Reviewerly/exHarmony huggingface dataset:
“If you know what to do, will you take action to avoid mobile phishing attacks”: Self-efficacy, anticipated regret, and gender

In recent years, mobile devices have increasingly become a key target for phishing attacks. As such, mobile users need to be able to confidently apply different security safeguards to thwart these mobile phishing threats. Thus, anti-phishing self-efficacy can be instrumental in explaining mobile phishing avoidance behavior. Therefore, the present study proposed a model to explain how anti-phishing self-efficacy along with anticipated regret and gender influences mobile users phishing avoidance behavior. Using data from 231 respondents, the study showed that besides the direct positive influence of both anti-phishing self-efficacy and anticipated regret on mobile phishing avoidance motivation and behavior, the interaction of these factors also has a positive and significant influence on avoidance behavior. Additionally, gender significantly moderated the influence of anti-phishing self-efficacy on both avoidance motivation and behavior with the relationships being more pronounced for women than for men. These results provide an enriched understanding of mobile phishing avoidance behavior, especially by showing the interactions of anti-phishing self-efficacy with anticipated regret and gender.
Sentiment mining in a collaborative learning environment: capitalising on big data

The ability to exploit students’ sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students’ data using machine learning techniques, and (ii) modelling and predicting students’ emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders.
Security Analysis on Remote User Authentication Methods

Remote user authentication has been framed to provide security to the users by hiding their details with the help of some technique which can bear all the forgery attacks, but not reveal any details. All the users want CIA (confidentiality, integrity, and availability) to be there if they are working in a network. Various protocols have been proposed and implemented in literature. But the efficient tool is still lacking. Here we find that many of the schemes and protocols suggested are vulnerable to forgery attacks. We have found some problems in secure remote user authentication scheme based on smart card.
Fine-grained ship classification based on deep residual learning for high-resolution SAR images

As the resolution of Synthetic Aperture Radar (SAR) images increases, the fine-grained classification of ships has become a focus of the SAR field. In this paper, a ship classification framework based on deep residual network for high-resolution SAR images is proposed. In general, networks with more layers have higher classification accuracy. However, the training accuracy degradation and the limited dataset are major problems in the training process. To build deeper networks, residual modules are constructed and batch normalization is applied to keep the activation function output. Different fine tuning strategies are used to select the best training scheme. To take advantage of the proposed framework, a dataset including 835 ship slices is augmented by different multiples and then used to validate our method and other Convolutional Neural Network (CNN) models. The experimental results show that, the proposed framework can achieve a 99% overall accuracy on the augmented dataset under the optimal fine-tuning strategy, 3% higher than that in other models, which demonstrates the effectiveness of our proposed approach.
Dead Reckoning of Dynamically Positioned Ships: Using an Efficient Recurrent Neural Network

When a ship experiences a loss of position reference systems, its navigation system typically enters a mode known as dead reckoning (DR) to maintain an estimate of its position. Commercial systems perform this task using a state estimator that includes mathematical model knowledge. Such a model is nontrivial to derive and needs tuning if the vessel's dynamic properties change. To this end, we propose using machine learning to estimate the horizontal velocity of the vessel without the help of position, velocity, or acceleration sensors. A simulation study was conducted to demonstrate the ability to maintain position estimates during a Global Navigation Satellite System (GNSS) outage. Comparable performance is seen relative to the established Kalmanfilter (KF) model-based approach.
Learning From Demonstration in the Wild

Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically relies on manually generated demonstrations or specially deployed sensors and has not generally been able to leverage the copious demonstrations available in the wild: those that capture behaviours that were occurring anyway using sensors that were already deployed for another purpose, e.g., traffic camera footage capturing demonstrations of natural behaviour of vehicles, cyclists, and pedestrians. We propose video to behaviour (ViBe), a new approach to learn models of behaviour from unlabelled raw video data of a traffic scene collected from a single, monocular, initially uncalibrated camera with ordinary resolution. Our approach calibrates the camera, detects relevant objects, tracks them through time, and uses the resulting trajectories to perform LfD, yielding models of naturalistic behaviour. We apply ViBe to raw videos of a traffic intersection and show that it can learn purely from videos, without additional expert knowledge.
Social and educational environment modeling in future vision: infosphere tools

