Reviewerly/exHarmony huggingface dataset:
DTFA: Adversarial attack with discrete cosine transform noise and target features on deep neural networks

Abstract Image recognition on deep neural network is vulnerable to adversarial sample attacks. The adversarial attack accuracy is low when only limited queries on the target are allowed with the current black box environment. This paper proposes a target adversarial attack algorithm discrete cosine transform‐mean target feature attack (DTFA) based on the target features and a limited‐area sampling method. The algorithm first examines the original image and a target image to generate an initial adversarial example. Then the disturbance is sampled from the low‐frequency region intercepted by Gaussian noise after discrete cosine transform. The authors determine the size of the disturbance according to the difference between the adversarial example and the original image with consideration of the number of iterations and the position of the target feature region. The disturbance is applied on the initial adversarial example to generate the new adversarial example with the difference from the original image reduced. To evaluate the proposed algorithm, based on the common image classification model InceptionV3, and with identical queries accessing the same target model, the authors conduct experiments to compare the attack effectiveness of DTFA and the benchmark algorithms on the same image and target datasets. Experimental results show that the generated adversarial examples by the proposed algorithm are superior to 94% of those by the similar attack algorithms with less than 10,000 access queries on the target model.
Provable image encryption scheme via trapdoor permutation

Abstract Nowadays, due to the advancement in networks, it has become easy to share multimedia information such as images, voice, and videos in real‐time. In the process of multimedia storage and transmission, it is crucial to ensure the security and privacy of multimedia data. The attendant issue is how to encrypt images or videos safely and efficiently. Here, a provable security model of image encryption is presented. Then, a new public image encryption scheme based on one‐way trapdoor permutation, with security in the presence of honest adversaries, is proposed. Furthermore, random reusable technology is adopted to improve efficiency and shorten ciphertext. Finally, the simulation results show the validity of the encryption scheme.
Credit Where Credit Is Due: Data and Software in the Space Weather Community

Abstract This editorial aims to improve awareness of the current best practices in open research, and stimulate discussion on the practical implementation of AGU's data and software policy in key areas of space weather research. We also further aim to encourage authors to take additional steps to ensure clear credit to all contributors to the work, whether that is underlying data, key software, or direct contributions to the manuscript.
SE-PSNet: Silhouette-based Enhancement Feature for Panoptic Segmentation Network

In this work, we propose a panoptic segmentation model that integrates bottom-up and top-down methods. Our framework is designed to guarantee both the performance and the inference speed. We also focus on improving the quality of semantic and instance masks. The proposed auxiliary task with the silhouette-based enhanced features can help the model improve the prediction quality of mask contours. Additionally, we introduce a new mask quality score intending to solve the occlusion problem. The model has less chance of ignoring small objects, which often have lower confidence scores than larger objects behind them. The results show that the proposed mask quality score can better distinguish the priority of objects when the occlusion occurs. We demonstrate the results of our work on two datasets: the COCO dataset and the CityScapes dataset. Via our approach, we obtained competitive results with fast inference time.
An optimal solution to reduce virtual machine migration SLA using host power

The growing demand for cloud services from users, organizations are planning to build more and more data centers to meet the demand. This is expected to increase energy consumption, which in turn will lead to an increase in operating costs and CO2 emissions. Virtual machine live migration technology can be used to reduce the amount of energy consumed by virtual machines. By doing so, virtual machines can be migrated from one server to another and therefore cause a delay in delivering the services to the users. Service Level Agreement (SLA) violation is one of the important parameters to be considered for better utilization of cloud services. By applying the proposed algorithm, SLA violations will be reduced through the process of selecting the host to which the Virtual Machine will be migrated according to the remaining power of the host, and thus the SLAs will be more enforced in the long run. The proposed algorithm has been implemented in CloudSim. In the comparison of the results achieved with the existing algorithms and the proposed algorithm, it is found that the proposed algorithm is capable of reducing the violation of the SLA by 13.6% when compared to the existing algorithms.
On Composing Finite Forests with Modal Logics

