Reviewerly/exHarmony huggingface dataset:
Human Security Behavior Assistance in the Cyber-Physical Space

With the development of human-computer interaction technology, the concept of space is constantly being extended. As technology continues to be embedded in daily life, the boundaries between the physical world and cyberspace are blurring. We consider this composite environment as a cyber-physical space, which is not only closely connected with the external physical world, but also has the characteristics of technology, virtuality and independence. Human security behaviors in cyber-physical space will be affected by more factors in different environments because they occur in parallel in the two spaces. Therefore, in this new socio-technical system, we need to reconsider how to guide and assist human security behavior. In this paper, we focus on the security behavior in cyber-physical space and the research question of how to provide assistance for it. On the one hand, the characteristics of the environment are considered, that is, the cyber-physical space is the product of technological development. On the other hand, human behavior is affected by their own psychological characteristics and bounded rationality, so the characteristics of the behavior subject are also considered. Starting from the tech-psychological and tech-economic perspectives, we propose that by changing motivation, changing ability, providing appropriate triggers, or using people’s status-quo bias, regress aversion bias, social influence to assist human security behavior based on the persuasive technology and nudge theory. Finally, we summarize the design ideas of human security behavior assistance in cyber-physical space.
An Analysis of Process Parameters for the Optimization of Specific Emitter Identification Under Rayleigh Fading

Specific Emitter Identification (SEI) was introduced over twenty-five years ago to grant electronic warfare systems the ability to uniquely, distinguish between RADARs of the same type using intra-pulse modulation features. The demonstrated success of RADAR SEI led to its application in the identification of wireless transmitters for the purpose of augmenting digital security measures within public and private communication networks. The majority of SEI work has focused on wireless channels comprised of noise only, unknown multipath fading conditions, or fixed fading channels. Our recent works focused on SEI performance within multipath fading channels that conform to a known model/behavior and change from one transmission to another. However, these works did not design the SEI process (i.e., from waveform collection to final radio identification decision) with the singular purpose of maximizing SEI performance under multipath fading. In order to maximize SEI performance under Rayleigh fading, this work analyzes the impacts of the: (i) filter type, order, and bandwidth; (ii) Gabor Transform analysis window width; as well as (iii) the number of candidate signals used by the Nelder-Mead (N-M) channel estimator. The result is a 11.9% average percent correct classification performance improvement for a length five Rayleigh fading channel at a signal-to-noise ratio of 9 dB.
Survey of Hypergraph Neural Networks and Its Application to Action Recognition

With the development of deep learning, graph neural networks have attracted ever-increasing attention due to their exciting results on handling data from non-Euclidean space in recent years. However, existing graph neural networks frameworks are designed based on simple graphs, which limits their ability to handle data with complex correlations. Therefore, in some special cases, especially when the data have interdependence, the complexity of the data poses a significant challenge to traditional graph neural networks algorithm. To overcome this challenge, researchers model the complex relationship of data by constructing hypergraph, and use hypergraph neural networks to learn the complex relationship within data, so as to effectively obtain higher-order feature representations of data. In this paper, we first review the basics of hypergraph, then provide a detailed analysis and comparison of some recently proposed hypergraph neural networks algorithm, next some applications of hypergraph neural networks for action recognition are listed, and finally propose potential future research directions of hypergraph neural networks to provide ideas for subsequent research.
UI Layers Group Detector: Grouping UI Layers via Text Fusion and Box Attention

Graphic User Interface (GUI) is facing great demand with the popularization and prosperity of mobile apps. Automatic UI code generation from UI design draft dramatically simplifies the development process. However, the nesting layer structure in the design draft affects the quality and usability of the generated code. Few existing GUI automated techniques detect and group the nested layers to improve the accessibility of generated code. In this paper, we proposed our UI Layers Group Detector as a vision-based method that automatically detects images (i.e., basic shapes and visual elements) and text layers that present the same semantic meanings. We propose two plug-in components, text fusion and box attention, that utilize text information from design drafts as a priori information for group localization. We construct a large-scale UI dataset for training and testing, and present a data augmentation approach to boost the detection performance. The experiment shows that the proposed method achieves a decent accuracy regarding layers grouping.
A Secure Image-Video Retrieval Scheme with Attribute-Based Encryption and Multi-feature Fusion in Smart Grid

