Reviewerly/exHarmony huggingface dataset:
Review on application progress of federated learning model and security hazard protection

Federated learning is a new type of distributed learning framework that allows multiple participants to share training results without revealing their data privacy. As data privacy becomes more important, it becomes difficult to collect data from multiple data owners to make machine learning predictions due to the lack of data security. Data is forced to be stored independently between companies, creating "data silos". With the goal of safeguarding data privacy and security, the federated learning framework greatly expands the amount of training data, effectively improving the shortcomings of traditional machine learning and deep learning, and bringing AI algorithms closer to our reality. In the context of the current international data security issues, federated learning is developing rapidly and has gradually moved from the theoretical to the applied level. The paper first introduces the federated learning framework, analyzes its advantages, reviews the results of federated learning applications in industries such as communication and healthcare, then analyzes the pitfalls of federated learning and discusses the security issues that should be considered in applications, and finally looks into the future of federated learning and the application layer.
Disturbance inspired equilibrium optimizer with application to constrained engineering design problems

This work proposes a novel adaptive global optimization algorithm called Disturbance Inspired Equilibrium Optimizer. The purpose of this study is to enhance the exploitation ability of the newly developed Equilibrium Optimizer, and to address the issue of getting trapped in local minima. The proposed algorithm is benefited from the novel disturbance-based hybrid initialization strategy, the new form of time factor, and the new update rule of particle's position. In addition, a novel boundary check strategy and an adaptive global position disturbance mechanism are proposed and installed into our algorithm. Based on the disturbance-inspired modifications, the exploration and exploitation ability of the standard Equilibrium Optimizer are significantly improved. The performance of the proposed algorithm is evaluated using representative different benchmark functions, consisting of three well-known mathematical benchmark functions, six complex composite functions, and four challenge functions proposed on 2017 IEEE Congress on Evolutionary Computation. Also, the proposed algorithm is conducted to optimize three engineering designs to examine its applicability in constrained real-world problems. In all experiments, the developed algorithm is compared with six other state-of-the-art metaheuristics. Experimental results and the average rank of Friedman test show that our algorithm provides promising results in solving mathematical problems and constrained real-world engineering optimization problems. Therefore, the proposed algorithm is competitive compared to the other state-of-the-art metaheuristic algorithms and is an effective solution to real-world engineering problems.
Designing of Augmented Breast Cancer Data using Enhanced Firefly Algorithm

Breast cancer is the world’s most widespread disease. Specific hereditary gene mutations considerably increase breast cancer risk. Treatment for breast cancer can be quite beneficial, with survival rates of Ninety percent or greater, specifically when the disease is detected early. In the system to identify cancer in its early stages, computational techniques are used to create augmented data based on its importance. Based on the specified features in the dataset, the system employs multiple machine learning techniques to predict whether the cancer is mild or severe. The proposed method uses these three datasets (Wisconsin, Prognostic, and Coimbra), provided in the Kaggle and UCI data repositories. optimization for feature selection performs on these three datasets. This model uses a new metaheuristic algorithm called the Enhanced Firefly algorithm, which is inspired more by illuminating behavior of fireflies. With this algorithm used in the system the Wisconsin dataset reduces from 32 to 6 features, the Prognostic dataset reduces from 32 to 9, and the Coimbra dataset reduces from 10 to 4. This Enhanced firefly algorithm uses accuracy and reduction of features as fitness functions. The major goal of this model is to design a generalized dataset that can work to predict the optimal accuracy using any stacking classifiers.
Reddit Comment Toxicity Score Prediction through BERT via Transformer Based Architecture

Hateful and offensive language on social media platforms has a severe influence on users' mental health and engagement of people from various backgrounds. Automatic detection of foul language has traditionally relied heavily on datasets with categorical data. However, the degree of offensiveness of comments varies. The proposed model uses tfidf followed by Ridge Regression,Catboost Regression and BERT followed by dense layers. The study uses a dataset containing Reddit-comments written in English language with precise and calculated values ranging from -1 to 1. Best-Worst Scaling was used to annotate the dataset, a type of comparative annotation that has been found to reduce the biases associated with rating scales. It has been demonstrated that the technique gives extremely accurate offensiveness scores. The proposed method offers user to customize their own threshold of offensiveness. The experiments has been conducted with different n-gram ranges. The result reveals better performance than state of the art.
Non-Fungible Tokens Applications: A Systematic Mapping Review of Academic Research

