Reviewerly/exHarmony huggingface dataset:
Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks

SQL injection attack is considered one of the most dangerous vulnerabilities exploited to leak sensitive information, gain unauthorized access, and cause financial loss to individuals and organizations. Conventional defense approaches use static and heuristic methods to detect previously known SQL injection attacks. Existing research uses machine learning techniques that have the capability of detecting previously unknown and novel attack types. Taking advantage of deep learning to improve detection accuracy, we propose using a probabilistic neural network (PNN) to detect SQL injection attacks. To achieve the best value in selecting a smoothing parament, we employed the BAT algorithm, a metaheuristic algorithm for optimization. In this study, a dataset consisting of 6000 SQL injections and 3500 normal queries was used. Features were extracted based on tokenizing and a regular expression and were selected using Chi-Square testing. The features used in this study were collected from the network traffic and SQL queries. The experiment results show that our proposed PNN achieved an accuracy of 99.19% with a precision of 0.995%, a recall of 0.981%, and an F-Measure of 0.928% when employing a 10-fold cross-validation compared to other classifiers in different scenarios.
The role of mobile money innovations in transforming unemployed women to self-employed women in sub-Saharan Africa

The study examines how mobile money innovations transform unemployed women to self-employed women. The empirical evidence is based on interactive quantile regressions focusing on data in 44 countries from sub-Saharan Africa for the period 2004 to 2018. The hypothesis that mobile money innovations transform female unemployment to female self-employment is tested. Eight mobile money innovation dynamics presented in four categories are employed. Three main common findings are apparent from interactions between female unemployment, eight mobile money innovation dynamics and female self-employment: (i) the investigated hypothesis is valid exclusively at the top quantiles of female self-employment; (ii) the net effects are consistently negative and (iii) the corresponding conditional or interactive effects upon which the net effects are based are consistently positive. This is an indication that critical masses at which money innovation innovations have an overall positive net effect on female self-employment are apparent. The corresponding mobile money innovation policy thresholds at which the net effects on female self-employment change from negative to positive are provided. Policy implications are discussed.
Quantum Nondemolition Measurements with Optical Parametric Amplifiers for Ultrafast Universal Quantum Information Processing

Realization of a room-temperature ultra-fast photon-number-resolving (PNR) quantum nondemolition (QND) measurement would have significant implications for photonic quantum information processing (QIP), enabling, e.g., deterministic quantum computation in discrete-variable architectures, but the requirement for strong coupling has hampered the development of scalable implementations. In this work, we propose and analyze a nonlinear-optical route to PNR QND using quadratic (i.e., $\chi^{(2)}$) nonlinear interactions. We show that the coherent pump field driving a phase-mismatched optical parametric amplifier (OPA) experiences displacements conditioned on the number of signal Bogoliubov excitations. A measurement of the pump displacement thus provides a QND measurement of the signal Bogoliubov excitations, projecting the signal mode to a squeezed photon-number state. We then show how our nonlinear OPA dynamics can be utilized for deterministically generating Gottesman-Kitaev-Preskill states only with additional Gaussian resources, offering an all-optical route for fault-tolerant QIP in continuous-variable systems. Finally, we place these QND schemes into a more traditional context by highlighting analogies between the phase-mismatched optical parametric oscillator and multilevel atom-cavity QED systems, by showing how continuous monitoring of the outcoupled pump quadrature induces conditional localization of the intracavity signal mode onto squeezed photon-number states. Our analysis suggests that our proposal may be viable in near-term $\chi^{(2)}$ nonlinear nanophotonics, highlighting the rich potential of OPA as a universal tool for ultrafast non-Gaussian quantum state engineering and quantum computation.
Evaluation of the Impact of the Sustainable Development Goals on an Activity Recognition Platform for Healthcare Systems

The Sustainable Development Goals (SDGs), also known as the Global Goals, were adopted by the United Nations in 2015 as a universal call to end poverty, protect the planet and ensure peace and prosperity for all by 2030. The 17 SDGs have been designed to end poverty, hunger, AIDS and discrimination against women and girls. Despite the clear SDG framework, there is a significant gap in the literature to establish the alignment of systems, projects or tools with the SDGs. In this research work, we assess the SDG alignment of an activity recognition platform for healthcare systems, called ACTIVA. This new platform, designed to be deployed in environments inhabited by vulnerable people, is based on sensors and artificial intelligence, and includes a mobile application to report anomalous situations and ensure a rapid response from healthcare personnel. In this work, the ACTIVA platform and its compliance with each of the SDGs is assessed, providing a detailed evaluation of SDG 7-ensuring access to affordable, reliable, sustainable and modern energy for all. In addition, a website is presented where the ACTIVA platform's compliance with the 17 SDGs has been evaluated in detail. The comprehensive assessment of this novel platform's compliance with the SDGs provides a roadmap for the evaluation of future and past systems in relation to sustainability.
Objective assessment of IPM denoising quality of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si161.svg" display="inline" id="d1e1073"><mml:mi>φ</mml:mi></mml:math>-OTDR signal

