Reviewerly/exHarmony huggingface dataset:
Deep Learning Architecture for Detecting SQL Injection Attacks Based on RNN Autoencoder Model

SQL injection attacks are one of the most common types of attacks on Web applications. These attacks exploit vulnerabilities in an application’s database access mechanisms, allowing attackers to execute unauthorized SQL queries. In this study, we propose an architecture for detecting SQL injection attacks using a recurrent neural network autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then, it was compared with several other machine learning models, including ANN, CNN, decision tree, naive Bayes, SVM, random forest, and logistic regression models. The experimental results showed that the proposed approach achieved an accuracy of 94% and an F1-score of 92%, which demonstrate its effectiveness in detecting QL injection attacks with high accuracy in comparison to the other models covered in the study.
Zero trust cybersecurity: Critical success factors and A maturity assessment framework

Zero trust cybersecurity is beginning to replace traditional perimeter-based security strategies and is being adopted by organizations across a wide range of industries. However, the implementation of zero trust is a complex undertaking, different from traditional perimeter-based security, and requires a fresh approach in terms of its management. As such, a clear set of critical success factors (CSFs) will help organizations to better plan, assess, and manage their zero trust cybersecurity. In response, we investigated the CSFs for implementing zero trust cybersecurity by conducting a three-round Delphi study to obtain the consensus from a panel of 12 cybersecurity experts. We built a multi-dimensional CSFs framework that comprises eight dimensions, namely identity, endpoint, application and workload, data, network, infrastructure, visibility and analytics, and automation and orchestration. Based on the CSFs, we developed a maturity assessment framework enabling organizations to evaluate their zero trust maturity. This paper contributes to a theoretical understanding of how to deploy zero trust from multiple dimensions and offers a viable guidance framework for organizations from a practical perspective. This paper is useful for organizational stakeholders who are in the process of planning, reviewing, or implementing zero trust cybersecurity.
A systematic literature review of blockchain technology and environmental sustainability: Status quo and future research

This study conducted a systematic literature review on the function and potential of blockchain technology in promoting environmental sustainability to achieve sustainable development goals (SDG 7 and SDG 13). We chronologically explored theoretical viewpoints, analytical frameworks, measures, relationships, and procedures from 2015 to 2022 to examine the relationship between blockchain technology and environmental sustainability. We followed a four-step methodology (planning, execution, analysis, and reporting) based on Tranfield et al.’s (2003) approach. The extensively preferred PRISMA technique was adopted for conducting a systematic literature review. VOSviewer program was used in this study for bibliometric analysis. The contribution of this study is that this is the first one to use a bibliometric analysis and SLR on the topic of blockchain technology, environment, and green finance from the standpoint of the SDGs, specifically SDG 7 and SDG 13. The findings suggest that to attain environmental sustainability, new approaches are required. New methods to achieve environmental sustainability are possible because of emerging technology like blockchain. Policymakers will benefit from understanding the effect of market interference and the available choices while considering their disruptive consequences. The findings are beneficial for political institutions in establishing the political and legal environment required to develop successful green blockchain applications.
Directive (EU) 2019/790 of the European Parliament and of the Council of 17 April 2019 on copyright and related rights in the Digital Single Market and amending Directives 96/9/EC and 2001/29/EC

Subject EU Law Intellectual Property Law Collection: Oxford Scholarship Online
Summary of insulator defect detection based on deep learning

Insulator is a kind of key electrical equipment in transmission line. Its long-term operation in strong electric field, high temperature sunshine, mechanical stress, humidity, dirt and other complex environment. This is likely to lead to self-detonation, pollution, flashover and leakage current and other defects, which will reduce insulation performance and seriously affect the safety of power grid. Therefore, power insulator defect detection is an important task in power system detection. How to identify insulator defects quickly and accurately has been an important problem for many scholars. Firstly, this paper reviews the insulator defect detection technology based on traditional detection algorithm. Secondly, the application and key technologies of deep learning algorithm in insulator defect detection are introduced in detail. Then it introduces the challenges and future development trend of this field; Finally, it summarizes this paper and puts forward some suggestions for the development of this field.
AdvMask: A sparse adversarial attack-based data augmentation method for image classification