With the continuous development of smart grid, massive sensitive videos of power grid are stored in semi-trusted cloud servers, which leads to various security threats. And the existing secure video retrieval solutions are difficult to achieve both high retrieval rate and accuracy. We propose a novel secure video retrieval scheme in smart grid based on image content, by utilizing symmetric searchable encryption technology with attribute-based encryption and multi-feature fusion. Fine-grained access control is achieved for sensitive video data to eliminate illegal authorized access. This scheme converts the video into a keyframe set and transforms the difficult video secure retrieval problem to the more mature image secure retrieval region, which can effectively reduce the overhead of video secure retrieval and improve the retrieval accuracy. Security analysis demonstrates that the proposed scheme can preserve the privacy of data, resist known ciphertext attacks, known background attacks and collusion attacks. The experimental results indicate that the proposed scheme has the best balance between retrieval efficiency and retrieval accuracy among the three comparison schemes. The proposed scheme is feasible in the smart grid application scenario.
EdgeVR360: Edge-Assisted Multiuser-Oriented Intelligent 360-degree Video Delivery Scheme over Wireless Networks

The COVID-19 situation has determined many people all over the world to experience remote work, study and play although most of them were not prepared for such a change in their lifestyle. With the coming of the high demand of virtual interaction, 360-degree Virtual Reality (VR) technologies and applications have established stronger relationships with your peers and friends if it applies. However, higher quality of VR streaming brings users deeper immersive experience which requires greater network bandwidth and latency, and more powerful computation capability for individuals. To address these issues, the proposed intelligent video delivery scheme in this paper takes advantage of the edge-assisted computational power to improve the multi-user oriented watching experience of high quality 360-degree video over wireless networks, which reduces network resource utilization, and also optimizes edge cache hit ratio and user’s Field of View (FoV) quality.
Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency

The intelligent video surveillance system (IVSS) can automatically analyze the content of the surveillance image (SI) and reduce the burden of the manual labour. However, the SIs may suffer quality degradations in the procedure of acquisition, compression, and transmission, which makes IVSS hard to understand the content of SIs. In this paper, we first conduct an example experiment (i.e. the face detection task) to demonstrate that the quality of the SIs has a crucial impact on the performance of the IVSS, and then propose a saliency-based deep neural network for the blind quality assessment of the SIs, which helps IVSS to filter the low-quality SIs and improve the detection and recognition performance. Specifically, we first compute the saliency map of the SI to select the most salient local region since the salient regions usually contain rich semantic information for machine vision and thus have a great impact on the overall quality of the SIs. Next, the convolutional neural network (CNN) is adopted to extract quality-aware features for the whole image and local region, which are then mapped into the global and local quality scores through the fully connected (FC) network respectively. Finally, the overall quality score is computed as the weighted sum of the global and local quality scores. Experimental results on the SI quality database (SIQD) show that the proposed method outperforms all compared state-of-the-art BIQA methods.
Saliency Map-Based Local White-Box Adversarial Attack Against Deep Neural Networks

The current deep neural networks (DNN) are easily fooled by adversarial examples, which are generated by adding some small, well-designed and human-imperceptible perturbations to clean examples. Adversarial examples will mislead deep learning (DL) model to make wrong predictions. At present, many existing white-box attack methods in the image field are mainly based on the global gradient of the model. That is, the global gradient is first calculated, and then the perturbation is added into the gradient direction. Those methods usually have a high attack success rate. However, there are also some shortcomings, such as excessive perturbation and easy detection by the human’s eye. Therefore, in this paper we propose a Saliency Map-based Local white-box Adversarial Attack method (SMLAA). The saliency map used in the interpretability of artificial intelligence is introduced in SMLAA. First, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to provide a visual interpretation of model decisions to find important areas in an image. Then, the perturbation is added only to important local areas to reduce the magnitude of perturbations. Experimental results show that compared with the global attack method, SMLAA reduces the average robustness measure by 9%–24% while ensuring the attack success rate. It means that SMLAA has a high attack success rate with fewer pixels changed.
Fast and almost unbiased weighted least squares fitting of circles

The algebraic fitting of circles was first proposed by Delogne (1972) and Kasa (1976). We extend the work by Delogne (1972) and Kasa (1976) and propose fast and almost unbiased weighted least squares to best fit circles. The simulations have shown that two non-iterative versions of the bias-corrected weighted LS method are fast and almost unbiased and perform much better than the naive weighted LS method (without applying any bias correction), the ordinary LS-based methods and the gradient-based weighted LS method in terms of both biases and mean squared errors, no matter whether fitting of circles is strongly or weakly constrained geometrically. Nevertheless, a weak geometrical constraint results in a poor fitting of circles. We accordingly propose the regularized variants of the bias-corrected weighted LS method to fit circles from data with a weak geometrical constraint. The simulations have also shown that the two non-iterative regularized variants fit circles satisfactorily well and consistently perform the best among all the regularization methods under study for ill-conditioned problems of fitting circles.
Conceptual Modeling-Based Cardiopathies Data Management

