Reviewerly/exHarmony huggingface dataset:
On effort-aware metrics for defect prediction

Abstract Context Advances in defect prediction models, aka classifiers, have been validated via accuracy metrics. Effort-aware metrics (EAMs) relate to benefits provided by a classifier in accurately ranking defective entities such as classes or methods. PofB is an EAM that relates to a user that follows a ranking of the probability that an entity is defective, provided by the classifier. Despite the importance of EAMs, there is no study investigating EAMs trends and validity. Aim The aim of this paper is twofold: 1) we reveal issues in EAMs usage, and 2) we propose and evaluate a normalization of PofBs (aka NPofBs), which is based on ranking defective entities by predicted defect density. Method We perform a systematic mapping study featuring 152 primary studies in major journals and an empirical study featuring 10 EAMs, 10 classifiers, two industrial, and 12 open-source projects. Results Our systematic mapping study reveals that most studies using EAMs use only a single EAM (e.g., PofB20) and that some studies mismatched EAMs names. The main result of our empirical study is that NPofBs are statistically and by orders of magnitude higher than PofBs. Conclusions In conclusion, the proposed normalization of PofBs: (i) increases the realism of results as it relates to a better use of classifiers, and (ii) promotes the practical adoption of prediction models in industry as it shows higher benefits. Finally, we provide a tool to compute EAMs to support researchers in avoiding past issues in using EAMs.
Driver Behaviors Recognizer Based on Light-Weight Convolutional Neural Network Architecture and Attention Mechanism

Driving is a set of behaviors that need high concentration. Sometimes these behaviors are dominated by other acts such as smoking, eating, drinking, talking, phone calls, adjusting the radio, or drowsiness. These are also the main causes of current traffic accidents. Therefore, developing applications to warn drivers in advance is essential. This research introduces a light-weight convolutional neural network architecture to recognize driver behaviors, helping the warning system to provide accurate information and to minimize traffic collisions. This network is a combination of feature extraction and classifier modules. The feature extraction module uses the advantages of the standard convolution layers, depthwise separable convolution layers, average pooling layers, and proposed adaptive connections to extract the feature maps. The benefit of the convolution block attention module is deployed in the feature extraction module that guides the network in learning the salient features. The classifier module is comprised of a global average pooling and softmax layer to calculate the probability of each class. The overall design optimizes the network parameters and maintains classification accuracy. The entire network is trained and evaluated on three benchmark datasets: the State Farm Distracted Driver Detection, the American University in Cairo version 1, and the American University in Cairo version 2. As a result, the accuracies on overall classes (ten classes) are 99.95&#x0025;, 95.57&#x0025;, and 99.61&#x0025;, respectively. Also, several video tests with VGA (Video Graphics Array), HD (High Definition), and FHD (Full High Definition) resolution were conducted, and they can be seen at <uri>https://bit.ly/3GY2iJl</uri>.
FIBS-Unet: Feature Integration and Block Smoothing Network for Single Image Dehazing

The dehazing algorithms are based on the hazy simulation equation to remove haze and restore the input image feature maps by estimating the intensity coefficient of the atmospheric light source and the scattering coefficient of the atmosphere. However, the coefficient prediction isn&#x2019;t good, resulting in artifact noise in the dehazed output image. The increasing expansion of deep learning algorithms in computer vision applications to combat noise and interference in the hazy picture is growing. This paper proposed an efficient framework for Feature Integration and Block Smoothing (FIBS-Unet) Unet architecture using encoder-decoder processing with intensity attention block. We modified the Res2Net residual block with customized convolution and added instance normalization to improve the encoder feature extraction efficiency. Besides, we designed the Intensity Attention Block (IAB) using Sub-Pixel Layer and convolution (<inline-formula> <tex-math notation="LaTeX">$1\times 1$ </tex-math></inline-formula>) to amplify input feature and fusion feature maps. We developed an efficient decoder employing sub-pixel convolutions, concatenations, contrive convolutions, and multipliers to recover smooth and high-quality feature maps at the framework. The proposed FIBS-Unet has minimized the Mean Absolute Error (MAE) at perceptual loss function with the RESIDE dataset. We calculated the Peak Signal-to-Noise Ratio (PSNR), the Similarity Index Measure (SSIM), and a subjective visual color difference to evaluate the model&#x2019;s effectiveness. The proposed FIBS-Unet achieved better quality dehazing image results of PSNR:34.122 and SSIM:0.9890 in the outdoor scenarios at dense haze and backlight image for the Synthetic Objective Testing Set (SOTS). Our extensive experimental results specify that proposed FIBS-Unet is extendable to real-time applications.
Transition Information Enhanced Disentangled Graph Neural Networks for session-based recommendation