Data augmentation has been an essential technique for improving the generalization ability of deep neural networks in image classification tasks. However, intensive changes in appearance and different degrees of occlusion in images are the key factors that severely affect the generalization ability of image classification models. Therefore, in order to enhance the generalization performance and robustness of deep models, data augmentation approaches by providing models with more diverse training data in various scenarios are widely applied. Although many existing data augmentation methods simulate occlusion in the augmented images to enhance the generalization of models, these methods randomly delete some areas in images without considering the semantic information of images. In this work, we propose a novel data augmentation method named AdvMask for image classification based on sparse adversarial attack techniques. AdvMask first identifies the key points that have the greatest influence on the classification results via a proposed end-to-end sparse adversarial attack module. During the data augmentation process, AdvMask efficiently generates diverse augmented data with structured occlusions based on the key points. By doing so, AdvMask can force deep models to seek other relevant content while the most discriminative content is hidden. Extensive experimental results on various benchmark datasets and deep models demonstrate that our proposed method can effectively improve the generalization performance of deep models and significantly outperforms previous data augmentation methods. Code for reproducing our results is available at https://github.com/Jackbrocp/AdvMask.
Deep fidelity in DNN watermarking: A study of backdoor watermarking for classification models

Backdoor watermarking is a promising paradigm to protect the copyright of deep neural network (DNN) models. In the existing works on this subject, researchers have intensively focused on watermarking robustness, while the concept of fidelity, which is concerned with the preservation of the model’s original functionality, has received less attention. In this paper, focusing on deep image classification models, we show that the existing shared notion of the sole measurement of learning accuracy is inadequate to characterize backdoor fidelity. Meanwhile, we show that the analogous concept of embedding distortion in multimedia watermarking, interpreted as the total weight loss (TWL) in DNN backdoor watermarking, is also problematic for fidelity measurement. To address this challenge, we propose the concept of deep fidelity, which states that the backdoor watermarked DNN model should preserve both the feature representation and decision boundary of the unwatermarked host model. To achieve deep fidelity, we propose two loss functions termed penultimate feature loss (PFL) and softmax probability-distribution loss (SPL) to preserve feature representation, while the decision boundary is preserved by the proposed fix last layer (FixLL) treatment, inspired by the recent discovery that deep learning with a fixed classifier causes no loss of learning accuracy. With the above designs, both embedding from scratch and fine-tuning strategies are implemented to evaluate the deep fidelity of backdoor embedding, whose advantages over the existing methods are verified via experiments using ResNet18 for MNIST and CIFAR-10 classifications, and wide residual network (i.e., WRN28_10) for CIFAR-100 task. PyTorch codes are available at https://github.com/ghua-ac/dnn_watermark.
No more privacy Concern: A privacy-chain based homomorphic encryption scheme and statistical method for privacy preservation of user’s private and sensitive data

Internet giants collects a huge volume of data using the modern technologies and apply them to data mining for prediction and decision making in numerous fields, for instance, health care, web analysis, insurance, market basket analysis and bioinformatics. However, the huge repositories collects variety of data, it inevitably gathers a mass of private and sensitive data about the users. The revelation of such private and sensitive information may harm the user’s privacy. In recent years, privacy is becoming an increasingly main concern in many data mining applications. Privacy preserving data mining (PPDM) has been evolved as the solution for the privacy issues. PPDM deals with protecting the individual’s data without compromising the data utility. In this paper, privacy-chain based homomorphic encryption scheme along with the statistical transformation method weight of evidence and information value is proposed to preserve the private and sensitive information about the user. The proposed statistical transformation with homomorphic encryption (STHE) algorithm will perturb both the numerical and categorical values from the adult income, bank marketing and lung cancer datasets without affecting the data utility. In STHE, the quasi-identifiers are initially perturbed with IV and then privacy-chain based homomorphic encryption using RSA is applied to preserve the data privacy. The performance of the STHE algorithm is made compared with the state-of-the-art algorithms using the classifier models, decision tree, random forest, extreme gradient boost, and support vector machines. The experimental results shows that the proposed STHE algorithm outperforms the existing techniques in terms of accuracy, performance, data transformation, data retrieval and privacy preservation level along with the data utility.
OCRUN: An oppositional Runge Kutta optimizer with cuckoo search for global optimization and feature selection

