Reviewerly/exHarmony huggingface dataset:
Combining dimensional and statistical analysis for efficient data driven modelling of complex systems

Dimensional analysis is a well-known approach to model building in engineering, because it can contribute to identifying more parsimonious and meaningful equations for describing complex phenomena. Unfortunately, it is not always exploited to the full, because it is typically applied prior to any form of statistical evaluation, often resulting in poor choices of the dimensionless quantities and consequently in suboptimal models. A completely general and data driven technique is proposed, which integrates dimensional and statistical analysis with the help of genetic programming supported symbolic regression and neural computing. The methodology exploits the potential of various machine-learning techniques and allows extracting mathematical models in terms of dimensionless quantities directly from the dimensional databases available. A battery of numerical tests and examples from fluid dynamics and thermonuclear fusion illustrate the unquestionable advantages of the approach for statistical inference and for the interpretation of the large amounts of data produced by modern physics experiments and engineering studies.
Remote Code Execution

Data needs to be protected first and foremost these days. The biggest threat to any organization is when someone has access to its data, credentials, or sensitive information. Every firm is establishing its services online as we move toward a digital future, and for those services, data loss, disruption, or corruption pose a serious hazard . Web apps are becoming more and more popular because of their ability to meet both consumer and commercial needs. The most effective and efficient way to deliver business services to stakeholders is now possible with web applications. Many services are now offered through web apps, and the efficiency of those services is gauged by the speed at which they are processed and their informational capabilities. However, because of incorrect validation, those services may also be threatened. Currently, cyber-attacks are a serious concern for any global digital transition. This chapter presents a case study on a critical criteria vulnerability named RCE.
Gradient Adaptive Planar Prediction for High-Fidelity Images

A good pixel prediction strategy is essential to have a good Prediction Error- Expansion (PEE) based reversible data hiding (RDH) technique. Several pixel prediction strategies exist in literature. Gradients are used to predict the current pixel. Several Researchers have been proposed based on the gradient estimation to predict the current pixel for embedding more data in the current pixel. In this paper Threshold controlled gradient adaptive planar prediction with gradients of the image using context on 3 x 3 neighbourhood has been proposed for better predicting the current pixel. Adaptive histogram bin shifting has been used to insert more data with less distortion, depending on the local complexity of the pixel. The experimental study shows that the proposed technique performs better than some existing methods.
Advanced Analytics for Smart Farming in a Big Data Architecture Secured by Blockchain and pBFT

In this paper, we adopted the strategy of utilizing Blockchain technology for data processing. This concept is implemented as a unique data structure tasked with keeping historical data on creative farm transactions. Using this security mechanism, users will be able to store sensitive data and share it with one another without the need for a centralized authority. From the standpoint of federated business intelligence analytics, the Blockchain is the technological engine of crypto city data or crypto farming data. Given that Big Data is the only source of all the useful information for the critical key performance indicators of intelligent farming data analytics, Big Data is the only source of truth. In this study, we also demonstrated how the Big Data Lake and Blockchain architectures may be integrated using the design of distributed systems. In addition, we determined how this design would be implemented in the real world and detailed the functional and technological components.
Guideline of Test Suite Construction for GUI Software Centered on Grey-Box Approach

In this paper, the test suite construction for GUI (Graphical User Interface) software may be executed centered on grey-box approach with the prior test design of window access controls for unit testing, including front-end method of white box and follow-up black box method for integration testing.Moreover, two key opinions are proposed for the test suite construction for GUI software, the first one is that the "Triple-step method" should be used for unit testing with the prior disposing of data boundary value testing of input controls, and another one is that the "Grey-box approach" should be applied in integration testing for GUI software with necessary testing preparation in the precondition.At the same time, the testing of baseline version and the incremental testing should be considered for the test case construction to coordinate with the whole evolution of software product today.Additionally, all our opinion and thought are verified and tested with a typical case of GUI software-PQMS (Product Quality Monitoring Software/System), and results indicate that these methods and specific disposing are practical and effective.
CaTabRa: Efficient Analysis and Predictive Modeling of Tabular Data

