Reviewerly/exHarmony huggingface dataset:
VulDistilBERT: A CPS Vulnerability Severity Prediction Method Based on Distillation Model

With the vigorous development of the Internet, the ecosystem of cyber-physical systems is also developing at a high speed, but cyber-physical systems may be accompanied by unknown vulnerabilities in the process of concrete implementation. Thus, the number of vulnerabilities in cyber-physical systems has been increasing year by year. The vulnerability evaluation speed cannot keep up with the vulnerability exposure speed. The traditional manual evaluation method can no longer effectively deal with such large-scale vulnerabilities, resulting in a backlog of vulnerabilities. Therefore, the vulnerability evaluation results have a certain lag. To address this problem, the paper proposes a vulnerability severity assessment method based on the distillation model. The method first uses data augmentation and integration of optimal subsets to improve the amount of information in the vulnerability description text, then uses the DistilBERT model to characterize the text of the vulnerability description text, and then the characterized feature vectors are classified based on the linear layer to achieve the purpose of assessing vulnerability severity. Compared with the current method of manual assessment based on the CVSS metric system, this method can automate the assessment of vulnerabilities based on vulnerability description text, which improves the speed of vulnerability assessment, and the assessment accuracy and other metrics achieved by this method are improved compared with similar studies. This approach provides an automated solution for cyber-physical systems vulnerability assessment and can better address the current situation where cyber-physical systems vulnerabilities are being exposed at an accelerated rate.
Taylor CFRO-Based Deep Learning Model for Service-Level Agreement-Aware VM Migration and Workload Prediction-Enabled Power Model in Cloud Computing

In this research, Taylor Chaotic Fruitfly Rider Optimization (TaylorCFRO)-based Deep Belief Network (DBN) approach is designed for workload prediction and Service level agreement (SLA)-aware Virtual Machine (VM) migration in the cloud. In this model, the round robin technique is applied for the task scheduling process. The Chaotic Fruitfly Rider Optimization driven Neural Network (CFRideNN) is also introduced in order to perform workload prediction. The DBN classifier is employed to detect SLA violations, and the DBN is trained using devised optimization model, named the TaylorCFRO technique. Accordingly, the introduced TaylorCFRO approach is newly designed by incorporating the Taylor series, Chaotic Fruitfly Optimization Algorithm (CFOA), and Rider Optimization Algorithm (ROA). The developed TaylorCFRO-based DBN scheme outperformed other workload and SLA Violation (SLAV) detection methods with violation detection rate of 0.8048, power consumption of 0.0132, SLAV of 0.0215, and load of 0.0033.
Quantitative Methoden in diachronen Studien<b>: Anwendung am Beispiel der Wortstellungsvariation in deutschen Infinitiven</b>

Abstract On the basis of a case study on the linearisation of German infinitival complements, the present paper illustrates the advantages of selected quantitative and statistical methods in diachronic studies. In particular, it first discusses, the problem of the availability of balanced diachronic corpora and how mixed-effects modelling can help make the best of “bad data” and second, it deals with the question of periodisation and shows the advantages of a data-driven method.
LAU-Net: A low light image enhancer with attention and resizing mechanisms

Nighttime environments with sub-optimal lighting conditions significantly degrade the quality of captured images. Even though many notable state-of-the-art methods had been proposed to enhance low-light images, many of the enhanced outcomes exhibit color distortion, and uneven light adjustment problems. To remedy these issues, we propose an effective supervised network, Low-light Advanced U-Net (LAU-Net), which restructures the regular U-Net to offer a better network for low-light image enhancement. Specifically, we merged several efficacious components into our LAU-Net, namely the Parallel Attention Unit (PAU), the Internal Resizing Module (IRM), and external convolutional layers. The PAU places two attention modules in parallel to extract features along the convolutional streams. Meanwhile, the IRM comprises resizing components to optimize the information flow from encoder blocks to decoder blocks, whereas the external convolutional layers simulate the autoencoder to suppress noises. We employed the LOL dataset, which is composed of 500 paired images, to train, validate, and test the proposed network. Rigorous experiments showed that our model delivered remarkable performance both in qualitative and quantitative assessments and outperforms state-of-the-art approaches. Moreover, ablation studies also justified the necessity of each module in our proposed design. Lastly, we demonstrated that the proposed method could serve as an excellent pre-processing tool for image classification tasks in challenging nighttime environments, as it has successfully improved the object classification accuracy of a ResNet-50 model when applied onto low-light images from the ExDark dataset.
Capturing the form of feature interactions in black-box models