Non-fungible tokens (NFTs) represent unique and non-interchangeable assets. The concept of tokenizing the unique nature of digital items originated with the Ethereum blockchain's token standard. NFTs have quickly gained popularity and growing attention in different application areas. In this paper, we aim to identify literature that seeks to adopt NFTs and present a systematic mapping review of peer-reviewed and non-peer-reviewed research publications about the applications of NFTs. We obtained all research papers from the primary scientific databases and using the systematic mapping method, we narrowed them down to 106 relevant papers. We classified these papers to discover yearly trends of development and research. We included existing research and different applications. Finally, we identified the research gap and future opportunities.
OTPose: Occlusion-Aware Transformer for Pose Estimation in Sparsely-Labeled Videos

Although many approaches for multi-human pose estimation in videos have shown profound results, they require densely annotated data which entails excessive man labor. Furthermore, there exists occlusion and motion blur that inevitably lead to poor estimation performance. To address these problems, we propose a method that leverages an attention mask for occluded joints and encodes temporal dependency between frames using transformers. First, our framework composes different combinations of sparsely annotated frames that denote the track of the overall joint movement. We propose an occlusion attention mask from these combinations that enable encoding occlusion-aware heatmaps as a semi-supervised task. Second, the proposed temporal encoder employs transformer architecture to effectively aggregate the temporal relationship and keypoint-wise attention from each time step and accurately refines the target frame’s final pose estimation. We achieve state-of-the-art pose estimation results for PoseTrack2017 and PoseTrack2018 datasets and demonstrate the robustness of our approach to occlusion and motion blur in sparsely annotated video data.
A Combination of Decimal- and Bit-Level Secure Multimedia Transmission

The proliferation of multimedia production and exchange over the Internet has created a dire need for security measures to protect it. In this paper, we propose a hybrid method for the encryption of images. This kind of hybrid strategy calls for the application of two (varying in nature) components, arithmetic (in the form of a dot product operation applied on the decimal level) and bit-level operations (in the form of an application of a PRNG key data change), alongside an S-Box (as a confusion application element). The incorporation of an arithmetic component leads to the development of an encryption scheme that is fundamentally distinct from the majority of algorithms in the state-of-the-art. The proposed scheme demonstrates strong encryption performance, as can be seen from the results of the conducted tests.
The effects of mobile technology usage on cognitive, affective, and behavioural learning outcomes in primary and secondary education: A systematic review with meta‐analysis

Abstract Background The impact of mobile technology usage on student learning in various educational stages has been the subject of ongoing empirical and review research. The most recent meta‐analyses on various types of mobile technology use for potential benefits of learning covered the empirical studies up to about nine years ago. Since then, the use of mobile technology in primary and secondary education has increased tremendously, and numerous empirical studies have been conducted on this topic, but their conclusions were inconsistent. Objectives The purpose of this systematic review is to re‐examine this issue by meta‐analyzing the empirical research studies from the last nine years, with a focus on cognitive, affective, and behavioral learning outcomes in primary and secondary education, and to examine the potential moderators that may have contributed to the heterogeneity across findings. Methods Based on our inclusion and exclusion criteria, we found 85 studies of 78 peer‐reviewed papers ( N = 9157) from electronic databases and major journals in educational technology and mobile learning between 2014 and 2022. We then examined 15 moderators that were expected to affect student learning outcomes. Results and Conclusions Compared with traditional technology and non‐technology groups, using mobile technology produced medium positive and statistically significant effects on primary and secondary students' learning, in terms of cognitive ( g = 0.498, 95% CI [0.382, 0.614]), affective ( g = 0.449, 95% CI [0.301, 0.598]) and behavioural ( g = 0.339, 95% CI [0.051, 0.627]) learning outcomes. Further moderator analyses revealed that student factors (i.e., community type, students’ socioeconomic status), learning process (i.e., hardware used, student‐to‐hardware ratio, teaching method) and study quality (i.e., learning topic/content equivalence, degree of technology use in the control group) were among the variables that moderated the summary effect sizes for at least one learning outcome dimension significantly. The findings and their implications for researchers, policymakers and practitioners are discussed.
Forecasting of Energy Consumption for Anomaly Detection in Automated Guided Vehicles: Models and Feature Selection

