Reviewerly/exHarmony huggingface dataset:
Embracing modern C++ features: An empirical assessment on the KDE community

Abstract Similar to software systems, programming languages evolve substantially over time. Indeed, the community has more recently seen the release of new versions of mainstream languages in shorter and shorter time frames. For instance, the C++ working group has begun to release a new version of the language every 3 years, which now has a greater number of modern C++ features and improvements in modern standards (C++11, C++14, C++17, and C++ 20). Nonetheless, there is little empirical evidence on how developers are transitioning to use modern C++ constructs in legacy systems, and not understanding the trends and reasons for adopting these new modern C++ features might hinder software developers in conducting rejuvenation efforts. In this paper, we conduct an in‐depth study to understand the development practices of KDE contributors to evolve their projects toward the use of modern C++ features. Our results show a trend in the widespread adoption of some modern C++ features ( lambda expressions , auto‐typed variables , and range‐based for ) in KDE community projects. We also found that developers in the KDE community are making large efforts to modernize their programs using automated tools, and we present some modernization scenarios and the benefits of adopting modern C++ features of the C++ programming language. Our results might help C++ software developers, in general, to evolve C++ legacy systems and tools builders to implement more effective tools that could help in rejuvenation efforts.
Abnormal event detection model using an improved ResNet101 in context aware surveillance system

Abstract Surveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance‐system utilising hybrid ResNet101‐ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre‐processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre‐processed frame is fetched into hybrid ResNet101‐ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal‐events detected the context aware services generate alert to the user for preventing abnormal‐activities. Accuracy, precision, recall, and error values reached for the proposed‐model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.
Robust weighted fusion Kalman filters under linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements

Abstract The paper solves the robust weighted fusion Kalman filtering problem for systems with linearly correlated noise and mixed uncertainties of noise variances, multiplicative noises, and multiple networked inducements including missing measurements, packets dropouts, and two‐step random measurement delays. It is assumed that system noise variances are uncertain but bounded above, and the other four uncertainties are compensated to fictitious white noise by the proposed model‐transformation method. For the transformed local multimodel system with correlated fictitious noise, the robust local time‐varying recursive Kalman filters are presented by decorrelation technique and minimax robust filtering principle. Then the six weighted fusion robust Kalman filters are presented in a unified form. The robustness of local and fused robust Kalman filters is proved by the extended Lyapunov equation approach, matrix factorization, and elementary transformation. Further, the local and fused steady‐state robust Kalman filters are designed. Finally, a simulation study applied to F404 aircraft engine system is provided to examine effectiveness and applicability of the proposed algorithm.
Thwarting code-reuse and side-channel attacks in embedded systems

Embedded devices are increasingly present in our everyday life. They often process critical information, and hence, rely on cryptographic protocols to achieve security. However, embedded devices remain particularly vulnerable to attackers seeking to hijack their operation and extract sensitive information by exploiting side channels and code reuse. Code-Reuse Attack (CRA) can steer the execution of a program to malicious outcomes, altering existing on-board code without direct access to the device memory. Moreover, Side-Channel Attacks (SCAs) may reveal secret information to the attacker based on mere observation of the device. Thwarting Code-Reuse Attacks (CRAs) and Side-Channel Attacks (SCAs) against embedded devices is especially challenging because embedded devices are usually resource constrained. Fine-grained code diversification can hinder CRAs by introducing uncertainty to the binary code; while software mechanisms can thwart timing or power SCAs. The resilience to either attack may come at the price of the overall efficiency. Moreover, a unified approach that preserves these mitigations against both CRAs and SCAs is not available. In this paper, we propose a novel Secure Diversity by Construction (SecDivCon) approach that tackles this challenge. SecDivCon is a combinatorial compiler-based approach that combines software diversification against CRAs with software mitigations against SCAs. SecDivCon restricts the performance overhead introduced by the generated code that thwarts the attacks and hence, offers a secure-by-design approach enabling control over the performance-security trade-off. Our experiments, using 16 benchmark programs, show that SCA-aware diversification is effective against CRAs, while preserving SCA mitigation properties at a low, controllable overhead. Given the combinatorial nature of our approach, SecDivCon is suitable for small, performance-critical functions that are sensitive to SCAs. SecDivCon may be used as a building block to whole-program code diversification or in a re-randomization scheme of cryptographic code.
An analytical solution for a Spin-1 Hamiltonian in the adiabatic approximation