The article is devoted to the study of information and communication environment tools for constructing a vision of the future. The research methodology is determined by the information paradigm providing a general theoretical basis for rational explanation of orienting interactions in socio-technical environment / system. The information model of interactions management in a social environment is noted to be of code nature, which is typical of an indirect influence on contemporaries' consciousness. The infosphere tools for the future modeling are presented by their structural, functional and motivational aspects. In the context of social and educational environment modeling, one should point out the material needs driven by the market code and background knowledge transmitting the stereotypes of thinking and behavior. Further, the authors specify the role of the worldview and educational paradigm in the future modeling. The results of mass expectations monitoring are presented, such as dynamic analysis of social sentiment indicators in Russia, youth assessment of the university’s image and prospects for online learning. The study materials include data of sociological surveys among Peter the Great St.Petersburg Polytechnic University students. In conclusion, the authors emphasize the need for mental protection of a person in the digital hyper-net, which transforms the infosphere of communication thus creating potential threat of community and mental structures disorganization.
A Switchable Deep Learning Approach for In-loop Filtering in Video Coding

Deep learning provides a great potential for in-loop filtering to improve both coding efficiency and subjective quality in video coding. State-of-the-art work focuses on network structure design and employs a single powerful network to solve all problems. In contrast, this paper proposes a deep learning based systematic approach that includes an effective Convolutional Neural Network (CNN) structure, a hierarchical training strategy, and a video codec oriented switchable mechanism. First, we propose a novel CNN structure, i.e., Squeeze-and-Excitation Filtering CNN (SEFCNN), as an optional in-loop filter. To capture the non-linear interaction between channels, the SEFCNN is comprised of two subnets, i.e., Feature EXtracting (FEX) subnet and Feature ENhancing (FEN) subnet. Then, we develop a hierarchical model training strategy to adapt the two subnets to different coding scenarios. For high-rate videos with small artifacts, we train a single global model using the FEX for all types of frames, whereas for low-rate videos with large artifacts, different models are trained using both FEX and FEN for different types of frames. Finally, we propose an adaptive enhancing mechanism which is switchable between the CNN-based and the conventional methods. We selectively apply the CNN model to some frames or some regions in a frame. Experimental results show that the proposed scheme outperforms state-of-the-art work in coding efficiency, while the computational complexity is acceptable after GPU acceleration.
Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping

Assistive robotic systems endeavour to support those with movement disabilities, enabling them to move again and regain functionality. Main issue with these systems is the complexity of their low-level control, and how to translate this to simpler, higher level commands that are easy and intuitive for a human user to interact with. We have created a multi-modal system, consisting of different sensing, decision making and actuating modalities, leading to intuitive, human-in-the-loop assistive robotics. The system takes its cue from the user's gaze, to decode their intentions and implement low-level motion actions to achieve high-level tasks. This results in the user simply having to look at the objects of interest, for the robotic system to assist them in reaching for those objects, grasping them, and using them to interact with other objects. We present our method for 3D gaze estimation, and grammars-based implementation of sequences of action with the robotic system. The 3D gaze estimation is evaluated with 8 subjects, showing an overall accuracy of 4.68\pm 0.14cm. The full system is tested with 5 subjects, showing successful implementation of 100% of reach to gaze point actions and full implementation of pick and place tasks in 96%, and pick and pour tasks in 76% of cases. Finally we present a discussion on our results and what future work is needed to improve the system.
End-to-end transport for video QoE fairness

The growth of video traffic makes it increasingly likely that multiple clients share a bottleneck link, giving video content providers an opportunity to optimize the experience of multiple users jointly. But today's transport protocols are oblivious to video streaming applications and provide only connection-level fairness. We design and build Minerva, the first end-to-end transport protocol for multi-user video streaming. Minerva uses information about the player state and video characteristics to adjust its congestion control behavior to optimize for QoE fairness. Minerva clients receive no explicit information about other video clients, yet when multiple of them share a bottleneck link, their rates converge to a bandwidth allocation that maximizes QoE fairness. At the same time, Minerva videos occupy only their fair share of the bottleneck link bandwidth, competing fairly with existing TCP traffic. We implement Minerva on an industry standard video player and server and show that, compared to Cubic and BBR, 15-32%of the videos using Minerva experience an improvement in viewing experience equivalent to a jump in resolution from 720p to 1080p. Additionally, in a scenario with dynamic video arrivals and departures, Minerva reduces rebuffering time by an average of 47%.
Simple K-Medoids Partitioning Algorithm for Mixed Variable Data

