Reviewerly/exHarmony huggingface dataset:
VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types

Deep learning-based vulnerability prediction approaches are proposed to help under-resourced security practitioners to detect vulnerable functions. However, security practitioners still do not know what type of vulnerabilities correspond to a given prediction (aka CWE-ID). Thus, a novel approach to explain the type of vulnerabilities for a given prediction is imperative. In this paper, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VulExplainer</i> , an approach to explain the type of vulnerabilities. We represent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VulExplainer</i> as a vulnerability classification task. However, vulnerabilities have diverse characteristics (i.e., CWE-IDs) and the number of labeled samples in each CWE-ID is highly imbalanced (known as a highly imbalanced multi-class classification problem), which often lead to inaccurate predictions. Thus, we introduce a Transformer-based hierarchical distillation for software vulnerability classification in order to address the highly imbalanced types of software vulnerabilities. Specifically, we split a complex label distribution into sub-distributions based on CWE abstract types (i.e., categorizations that group similar CWE-IDs). Thus, similar CWE-IDs can be grouped and each group will have a more balanced label distribution. We learn TextCNN teachers on each of the simplified distributions respectively, however, they only perform well in their group. Thus, we build a transformer student model to generalize the performance of TextCNN teachers through our hierarchical knowledge distillation framework. Through an extensive evaluation using the real-world 8,636 vulnerabilities, our approach outperforms all of the baselines by 5%–29%. The results also demonstrate that our approach can be applied to Transformer-based architectures such as CodeBERT, GraphCodeBERT, and CodeGPT. Moreover, our method maintains compatibility with any Transformer-based model without requiring any architectural modifications but only adds a special distillation token to the input. These results highlight our significant contributions towards the fundamental and practical problem of explaining software vulnerability.
Classification of some permutation quadrinomials from self reciprocal polynomials over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

Let Fq be a finite field of order q. In this paper, for q even, we give a simple way to construct small degree polynomials over Fq with no roots in the set μq+1={a∈Fq2|aq+1=1} of the (q+1)-th roots of unity. Further, we use these small degree polynomials over Fq to construct and classify certain new families of permutation quadrinomials over Fq2.
Design of high performance and energy efficient convolution array for convolution neural network-based image inference engine

The energy efficiency of CNN-based inference engines predominately depends upon Giga-operations-per-second and power consumption. The sparse-based accelerator compresses the insignificant inputs (input feature maps & weights), skips the inefficient computations, and improves energy efficiency. A sparse accelerator for weights could impact the accuracy of the inference. Therefore, a sparse network for Input Feature Maps (IFMs) is considered. MATLAB-based sparsity analysis is done layer-wise on the pre-trained CNN models like AlexNet, VGG-16, VGG-19, ResNet-18 & ResNet-34. Layer-wise analysis reveals that ∼18%–90% of the IFMs are zeros. Besides, IFMs and Weights adopted the 16-bit Fix/Float data format to maintain an accuracy as close as 97% with Single Precision Floating Point (SPFP). A 3 × 1 Convolutional array with improved Zero-detect-Skip (CZS3×1) control units for multiplier and adder/subtractor arrays is proposed. The modified rectified linear unit (RELU) converts IFM values ≤ 0 to zero and sets Detection-Bit (DB) to 1. These DBs decide the mode of effective zero-skip operations in CZS3×1. A 3 × 3 Compressed Processing Element (CPE) is designed using three CZS3×1. The 20-CPEs convolution array architecture is implemented in 65 nm technology libraries. The performance of 90 Giga-operations per second (GOP/s) and energy efficiency of 3.42 Tera operations per second per watt (TOPS/W) were attained for the proposed CPE. The CPE with improved control strategy enhanced the performance by a factor of 2.45 while consuming 8.8 times less energy on average than the state-of-the-art CNN accelerators.
Multi‐input fully CNN for joint pilot decontamination and symbol detection in 5G massive MIMO