Detecting feature interactions is an important post-hoc method to explain black-box models. The literature on feature interactions mainly focus on detecting their existence and calculating their strength. Little attention has been given to the form how the features interact. In this paper, we propose a novel method to capture the form of feature interactions. First, the feature interaction sets in black-box models are detected by the high dimensional model representation-based method. Second, the pairwise separability of the detected feature interactions is determined by a novel model which is verified theoretically. Third, the set separability of the feature interactions is inferred based on pairwise separability. Fourth, the interaction form of each feature in product separable sets is explored. The proposed method not only provides detailed information about the internal structure of black-box models but also improves the performance of linear models by incorporating the appropriate feature interactions. The experimental results show that the accuracy of recognizing product separability in synthetic models is 100%. Experiments on three regression and three classification tasks demonstrate that the proposed method can capture the product separable form of feature interactions effectively and improve the prediction accuracy greatly.
Domain adaptive crowd counting via dynamic scale aggregation network

Abstract Crowd counting is an important research topic in computer vision. Its goal is to estimate the people's number in an image. Researchers have dramatically improved counting accuracy in recent years by regressing density maps. However, because of the inherent domain shift, the model trained on an expensive manually labelled dataset (source domain) does not perform well on a dataset with scarce labels (target domain). For this issue, a novel dynamic scale aggregation network (DSANet) is proposed to reduce the gaps in style and cross‐domain head scale variations. Specifically, a practical style transfer layer is introduced to reduce the appearance discrepancy between the source and target domains. Then, the translated source and target domain samples are encoded by a generator consisting of the VGG16 network and the dynamic scale aggregation modules (DSA Modules) and produce corresponding density maps. The DSA module can adaptively adjust parameters according to the input features and effectively fuse multi‐scale information to overcome the cross‐domain head scale variations. Next, a discriminator judges the input density map from the source or target domain. Last, domain distributions are aligned through adversarial between the generator and the discriminator. The experiments show that our network outperforms the current state‐of‐the‐art methods and can improve the target domain's performance while maintaining the source domain's performance without significant degradation.
Pointing in the third-person: an exploration of human motion and visual pointing aids for 3D virtual mirrors

Abstract A “virtual mirror” is a promising interface for virtual or augmented reality applications in which users benefit from seeing themselves within the environment, such as serious games for rehabilitation exercise or biological education. While there is extensive work analyzing pointing and providing assistance for first-person perspectives, mirrored third-person perspectives have been minimally considered, limiting the quality of user interactions in current virtual mirror applications. We address this gap with two user studies aimed at understanding pointing motions with a mirror view and assessing visual cues that assist pointing. An initial two-phase preliminary study had users tune and test nine different visual aids. This was followed by in-depth testing of the best four of those visual aids compared with unaided pointing. Results give insight into both aided and unaided pointing with this mirrored third-person view, and compare visual cues. We note a pattern of consistently pointing far in front of targets when first introduced to the pointing task, but that initial unaided motion improves after practice with visual aids. We found that the presence of stereoscopy is not sufficient for enhancing accuracy, supporting the use of other visual cues that we developed. We show that users perform pointing differently when pointing behind and in front of themselves. We finally suggest which visual aids are most promising for 3D pointing in virtual mirror interfaces.
Competing Views of Word Meaning: Word Embeddings and Word Senses

Abstract At least since the invention of writing, people have been troubled by the problem of what a word means. Dictionaries have traditionally been written with numbered word senses, giving the impression that the different senses of a word are fixed abstract entities, which can be used to separate usages into neat piles according to their different meanings. Adam Kilgarriff’s daring 1997 article ‘I Don’t Believe in Word Senses’ challenged this traditional view of word meaning, presenting an account in which ‘the basic units are occurrences of the word in context’. Kilgarriff went on to develop the Sketch Engine, a statistical tool that enables lexicographers and NLP researchers, teachers, and students to explore the relationship between meanings and collocations (words and their contexts). In this review article, we compare the information provided by Kilgarriff’s Word Sketches with the recently developed Word Embedding techniques and with the results of Corpus Pattern Analysis.
NEAT—Named Entities in Archaeological Texts: A semantic approach to term extraction and classification

Abstract The lack of annotated datasets affects the development of Natural Language Processing applications and heavily impacts the access to textual data, in particular for specific domains and specific languages. In this paper, we propose a methodology to annotate texts concerning domain-specific knowledge, to provide a reliable source of data for the task of Named Entity Recognition (NER) in the domain of archaeology for the Italian laguage. This method integrates syntactic and semantic information from several structured sources to annotate entities’ mentions in unstructured texts. Furthermore, we make use of an ontology to label entities with the specific type they refer to. By using a corpus made up of item descriptions from Europeana’s Archaeology Collection, we first test our proposed methodology on a mock dataset composed of 1,000 texts. After several steps of improvements, we use the final process to create a complete dataset composed of 5,000 descriptions. The resulting dataset, Named Entities in Archaeological Texts has a total of 41,002 spans of texts annotated with their domain-specific entity classification according to the CIDOC Conceptual Reference Model.
Data-I – Interactive Experience of IoT Data