The recently proposed swarm intelligence algorithm, Runge–Kutta Optimization (RUN), is rooted in the fourth-order Runge–Kutta method. Compared with its counterparts, RUN boasts an advantage of having a more concrete theoretical foundation embodying a more powerful optimization efficacy, free from any metaphor. However, RUN still has its shortcomings. The compelling enhanced solution function leads to insufficient exploration ability of the algorithm, and resulting in an imbalance between exploration and exploitation that cannot be mitigated. An improved version based on opposition-based learning and cuckoo search is proposed to compensate for the above deficiencies, called OCRUN. OCRUN is tested on 30 test functions of CEC2014 with 10 classical metaheuristics and 9 advanced metaheuristics, respectively. Combining the experimental results and the Wilcoxon signed-rank test, OCRUN exhibits excellent performance. At the same time, parameter sensitivity analysis experiments are also carried out on this test set. Furthermore, a binary implementation of the algorithm was constructed specifically for feature selection cases, labeled as BOCRUN. BOCRUN is compared with 5 existing binary metaheuristics on 15 public datasets. The experimental results show that the improved algorithm performs well in feature selection. Therefore, OCRUN is an effectively improved optimizer. Finally, the OCRUN method offers high-quality solutions to engineering problems and contributes significantly to engineering design under practical constraints. The method has been successfully applied to various design scenarios, such as reducer design, cantilever beam design, and tension/compression spring design. The OCRUN method outperforms other similar products in terms of performance and effectiveness.
Convergent Fuzzy Cognitive Modelling of Regional Youth Policy Strategy

The article aims to accelerate strategic meetings in the country's regional governments. The fuzzy cognitive modelling and fuzzy cognitive maps (FCM), along with the author's convergent methodology, have helped to make this goal achievable. The convergent methodology suggests a unique approach for structuring information generated during the strategic meeting by applying the inverse problem-solving method to the fuzzy cognitive modelling to transform the divergent strategic discussion into a convergent one and make a strategic meeting sustainable and purposeful. The FCM helps to represent the non-formalisable cognitive semantics of computer models. The relevant big data analysis was used to justify fuzzy cognitive models and create them automatically. A high level of accuracy was achieved for verifying models; however, the accuracy of the latter process needed to be higher. The practical objective was to speed up the collective creation of a strategy for youth policy development for one of the country's regions. Three dozen participants, including remote experts and the youth policy department authorities, took part in the strategic meeting. It took five hours to collective building the draft of the strategy.
Financial development, income inequality, and institutional quality: A multi-dimensional analysis

Ambiguous impacts of financial development on income inequality in the literature imply that the impacts can be affected by other variables and may depend on different dimensions of financial development. This paper studies the effects of financial development with multi-dimensional analysis (financial depth, financial access, and financial efficiency) of two main categories (financial institutions and financial markets) and institutional quality on income inequality in 30 Asian countries in the period 2000–2019. Results show that the financial institutions development (FI), the financial institutions access (FIA), the financial institutions efficiency (FIE), and the financial markets access (FMA) reduce income inequality; but the overall financial development (OFD), the financial markets development (FM), the financial institutions depth (FID), and the financial markets depth (FMD) increase it. Notably, better institutional quality not only lessens income inequality but also moderates the effects of financial development on income inequality. Specifically, the improvement of institutional quality strengthens the beneficial effects of FI, FIA, FIE, and FMA on income inequality. Meanwhile, OFD, FM, FID, and FMD initially exacerbate income inequality until respective thresholds of institutional quality, and then beyond those levels of IQ, these indicators of financial development reduce income inequality. Results are robust with various estimators. These findings strongly support the importance of financial development with multi-dimensions and institutional reform in Asian countries as they have both direct and indirect impacts on income inequality through their mutual interactions.
Threshold BBS+ Signatures for Distributed Anonymous Credential Issuance

We propose a secure multiparty signing protocol for the BBS+ signature scheme; in other words, an anonymous credential scheme with threshold issuance. We prove that due to the structure of the BBS+ signature, simply verifying the signature produced by an otherwise semi-honest protocol is sufficient to achieve composable security against a malicious adversary. Consequently, our protocol is extremely simple and efficient: it involves a single request from the client (who requires a signature) to the signing parties, two exchanges of messages among the signing parties, and finally a response to the client; in some deployment scenarios the concrete cost bottleneck may be the client’s local verification of the signature that it receives. Furthermore, our protocol can be extended to support the strongest form of blind signing and to serve as a distributed evaluation protocol for the Dodis-Yampolskiy Oblivious VRF. We validate our efficiency claims by implementing and benchmarking our protocol.
smartFHE: Privacy-Preserving Smart Contracts from Fully Homomorphic Encryption