Familiar cardiopathies are a group of heterogeneous disorders that affect the heart. Although their origin has a strong genetic component, most patients’ variations have not been reported and are not classified. Thus, cardiologists struggle to assess whether the origin of the disease is genetic and are unable to provide an appropriate early diagnosis. Providing an enriched context for understanding the role of such variations in these disorders is crucial. This work reports the identification of the conceptual dimensions that are required to provide such context. Then, we instantiate the identified conceptual dimensions in a use case. Finally, the instantiated dimensions are integrated to provide semantic interoperability.
An Initial Empirical Assessment of an Ontological Model of the Human Genome

Conceptual modeling is used to model application domains for which an information system is needed. One of the most complex domains to which conceptual modeling has been applied is that of the human genome. Due to its complexity, its understanding is often left to domain experts. Conceptual models represent genomics-related concepts, with various purposes, including domain clarification or data structures design for facilitating data integration. However, traditional conceptual models, which might be expressed, for example, with UML, may not be appropriate for properly explaining such a complex domain, thus requiring an additional layer to ground the model on well-accepted ontological foundations. To achieve this result, an “ontological unpacking” method has been proposed that uses OntoUML as a visual formalism. In this research, we carry out an empirical study to compare the two mentioned representations. The study involved a small group of participants, who responded to a set of questions by reading either a UML model or its related OntoUML unpacked version; the results enabled us to assess their understanding of the domain. We aim to initiate a practical evaluation framework to assess the effectiveness, efficiency and user beliefs of models derived by ontologically unpacking traditional conceptual models. The results of the analysis provide the basis for a broader assessment.
Understanding Algebraic Effect Handlers via Delimited Control Operators

Algebraic effects and handlers are a powerful and convenient abstraction for user-defined effects. We aim to understand effect handlers through the lens of control operators, a similar but more well-studied tool for expressing effects. In this paper, we establish two program transformations and a type system for effect handlers, all by reusing the existing results about control operators and their relationship to effect handlers.
MCTSR: A super‐resolution method for defects in gas‐insulated switchgear X‐ray images based on multi‐scale contextual transformers

Abstract As the core equipment of transmission and distribution hubs, the operational status of gas‐insulated switchgear (GIS) is closely linked to the safety of the power system. Recently, X‐ray digital imaging technology has been extensively used in GIS equipment fault detection. However, the X‐ray image of GIS is blurred, which is not conducive to the detection of tiny defects. Thus, a super‐resolution method for GIS X‐ray images based on multi‐scale context transformers is proposed in this study, namely MCTSR. Firstly, a second‐order image degradation model is introduced to generate GIS X‐ray low‐resolution images that more closely resemble the real world. Secondly, a contextual transformer gate module is constructed to improve attention to tiny defects in GIS X‐ray images. Thirdly, a U‐Net discriminator network based on multi‐scale contextual transformers is intended to enrich the information of the generated images. Finally, the proposed discriminator is combined with the existing generator to compose a super‐resolution method applicable to GIS X‐ray images. The experimental results demonstrate that the method outperforms other methods in peak signal‐to‐noise ratio and structural similarity on the constructed GIS X‐ray image dataset. In addition, the output image of the proposed method facilitates the subsequent defect detection.
Learner Characteristics and Competencies

Abstract The advances in technology and the demand for open, distance, and digital education redefined the characteristics and competencies of learners in these learning environments. Although technology ownership and access to an Internet connection are growing worldwide, there is still a significant number of learners in need of a computer and Internet access to benefit from the advantages offered by new learning environments. As a result, the obstacles in technology ownership and access put a group of learners from a low socioeconomic status and minorities in a disadvantaged position and prevent them from engaging in learning experiences. Indirectly, these learners also struggle to develop digital competencies due to their limited access to technology-rich environments. This chapter elaborates on the characteristics of learners in open, distance, and digital education environments with respect to their technology ownership and access, presents a number of competency frameworks, and discusses how the competencies included in the frameworks apply to open, distance, and digital education. The chapter opens with the role of technology in new learning environments and how technology ownership and access vary among certain demographics as well as developed, developing, and underdeveloped countries. The introduction is followed by learner characteristics with respect to varying technology access and ownership among demographics and the implications of technology ownership and access for open, distance, and digital education. Finally, a number of competency frameworks were presented and discussed in regard to their contributions to the development of a competency set for learners in open, distance, and digital education environments.
Energy Saving Techniques for Cloud Data Centres: An Empirical Research Analysis