The denoising quality of the image processing method (IPM) is usually evaluated by the traditional peak signal-to-noise ratio (PSNR), but its noise level is usually uncertain. Therefore, the assessment index of the denoising quality of different IPMs is not objective. This paper proposes an objective index, first cycle lag of autocorrelation function (FCL-ACF), to evaluate the denoising quality of IPM, including non-local means (NLM), bilateral filter (BF), and wavelet denoise (WD). Compared with PSNR and structural similarity (SSIM), the FCL-ACF is validated based on an assumed noisy image, and further verified by a practical φ-OTDR signal. The evaluation results show that the denoising quality of NLM, WD, and BF is ranked from high to low. The proposed index is helpful to objectively assess the denoising abilities of different IPMs to denoise the φ-OTDR vibration signal and other periodic signals.
Artificial intelligence and prediction of cardiometabolic disease: Systematic review of model performance and potential benefits in indigenous populations

Indigenous peoples often have higher rates of morbidity and mortality associated with cardiometabolic disease (CMD) than non-Indigenous people and this may be even more so in urban areas. The use of electronic health records and expansion of computing power has led to mainstream use of artificial intelligence (AI) to predict the onset of disease in primary health care (PHC) settings. However, it is unknown if AI and in particular machine learning is used for risk prediction of CMD in Indigenous peoples.We searched peer-reviewed literature using terms associated with AI machine learning, PHC, CMD, and Indigenous peoples.We identified 13 suitable studies for inclusion in this review. Median total number of participants was 19,270 (range 911-2,994,837). The most common algorithms used in machine learning in this setting were support vector machine, random forest, and decision tree learning. Twelve studies used the area under the receiver operating characteristic curve (AUC) to measure performance. Two studies reported an AUC of >0.9. Six studies had an AUC score between 0.9 and 0.8, 4 studies had an AUC score between 0.8 and 0.7. 1 study reported an AUC score between 0.7 and 0.6. Risk of bias was observed in 10 (77 %) studies.AI machine learning and risk prediction models show moderate to excellent discriminatory ability over traditional statistical models in predicting CMD. This technology could help address the needs of urban Indigenous peoples by predicting CMD early and more rapidly than conventional methods.
Improving the invisibility of adversarial examples with perceptually adaptive perturbation

Deep neural networks (DNNs) are vulnerable to adversarial examples generated by adding subtle perturbations to benign inputs. While these perturbations are somewhat small due to the Lp norm constraint, they are still easily spotted by human eyes. This paper proposes Perceptual Sensitive Attack (PS Attack) to address this flaw with a perceptually adaptive scheme. We add Just Noticeable Difference (JND) as prior information into adversarial attacks, making image changes in areas that are insensitive to the human eyes. By integrating the JND matrix into the Lp norm, PS Attack projects perturbations onto the JND space around clean data, resulting in more imperceivable adversarial perturbations. PS Attack also mitigates the trade-off between the imperceptibility and transferability of adversarial images by adjusting a visual coefficient. Extensive experiments manifest that combining PS attacks with state-of-the-art black-box approaches can significantly promote the naturalness of adversarial examples while maintaining their attack ability. Compared to the state-of-the-art transferable attacks, our attacks reduce LPIPS by 8% on average when attacking typically-trained and defense models.
User-Friendly Public-Key Authenticated Encryption With Keyword Search for Industrial Internet of Things

The Industrial Internet of Things (IIoT) incorporates massive physical devices to collect ambient data. Due to the limited types of equipment in IIoT, most of the data has to be saved on a cloud server before it can be processed and analyzed. The ciphertext generated by traditional encryption techniques is difficult to search in subsequent use. Public-key encryption with keyword search (PEKS) can provide data encryption as well as confidential searching, but traditional PEKS schemes are susceptible to internal keyword guessing attacks (IKGAs) caused by the limited space of commonly used keywords. To address this issue, the cryptographic primitive of public-key authenticated encryption with keyword search (PAEKS) was proposed, while most of the existing schemes are not appropriately applied to IIoT for involving time-consuming bilinear pairing operations. In this article, we first propose a user-friendly PAEKS scheme that totally circumvents bilinear pairing operations during generating keyword ciphertext and trapdoor. Then, we prove its multiciphertext indistinguishability (MCI) and trapdoor privacy based on decisional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -ABDHE and computational Diffie–Hellman assumptions in the random oracle model together with conducting the theoretical and experimental comparisons. The results show that the computational overhead of our proposal is significantly reduced comparing with most existing classical PAEKS schemes without causing other communication costs or security loss. Due to its better performance and security, our scheme is better suited for lightweight devices in the IIoT.
Proximal Alternating-Direction-Method-of-Multipliers-Incorporated Nonnegative Latent Factor Analysis