We present CaTabRa, a novel open-source Python package for the efficient and largely automated analysis of tabular data. It combines a variety of established frameworks and libraries for data processing, automated machine learning, explainable AI and out-of-distribution detection into one coherent system. Thanks to its simple user interface, CaTabRa can be used by practitioners who want to quickly gain insights into their data and the potential of predictive modeling, but it also provides added value for data-science experts through its function library. We demonstrate CaTabRa’s usefulness in two example applications.
An Efficient Deep Learning-Based Breast Cancer Detection Scheme with Small Datasets

Breast cancer is the second major reason of cancer death among women. Automatic and accurate detection of cancer at an early stage, allow proper treatment to the patients and drastically reduces the death rate. Usually, the performance of conventional deep learning networks degrades in small databases due to a lack of data for proper training. In this work, a deep learning-based breast cancer detection framework is suggested in which EfficientNet is employed to deliver excellent performance even in small databases. The uniform and adaptive scaling of depth, width, and resolution result in an efficient detection framework by maintaining a proper tradeoff between classification performance and computational cost. Moreover, an Laplacian of Gaussian-based modified high boosting (LoGMHB) is applied in addition to data augmentation as a preprocessing step prior to the deep learning model to boost performance further. Here, performance analysis is conducted in both mammogram and ultrasound modalities to display the proposed method's superiority. The experimental results with five-fold cross-validation show that the proposed breast cancer scheme outperforms other comparison methods in all the performance measures.
Med Card: An Innovative Way to Keep Your Medical Records Handy and Safe

The healthcare sector has advanced a lot from new machines to do effective surgeries and rolling out medicines to even cure diseases which were considered incurable once. But the technical advancements have not reached their records yet. The traditional method of recording everything on paper is still in use. The only difference that is there is that now the printed material is being provided in spite of the handwritten one. But these papers are indeed very costly and hefty things to manage in sectors like health care. Today when everything is related to privacy, why not have a look at our medical records in a similar way? We propose a system where patients can keep their records handy in a medical card issued to them. All their medical history and prescription will be stored over there. All the clinical records are well maintained and kept safe without any breach to them. Our proposed system is cost-effective and very much possible and also quite feasible as no resources are being used at the users’ end. Here, we tried presenting a prototype which is very similar to the actual layout of our project. The application of this will save a lot of resources that are time-consuming and inefficient. We are also planning to have a duration period for which a person wants to opt for the service of this med card. Each individual who will take the med card, contingent upon card esteem, first pays Card Value to the Organization. This data is shared with every one of the parts of the Hospital branches with the goal that they can keep up with worldwide data gathering. In the event that the user of the Med Card is enduring illness, they will be given starting treatment with next to no consultancy charges.
Diffusion Maps: Using the Semigroup Property for Parameter Tuning

Diffusion maps (DM) constitute a classic dimension reduction technique, for data lying on or close to a (relatively) low-dimensional manifold embedded in a much larger dimensional space. It consists in constructing a spectral parametrization for the manifold from simulated random walks or diffusion paths on the dataset. However, DM is hard to tune in practice. In particular, the task to set a diffusion time t when constructing the diffusion kernel matrix is critical. We address this problem by using the semigroup property of the diffusion operator. We propose a semigroup criterion for picking the "right" value for t. Experiments show that this principled approach is effective and robust.
Optical cascaded reservoir computing for realization of dual-channel high-speed OTDM chaotic secure communication via four optically pumped VCSEL

In this work, we propose a chaotic secure communication system with optical time division multiplexing (OTDM), using two cascaded reservoir computing systems based on multi beams of chaotic polarization components emitted by four optically pumped VCSELs. Here, each level of reservoir layer includes four parallel reservoirs, and each parallel reservoir contains two sub-reservoirs. When the reservoirs in the first-level reservoir layer are well trained and the training errors are far less than 0.1, each group of chaotic masking signals can be effectively separated. When the reservoirs in the second reservoir layer are effectively trained and the training errors are far less than 0.1, the output for each reservoir can be well synchronized with the corresponding original delay chaotic carrier-wave. Here, the synchronization quality between them can be characterized by the correlation coefficients of more than 0.97 in different parameter spaces of the system. Under these high-quality synchronization conditions, we further discuss the performances of dual-channel OTDM with a rate of 4×60 Gb/s. By observing the eye diagram, bit error rate and time-waveform of each decoded message in detail, we find that there is a large eye-openings in the eye diagrams, low bit error rate and higher quality time-waveform for each decoded message. Except that the bit error rate of one decoded message is lower than 7 × 10-3 in different parameter spaces, and those of the other decoded messages are close to 0, indicating that high-quality data transmissions are expected to be realized in the system. The research results show that the multi-cascaded reservoir computing systems based on multiple optically pumped VCSELs provide an effective method for the realization of multi-channel OTDM chaotic secure communications with high-speed.
Smart home cybersecurity awareness and behavioral incentives