In this work we present a complete analytic solution for a Hamiltonian that represents a three-level system described in terms of the spin 1 matrices, Jˆx and Jˆz, in interaction with a bosonic field written in terms of creation and annihilation operators, in the adiabatic approximation. This model is an immediate extension of the Quantum Rabi Model since we start from a Hamiltonian with the same structure but we replace the Pauli matrices of spin 1/2 by the Jˆ matrices of spin 1. We follow a procedure presented by Li and Batchelor in 2021 applied to a two-level system, extending it to a three-level system. In this case, the Hamiltonian is studied by blocks from two different perspectives: nine blocks of dimension n×n, where it is possible to identify displaced harmonic oscillators whose non-simultaneous solutions help us to build an eigenbase for Hˆ, and n2 blocks of dimension 3 × 3 which, in the adiabatic approximation regime, are reduced to n blocks that can be analytically diagonalized in a simple way. This allows us to generate all the eigenenergies of the Hamiltonian and their respective eigenvectors.
One-pixel and X-pixel adversarial attacks based on smell bees optimization algorithm

In the present paper, we propose two new black-box attacks to generate adversarial images. The first proposition is a new version One-pixel attack. It consists at reducing the search space of pixels by segmenting the image at the first stage. Then, a nature-inspired algorithm is applied to find the optimal pixel able to modify the decision of the deep learning model. The second proposition is X-pixel attack introduced to overcome the drawbacks of the One-pixel attack. It consists at firstly construct a large sub-set of potential pixels, then at apply a feature selection approach to optimize it. Many experiments are undertaken with CNN and ResNet learning models on MNIST and CIFAR-10 datasets. Some comparisons are also done with FGSM, PGD and JSMA attacks.
Vectorial bent functions and linear codes from quadratic forms

In this paper, we study the vectorial bentness of an arbitrary quadratic form and construct two classes of linear codes of few weights from the quadratic forms. Let q be a prime power, m be a positive integer and $$Q:\mathbb {F}_{q^m}\rightarrow \mathbb {F}_q$$ be a quadratic form. We first show that Q is a vectorial bent function if and only if Q is non-degenerate and $$(q+1)m$$ is even (i.e. either q is odd or m is even). Furthermore, if $$2\mid (q+1)m$$ and $$Q(x)= \sum \limits _{i=0}^{m-1} \textrm{Tr}_{q^m/q}(a_i x^{q^i+1})\ (a_i\ne 0)$$ , we show that Q is vectorial bent if and only if the associated additive polynomial $$L_Q(x)=\sum _i (a_i + a_{m-i}^{q^{i}}) x^{q^i}$$ is a permutation polynomial over $$\mathbb {F}_{q^m}$$ . If there is only one $$a_i\ne 0$$ , we recover the constructions of Sidelnikov, Dembowski-Ostrom and Kasami of quadratic vectorial bent functions. We then construct two classes of linear codes $$\mathcal {C}'_Q$$ and $$\mathcal {C}_Q$$ over $$\mathbb {F}_q$$ from Q and completely determine the weight distributions of our codes, showing that they are two-, three- or four-weight codes and contain optimal codes satisfying the Griesmer and Singleton bounds.
An Improved High-Resolution Network-Based Method for Yoga-Pose Estimation

In this paper, SEPAM_HRNet, a high-resolution pose-estimation model that incorporates the squeeze-and-excitation and pixel-attention-mask (SEPAM) module is proposed. Feature pyramid extraction, channel attention, and pixel-attention masks are integrated into the SEPAM module, resulting in improved model performance. The construction of the model involves replacing ordinary convolutions with the plug-and-play SEPAM module, which leads to the creation of the SEPAMneck module and SEPAMblock module. To evaluate the model’s performance, the YOGA2022 human yoga poses teaching dataset is presented. This dataset comprises 15,350 images that capture ten basic yoga pose types—Warrior I Pose, Warrior II Pose, Bridge Pose, Downward Dog Pose, Flat Pose, Inclined Plank Pose, Seated Pose, Triangle Pose, Phantom Chair Pose, and Goddess Pose—with a total of five participants. The YOGA2022 dataset serves as a benchmark for evaluating the accuracy of the human pose-estimation model. The experimental results demonstrated that the SEPAM_HRNet model achieved improved accuracy in predicting human keypoints on both the common objects in context (COCO) calibration set and the YOGA2022 calibration set, compared to other state-of-the-art human pose-estimation models with the same image resolution and environment configuration. These findings emphasize the superior performance of the SEPAM_HRNet model.
Capsule Network with Its Limitation, Modification, and Applications—A Survey