Despite the great potential and flexibility of smart contract-enabled blockchains, building privacy-preserving applications using these platforms remains an open question. Existing solutions fall short since they ask end users to coordinate and perform the computation off-chain themselves. While such an approach reduces the burden of the miners of the system, it largely limits the ability of lightweight users to enjoy privacy since performing the actual computation on their own and attesting to its correctness is expensive even with state-of-the-art proof systems.To address this limitation, we propose smartFHE, a framework to support private smart contracts using fully homomorphic encryption (FHE). To the best of our knowledge, smartFHE is the first to use FHE in the blockchain model; moreover, it is the first to support arbitrary privacy-preserving applications for lightweight users under the same computation-on-demand model pioneered by Ethereum. smartFHE does not overload the user since miners are instead responsible for performing the private computation. This is achieved by employing FHE so miners can compute over encrypted data and account balances. Users are only responsible for proving well-formedness of their private inputs using efficient zero-knowledge proof systems (ZKPs). We formulate a notion for a privacy-preserving smart contract (PPSC) scheme and show a concrete instantiation of our smartFHE framework. We address challenges resulting from using FHE in the blockchain setting—including concurrency and dealing with leveled schemes. We also show how to choose suitable FHE and ZKP schemes to instantiate our framework, since naively choosing these will lead to poor performance in practice. We formally prove correctness and security of our construction. Finally, we conduct experiments to evaluate its efficiency, including comparisons with a state-of-the-art scheme and testing several private smart contract applications. We have open-sourced our (highly optimized) ZKP library, which could be of independent interest.
Efficient and secure chaotic PRNG for color image encryption

Random number generation is considered a major problem for any digital color image cipher algorithm and secret communication protocol. Pseudo-random number generators (PRNGs) are used in many security applications because they afford both features, the randomness efficiency and quality of the PRNG. The paper proposes a new PRNG depending on a combination of the Secure Hash Algorithm (SHA-2(256)) with the results of three chaotic maps: the 2D Henon map, the 2D Kaplan-Yorke map, and the 2D Tinkerbell map. In the proposed chaotic generator, 6 initial floating-point numbers with 10−16 precision are inputs to SHA-2(256) for generating the initial values of the three chaotic maps. Then the chaotic maps are iterated to produce a binary sequence that is processed to enhance the sequence complexity based on binary XOR addition operation and 2D shifting scrambling method. The random binary sequences produced by the proposed PRNG algorithm were tested using several number of analyses like the national statistical test suite, correlation analyses, key space analyses, Sum of absolute analyses (SAD), key sensitivity analyses, and histogram analyses. The results proved that scrambling binary sequences have passed successfully the NIST tests where the ratio η of p-value concerns individual sequences between [0.9805, 0.9994], the correlation between the produced sequences is very small where the distributions of Pearson's correlation coefficients belong to [-0.03, 0.03] and the distributions of Hamming distance coefficients belonging to [0.49, 0.52], all the results of the SAD between [0.33237, 0.33451] which are almost close ideal values, and the key space of proposed PRNG is very large which is 2319. The proposed PRNG was applied as a random key generation algorithm on a new color image cipher algorithm, with strong permutation and substitution methods to encrypt and decrypt each image color channel. The practicableness of the algorithm was tested by different statistical and security analyses.
Design of IoT based Rural Health Helper using Natural Language Processing

A proposed system that utilizes machine learning and natural language processing aims to facilitate communication among individuals who speak different languages. Specifically, doctors can use a prototype of this system to access information from a database about the health of their patients. The paper suggests that Natural Language Processing (NLP) can be used to minimize communication delays during online consultations between doctors and patients. NLP plays a crucial role in smart healthcare by processing textual data and enabling human-machine communication. Through the use of machine learning algorithms, NLP can teach computers to comprehend natural human language, including speech and text, and extract useful information from unstructured data. This system can overcome language barriers, allowing patients to communicate in their native language, while doctors can respond in English or other languages, enabling a better user experience for everyone.
Evaluating YOLOV5, YOLOV6, YOLOV7, and YOLOV8 in Underwater Environment: Is There Real Improvement?

This paper compares several new implementations of the YOLO (You Only Look Once) object detection algorithms in harsh underwater environments. Using a dataset collected by a remotely operated vehicle (ROV), we evaluated the performance of YOLOv5, YOLOv6, YOLOv7, and YOLOv8 in detecting objects in challenging underwater conditions. We aimed to determine whether newer YOLO versions are superior to older ones and how much, in terms of object detection performance, for our underwater pipeline dataset. According to our findings, YOLOv5 achieved the highest mean Average Precision (mAP) score, followed by YOLOv7 and YOLOv6. When examining the precision-recall curves, YOLOv5 and YOLOv7 displayed the highest precision and recall values, respectively. Our comparison of the obtained results to those of our previous work using YOLOv4 demonstrates that each version of YOLO detectors provides significant improvement.