Abstract This paper presents a multi‐input deep learning‐based joint pilot decontamination and symbol detection (SD) technique for 5G massive multiple‐input multiple‐output (MAMIMO) systems. It consists of a fully convolutional neural network (FCNN) that finds the 5G channel coefficients using pre‐known sounding reference signals (SRSs) under pilot contamination and a symbol detector derived from projected gradient descent iterations. The study considers pilot contamination caused by inter‐cell interferences between SRSs during channel estimation (CE). The proposed scheme accepts entire 5G orthogonal frequency modulated (OFDM) data and least square estimates and produces the transmitted OFDM signal. Simulation experiments demonstrated that the proposed technique has better CE and SD performance with reduced trainable parameters. Moreover, it is faster due to the lowest elapsed time during end‐to‐end OFDM symbol detection. This paper proposes a joint pilot decontamination and signal detection for 5G MAMIMO systems. It achieves better detection performance with the lowest number of trainable parameters and memory requirements. It is applicable in 5G OFDM symbol detection and channel estimation under pilot contamination.
Surgicberta: a pre-trained language model for procedural surgical language

Abstract Pre-trained language models are now ubiquitous in natural language processing, being successfully applied for many different tasks and in several real-world applications. However, even though there is a wealth of high-quality written materials on surgery, and the scientific community has shown a growing interest in the application of natural language processing techniques in surgery, a pre-trained language model specific to the surgical domain is still missing. The creation and public release of such a model would serve numerous useful clinical applications. For example, it could enhance existing surgical knowledge bases employed for task automation, or assist medical students in summarizing complex surgical descriptions. For this reason, in this paper, we introduce SurgicBERTa , a pre-trained language model specific for the English surgical language, i.e., the language used in the surgical domain. SurgicBERTa has been obtained from RoBERTa through continued pre-training with the Masked language modeling objective on 300 k sentences taken from English surgical books and papers, for a total of 7 million words. By publicly releasing SurgicBERTa , we make available a resource built from the content collected in many high-quality surgical books, online textual resources, and academic papers. We performed several assessments in order to evaluate SurgicBERTa , comparing it with the general domain RoBERTa . First, we intrinsically assessed the model in terms of perplexity, accuracy, and evaluation loss resulting from the continual training according to the masked language modeling task. Then, we extrinsically evaluated SurgicBERTa on several downstream tasks, namely (i) procedural sentence detection, (ii) procedural knowledge extraction, (iii) ontological information discovery, and (iv) surgical terminology acquisition. Finally, we conducted some qualitative analysis on SurgicBERTa , showing that it contains a lot of surgical knowledge that could be useful to enrich existing state-of-the-art surgical knowledge bases or to extract surgical knowledge. All the assessments show that SurgicBERTa better deals with surgical language than a general-purpose pre-trained language model such as RoBERTa , and therefore can be effectively exploited in many computer-assisted applications in the surgical domain.
Reinforcement of Collaborative Work in the Metaverse Through Playful Activities

This article explores the potential of the metaverse as a tool to reinforce collaborative work and improve student participation. A Rally was conducted as a playful activity in the metaverse, and data were collected through a validated survey to assess the impact on student motivation, participation, and collaboration. The main idea was to take advantage of the features of the metaverse to create an interactive and motivating learning environment that fosters collaboration and the exchange of ideas among students. To achieve this, interdisciplinary teams were formed that allowed students to interact and work together in problem solving and knowledge construction. The results of the research show that the use of playful activities in the metaverse improves student participation and motivation, as well as their ability to collaborate and communicate effectively. In conclusion, the proposal presented in this article offers an innovative and effective alternative for the reinforcement of collaborative work in the context of online education and collaboration.
Evaluation of a Combined Conductive Fabric-Based Suspender System and Machine Learning Approach for Human Activity Recognition