Numerous advancements in various fields, including pattern recognition and image classification, have been made thanks to modern computer vision and machine learning methods. The capsule network is one of the advanced machine learning algorithms that encodes features based on their hierarchical relationships. Basically, a capsule network is a type of neural network that performs inverse graphics to represent the object in different parts and view the existing relationship between these parts, unlike CNNs, which lose most of the evidence related to spatial location and requires lots of training data. So, we present a comparative review of various capsule network architectures used in various applications. The paper’s main contribution is that it summarizes and explains the significant current published capsule network architectures with their advantages, limitations, modifications, and applications.
Rust-Shyper: A reliable embedded hypervisor supporting VM migration and hypervisor live-update

Traditional embedded hypervisors usually focus on resource utilization and performance while neglecting the reliability requirements. On the one hand, the reliability issues result from the use of C language that may introduce memory safety bugs. On the other hand, most existing embedded hypervisors lacks run-time fault tolerance and dynamic bug-fixing mechanisms in deployed embedded system. To address the above challenges, in this paper, we propose Rust-Shyper, an embedded type-1 hypervisor built with Rust, which has both high performance and high reliability. We make full use of the Rust language features in terms of code safety to reduce the number of bugs within the embedded hypervisor. Furthermore, we have proposed low-overhead VM migration and hypervisor live-update mechanisms to enable our embedded hypervisor to tolerate hardware faults at runtime and dynamically fix hypervisor bugs. To the best of our knowledge, we are the first work to propose live-update mechanisms for embedded hypervisor. We have systematically evaluated the performance and reliability of Rust-Shyper, and the results show that Rust-Shyper can significantly improve memory safety without compromising performance. Furthermore, the VM migration and hypervisor live-update mechanisms only incur a small performance overhead.
Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people. Hence, loss of life and severe brain damage can be avoided if stroke is recognized and diagnosed early. Healthcare professionals can discover solutions more quickly and accurately using artificial intelligence (AI) and machine learning (ML). As a result, we have shown how to predict stroke in patients using heterogeneous classifiers and explainable artificial intelligence (XAI). The multistack of ML models surpassed all other classifiers, with accuracy, recall, and precision of 96%, 96%, and 96%, respectively. Explainable artificial intelligence is a collection of frameworks and tools that aid in understanding and interpreting predictions provided by machine learning algorithms. Five diverse XAI methods, such as Shapley Additive Values (SHAP), ELI5, QLattice, Local Interpretable Model-agnostic Explanations (LIME) and Anchor, have been used to decipher the model predictions. This research aims to enable healthcare professionals to provide patients with more personalized and efficient care, while also providing a screening architecture with automated tools that can be used to revolutionize stroke prevention and treatment.
Environmental Visualization and Exploration in Mobile Augmented Reality Games: Redefining the Spatial Affordances

While spatiality in desktop and console interface video games has been widely studied, not many approaches have been made in the context of mobile augmented reality (MAR), an environment from which popular games such as Pokemon Go have emerged. On the one hand, this paper retakes the notion of spatial affordance that Janet Murray introduced for digital environments and proposes a reinterpretation of the concept throughout the integration of the MAR features. On the other, it seeks to approach the impact that these characteristics have on the gameplay experience. For this purpose, 46 mobile augmented reality video games of different genres were analyzed. The study reveals that augmented reality is still a superfluous element rather than a central aspect of the gameplay in most MAR video games, although trends involving new hybrid game experiences have been observed. Finally, we propose a tool for classifying MAR video games according to two axes that assess up to what extent physical space is integrated into the gameplay: gameworld spaces (representational layer) and gameplay spaces (interactive layer, interaction with the game rules and mechanics).
JADE: a board game to teach software ergonomics

JADE is an educational game we have imagined, designed, built, and used successfully in various contexts. This board game enables learning and practicing software ergonomics concepts. It is intended for beginners. We use it every year during several hours with our second-year computer science students at Lyon 1 University. In this paper, we present the classical version of the game, as well as the design and evaluation process that we applied. We also present the hybrid version of JADE, which relies on the use of QR codes and videos. We also present its use in our teaching (with about 850 learners for a total duration of 54 hours, which totals more than 2500 student-hours). We then discuss the results obtained and present the considered evolutions.
Optimal Staking and Liquid Token Holding Decisions in Cryptocurrency Markets

