rTuAUcR9Ak?usp=sharing.
Malware Analysis Based on Malicious Web URLs

Malicious URL distribution channels are used to host malicious data over the Internet. They are primarily responsible for transmitting spam, malware, adware, spoofing, inappropriate data, and resources. So victims get exploited for information disclosure, gaining unwanted access, financial loss, and extortion. Web applications are used by cyber attackers to gain remote access and covertly monitor sensitive data. Victims are driven by email, social media sites, or Web searches through malicious URLs. So, it gets a compromise. Traditional techniques like blacklisting, signature matching, and pattern matching are getting more complex due to the ever-growing volume of signatures, patterns, and features. Technology is changing over time and needs continuous innovation to sustain itself. In this paper, we propose a novel mechanism for classifying malicious URLs by comparing benign Web-based URLs with well-known classifiers, and our proposed procedure evaluates benign Web-based URLs while considering other malicious parameters as well.
Correct Orchestration of Federated Learning Generic Algorithms: Formalisation and Verification in CSP

Federated learning (FL) is a machine learning setting where clients keep the training data decentralised and collaboratively train a model either under the coordination of a central server (centralised FL) or in a peer-to-peer network (decentralised FL). Correct orchestration is one of the main challenges. In this paper, we formally verify the correctness of two generic FL algorithms, a centralised and a decentralised one, using the Communicating Sequential Processes calculus (CSP) and the Process Analysis Toolkit (PAT) model checker. The CSP models consist of CSP processes corresponding to generic FL algorithm instances. PAT automatically proves the correctness of the two generic FL algorithms by proving their deadlock freeness (safety property) and successful termination (liveness property). The CSP models are constructed bottom-up by hand as a faithful representation of the real Python code and is automatically checked top-down by PAT.
Continuous Integration of Neural Networks in Autonomous Systems

The perception of the autonomous driving software of the FS223, a low-level sensor fusion of Lidar and Camera data requires the use of a neural network for image classification. To keep the neural network up to date with updates in the training data, we introduce a Continuous Integration (CI) pipeline to re-train the network. The network is then automatically validated and integrated into the code base of the autonomous system. The introduction of proper CI methods in these high-speed embedded software applications is an application of state-of-the-art MLOps techniques that aim to provide rapid generation of production-ready models. It further serves the purpose of professionalizing the otherwise script-based software production, which is re-done almost completely every year as the teams change from one year to the next.
Synthesizing Understandable Strategies

The result of reinforcement learning is often obtained in the form of a q-table mapping actions to future rewards. We propose to use SMT solvers and strategy trees to generate a representation of a learned strategy in a format which is understandable for a human. We present the methodology and demonstrate it on a small game.
Towards LLM-Based System Migration in Language-Driven Engineering

In this paper we show how our approach of extending Language Driven Engineering (LDE) with natural language-based code generation supports system migration: The characteristic decomposition of LDE into tasks that are solved with dedicated domain-specific languages divides the migration tasks into portions adequate to apply LLM-based code generation. We illustrate this effect by migrating a low-code/no-code generator for point-and-click adventures from JavaScript to TypeScript in a way that maintains an important property: generated web applications can automatically be validated via automata learning and model analysis by design. In particular, this allows to easily test the correctness of migration by learning the difference automaton for the generated products of the source and the target system of the migration.
Prediction of Continuous Behavior to Domestic Diving Tour in Scuba Divers by Using Theory of Planned Behavior and Information-Motivation-Behavioral Skills Model

Purpose: This study aims to overcome the limitations of the existing hTeory of Planned Behavior (TPB) by predicting the continuous behavior of scuba divers in domestic diving tours. It achieves this by incorporating the Information-Motivation-Behavioral Skills Model (IMB) within a social psychological framework.BR Method: The final analysis utilized 255 survey questionnaires collected from participants in domestic scuba diving tours. The data was processed using a structural equation model in AMOS 22.0.BR Results: First, information positively influenced behavioral skills but did not significantly impact attitude and subjective norm. Motivation had a positive influence on behavioral skills, attitude, and subjective norm. Second, subjective norm exerted the most significant influence on the continuous intention to undertake domestic diving tours, followed by perceived behavioral control and attitude. Third, behavioral skills had the most substantial impact on continuous behavior in dometisc diving tours, followed by motivation, perceived behavioral control, and information. Continuous intention to engage in domestic diving tours showed no significant influence.BR Conclusion: These findings suggest that the TPB and IMB together constitute a theoretical model capable of explaining and predicting the continuous intention and behavior in domestic diving tours.
A Federated Learning Algorithms Development Paradigm

At present many distributed and decentralized frameworks for federated learning algorithms are already available. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. A solution to that challenge named Python Testbed for Federated Learning Algorithms (PTB-FLA) appeared recently. This solution is written in pure Python, it supports both centralized and decentralized algorithms, and its usage was validated and illustrated by three simple algorithm examples. In this paper, we present the federated learning algorithms development paradigm based on PTB-FLA. The paradigm comprises the four phases named by the code they produce: (1) the sequential code, (2) the federated sequential code, (3) the federated sequential code with callbacks, and (4) the PTB-FLA code. The development paradigm is validated and illustrated in the case study on logistic regression, where both centralized and decentralized algorithms are developed.
Performance evaluation of deep learning techniques for human activity recognition system

Abstract Human Activity Recognition (HAR) is crucial in various applications, such as sports and surveillance. This paper focuses on the performance evaluation of a HAR system using deep learning techniques. Features will be extracted using 3DCNN, and classification will be performed using LSTM. Meanwhile, 3DCNN and RNN are two additional, well-known classification techniques that will be applied in order to compare the effectiveness of the three classifiers. The 3DCNN-LSTM approach contributes the highest overall accuracy of 86.57%, followed by 3DCNN-3DCNN and 3DCNN-RNN with the overall accuracy of 86.07% and 79.60%, respectively. Overall, this paper contributes to the field of HAR and provides valuable insights for the development of activity recognition systems.