Accelerometer-based human activity recognition (HAR) wearable systems are location-centric and noisy, needing multiple sensors with complex signal processing and filtering mechanisms. A recently reported alternative approach using a wearable suspender integrated with strain sensors and machine learning presented a viable option for non-localized measurement with less noise and better recognition capabilities. The washability and wearability of the strain sensor instrumented suspenders due to the physical wires are limited, and the power consumption is higher, which needs to be minimized to extend the battery life of the wearable device. This article proposes an improved body-worn suspender-based HAR system built using a conductive knit jersey fabric material that overcomes the existing strain sensor based wearable device’s limitations and at the same time provides improved sensitivity. The proposed suspender system recognizes fourteen human activities using machine learning and deep learning algorithms with the best accuracy of 98.11%. A performance comparison of machine learning models based on two dimensionality reduction techniques using kernel and linear discriminatory analysis was conducted. The kernel-based method outperformed the linear one in recognizing human activities across all classifiers. The durability of the wearable is tested by washing the sensor, and the recognition capabilities were consistent before and after the wash.
Incomplete Multi-View Clustering With Complete View Guidance

In recent years, multi-view clustering has gained widespread attention in signal processing because multi-view data contains more information than a single view. However, multi-view data is often incomplete due to missing data in one or more random views. Therefore, several methods have been proposed for incomplete multi-view clustering to learn features that contain consensus information for clustering incomplete multi-view data (IMD). However, there is a part of the IMD that is not missing in any view, and most previous methods have not utilized this part to guide the process of learning consensus information. To address this issue, we design a knowledge distillation framework for incomplete multi-view clustering and propose an incomplete multi-view clustering with complete view guidance (IMC-CVG). We first train a robust teacher model with contrastive learning loss on the complete part of IMD to learn consensus features containing multi-view information. Then, we train a student model on all the IMD, where we mask partial views of the complete data to simulate missing data, and utilize the teacher model to guide the student model to learn consensus features that contain as much multi-view information as possible. Experiments show that our proposed method outperforms all the compared state-of-the-art methods.
Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic Arm

Robotic systems play an important role in the development and modernization processes of production, facilitation of labor, and human life. The robotic manipulators are outstanding among such systems. Such robots can be used for various spheres of their application. In this case, there is the manipulator’s effective control problem in the working area, of which there may be various obstacles. Therefore, a procedure is required to find the optimal path for moving the robotic arm. To develop such a procedure, the literature was reviewed, and the structural diagram of the control system of such a robot and its components was summarized. It proposed a mathematical formalization of the search for the optimal path to move the robot arm, an algorithm based on a modified method of navigation graphs, to realize the more natural movement of the robot arm. Experimental studies were conducted with different numbers of objects on the path of robot arm movement, which were combined into groups. The temporal results of this process are presented in a diagram.
Comparison of glass and digital slides for cervical cytopathology screening and interpretation

Cervical cancer is the second most common form of cancer and a leading cause of premature death among women aged 15 to 44 worldwide. In Brazil, there is a high prevalence of infection by the human papillomavirus - HPV. Digital pathology optimizes time and space for reading cervicovaginal cytology slides. We evaluated the feasibility of using whole slide images (WSI) for the routine interpretation of cytology exams. A total of 99 cases of vaginal cytology were selected from a reference laboratory in Northeastern Brazil. Three cytotechnicians participated in the study. Cellular atypia was the one that most presented concordance values. Two observers almost perfectly agreed (k = 0.86 and k = 0.84, respectively) on the negative diagnoses. The performance of the evaluators for NILM (negative for intraepithelial lesion and malignancy) showed high reproducibility and sensitivity in the digital slides, mainly between evaluators A and C. In contrast, the microbiology group showed disagreement between the diagnoses by digital slides and the standard- gold. The concordance between the digital diagnoses and the gold standard for ASCUS was 89%. In the inflammatory category, Spearman's test showed similar results between raters A, B, and C (rs = 0.47, rs = 0.41, and rs = 0.47, respectively). This study reports the diagnostic validation using digital slides in view of the need to optimize the cytology visualization process. Our experience shows good diagnostic agreement between digital and optical microscopy in several analyzed categories, but mainly in relation to cellular atypia and inflammatory processes.
Bio-inspired, task-free continual learning through activity regularization