In this teachable moment we present our approach and tools for an ongoing teaching-learning project that brings the focus of human-data interaction in classical human-computer education, namely a human-centered design process. We explain our motivation, coming from an HCI course open not only for computer science students but for multiple disciplines aiming at integrating an interdisciplinary perspective. We motivate why we chose to focus on simple IoT sensor data and why a hands-on approach is useful for students. We discuss our Sensorkit, which is designed to support students in all relevant stages of the HCD process, from initial exploration, gaining insights and understanding, to prototyping, to evaluating a prototype that collects and visualizes sensor data in a small study with participants.
Research on the application effect of machine vision technology based on simulation analysis in <scp>3D</scp> inspection of torque converter welds

Abstract With the development of machine vision technology, visual inspection technology has been widely used in many fields. Among them, the 3D visual detection technology based on line structured light has attracted the attention of relevant scholars because of its advantages of the easy acquisition of image information and high detection accuracy. Therefore, applying machine vision technology to the field of three‐dimensional weld inspection can improve the inspection effect, so a machine vision technology based on simulation analysis was proposed. First, the model parameter calibration and algorithm of the structured light imaging system are studied, and then a structured light‐based torque converter weld height detection system is designed and related models are constructed. Finally, the detection accuracy and speed of the constructed 3D vision measurement system are verified. The results show that the calibration parameters obtained by the optimization algorithm of this study are as follows, focal length = 17.16376; = 1798.55439; = 1276.85505; = 0.14072; Secondly, in the comparative experiment, the height of the wave crest coordinates of the measurement results of the three‐dimensional detection system of the torque converter weld seam in the global feature extraction process based on the machine vision technology based on simulation analysis is 9.1330 mm, and the maximum error value is 1.0403 mm; As a result of the calibration algorithm, the peak coordinate height is 10.3526 mm, and the maximum error value is 2.7482 mm. The error of the research system is better than Zhang Zhengyou's calibration method. The research content has important reference value for improving the three‐dimensional inspection effect of torque converter welds.
Emojis vs. facial expressions: An electrical neuroimaging study on perceptual recognition

The aim of this study was to investigate the neural underpinnings and the time course of emoji recognition through the recording of event-related potentials in 51 participants engaged in a categorization task involving an emotional word paradigm. Forty-eight happy, sad, surprised, disgusted, fearful, angry emojis, and as many facial expressions, were used as stimuli. Behavioral data showed that emojis were recognized faster and more accurately (92.7%) than facial expressions displaying the same emotions (87.35%). Participants were better at recognizing happy, disgusted, and sad emojis, and happy and angry faces. Fear was difficult to recognize in both faces and emojis. The N400 response was larger to incongruently primed emojis and faces, while the opposite was observed for the P300 component. However, both N400 and P300 were considerably later in response to faces than emojis. The emoji-related N170 component (150-190 ms) discriminated stimulus affective content, similar to face-related N170, but its neural generators did not include the face fusiform area but the occipital face area (OFA) for processing face details, and object-related areas. Both faces and emojis activated the limbic system and the orbitofrontal cortex supporting anthropomorphization. The schematic nature of emojis might determine an easier classification of their emotional content.
Yoga AI Assistant with ReactJS

Yoga is beneficial for maintaining physical and mental fitness. And these days, technology is helping yoga find a new companion as online yoga becomes more and more common, making it easy to access classes and instructions. TensorFlow’s pose net, a tool for pose detection, is used by the AI yoga web app. Pre-trained models for identifying human poses are immediately accessible in the browser using posenet.js, a toolkit for estimating yoga poses based on body count rate determined by the pose. A person can receive feedback on the accuracy of their pose and keep track of their fitness goal in the application using real-time camera data.
Adversarial example detection using semantic graph matching

Deep neural networks have recently been found to be vulnerable to adversarial examples, which can deceive attacked models with high confidence. This has given rise to significant security threats and raised doubts about the reliability of deploying deep learning models in security-critical domains. Therefore, effectively dealing with various adversarial examples has become an essential but challenging requirement. Adversarial example detection can predict the existence of adversarial examples in advance. However, existing detection methods are usually restricted to specific attacks, lacking generalization and decision-making bases. In this paper, we discover that the common characteristic of adversarial examples is to alter the semantic information recognized by models, which can effectively distinguish adversarial examples and provide interpretability. Based on this perspective, we propose a semantic graph matching (SeMatch) method to execute attack-agnostic adversarial example detection. SeMatch detects adversarial examples by comparing the constructed semantic graphs with the semantic graph prototypes of their predicted classes and further corrects their classification results. Experimental results demonstrate that SeMatch can effectively detect and classify adversarial examples in several attack settings, achieving an average detection and classification accuracy of 96.01% and 89.71%, respectively. When applied to unknown attack scenarios, SeMatch is more effective and interpretable than state-of-the-art methods.
How Do Academic Society Editorial Boards Handle Nonregulated Research?