Automated guided vehicles (AGV) provide a cost-efficient transportation method in smart industrial plants. Their continuous operation is crucial for production flow. However, while detection of typical failures, e.g., those related to battery voltage, can be performed in an automated manner, more complex scenarios require expert knowledge and human monitoring. In this paper, we evaluate recurrent neural network-based (RNN) energy consumption forecasting using other telemetry features. We aim to find models well suited for anomaly detection methods working on the analysis of error between forecasted and actual values. We compare the results of RNN architectures on our data and public vehicle energy datasets. We demonstrate that RNN-based forecasting, together with a proper selection of telemetry features used in prediction, can be effectively utilized on AGV telemetry data as a first step in anomaly detection schemes.
Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development

Bitcoin is yet to be assumed as a worthy cryptocurrency and rewarding asset in the global market. As polynomial-based neural networks (PBNNs) are very robust and more accurate in modeling stock price prediction, their advantage in Bitcoin pricing needs to be analyzed. In this study, the robustness of PBNNs, based on Chebyshev (CPBNN) and Legendre (LPBNN), is blended with the proposed algorithm, coined as the mutated climb monkey algorithm (MCMA), to control the estimation of network parameters to accurately predict the one-day-ahead Bitcoin price. The performance was evaluated by a comparative analysis of the testing of both CPBNN and LPBNN with each of the six algorithms under consideration on three different datasets collected within the same time interval. As the use of a few evaluation criteria will not be able to identify an efficient predictor model, this study also proposes the use of a Multi-Criteria Decision-Making (MCDM) framework to rank all models using 15 different evaluation criteria. The ranking of the models clearly indicates that the proposed MCMA algorithm outperforms all other algorithms under study. The convergence plots of the top two models for the datasets also indicate that the PBNN using MCMA for learning predicts better results.
Robust Medical Image Colorization with Spatial Mask-Guided Generative Adversarial Network

Color medical images provide better visualization and diagnostic information for doctors during clinical procedures than grayscale medical images. Although generative adversarial network-based image colorization approaches have shown promising results, in these methods, adversarial training is applied to the whole image without considering the appearance conflicts between the foreground objects and the background contents, resulting in generating various artifacts. To remedy this issue, we propose a fully automatic spatial mask-guided colorization with generative adversarial network (SMCGAN) framework for medical image colorization. It generates colorized images with fewer artifacts by introducing spatial masks, which encourage the network to focus on the colorization of the foreground regions instead of the whole image. Specifically, we propose a novel spatial mask-guided method by introducing an auxiliary foreground segmentation branch combined with the main colorization branch to obtain the spatial masks. The spatial masks are then used to generate masked colorized images where most background contents are filtered out. Moreover, two discriminators are utilized for the generated colorized images and masked generated colorized images, respectively, to assist the model in focusing on the colorization of foreground regions. We validate our proposed framework on two publicly available datasets, including the Visible Human Project (VHP) dataset and the prostate dataset from NCI-ISBI 2013 challenge. The experimental results demonstrate that SMCGAN outperforms the state-of-the-art GAN-based image colorization approaches with an average improvement of 8.48% in the PSNR metric. The proposed SMCGAN can also generate colorized medical images with fewer artifacts.
Automated Banking Fraud Detection for Identification and Restriction of Unauthorised Access in Financial Sector

Modern techniques have allowed for using advanced technology and tools in order detect fraud within an organisation. It is essential for any Banking sector or financial sector to identify those fraudulent activities that might be associated within their network. Different AI-based technology and ML algorithms are used within banking sectors of India and other countries of this world to determine frauds and unauthorised access. This research article have identified that Data mining is effective for the banking sector to target customers, who might develop fraudulent activities through the credit process. SVM, Logistic regression, Decision tree, Neural networks are used as data mining tools within banking sector’s fraud detection process, however all these tools required a data balancing method before structuring the model. Autoencoder is a model that is proposed for fraud detection without any data balancing. A secondary thematic data analysis process is developed within this research article to investigate of different variables relevant to this topic. It has been found that, proposed model for cyber-crime detection, AI-enabled automated fraud controlling system are effective to reduce risk within banking sector. Moreover, SAS is effective software used by banking sector to detect fraud. It generates alert signals for any fraudulent behaviour with system of banks. However, banking sectors need to engage good governance to mitigate challenge regarding lack of skill and expertise within their data mining process.
Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even obtain worse outcomes after therapy. Hence, predictors of treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potential of breast cancer stromal histology via a deep learning (DL)-based approach and proposed the tumor-associated stroma score (TS-score) for predicting pathological complete response (pCR) to NAC with a multicenter dataset. The TS-score was demonstrated to be an independent predictor of pCR, and it not only outperformed the baseline variables and stromal tumor-infiltrating lymphocytes (sTILs) but also significantly improved the prediction performance of the baseline variable-based model. Furthermore, we discovered that unlike lymphocytes, collagen and fibroblasts in the stroma were likely associated with a poor response to NAC. The TS-score has the potential to better stratify breast cancer patients in NAC settings.
Stochastic dynamics of a metal magnon parametron