Purpose Smart-home security involves multilayered security challenges related to smart-home devices, networks, mobile applications, cloud servers and users. However, very few studies focus on smart-home users. This paper aims to fill this gap by investigating the potential interests of adult smart-home users in cybersecurity awareness training and nonfinancial rewards that may encourage them to adopt sound cybersecurity practices. Design/methodology/approach A total of 423 smart-home users between the ages of 25 and 64 completed a survey questionnaire for this study, with 224 participants from Japan and 199 from the UK. Findings Cultural factors considerably influence adult smart-home users’ attitudes toward cybersecurity. Specifically, cultural differences impact their willingness to participate in cybersecurity awareness training, their views on the importance of cybersecurity training for children and senior citizens and their preference for nonfinancial rewards as an incentive for good cybersecurity behavior. These results highlight the need to consider cultural differences and their potential impact when developing and implementing cybersecurity programs that target smart-home users. Practical implications This research has two main implications. First, it provides insights for information security professionals on the importance of designing cost-effective and time-efficient cybersecurity awareness training programs for smart-home users. Second, the findings may assist governments in establishing nonfinancial incentives to encourage greater uptake of cybersecurity practices among smart-home users. Originality/value The paper investigates whether adult smart-home users are willing to spend time and money to engage in cybersecurity awareness training and to encourage their children and elderly parents to participate in training, as well. In addition, the paper examines incentives, especially nonfinancial rewards, that may motivate adult smart-home users to adopt cybersecurity behaviors at home. Furthermore, the paper analyses demographic differences among smart-home users in Japan and the UK.
Nearly Optimal Parallel Algorithms for Longest Increasing Subsequence

The paper presents parallel algorithms for multiplying implicit simple unit-Monge matrices (Krusche and Tiskin, PPAM 2009) of size n x n in the EREW PRAM model. We show implicit simple unit-Monge matrices multiplication of size n x n can be achieved by a deterministic EREW PRAM algorithm with O(n log n log log n) total work and O(log3 n) span. This implies that there is a deterministic EREW PRAM algorithm solving the longest increasing subsequence (LIS) problem in O(n log2 n log log n) work and O(log 4 n) span. Furthermore, with randomization and bitwise operations, implicitly multiplying two simple unit-Monge matrices can be improved to O(n log n) work and O(log3n) span, which leads to a randomized EREW PRAM algorithm obtaining LIS in O(nlog2n) work and O(log4n) span with high probability. In the regime where the LIS has length k = Ψ(log3n), our results improve the span from Õ(n2/3) (Krusche and Tiskin, SPAA 2010) and O(klog n) (Gu, Men, Shen, Sun, and Wan, SPAA 2023) to O(log4 n) while the total work remains near optimal Õ (n).
SMINet:Semantics-aware multi-level feature interaction network for surface defect detection

To boost the product quality, numerous saliency-based surface defect detection methods have been devoted to the areas of industrial production, construction consumable, road construction. However, the existing salient object detection (SOD) methods not only consume a significant amount of computing resources but also fail to meet the detection efficiency requirements of enterprises. Therefore, this paper proposes a lightweight semantics-aware multi-level feature interaction network (SMINet), to address the above issues. In the encoder phase, we integrate multiple adjacent level features in the cross-layer feature fusion (CFF) module to alleviate the discrepancy between multi-scale features. In the decoder phase, we first employ the semantic-aware feature extraction (SFE) module to mine the location cues embedded in the high-level features. Afterwards, we introduce the detail-aware context attention (DCA) module based on the attention mechanism to recover more spatial details. Extensive experiments on four surface defect datasets validate that our SMINet outperforms the existing state-of-the-art methods.
Exploiting Security Issues in Human Activity Recognition Systems (HARSs)

Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application.
Similarity based deep learning model for movie recommendation system

