Reviewerly/exHarmony huggingface dataset:
Nesting spatiotemporal attention networks for action recognition

Action recognition is an important yet challenging problem. While attention mechanism is widely used to extract informative features for action recognition, most previous attention models regard spatial attention and temporal attention to be independent. In this paper, we propose a novel nesting spatiotemporal attention network (NST) model in which the spatial attention and the temporal attention closely nests and interact with each other. A nesting spatiotemporal attention block contains a spatial attention module and a nested temporal attention module. The spatial attention module learns features to assign different weights for the spatial areas in each video frame. Based on the spatial attention areas, the temporal attention module assigns different weights for different frames. In this way, the most informative regions in each frame and the most key frames in the video sequence are jointly mined and enhanced. An overall architecture is constructed by inserting the nesting spatiotemporal attention blocks into base networks for action recognition. The proposed model was tested on challenging datasets and the experimental results show that our method outperforms other comparison methods.
Embracing virtuality: User acceptance of virtual settings for learning

Immersion in virtual settings allows participants to perceive, understand, and feel present in a computer-generated environment. User experience in a virtual setting is highly dependent on the fidelity of represented objects and surroundings, and the degree of interaction it provides. Understanding the users’ perception and acceptance of virtual settings is a priority to predict the potential of its incorporation in the curriculum. An instrument was applied to 85 university students measuring ease of use, enjoyment, and emotional immersion on a 1 to 7 Likert scale where 7 represents the highest score. The ease of use acceptance achieved a mean of 6.61, enjoyment 7, and emotional immersion 6.81. The results emphasize that users were enthusiastic and participative; these positive emotions reassure the high potential of virtuality in educational settings.
Let numbers tell the tale

Motivated by the recent push towards adopting new standards and the discovery of numerous vulnerabilities in both new and old protocols, this paper analyzes the security of Wi-Fi networks. Our analysis is based on publicly available datasets and our own survey covering 250,137 networks across four countries in three continents. We present several key insights, including the continued use of outdated security configurations and vulnerable protocols, the adoption rates of modern protocols, the increasing presence of mesh networks as part of smart city infrastructure, and the vast differences depending on the surveyed geographic region and frequency spectrum. Additionally, we identify and improve upon shortcomings in previous surveys, and recommend best practices for future surveying. In summary, our work provides a more fine-grained understanding on Wi-Fi network security in the real-world. Finally, we publish our tools used for extracting security statistics, and make all anonymized datasets available to other researchers.
Self-SAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks.
Parameter-Free Consensus Embedding Learning for Multiview Graph-Based Clustering

Finding a consensus embedding from multiple views is the mainstream task in multiview graph-based clustering, in which the key problem is to handle the inconsistence among multiple views. In this article, we consider clustering effectiveness and practical applicability collectively, and propose a parameter-free model to alleviate the inconsistence of multiple views cleverly. To be specific, the proposed model considers the diversities of multiple views as two-layers. The first layer considers the inconsistence among different features of each view and the second layer considers linking the preembeddings of multiple views attentively. By this way, a consensus embedding can be learned via kernel method effectively and the whole learning procedure is parameter-free. To solve the optimization problem involved in the proposed model, we propose an alternative algorithm which is efficient and easy to implement in practice. In the experiments, we evaluate the proposed model on synthetic and real datasets and the experimental results demonstrate its effectiveness.
Can I Solve It? Identifying APIs Required to Complete OSS Tasks

Open Source Software projects add labels to open issues to help contributors choose tasks. However, manually labeling issues is time-consuming and error-prone. Current automatic approaches for creating labels are mostly limited to classifying issues as a bug/non-bug. In this paper, we investigate the feasibility and relevance of labeling issues with the domain of the APIs required to complete the tasks. We leverage the issues' description and the project history to build prediction models, which resulted in precision up to 82% and recall up to 97.8%. We also ran a user study (n=74) to assess these labels' relevancy to potential contributors. The results show that the labels were useful to participants in choosing tasks, and the API-domain labels were selected more often than the existing architecture-based labels. Our results can inspire the creation of tools to automatically label issues, helping developers to find tasks that better match their skills.
Eye tracking in Child Computer Interaction: Challenges and opportunities