High-dimensional and incomplete (HDI) data subject to the nonnegativity constraints are commonly encountered in a big data-related application concerning the interactions among numerous nodes. A nonnegative latent factor analysis (NLFA) model can perform representation learning to HDI data efficiently. However, existing NLFA models suffer from either slow convergence rate or representation accuracy loss. To address this issue, this paper proposes a proximal alternating-direction-method-of-multipliers-based nonnegative latent factor analysis (PAN) model with two-fold ideas: 1) adopting the principle of alternating-direction-method-of-multipliers to implement an efficient learning scheme for fast convergence and high computational efficiency; and 2) incorporating the proximal regularization into the learning scheme to suppress the optimization fluctuation for high representation learning accuracy to HDI data. Theoretical studies verify that PAN converges to a Karush-Kuhn-Tucker (KKT) stationary point of its nonnegativity-constrained learning objective with its learning scheme. Experimental results on eight HDI matrices from real applications demonstrate that the proposed PAN model outperforms several state-of-the-art models in both estimation accuracy for missing data of an HDI matrix and computational efficiency.
Debiased Contrastive Curriculum Learning for Progressive Generalizable Person Re-Identification

Domain generalization (DG) in person re-identification (ReID) is an extremely challenging but essential task, which aims to learn a generalizable model over multiple labeled source domains that can perform well on unseen target domains. Most existing DG strategies in ReID directly aggregate multiple source data together for training, incurring a large inter-domain bias and unstable model optimization that lead the model apt to overfitting domain bias and the model training more time-consuming respectively, thus hampering the generalization and convergence speed of the model. To tackle the aforementioned issues, inspired by Curriculum Learning that mimics the process of human lifelong DG learning (from easy to hard), we put forward a novel Debiased Contrastive Curriculum Learning (DCCL) strategy for DG ReID, which is designed to incrementally enhance generalization in an easy-to-hard training way that can continuously accumulate learning experience to make learning in unknown domains easier and effectively eliminate the domain bias to help the model learn rich domain-invariant discriminative features, thereby strengthening generalization and accelerating convergence for the model. In addition, to simultaneously learn class-level and instance-level discriminative representations, we raise a non-parametric hybrid contrastive loss to equip the DCCL model. We also particularly design an inter-domain mix module to variegate the features of the newly added source domain at each stage of DCCL, further establishing the advantages of DCCL. Extensive experimental results on four public ReID benchmarks fully demonstrate that our DCCL can effectively strengthen the generalization capacity of the model to unseen domains and outperform the state-of-the-art methods.
Experimental Evaluation and Analysis of Federated Learning in Edge Computing Environments

Federated learning (FL) is a machine learning system that allows a network of devices to train a model without centralized data.This characteristic makes FL an ideal choice for machine or deep learning using user data while maintaining privacy.Many applications, such as autonomous vehicles that adapt to pedestrian behavior and smart healthcare devices, require machine learning to be performed in edge computing environments.However, the performance of FL in edge computing environments has received limited research attention, and previous studies have not comprehensively evaluated heterogeneity aspects, such as system heterogeneity, statistical heterogeneity, and communication bandwidth.This study aims to fill this research gap by conducting experimental evaluations and detailed analyses of FL in edge computing environments.Specifically, we set up an experimental testbed based on the FL framework on the KubeEdge platform and evaluate the diverse effects of heterogeneity aspects on the model convergence time such as homogeneous versus heterogeneous hardware resources, balanced versus imbalanced data distributions, duplicate versus nonduplicate datasets, client participation ratio at each round, the client selection algorithm, and the communication bandwidth.Overall, this study contributes to the advancement of the field of FL by expanding the understanding of its performance in edge computing environments and providing guidelines for efficient and effective FL systems in heterogeneous environments, which can ultimately benefit various industries and domains.
OSIN: Object-Centric Scene Inference Network for Unsupervised Video Anomaly Detection