We study the expressivity and complexity of two modal logics interpreted on finite forests and equipped with standard modalities to reason on submodels. The logic \(\mathsf {ML} ({\color{black}{{\vert\!\!\vert\!\vert}}})\) extends the modal logic K with the composition operator \({\color{black}{{\vert\!\!\vert\!\vert}}}\) from ambient logic whereas \(\mathsf {ML} (\mathbin {\ast })\) features the separating conjunction \(\mathbin {\ast }\) from separation logic. Both operators are second-order in nature. We show that \(\mathsf {ML} ({\color{black}{{\vert\!\!\vert\!\vert}}})\) is as expressive as the graded modal logic \(\mathsf {GML}\) (on trees) whereas \(\mathsf {ML} (\mathbin {\ast })\) is strictly less expressive than \(\mathsf {GML}\) . Moreover, we establish that the satisfiability problem is Tower -complete for \(\mathsf {ML} (\mathbin {\ast })\) , whereas it is (only) AExp Pol -complete for \(\mathsf {ML} ({\color{black}{{\vert\!\!\vert\!\vert}}})\) , a result that is surprising given their relative expressivity. As by-products, we solve open problems related to sister logics such as static ambient logic and modal separation logic.
Game-based security proofs for verifiable secret sharing schemes in the standard model

ABSTRACTABSTRACTIn a verifiable secret sharing (VSS) scheme, each participant can check cheating by the dealer or the other participants. In this work, we construct a VSS scheme with the strongest level of computational security and prove its security in the standard model. This scheme is secure against active attack and does not use bilinear pairing and hash function.KEYWORDS: Cryptologyverifiable secret sharinghybrid schemestandard modelmonotone span program Disclosure statementNo potential conflict of interest was reported by the author.
Collaborative Fault Detection in Resilient Navigation

Fault diagnosis is a key component of navigation integrity assurance for aerial swarms. The redundancy of a navigation system is enhanced with the introduction of observations from optimally configured collaborative partners, thus improving the capabilities of fault detection, identification, and isolation. In this chapter, fault detection, identification, and exclusion based on collaborative integrity augmentation are discussed. Multifault detection based on multiple hypothesis solution separations in collaborative resilient navigation fusion for an aerial swam is proposed, with simulated examples.
Data everyday as community‐driven science: Athletes' critical data literacy practices in collegiate sports contexts

Abstract In this article, we investigate the community‐driven science happening organically in elite athletics as a means of engaging a community of learners—collegiate athletes, many of whom come from underrepresented groups—in STEM. We aim to recognize the data literacy practices inherent in sports play and to explore the potential of critical data literacy practices for enabling athletes to leverage data science as a means of addressing systemic racial, equity, and justice issues inherent in sports institutions. We leverage research on critical data literacies as a lens to present case studies of three athletes at an NCAA Division 1 university spanning three different sports. We focus on athletes' experiences as they engage in critical data literacy practices and the ways they welcome, adapt, resist, and critique such engagements. Our findings indicate ways in which athletes (1) readily accept data practices espoused by their coaches and sport, (2) critique and intentionally disengage from such practices, and (3) develop their own new data productions. In order to support community‐driven science, our findings point to the critical role of athletics' organizations in promoting athletes' access to, as well as engagement and agency with data practices on their teams.
Deep adaptation of <scp>CNN</scp> in Chinese named entity recognition

Abstract Named entity recognition (NER) is an important task in the field of natural language processing, but it is more challenging in Chinese because of the lack of natural delimiters. The traditional character‐based Chinese NER model directly uses long short‐term memory (LSTM), gated recurrent units, and other sequence models to extract sentence‐level information from character sequences, resulting in the lack of word‐level information in the model. Therefore, a Chinese NER model called ChineseBERT‐CNNs‐BiLSTM‐CRF was proposed, which uses the ChineseBERT pretrained model as the embedding layer so that the vector representation of each Chinese character contained pinyin, glyph, and conventional character information. In addition, a CNN‐based neural network structure called CNNs was presented to extract word‐level information from character sequences and alleviate the problem of entity boundary recognition. BiLSTM was used to extract global features (i.e., sentence‐level information) and predict the corresponding labels of character sequences. Further, conditional random field (CRF) was employed to impose certain rules on the prediction of BiLSTM to enhance the recognition effect of the model. The experimental results revealed that the F1 values of the model on MSRA, people's Daily, and Weibo datasets reached 95.76, 96.61, and 70.00%, respectively, highlighting the effectiveness of the model.
Collaborative Observation-Based Resilient Navigation Fusion

The collaborative observation-based framework is another typical approach for realizing resilient navigation fusion. In this approach, the relative observations from collaborating anchor members are directly fused with the local measurements without performing localization first. On the basis of collaborative observation modelling, this chapter discusses collaborative observation-based navigation algorithms with hierarchical and parallel collaborative navigation structures. Simulated examples for both situations are also provided.
Collaborative Integrity Augmentation in Resilient Navigation