Eye trackers allow for the collection of eye gaze data. These data can include eye gaze points which are two dimensional Cartesian coordinates (x,y) that represent a point on a screen (for screen-based eye trackers) or points in a natural real-world environment (e.g., at home, school or a supermarket). Advances in eye tracking technology have resulted in less intrusive devices being developed that open up opportunities for the Child Computer Interaction community (CCI) to understand how children interact with digital technologies and offer new insights into their cognitive processing. This paper reports the results of a literature review of three digital libraries using the Quorom technique, to identify research on eye tracking that aligns with the core themes of CCI researchers. In total 66 papers were identified that related to the themes of design, education, programming and interaction design. The results highlighted that eye tracking studies with children have relatively small sample sizes (median n=11) and that the median age of the children participating in the studies is 10.3 (inter-quartile range = 3.96). The metrics used within the research studies are predominately fixation duration and fixations within areas of interest. The paper contributes to a critique of existing methodological challenges to ensure quality data is obtained from studies involving children. In addition, the review identified that there are clear opportunities to further understand children's interaction with digital technology using other metrics such as pupillometry and blinks; these can assist to understand phenomenon including cognitive load and arousal which may be useful for user experience research.
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods

In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment. In the current experimental setting, multiple different scores are employed to assess different aspects of model performance. We analyze the informativeness of these evaluation measures and identify several shortcomings. In particular, we demonstrate that all existing scores can hardly be used to compare results across different datasets. Therefore, we propose adjustments to the evaluation and demonstrate empirically how this supports a fair, comparable, and interpretable assessment of model performance.
Real-Time Deep Neuro-Vision Embedded Processing System for Saliency-based Car Driving Safety Monitoring

Recently, much interest has been aroused by scientific community regarding visual saliency-based applications. The proposed approach contributes to the progressive growth of knowledge of video saliency applications in the automotive field. Through the visual saliency detection, the car driver assistance systems (ADAS i.e. Advanced Driver Assistance Systems) are able to process the driving observation scene selectively. Specifically, it has been observed that the drivers along the driving route, focuses his/her gaze on some objects rather than others. This is determined by the perceptual activity of the brain which through the visual saliency determine the focused scene. We propose a driving safety assessment pipeline which combines a near-real time drowsiness car driver monitoring system driven by a visual saliency detection applied to the acquired driving scene. The proposed approach includes ad-hoc 3D pre-trained Semantic Segmentation Deep Network combined with ad-hoc 1D temporal Deep Dilated Convolutional Neural Network. This architecture was developed for the embedded platform based on STA1295 Accordo5 core (ARM A7 Dual-Cores) embedding an hardware graphics accelerator. The proposed system embeds a bio-sensor which will be placed on the steering wheel of the car having the target to collect the driver's Photoplethysmography (PPG) signal and which will result in a control of driver attention level. The so collected PPG time-series will be classified by the mentioned 1D Temporal Deep Convolutional Network which provides an assessment of the driver attention level. A final analyzer block verifies if the car driver attention level is adequate for the saliency-based scene classification. The performed tests confirmed the effectiveness of the overall proposed pipeline.
Vehicle Re-Identification based on Ensembling Deep Learning Features including a Synthetic Training Dataset, Orientation and Background Features, and Camera Verification

Vehicle re-identification has the objective of finding a specific vehicle among different vehicle crops captured by multiple cameras placed at multiple intersections. Among the different difficulties, high intra-class variability and high inter-class similarity can be highlighted. Moreover, the resolution of the images can be different, which also means a challenge in the re-identification task. Intending to face these problems, we use as baseline our previous work based on obtaining different deep learning features and ensembling them to get a single, stable and robust feature vector. It also includes post-processing techniques that explode all the information provided by the CityFlowV2-ReID dataset, including a re-ranking step. Then, in this paper, several newly included improvements are described. Background and orientation similarity matrices are added to the system to reduce bias towards these characteristics. Furthermore, we take into account the camera labels to penalize the gallery images that share camera with the query image. Additionally, to improve the training step, a synthetic dataset is added to the original one.
Towards a Computational Model of Argumentation Schemes in Agent-Oriented Programming Languages