A magnon parametron is a calculating element that carries binary information by a discretized oscillating phase: 0 and π. Owing to the strong non-linearity of the magnetization dynamics, the oscillation phase flips to another stochastically, which can give a rise to unconventional computing functionalities, including probabilistic computing. Here, we investigated the stochastic dynamics of ferromagnetic-metal magnon parametron, of which the precession phase is discretized into two (0 and π) by parametric pumping of magnons. We found that an AC magnetic field perpendicular to an external field can control the precession phase in the magnon parametron, of which amplitude dependence follows the sigmoid function, a requirement for probabilistic bit operation. We also found that the time scale for flipping between different precession phases grows exponentially as pumping microwave power. Our finding ensures that the magnon parametron can be used as a calculating element for unconventional computing schemes based on bistable systems.
Profile evaluation of rail joint in a 3‐m wavelength based on unsupervised learning

Abstract Previously, electronic straightedges with a length of 1 m were widely used to measure the longitudinal profiles of rail joints. However, owing to the lack of an efficient measurement device, rail joints with 3‐m wavelengths are seldom studied. In this study, a rail measurement trolley based on the chord‐reference method was developed with a measurement wavelength of up to 3 m. A field measurement was performed on a 53‐km metro line, and the waveforms of 4340 rail joints were obtained. First, to visualize the distribution of the dataset and to find out the common features, t ‐distributed stochastic neighbor embedding dimensionality reduction was applied to the rail joint dataset, and each rail joint waveform was mapped to a point in a two‐dimensional space. Second, K ‐means was applied to the rail joint dataset, and six categories of rail joints were obtained. The results indicated that there are two types of rail joints: M‐type and W‐type, accounting for 18.41% and 76.08% of the total number of joints, respectively, and the remainder are bolted rail joints. Third, to better evaluate rail joint status, the concept of rail joint triangle (RJT) is proposed, and five shape‐based features of a rail joint in 3‐m wavelength are defined. Finally, using RJT distribution analysis, we observed that the shape‐based features provide more essential information about a rail joint, such as symmetry, asymmetry, M‐type, or W‐type, compared with conventional indexes such as the quality index. Notably, compared with the waveform of a rail joint at 1 m, a 3‐m waveform provides significantly more essential information, which can be meaningful for future research on the dynamic impact of rail joints, as well as profile grinding around rail joints. To help other researchers follow our research, our dataset is available on Mendeley Data (RWJ‐3 m dataset).
A novel quantum recommender system

Abstract Recommendation system is a kind of information filtering system, which plays an increasingly important role in the era of big data. In this work, we present a novel quantum recommender system, which can also be regarded as a quantum version of the matrix reconstruction algorithm. In order to obtain two factors in the quantum state form, a quantum version of the alternating least squares algorithm is designed. And based on the factor matrices, the reconstruction matrix of the original rating matrix is calculated. The complexity analysis shows that our quantum algorithm may achieve an exponential speedup relative to the classical counterpart under certain conditions.
Model-driven development for the seL4 microkernel using the HAMR framework

Verified microkernels such as seL4 provide trustworthy foundations for safety- and security-critical systems. However, their full potential remains unrealized due, in part, to the lack of application development environments that help engineers integrate the microkernel's configuration and hosting of application code with modeling, analysis, and verification tools that address broader aspects of the development lifecycle. This paper presents a model-driven tool chain for the seL4 microkernel based on the open source High Assurance Modeling and Rapid engineering (HAMR) code generation framework for the Architecture and Analysis Definition Language (AADL). We describe how the semantics of AADL communication and threading can be realized in terms of the access primitives and strong spatial and temporal partitioning mechanisms provided by seL4. For AADL users, seL4 provides a high-assurance platform with formally verified enforcement of component boundaries and communication pathways. For seL4 users, AADL provides high-level abstractions for developing seL4 applications, along with an ecosystem of system engineering and analysis tools. We illustrate the framework by applying a model-based development environment for increasing resiliency against cyber attacks to an unmanned aircraft flight control system.