Navigation integrity is critical for the reliability of aerial swarms. The geometric configuration of the selected partners for collaboration has a significant impact on the effectiveness and efficiency of the collaborative integrity enhancement. In this chapter, an improved protection level is introduced to quantitatively evaluate navigation integrity in collaborative resilient navigation fusion. The influence of geometry on integrity in collaborative resilient navigation fusion is discussed in both the GNSS-augmented and GNSS-denied situations. Integrity optimization algorithms based on the geometric analysis method and the algebraic search method are proposed, with simulated examples.
Comparative Dynamical Study of a Bound Entangled State

The bound entangled state carries noisy entanglement and it is very hard to distill but the usefulness of bound entangled states has been depicted in different applications. This article represents a comparative dynamical study of an open quantum system for one of the bound entangled states proposed by Bennett et al. The study is conducted under the influence of Heisenberg, bi-linear bi-quadratic and Dzyaloshinskii–Moriya (DM) interaction. During the study, an auxiliary qutrit interacts with one of the qutrits of the selected two qutrit bound entangled state through different interactions. The computable cross-norm or realignment (CCNR) criterion has been used to detect the bound entanglement of the state and the negativity has been applied to measure the free entanglement. From this three-fold study it is observed that, although the auxiliary qutrit plays a significant role during the interaction, the probability amplitude of the qutrit does not affect the open quantum system. Further, it is found that the Dzyaloshinskii–Moriya (DM) interaction performs better to activate the chosen bound entangled state among all the interactions.
Design and Development Process of History Teaching in 3D Virtual Environments

This chapter aimed to reveal how the analysis, design, and development processes should be carried out while history teaching environments are being developed in 3D virtual environments and to set a concrete definition of teaching process in 3D virtual environments. In the history teaching session, the participants were represented in the 3D virtual environment developed in OpenSim simultaneously with their own avatars. They role-played the historical characters and actively participated in the events of that period by using historical reenactment technique in historical spaces. Additionally, this chapter describes the development process of a 3D virtual teaching environment from the design of the teaching process to the implementation. The procedures performed during the preparation of the 3D virtual teaching environment are presented with a flowchart. It is expected that this flowchart, suggested as a 3D virtual instructional design model, will guide researchers through the process of developing teaching implementations in 3D virtual environments in future studies.
Teaching and Learning English Online

This study aims to examine the perspectives of teachers and students towards teaching and learning English online and to determine the performance of students during online and traditional learning. This study mainly adopted a mixed methods research design. The data were collected using closed-ended and 5-point Likert scale questions on Google documents. The students' performance is similar according to the type of education they received, but their academic performance is slightly better in the blended learning period, and also the female students do slightly better than their male peers. The findings show that students are not completely in favor of learning English online and they also need psychological support during online education. The findings also revealed that teachers' computer literacy levels, preparedness to implement e-learning, awareness about students' psychological conditions, and their needs for methodological support for online teaching are indispensable necessities in eradicating the negative sides of online education to maximize the learning outcomes.
Teachers' Perceptions of the Use of the Metaverse in Foreign Language Teaching and Learning

The use of the metaverse may provide unique opportunities for teaching and learning a foreign language depending on teachers' pedagogical, personal, and linguistic skills; competencies; technical knowledge and skills; beliefs; attitudes; and perceptions. However, a serious lack of research exists on English as a foreign language (EFL) teachers' perceptions of and attitudes toward its utilization in the teaching and learning processes. The current study investigates foreign language teachers' perceived knowledge about the metaverse, attitudes toward the use of the metaverse in the teaching and learning process, perceived self-confidence in integrating the metaverse into their teaching pedagogy, and perceptions of school climate and support concerning the metaverse use. The results showed that EFL teachers had a relatively low familiarity with its utilization in the foreign language teaching and learning processes, positive attitudes towards using it, and neutral perceptions of self-confidence in integrating the metaverse into the mentioned processes.
Efficient Transformer-Based Compressed Video Modeling via Informative Patch Selection