There are many benefits of using argumentation-based techniques in multi-agent systems, as clearly shown in literature. Such benefits come not only from the expressiveness that such approaches bring to agent communication but also from the reasoning and decision-making capabilities under conditions of conflicting and uncertain information that argumentation brings to autonomous agents. When developing multi-agent applications in which argumentation will be used to improve agent communication and reasoning, argumentation schemes (i.e., reasoning patterns for argumentation) are useful in addressing the requirements of the application domain in regards to argumentation (e.g., defining the scope in which argumentation will be used by agents in that particular application). In this work, we propose a general argumentation framework that takes into account the particular structure of argumentation schemes at its core. This paper formally defines that framework and experimentally evaluates its implementation.
Forecasting a post-COVID-19 economic crisis using fuzzy cognitive maps: a Spanish tourism-sector perspective

Those in positions of leadership are accustomed to having to deal with complex and uncertain situations. However, the on-going COVID-19 pandemic has taken this challenge to a new level of complexity. Although econometric models are being used to predict economic scenarios relating to the fall-out from the pandemic, these forecasts do not factor-in the uncertainty generated by new changes announced weekly by policymakers. The aim of the present study is therefore to apply a fuzzy approach to develop a method for providing consistent and reliable forecasting scenarios that facilitate managers' and policymaker's decision-making in complex and uncertain situations. The chosen context of the study is the case of the potential consequences of COVID-19 for the international tourism sector in Spain, using fuzzy cognitive maps. This semi-quantitative model can help researchers to forecast the potential impact of major events in fuzzy or uncertain environments by constructing flexible and adaptable scenarios.
CoRM

Distributed memory systems are becoming increasingly important since they provide a system-scale abstraction where physically separated memories can be addressed as a single logical one. This abstraction enables memory disaggregation, allowing systems as in-memory databases, caching services, and ephemeral storage to be naturally deployed at large scales. While this abstraction effectively increases the memory capacity of these systems, it faces additional overheads for remote memory accesses. To narrow the difference between local and remote accesses, low latency RDMA networks are a key element for efficient memory disaggregation. However, RDMA acceleration poses new obstacles to efficient memory management and particularly to memory compaction: network controllers and CPUs can concurrently access memory, potentially leading to inconsistencies if memory management operations are not synchronized. To ensure consistency, most distributed memory systems do not provide memory compaction and are exposed to memory fragmentation. We introduce CoRM, an RDMA-accelerated shared memory system that supports memory compaction and ensures strict consistency while providing one-sided RDMA accesses. We show that CoRM sustains high read throughput during normal operations, comparable to similar systems not providing memory compaction while experiencing minimal overheads during compaction. CoRM never disrupts RDMA connections and can reduce applications' active memory up to 6x by performing memory compaction.
Internet of Things Security Detection Technology Based on Grey Association Decision Algorithm

This paper analyzes the current real-time monitoring system based on grey-related IoT security sensors for the detection of risk factors in the production environment of the Internet of Things and proposes a design plan for the Internet of Things environment monitoring based on the grey-related Internet of Things security sensor network, and according to the reliability guarantee mechanism of the system, a three-dimensional uniform IoT node deployment method suitable for IoT security monitoring is proposed. Based on the grey correlation analysis, it can provide a quantitative measurement analysis for the development and change state of a system, which is very suitable for the analysis of dynamic operating systems. As a real-time dynamic system of the Internet of Things, the use of grey correlation method to analyze its network security status has good operability and practical value. According to the multisource information processing technology, the monitoring data are preprocessed by dynamic limiting filtering, and then the data are fused at the data level with the optimal weighting algorithm. Through the use of grey correlation analysis to quantify the relative impact of cyberattacks on the network within a certain period of time, the quantitative assessment of the security environment and status of the entire network is realized. Finally, the characteristics of grey relational analysis and rough set theory attribute reduction are used to form the basis of grey correlation decision-level fusion algorithm, to achieve effective processing of the data of the Internet of Things security monitoring system.
Blockchain Technology and Its Brunt on Digital Marketing