“Movie Recommendation Systems” helps user get relative &amp; relevant items within millions of items. “Movie recommendation system’s” main task is to offer personalized content through information filtering. Here through this paper, we want to develop Similarity Based Deep Learning Model (SDLM) for automatic movie recommendation system. The projected technique is developed to identify the best rated movies and automatic movie recommendation system. This SDLM is a combination of “Spiking Neural Network (SNN)” and “Ebola Optimization Search Algorithm (EOSA)”. In the SNN, the EOSA is utilized to select optimal weighting parameters. The User Profile Correlation-Based Similarity (UPCS) is utilized along with proposed techniques to enable efficient movie recommendation system. To validate the proposed methodology, the movie databases is obtained from the online solutions. The proposed methodology is executed in MATLAB in addition performances can be assessed by “performance measures like recall, precision, accuracy, recall, specificity, sensitivity and F_Measure”. The projected methodology can be compared with the conventional methods such as “ODLM, Recurrent Neural Network (RNN) and Artificial Neural Network (ANN)” respectively.
Element detection and segmentation of mathematical function graphs based on improved Mask R-CNN

There are approximately 2.2 billion people around the world with varying degrees of visual impairments. Among them, individuals with severe visual impairments predominantly rely on hearing and touch to gather external information. At present, there are limited reading materials for the visually impaired, mostly in the form of audio or text, which cannot satisfy the needs for the visually impaired to comprehend graphical content. Although many scholars have devoted their efforts to investigating methods for converting visual images into tactile graphics, tactile graphic translation fails to meet the reading needs of visually impaired individuals due to image type diversity and limitations in image recognition technology. The primary goal of this paper is to enable the visually impaired to gain a greater understanding of the natural sciences by transforming images of mathematical functions into an electronic format for the production of tactile graphics. In an effort to enhance the accuracy and efficiency of graph element recognition and segmentation of function graphs, this paper proposes an MA Mask R-CNN model which utilizes MA ConvNeXt as its improved feature extraction backbone network and MA BiFPN as its improved feature fusion network. The MA ConvNeXt is a novel feature extraction network proposed in this paper, while the MA BiFPN is a novel feature fusion network introduced in this paper. This model combines the information of local relations, global relations and different channels to form an attention mechanism that is able to establish multiple connections, thus increasing the detection capability of the original Mask R-CNN model on slender and multi-type targets by combining a variety of multi-scale features. Finally, the experimental results show that MA Mask R-CNN attains an 89.6% mAP value for target detection and 72.3% mAP value for target segmentation in the instance segmentation of function graphs. This results in a 9% mAP improvement for target detection and 12.8% mAP improvement for target segmentation compared to the original Mask R-CNN.
Scalable multi‐site photovoltaic power forecasting based on stream computing

Abstract Photovoltaic (PV) is essential for global carbon neutrality, it is imperative to forecast PV generation accurately for power operation. With the rapid growth of distributed PV sites, a scalable cloud service tends to play a vital role in PV forecasting to address the increasing cost of computing resources and data subscriptions. Such a scheme creates a possibility to further enhance forecasting performance by re‐using forecasting model, data, and computing resources all in the cloud. In order to achieve this goal, this work proposes a multi‐site PV forecasting system design with a message queue (MQ) and stream computing engine, where a hybrid neural network model is trained and continuously updated using real‐time data. A performance benchmark with up to 60 sites served simultaneously was performed to verify the scalability of the stream computing based approach. Moreover, after incremental updating of the forecasting model, a decrease in normalized root mean square error and normalized mean absolute error of PV forecasting were observed, demonstrating that better short‐term forecasting accuracy was achieved.
Ethereum phishing detection based on graph neural networks

Abstract With the development of blockchain, cryptocurrencies are also showing a boom. However, due to the decentralized and anonymous nature of blockchain, cryptocurrencies have inevitably become a hotbed for fraudulent crimes. For example, phishing scams are frequent, which not only jeopardize the financial security of blockchain, but also hinder the promotion of blockchain technology. To solve this problem, this paper proposes a graph neural network‐based phishing detection method for Ethereum, and validates it using Ethereum datasets. Specifically, this paper proposes a feature learning algorithm named TransWalk, which consists of a random walk strategy for transaction networks and a multi‐scale feature extraction method for Ethereum. Then, an Ethereum phishing fraud detection framework is built based on TransWalk, and conduct extensive experiments on the Ethereum dataset to verify the effectiveness of this scheme in identifying Ethereum phishing detection.