Recently, Transformer-based video recognition models have achieved state-of-the-art results on major video recognition benchmarks. However, their high inference cost significantly limits research speed and practical use. In video compression, methods considering small motions and residuals that are less informative and assigning short code lengths to them (e.g., MPEG4) have successfully reduced the redundancy of videos. Inspired by this idea, we propose Informative Patch Selection (IPS), which efficiently reduces the inference cost by excluding redundant patches from the input of the Transformer-based video model. The redundancy of each patch is calculated from motions and residuals obtained while decoding a compressed video. The proposed method is simple and effective in that it can dynamically reduce the inference cost depending on the input without any policy model or additional loss term. Extensive experiments on action recognition demonstrated that our method could significantly improve the trade-off between the accuracy and inference cost of the Transformer-based video model. Although the method does not require any policy model or additional loss term, its performance approaches that of existing methods that do require them.
Analyzing Public Opinions Regarding Virtual Tourism in the Context of COVID-19: Unidirectional vs. 360-Degree Videos

Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidirectional videos is enormous and might differ between the two kinds of videos. This presents the problem of efficiently evaluating user opinions with respect to which type of video will be more appealing to viewers, positive comments, or interest. This paper aims to study SentiStrength-SE and SenticNet7 techniques for sentiment analysis. The findings demonstrate that the sentiment analysis obtained from SenticNet7 outperforms that from SentiStrength-SE. It is revealed through the sentiment analysis that sentiment disparity among the viewers of 360-degree and unidirectional videos is low and insignificant. Furthermore, the study shows that unidirectional videos garnered the most traffic during COVID-19 induced global travel bans. The study elaborates on the capacity of unidirectional videos on travel and the implications for industry and academia. The second aim of this paper also employs a Convolutional Neural Network and Random Forest for sentiment analysis of YouTube viewers’ comments, where the sentiment analysis output by SenticNet7 is used as actual values. Cross-validation with 10-folds is employed in the proposed models. The findings demonstrate that the max-voting technique outperforms compared with an individual fold.
Kernel-Based Container File Access Control Architecture to Protect Important Application Information

Container platforms ease the deployment of applications and respond to failures. The advantages of container platforms have promoted their use in information services. However, the use of container platforms is accompanied by associated security risks. For instance, malware uploaded by users can leak important information, and malicious operators can cause unauthorized modifications to important files to create service errors. These security threats degrade the quality of information services and reduce their reliability. To overcome these issues, important container files should be protected by file-access control functions. However, legacy file-access control techniques, such as umask and SecureOS, do not support container platforms. To address this problem, we propose a novel kernel-based architecture in this study to control access to container files. The proposed container file-access control architecture comprises three components. The functionality and performance of the proposed architecture were assessed by implementing it on a Linux platform. Our analysis confirmed that the proposed architecture adequately controls users’ access to container files and performs on par with legacy file-access control techniques.
Unsupervised Domain Adaptation for Image Classification Using Non-Euclidean Triplet Loss

In recent years, computer vision tasks have increasingly used deep learning techniques. In some tasks, however, due to insufficient data, the model is not properly trained, leading to a decrease in generalizability. When trained on a dataset and tested on another similar dataset, the model predicts near-random results. This paper presents an unsupervised multi-source domain adaptation that improves transfer learning and increases generalizability. In the proposed method, a new module infers the source of the input data based on its extracted features. By making the features extractor compete against this objective, the learned feature representation generalizes better across the sources. As a result, representations similar to those from different sources are learned. That is, the extracted representation is generic and independent of any particular domain. In the training stage, a non-Euclidean triplet loss function is also utilized. Similar representations for samples belonging to the same class can be learned more effectively using the proposed loss function. We demonstrate how the developed framework may be applied to enhance accuracy and outperform the outcomes of already effective transfer learning methodologies. We demonstrate how the proposed strategy performs particularly well when dealing with various dataset domains or when there are insufficient data.
Classification of Product Backlog Items in Agile Software Development Using Machine Learning

In agile software development, product backlog items (PBI) are used to capture the user requirements prior to the product implementation. Many types of requirements can be observed within a software project. Proper classification of PBI can positively impact the software development process. PBI can be classified into three categories: user stories, foundational stories, and spikes. After the extreme literature survey, no research was held on classifying the PBI into the categories mentioned above. This paper proposed a machine learning (ML) based approach to classify the PBI into three categories. 4,721 PBI were collected from different software projects and manually labelled into the three classes mentioned above. Then the PBI were cleaned using different pre-processing techniques. Classification models were constructed using ML techniques. The performance of each ML model was evaluated using accuracy, precision, recall, and F1 score. Support vector machine (SVM) outperformed other ML models by providing 88% accuracy.