The proliferation of information and communication technology and online business platforms joined together with smartphone technology and thereby changed the business-to-consumer business landscape, restructured organizations, and refurbished the process of value creation. Thus, it facilitates the businesspeople to reach a wider customer base by the customized customer targets; in so doing, it increases customers' trust and loyalty towards a brand. The internet-enabled technologies help the businesspeople to develop new digital markets and to make new demands for their products and services. Thus, the internet-enabled technologies are contributing a lot to the market engagement and customer target. Inline, a new technology called blockchain has transformed the digital marketing process completely and helps to do the business effectively with accurate and up-to-date data. With this backdrop, this chapter is designed to demonstrate how blockchain technology acts as a cutting edge technology in the consumer-centric pattern.
A Survey and Tutorial on Security and Resilience of Quantum Computing

Present-day quantum computers suffer from various noises or errors such as, gate error, relaxation, dephasing, readout error, and crosstalk. Besides, they offer a limited number of qubits with restrictive connectivity. Therefore, quantum programs running these computers face resilience issues and low output fidelities. The noise in the cloud-based access of quantum computers also introduce new modes of security and privacy issues. Furthermore, quantum computers face several threat models from insider and outsider adversaries including input tampering, program misallocation, fault injection, Reverse Engineering (RE) and Cloning. This paper provides an overview of various assets embedded in quantum computers and programs, vulnerabilities and attack models and the relation between resilience and security. We also cover countermeasures against the reliability and security issues and present future outlook for security of quantum computing.
Spectral Characteristics of the Antarctic Vegetation: A Case Study of Barton Peninsula

Spectral information is a proxy for understanding the characteristics of ground targets without a potentially disruptive contact. A spectral library is a collection of this information and serves as reference data in remote sensing analyses. Although widely used, data of this type for most ground objects in polar regions are notably absent. Remote sensing data are widely used in polar research because they can provide helpful information for difficult-to-access or extensive areas. However, a lack of ground truth hinders remote sensing efforts. Accordingly, a spectral library was developed for 16 common vegetation species and decayed moss in the ice-free areas of Antarctica using a field spectrometer. In particular, the relative importance of shortwave infrared wavelengths in identifying Antarctic vegetation using spectral similarity comparisons was demonstrated. Due to the lack of available remote sensing images of the study area, simulated images were generated using the developed spectral library. Then, these images were used to evaluate the potential performance of the classification and spectral unmixing according to spectral resolution. We believe that the developed library will enhance our understanding of Antarctic vegetation and will assist in the analysis of various remote sensing data.
Fine-Grained Image Quality Assessment: A Revisit and Further Thinking

Image quality assessment (IQA) plays a central role in many image processing algorithms and systems. Although many popular IQA models achieves high performance on existing released databases, they are still not well accepted in practical applications due to the not-always satisfactory accuracy on real-world data and situations. In this paper, we revisit the IQA research, and point out an ignored but interesting problem in IQA: the coarse-grained (i.e., when quality variation is sufficiently big, as the setting of most IQA databases up to date) statistical results evaluated on existing databases mask the fine-grained differentiation. Accordingly, we present a survey on image quality assessment from a new perspective: fine-grained image quality assessment (FG-IQA). Recent FG-IQA research on five major kinds of images is introduced, and some popular IQA methods are analyzed from FG-IQA perspective. The potential problems for current IQA research based on existing coarse-grained databases are analyzed and the necessity of more FG-IQA research is justified. Finally, we discuss some challenges and possible directions for future works in FG-IQA.