Video Anomaly Detection (VAD) is an essential yet challenging task in the signal processing community, which aims to understand the spatial and temporal contextual interactions between objects and surrounding scenes to detect unexpected events in surveillance videos. However, existing unsupervised methods either use a single network to learn global prototype patterns without making a unique distinction between foreground objects and background scenes or try to strip objects from frames, ignoring that the essence of anomalies lies in unusual object-scene interactions. To this end, this letter proposes an Object-centric Scene Inference Network (OSIN) that uses a well-designed three-stream structure to learn both global scene normality and local object-specific normal patterns as well as explore the object-scene interactions using scene memory networks. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed OSIN model, which achieves frame-level AUCs of 91.7%, 79.6%, and 98.3% on the CUHK Avenue, ShanghaiTech, and UCSD Ped2 datasets, respectively.
Development of a Novel Artificial Intelligence Model for Better Balancing Exploration and Exploitation

Grey Wolf optimizer (GWO) has been used in several fields of research. The main advantages of this algorithm are its simplicity, little controlling parameter, and adaptive exploratory behavior. However, similar to other metaheuristic algorithms, the GWO algorithm has several limitations. The main drawback of the GWO algorithm is its low capability to handle a multimodal search landscape. This drawback occurs because the alpha, beta, and gamma wolves tend to converge to the same solution. This paper presents HDGM – a novel hybrid optimization model of dragonfly algorithm and grey wolf optimizer, aiming to overcome the disadvantages of GWO algorithm. Dragonfly algorithm (DA) is combined with GWO in this study because DA has superior exploration ability, which allows it to search in promising areas in the search space. To verify the solution quality and performance of the HDGM algorithm, we used twenty-three test functions to compare the proposed model’s performance with that of the GWO, DA, particle swam optimization (PSO) and ant lion optimization (ALO). The results show that the hybrid algorithm provides more competitive results than the other variants in terms of solution quality, stability, and capacity to discover the global optimum.
Academic word difficulty and multidimensional lexical sophistication: An English‐for‐academic‐purposes‐focused conceptual replication of Hashimoto and Egbert (2019)

Abstract This article presents a conceptual replication of Hashimoto and Egbert ( https://doi.org/10.1111/lang.12353 ), a study that featured multivariate models where lexical sophistication variables accounted for word difficulty (yes–no recognition) better than frequency alone among learners of English as a second or foreign language from North America. This current study ( n words = 88; n people = 128) conceptually replicated Hashimoto and Egbert with data from three Asian university English‐for‐academic‐purposes sites. Methodological differences included a more conservative lexical sophistication operationalization process and avoidance of stepwise regression. Like the original study, the replication's findings favored multivariate models over frequency, which predicted 36% of word difficulty's variance alone. In a multiple regression model accounting for word difficulty, R 2 = .52, frequency accounted for 17% of the predicted variance with age of acquisition (AoA: 18%) and word naming reaction time (WN_RT: 16%) also being significant predictors. This replication also extended the testing approach by using a mixed‐effect model, involving person and site intercepts as random effects. The model's ability to predict word difficulty fell, marginal R 2 = .22, conditional R 2 = .40, but frequency, AoA, and WN_RT remained the strongest predictors. Taken together, this replication successfully supports the original study's more‐than‐frequency conclusion while highlighting the need for further research into the area.
Wireless Communications Implementation Using Blockchain as Well as Distributed Type of IOT

Electronic gadgets now work closely together as a result of the growth of the Internet of Things (IoT) and Mobile Edge Computing (MEC). Strong dependability and integrity of the technologies are required. But the present trust procedures have the big drawback: (1) they significantly depend on a trustworthy third party, which might have serious security problems if it were compromised; and (2) they subject the associated devices to malicious assessments that may skew their trustrank. In this research, we propose a blockchain-based done to assure for global IoT devices that incorporates the ideas of financial planning and cryptography. The link prediction mechanism uses norms trust but also risk metrics to quantify trustrank, and a novel storage structure is created to make it easier for the domain secretariat to spot and remove fraudulent assessments of the objects. Evidence suggests that the suggested trust system can protect IoT devices from malicious assaults while also guaranteeing data exchange and consistency.
Artificial Intelligence-Based System for Advocate Assistance

India experienced it through the Digital Revolution several years ago; however, technology has never drastically altered other facets of society. This study is significant because if legal research is deficient, the implications of AI & Edge Devices to assist Advocates as legal rights which directly beneficial to the legal phenomenon. By implementing automation in lower courts, the judiciary may be able to overcome some of the substantial difficulties brought along by arrears, caseloads, delays, client interaction, etc. Although: there are many facets to these problems, improvements in increased performance, coherence, transparency, & speed that coming innovations of Industry 4.0 might offer might greatly enhance and relieve problems to retrieve this problem Edge Device can assist the advocate, This AI-based device is an innovative one which is connected to the database. In the Advocate office, the client come to the Office this system talks to the client & listens to the whole facts and circumstances of the case & this device tells the relevant Act which would be applicable in the related case and also provide a printed copy This study signifies the impact, outcome coming out of the device is effective and how this device functions under the legal system.