Session-based recommendation (SBR) is a practical task that predicts the next item based on an anonymous behavior sequence. Most of current methods employ graph neural network to model neighboring item transition information from global and local contexts (i.e, other and current sessions). However, they treat neighbors from other sessions equally without considering its items, which may have different contributions with the target item on varied aspects. In other words, they have not explored finer-granularity transition information in the global context, leading to sub-optimal performance. This paper fills this gap by proposing a novel method called Transition Information Enhanced Disentangled Graph Neural Network (TIE-DGNN) to capture finer-granular transition information between items and try to interpret the transition reason by modeling various factors of items. Specifically, we first propose a position-aware global graph to model neighboring item transition in the global context. Then, we slice item embeddings into blocks, each of which represents a factor, and use global-level disentangling layers to separately learn factor embeddings. Meanwhile, we train local-level item embeddings by using attention mechanisms to capture transition information from the current session. Further, inter-session and intra-session embeddings are generated by two types of item embeddings, respectively. Finally, we use contrastive learning techniques to enhance the robustness of two session embeddings. To this end, our model considers two levels of transition information. Especially in global context, it not only consider finer-granularity transition information between items but also take user intents at factor-level into account to interpret the key reason for the transition. Extensive experiments on three benchmark datasets demonstrate its superiority over state-of-the-art methods.
Event driven bio-inspired attentive system for the iCub humanoid robot on SpiNNaker

Abstract Attention leads the gaze of the observer towards interesting items, allowing a detailed analysis only for selected regions of a scene. A robot can take advantage of the perceptual organisation of the features in the scene to guide its attention to better understand its environment. Current bottom–up attention models work with standard RGB cameras requiring a significant amount of time to detect the most salient item in a frame-based fashion. Event-driven cameras are an innovative technology to asynchronously detect contrast changes in the scene with a high temporal resolution and low latency. We propose a new neuromorphic pipeline exploiting the asynchronous output of the event-driven cameras to generate saliency maps of the scene. In an attempt to further decrease the latency, the neuromorphic attention model is implemented in a spiking neural network on SpiNNaker, a dedicated neuromorphic platform. The proposed implementation has been compared with its bio-inspired GPU counterpart, and it has been benchmarked against ground truth fixational maps. The system successfully detects items in the scene, producing saliency maps comparable with the GPU implementation. The asynchronous pipeline achieves an average of 16 ms latency to produce a usable saliency map.
Neural Network Robustness as a Verification Property: A Principled Case Study

Abstract Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields. However, their usefulness is hampered by their susceptibility to adversarial attacks . Recently, many methods for measuring and improving a network’s robustness to adversarial perturbations have been proposed, and this growing body of research has given rise to numerous explicit or implicit notions of robustness. Connections between these notions are often subtle, and a systematic comparison between them is missing in the literature. In this paper we begin addressing this gap, by setting up general principles for the empirical analysis and evaluation of a network’s robustness as a mathematical property—during the network’s training phase, its verification, and after its deployment. We then apply these principles and conduct a case study that showcases the practical benefits of our general approach.
Resilient government requires data science reform

Data has tremendous potential to build resilience in government. To realize this potential, we need a new, human-centred, distinctly public sector approach to data science and AI, in which these technologies do not just automate or turbocharge what humans can already do well, but rather do things that people cannot.
A Systematic Review of Virtual Reality-Based Education Research Using Latent Dirichlet Allocation: Focus on Topic Modeling Technique

Using virtual reality in the context of education is becoming important since this technology enhances learners’ motivation and performance with transfer of learning, problem-solving skills, educational equity, and multisensory learning. Along with the 4th Industrial Revolution and COVID-19, the attention on virtual reality has been increased again. Some studies reviewed the trends of research on virtual reality-based education, mostly focusing on specific subject matters or areas. The purpose of the study is to investigate the change of research trends in the virtual reality-based education field by implementing the topic modeling analysis based on latent Dirichlet allocation (LDA) technique with 6,755 articles published in the last 30 years (between 1992 and 2022). As a result of this study, it was revealed that the research on virtual reality-based education was clearly divided into the following four periods; 1992–2011, 2012–2016, 2017–2019, and 2020–2022. The main topics for each period were suggested. Here are three major findings of this study. First, it is identified that the weight of “virtual reality for learning and teaching” has increased in recent years. Second, conversely, it is identified that the proportion of “virtual reality in medical education” has decreased relatively recently. It means it is possible to interpret that the proportion of other topics has relatively increased. Third, the topics of “virtual reality education platform” and “virtual reality-based education in rehabilitation” continue to maintain a certain weight. Limitations of this study and further research suggestions are provided based on the results of the study.
A Hyperheuristic Algorithm Based on Evolutionary Strategy for Complex Mission Planning of AUVs in Marine Environment