Platforms for cloud computing make it possible for end users to get access to computing resources online. The emergence of cloud computing has led to an increase in the use of Apache Hadoop in cloud data centres. MapReduce and the open-source framework Hadoop have generated great interest among both researchers and corporates. Optimising Hadoop’s energy consumption has been identified as a critical issue, with numerous studies undertaken on the topic. FIFO, capacity, and fair scheduling algorithms are the most commonly used scheduling algorithms in Hadoop cloud data centres. This paper examines the nature and non-nature-oriented energy-efficient scheduling techniques that are frequently used in cloud data centres. The studies that have been explored are classified into node management, virtualisation and consolidation, task scheduling, data placement, and infrastructure change categories. A brief discussion of the rationale for each category is included, along with its strengths and weaknesses. Additionally, for computation and I/O-intensive workloads, the paper examines the CPU energy consumption of three commonly used cloud workload scheduling algorithms: FIFO, fair, and capacity. Our goal is to see if the energy usage among the three schedulers differs significantly between two types of workloads and different job sizes. Finally, the cloud computing research community has been provided with a futuristic perspective in the area, based on current trends in energy-efficient algorithms. The findings and directions presented in this study may be helpful to researchers interested in optimising cloud data centre energy consumption.
Implementing Robotic Path Planning After Object Detection in Deterministic Environments Using Deep Learning Techniques

Path planning is a significant activity which aims at providing a collision-free, feasible and low-cost dynamic path for a mobile robot to navigate around its environment. There are various methods and algorithms for navigation whose initial step would be to gain knowledge of the environment that the robot is presented to. Generally, robotic path planning can be either local or global. The basic difference is that in global path planning, the robot will have clear knowledge of the environment in the form of a map; i.e. the obstacles would be known and the environment will be static, whereas in local path planning, the environment and its obstacles are dynamic. So in a local path planning method, the robot has to learn through the environment to reach a target without colliding with its obstacles. Learning happens constantly by capturing the environment as a map image. This learning process is done using deep learning that aims to learn representations of data using a neural network with several nodes. The representations are learnt by feature identification and processing in a series of stages. A convolutional neural network (CNN) is used to identify both the obstacle and free-space in the given environment image. Further, after such classification, an optimal path towards the goal using three sampling-based path planning algorithms is done. The best path generated with minimum time and distance comparatively is tabulated after analyses.
Authoring Interactive Narrative Meets Narrative Interaction Design

Authoring stories and designing interactions are often discussed separately, within differing communitiesCommunities. At the same time, it would be useful to regard these two tasks as actually one integrated process for the creation of interactive narratives. With a focus on non-standardized interaction stylesInteractioninteraction style, this chapter discusses the authoring challenges involved in conceiving IDNs for locationLocation-based and immersiveImmersion (immersive) experiences with novelNovel technologies, one example being Augmented RealityAugmented Reality (AR). These challenges include the necessity to understand technologies and their users, as well as potential gaps between the platforms and circumstances of creation and experience. This chapter suggests approaches to these authoring problems, placing an emphasis on the integration of the story withPatterninteraction pattern repeatableRepeat (repeatable) interaction patternsInteractioninteraction pattern.
Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking

In order to reduce the negative impact of severe occlusion in dense scenes on the performance degradation of the tracker, considering that the head is the highest and least occluded part of the pedestrian’s entire body, we propose a new multiobject tracking method for pedestrians in dense crowds combined with head tracking. For each frame of the video, a head tracker is first used to generate the pedestrians’ head movement tracklets, and the pedestrians’ whole body bounding boxes are detected at the same time. Secondly, the degree of association between the head bounding boxes and the whole body bounding boxes are calculated, and the Hungarian algorithm is used to match the above calculation results. Finally, according to the matching results, the head bounding boxes in the head tracklets are replaced with the whole body bounding boxes, and the whole body motion tracklets of the pedestrians in the dense scene are generated. Our method can be performed online, and experiments suggested that our method effectively reduces the negative effects of false negatives and false positives on the tracker caused by severe occlusion in dense scenes.