In recent years in Japan, ethical review applications for research that is not subject to regulation by administrative guidelines or laws (nonregulated research) have been submitted to institutional research ethics committees. Ethics committee offices' previous studies have revealed that the main reason for such applications was to satisfy paper submission requirements. Some academic societies require committee approval for all research involving human subjects. If this situation is not addressed, it could negatively impact the quality of ethics committee deliberations, and for research involving human subjects, it could adversely affect the subjects' safety and protection. Therefore, this research conducted a questionnaire survey among the editorial committees of academic societies and asked them about their responses regarding nonregulated research.
Hot Topics in Security and Privacy Standardization at the IETF and Beyond

Deploying security and privacy enhancing technologies often requires alignment and collaboration across stakeholders. Technical specifications and standards help achieve alignment and facilitate collaboration. In this column, we discuss the role of specifications in emerging security and privacy technologies.
Power in Computer Security and Privacy: A Critical Lens

Critical theory is an approach to research focused on acknowledging and dismantling power structures. In this piece, we illustrate the ways in which security and privacy research already takes a critical approach and offer directions for increasing the criticality of our work along new dimensions.
The syndromes decoding algorithm in group codes

Our aim is the design of an efficient decoding algorithm in group codes. The algorithm is inspired by the well known syndrome decoding algorithm for linear codes and uses the decomposition of a semisimple group algebra KG as a direct sum of two-sided ideals, each of them generated by a central idempotent of KG. When G is an abelian group the algorithm can be modified to make it very simple and efficient. Some illustrative examples are presented.
A new construction for q-ary constant weight codes

For a given prime power q, a new method for constructing q-ary constant weight codes is presented, and then several classes of q-ary constant weight codes are given by using the trace map over finite fields. Especially, for q=2, some of these codes are optimal, and then the maximal possible sizes of two classes of binary constant weight codes are determined.
An efficient algorithm for constructing reversible quasi-cyclic codes via Chinese remainder theorem

Regarding quasi-cyclic codes as certain polynomial matrices, we show that all reversible quasi-cyclic codes are decomposed into reversible linear codes of shorter lengths corresponding to the coprime divisors of the polynomials with the form of one minus x to the power of m. This decomposition brings us an efficient method to construct reversible quasi-cyclic codes. We also investigate the reversibility and the self-duality of the linear codes corresponding to the coprime divisors of the polynomials. Specializing to the cases where the number of cyclic sections is not more than two, we give necessary and sufficient conditions for the divisors of the polynomials for which the self-dual codes are reversible and the reversible codes of half-length-dimension are self-dual. Our theorems are utilized to search reversible self-dual quasi-cyclic codes with two cyclic sections over binary and quaternary fields of lengths up to seventy and thirty-six, respectively, together with the maximums of their minimum weights.
The potential-Ramsey numbers <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:msub><mml:mi>r</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:msub><mml:mi>C</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msubsup><mml:mi>K</mml:mi><mml:mi>t</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:msubsup><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:…

A nonincreasing sequence ρ=(ρ1,…,ρn) of nonnegative integers is a graphic sequence if it is realizable by a simple graph G on n vertices. In this case, G is referred to as a realization of ρ. Given two graphs G1 and G2, Busch et al. introduced the potential-Ramsey number of G1 and G2, denoted rpot(G1,G2), is the smallest nonnegative integer m such that for every m-term graphic sequence ρ, there is a realization G of ρ with G1⊆G or with G2⊆G¯, where G¯ is the complement of G. For t≥2 and 0≤k≤⌊t2⌋, let Kt−k be the graph obtained from Kt by deleting k independent edges. Busch et al. determined rpot(Cn,Kt−k) and rpot(Pn,Kt−k) for k=0. Du and Yin determined rpot(Cn,Kt−k) and rpot(Pn,Kt−k) for k=1. In this paper, we further determine rpot(Cn,Kt−k) and rpot(Pn,Kt−k) for 2≤k≤⌊t2⌋.