Autonomous underwater vehicles (AUVs) have been widely implemented to explore marine resources or perform marine missions; meanwhile, complex mission planning has been expected to enhance the intelligence level, improve energy efficiency, and expand AUVs' application areas. The mission planning model needs to consider energy consumption, mission quality, and mission quantity of long-term working in the marine environment. In this article, one unified and robust model was proposed based on the above requirements in the complex marine environment to improve the automaticity of AUV. However, solving this mission planning model is a nontrivial problem, especially when high robustness, high efficiency, fast response, and near-optimal results are required. Therefore, we propose a novel hyperheuristic algorithm based on evolutionary strategy (ES-HH). The proposed ES-HH combines a metaheuristic framework with a selection function to evaluate the performance of low-level heuristic operators online. This evolutionary strategy endows the hyperheuristic algorithm with the online learning feature, giving the ES-HH algorithm better computing efficiency, robustness, and near-optimal results. The experiment results show that the ES-HH algorithm can achieve better convergence and higher robustness than other algorithms, such as ant colony optimization and biogeography-based optimization algorithms. Compared with ACO and BBO algorithms, the proposed ES-HH algorithm can improve the mission completion rate by 3.42% and reduce the average energy consumption of a single task by 8.02%.
Frequency Domain Feature Based Robust Malicious Traffic Detection

Machine learning (ML) based malicious traffic detection is an emerging security paradigm, particularly for zero-day attack detection, which is complementary to existing rule based detection. However, the existing ML based detection achieves low detection accuracy and low throughput incurred by inefficient traffic features extraction. Thus, they cannot detect attacks in realtime, especially in high throughput networks. Particularly, these detection systems similar to the existing rule based detection can be easily evaded by sophisticated attacks. To this end, we propose Whisper, a realtime ML based malicious traffic detection system that achieves both high accuracy and high throughput by utilizing frequency domain features. It utilizes sequential information represented by the frequency domain features to achieve bounded information loss, which ensures high detection accuracy, and meanwhile constrains the scale of features to achieve high detection throughput. In particular, attackers cannot easily interfere with the frequency domain features and thus Whisper is robust against various evasion attacks. Our experiments with 74 types of attacks demonstrate that, compared with the state-of-the-art systems, Whisper can accurately detect various sophisticated and stealthy attacks, achieving at most 18.36% improvement of AUC, while achieving two orders of magnitude throughput. Even under various evasion attacks, Whisper is still able to maintain around 90% detection accuracy.
Revisiting Higher-Order Masked Comparison for Lattice-Based Cryptography: Algorithms and Bit-Sliced Implementations

Masked comparison is one of the most expensive operations in side-channel secure implementations of lattice-based post-quantum cryptography, especially for higher masking orders.First, we introduce two new masked comparison algorithms, which improve the arithmetic comparison of D'Anvers et al. ( 2021) and the hybrid comparison method of Coron et al. ( 2021) respectively.We then look into implementation-specific optimizations, and show that small specific adaptations can have a significant impact on the overall performance.Finally, we implement various state-of-the-art comparison algorithms and benchmark them on the same platform (ARM-Cortex M4) to allow a fair comparison between them.We improve on the arithmetic comparison of D'Anvers et al. with a factor % 20% by using Galois Field multiplications and the hybrid comparison of Coron et al. with a factor % 25% by streamlining the design.Our implementation-specific improvements allow a speedup of a straightforward comparison implementation of % 33%.We discuss the differences between the various algorithms and provide the implementations and a testing framework to ease future research.
CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network