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Cones from maximum h-scattered linear sets and a stability result for cylinders from hyperovals

This paper mainly focuses on cones whose basis is a maximum h-scattered linear set. We start by investigating the intersection numbers of such cones with respect to the hyperplanes. Then we analyze two constructions of point sets with few intersection numbers with respect to the hyperplanes. In particular, the second one extends the construction of translation KM-arcs in projective spaces, having as part at infinity a cone with basis a maximum h-scattered linear set. As an instance of the second construction we obtain cylinders with a hyperoval as basis, which we call hypercylinders, for which we are able to provide a stability result. The main motivation for these problems is related to the connections with both Hamming and rank distance codes. Indeed, we are able to construct codes with few weights and to provide a stability result for the codes associated with hypercylinders.
Head Pose Estimation Patterns as Deepfake Detectors

The capacity to create ”fake” videos has recently raised concerns about the reliability of multimedia content. Identifying between true and false information is a critical step toward resolving this problem. On this issue, several algorithms utilizing deep learning and facial landmarks have yielded intriguing results. Facial landmarks are traits that are solely tied to the subject’s head posture. Based on this observation, we study how Head Pose Estimation (HPE) patterns may be utilized to detect deepfakes in this work. The HPE patterns studied are based on FSA-Net, SynergyNet, and WSM, which are among the most performant approaches on the state of the art. Finally, using a machine learning technique based on K-Nearest Neighbor and Dynamic Time Warping, their temporal patterns are categorized as authentic or false. We also offer a set of experiments for examining the feasibility of using deep learning techniques on such patterns. The findings reveal that the ability to recognize a deepfake video utilizing an HPE pattern is dependent on the HPE methodology. On the contrary, performance is less dependent on the performance of the utilized HPE technique. Experiments are carried out on the FaceForensics++ dataset, that presents both identity swap and expression swap examples. The findings show that FSA-Net is an effective feature extraction method for determining whether a pattern belongs to a deepfake or not. The approach is also robust in comparison to deepfake videos created using various methods or for different goals. In mean the method obtain 86% of accuracy on the identity swap task and 86.5% of accuracy on the expression swap. These findings offer up various possibilities and future directions for solving the deepfake detection problem using specialized HPE approaches, which are also known to be fast and reliable.
Multi-Instance Attention Network for Anomaly Detection from Multivariate Time Series

AbstractAnomaly detection and state prediction research using multivariate data is being actively conducted in various industrial fields. However, since most dynamically operating industrial machines perform different operating conditions, they contain different types of abnormal conditions, making it difficult to detect anomalies and predict the remaining life. This white paper proposes a condition diagnosis model based on multi-sensor data prediction for estimating the remaining lifespan of equipment while solving two complex problems of detecting four typical abnormal conditions and sensor omissions of industrial machines. First, we use a multi-sensor data generation model to learn relationships between sensors, and second, we use a sensor data prediction model to learn sensor-specific feature information. In order to extract the temporal and spatial characteristics of sensing information and to derive the relationship between the sensors, we propose an attention model with three types of cases. Finally, the state of the device is diagnosed through the difference between the model predicted value and the actual value, and future state information of the device is predicted through the accumulation of error information. In order to prove the robustness of the proposed model, extensive experiments were conducted focusing on the case where sensor omission occurred due to data from equipment with more than 4 types and conditions. Our model produces missing sensor data with about 92% accuracy and detects anomalies with about 88% accuracy, even if parts of the sensor are missing or the operating environments have been changed. The proposed model has improved anomaly detection accuracy compared to the comparative model, and has been proven to be applicable to real industrial problems.Keywords: Anomaly detectiondeep learningfault diagnosishierarchical predictionhydraulic equipmentprognostics and health Disclosure StatementNo potential conflict of interest was reported by the authors.
Anomaly Detection Using Machine Learning Techniques: A Systematic Review

Anomaly detection is an observation of irregular, uncommon events that leads to a deviation from the expected behaviour of a larger dataset. When data is multiplied exponentially, it becomes sparse, making it difficult to spot anomalies. The fundamental aim of anomaly detection is to determine odd cases as the data may be properly evaluated and understood to make the best decision possible. A promising area of research is detecting anomalies using modern ML algorithms. Many machines learning models that are used to learn and detect anomalies in their respective applications across various domains are examined in this systematic review study.