The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL.
Probable Prime Generation from a Quantum Randomness Source

Quantum random number generators (QRNGs) are an increasingly popular method to obtain information-theoretically provable random keys for cryptographic applications. Here, we experimentally demonstrate a probable prime generation protocol using a vacuum-based QRNG and compare its output with the one yielded by an electronic noise source. The quantum source significantly outperformed the classical scheme for lengths smaller than 128 bits, with increases of up to 837.1% in the number of unique primes being reported. By mapping the input noise to a uniform distribution, this figure was additionally increased by up to 844.3% and the output statistical distribution for small primes significantly improved.
Recurrent auto-encoder with multi-resolution ensemble and predictive coding for multivariate time-series anomaly detection

As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential role in diverse industries. It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data. However, multivariate time-series anomaly detection is challenging because real-world time-series data exhibit complex temporal dependencies. For this task, it is crucial to learn a rich representation that effectively contains the nonlinear temporal dynamics of normal behavior. In this study, we propose an unsupervised multivariate time-series anomaly detection model named RAE-MEPC which learns informative normal representations based on multi-resolution ensemble reconstruction and predictive coding. We introduce multi-resolution ensemble encoding to capture the multi-scale dependency from the input time series. The encoder hierarchically aggregates the multi-scale temporal features extracted from the sub-encoders with different encoding lengths. From these encoded features, the reconstruction decoder reconstructs the input time series based on multi-resolution ensemble decoding where lower-resolution information helps to decode sub-decoders with higher-resolution outputs. Predictive coding is further introduced to encourage the model to learn more temporal dependencies of the time series. Experiments on real-world benchmark datasets show that the proposed model outperforms the benchmark models for multivariate time-series anomaly detection.
Application of Immersive Virtual Environment Through Virtual Avatar Based On Rigid-body Tracking

This study proposes a rigid-body tracking based virtual avatar application method to increase the social presence and provide various experiences of virtual reality(VR) users in an immersive virtual environment. The proposed method estimates the motion of a virtual avatar through inverse kinematics based on real-time rigid-body tracking based on motion capture using markers. Through this, it aims to design a highly immersive virtual environment with simple object manipulation in the real world. Science experiment educational contents are produced to experiment and analyze applications related to immersive virtual environments through virtual avatars. In addition, audiovisual education, full-body tracking, and the proposed rigid-body tracking method were compared and analyzed through survey. In the proposed virtual environment, participants wore VR HMDs and conducted a survey to confirm immersion and educational effects from virtual avatars performing experimental educational actions from estimated motions. As a result, through the method of utilizing virtual avatars based on rigid-body tracking, it was possible to induce higher immersion and educational effects than traditional audiovisual education. In addition, it was confirmed that a sufficiently positive experience can be provided without much work for full-body tracking.
Bibliometric review of articles related to context-based learning in science education

This study provides a bibliometric study of publications on context-based learning in science education to give readers a better understanding of the current state of the field’s research. The major goal of this study is to provide bibliometric information on articles on context-based learning in science education published in periodicals listed in the Scopus Database between 2005 and 2023. A bibliometric analysis based on seven categories–number of articles and citations per year, most influential countries, most productive authors, most significant affiliations, funding institutions, and subject areas was performed on the information gathered from publications scanned and published within the study’s parameters. Network diagrams and bibliometric analyses were produced using the Scopus Database. The year in which the most articles were published is 2022. The top three most productive countries were the Netherlands, Germany, and Turkey, respectively. The number of citations to papers included in the Scopus Database grew steadily, reaching its peak in 2022 with 410 citations. Pilot, A., who published 13 times, was the most productive author on this subject. Most publications are affiliated with Universiteit Utrecht, Freudenthal Institute and Technische Universiteit Eindhoven. Horizon 2020 framework program was the top funding source in terms of articles published. Additionally, it was examined how the publications were distributed by subject. The publications’ respective topic areas were social sciences and computer sciences. This study offers a global view of context-based learning in science education as well as a vision for future research.