Although deep learning has demonstrated its capability in solving diverse scientific visualization problems, it still lacks generalization power across different tasks. To address this challenge, we propose CoordNet, a single coordinate-based framework that tackles various tasks relevant to time-varying volumetric data visualization without modifying the network architecture. The core idea of our approach is to decompose diverse task inputs and outputs into a unified representation (i.e., coordinates and values) and learn a function from coordinates to their corresponding values. We achieve this goal using a residual block-based implicit neural representation architecture with periodic activation functions. We evaluate CoordNet on data generation (i.e., temporal super-resolution and spatial super-resolution) and visualization generation (i.e., view synthesis and ambient occlusion prediction) tasks using time-varying volumetric data sets of various characteristics. The experimental results indicate that CoordNet achieves better quantitative and qualitative results than the state-of-the-art approaches across all the evaluated tasks. Source code and pre-trained models are available at https://github.com/stevenhan1991/CoordNet.
Robust Registration Algorithm for Optical and SAR Images Based on Adjacent Self-Similarity Feature

Because optical and synthetic aperture radar (SAR) images are complementary, their registration has received extensive attention in joint applications. However, robust optical and SAR image registration is challenging due to substantial geometric and radiometric differences. To address this problem, we propose a fast and robust registration algorithm for optical and SAR images based on a novel feature type known as the adjacent self-similarity (ASS). The ASS feature of the pixelwise feature representation is defined to quickly and finely capture the structural features of the image. The ASS feature is extracted by using an optimized offset mean filtering method in a neighborhood of the unit pixel radius to accelerate and refine calculations and the local statistics weighted difference operation to suppress coherent speckles. Based on the ASS feature, we extract the minimum self-similarity map (SSM) and the index map, which are robust against radiometric differences and speckles. Then, based on the excellent characteristics of the two maps, we propose a feature detector based on suppressing the local nonmaximum on the minimum SSM and a novel feature descriptor based on calculating the distribution histogram of the index map in a log-polar grid. In addition, we design a rotation invariance enhancement method for the descriptor to improve the rotation invariance robustness of the algorithm. We conduct experiments with both synthetic and real image pairs. The registration results demonstrate that the proposed algorithm has good scale and rotation invariance, as well as good antinoise ability, and that the algorithm performs better than existing state-of-the-art algorithms in terms of registration robustness, accuracy, and efficiency. The registration results on two real optical and SAR image pairs with complex image scenes show the adaptability of the proposed algorithm. The source code of ASS is publicly available<sup>1</sup>.
Chaotification of One-Dimensional Maps Based on Remainder Operator Addition

In this work, a chaotification technique is proposed that can be used to enhance the complexity of any one-dimensional map by adding the remainder operator to it. It is shown that by an appropriate parameter choice, the resulting map can achieve a higher Lyapunov exponent compared to its seed map, and all periodic orbits of any period will be unstable, leading to robust chaos. The technique is tested on several maps from the literature, yielding increased chaotic behavior in all cases, as indicated by comparison of the bifurcation and Lyapunov exponent diagrams of the original and resulting maps. Moreover, the effect of the proposed technique in the problem of pseudo-random bit generation is studied. Using a standard bit generation technique, it is shown that the proposed maps demonstrate increased statistical randomness compared to their seed ones, when used as a source for the bit generator. This study illustrates that the proposed method is an efficient chaotification technique for maps that can be used in chaos-based encryption and other relevant applications.
D-NISQ: A reference model for Distributed Noisy Intermediate-Scale Quantum computers

Quantum computing has entered its mature life thanks to the availability of cloud-based Noisy Intermediate-Scale Quantum (NISQ) technologies. These devices allow quantum researchers and practitioners to design, develop and test quantum algorithms on actual hardware, paving the way toward new approaches in solving problems intractable by classical computers. However, in spite of these achievements in quantum technologies, the size of the problems that can be actually solved by quantum algorithms is still limited by the small number of qubits and the considerable noise in computation that still characterizes NISQ devices. As a consequence, there is a strong need of introducing innovative computing architectures able to interconnect a set of NISQ devices to increase the capabilities of current quantum computers of solving hard problems in a reliable way. In this paper, the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) is introduced as a reference computational model by which designing innovative frameworks where quantum devices interact to solve a complex problem by working cooperatively. In detail, the D-NISQ model consists of a hybrid and hierarchical architecture where classical and quantum processors interact to iteratively split up a problem into a collection of sub-problems, solve each one of them by means of a proper quantum algorithm, and fuse output quantum information so as to compute the final solution to the problem posed. As demonstrated by two case studies based on the well-known Grover's algorithm, a multi-threaded implementation of the D-NISQ reference model allows for greater reliability in solving problems by quantum computation.