A simple and fast k-medoids algorithm that updates medoids by minimizing the total distance within clusters has been developed. Although it is simple and fast, as its name suggests, it nonetheless has neglected local optima and empty clusters that may arise. With the distance as an input to the algorithm, a generalized distance function is developed to increase the variation of the distances, especially for a mixed variable dataset. The variation of the distances is a crucial part of a partitioning algorithm due to different distances producing different outcomes. The experimental results of the simple k-medoids algorithm produce consistently good performances in various settings of mixed variable data. It also has a high cluster accuracy compared to other distance-based partitioning algorithms for mixed variable data.
Cyber risk assessment in cloud provider environments: Current models and future needs

Traditional frameworks for risk assessment do not work well for cloud computing. While recent work has often focussed on the risks faced by firms adopting or selecting cloud services, there has been little research on how cloud providers might assess their own services. In this paper, we use an in-depth review of the extant literature to highlight the weaknesses of traditional risk assessment frameworks for this task. Using examples, we then describe a new risk assessment model (CSCCRA) and compare this against three established approaches. For each approach, we consider its goals, the risk assessment process, decisions, the scope of the assessment and the way in which risk is conceptualised. This evaluation points to the need for dynamic models specifically designed to evaluate cloud risk. Our suggestions for future research are aimed at improving the identification, assessment, and mitigation of inter-dependent cloud risks inherent in a defined supply chain.
A Docker Container Anomaly Monitoring System Based on Optimized Isolation Forest

Container-based virtualization has gradually become a main solution in today&#x2018;s cloud computing environments. Detecting and analyzing anomaly in containers present a major challenge for cloud vendors and users. This paper proposes an online container anomaly detection system by monitoring and analyzing multidimensional resource metrics of the containers based on the optimized isolation forest algorithm. To improve the detection accuracy, it assigns each resource metric a weight and changes the random feature selection in the isolation forest algorithm to the weighted feature selection according to the resource bias of the container. In addition, it can identify abnormal resource metrics and automatically adjust the monitoring period to reduce the monitoring delay and system overhead. Moreover, it can locate the cause of the anomalies via analyzing and exploring the container log. The experimental results demonstrate the performance and efficiency of the system on detecting the typical anomalies in containers in both simulated and real cloud environments.
Liver Vessels Segmentation Based on 3d Residual U-NET

Recently, extraction of blood vessels has aroused widespread interests in medical image analysis. In this work, to accelerate convergence speed and enhance the representation for discriminative features, we introduce the residual block structure in the ResNet into the 3D U-Net, and construct a new 3D Residual U-Net architect to segment the hepatic and portal veins from abdominal CT volumes. In addition, we develop a weighted Dice loss function to cope with the challenges of pixel imbalance, vessel boundary segmentation and small vessels segmentation. Furthermore, based on the prediction results, the post-processing methods of 3D morphological closed operation and volume analysis are employed to smooth the surface of vessels and eliminate noise blocks, respectively. Compared with existing 3D DenseNet, FCN and 3D U-Net, the average Dice coefficients of our method in hepatic veins and portal veins segmentation are 71.7% and 76.5% respectively, which are superior to 55.3% and 53.9% of the 3D DenseNet, 60.2% and 75.6% of the FCN, and 66.4% and 73.9% of the 3D U-Net. Meanwhile, the cross validation results prove that our method is accurate and stable for liver vessel extraction.
Attentive Layer Separation for Object Classification and Object Localization in Object Detection

Object detection became one of the major fields in computer vision. In object detection, object classification and object localization tasks are conducted. Previous deep learning-based object detection networks perform with feature maps generated by completely shared networks. However, object classification focuses on the most discriminative object part of the feature map. Whereas, object localization requires a feature map that is focused on the entire area of the object. In this paper, we propose a novel object detection network by considering the difference between the two tasks. The proposed deep learning-based network mainly consists of two parts; 1) Attention network part where task-specific attention maps are generated, 2) Layer separation part where layers for estimating two tasks are separated. Comprehensive experimental results based on PASCAL VOC dataset and MS COCO dataset showed that proposed object detection network outperformed the state-of-the-art methods.
Communication-efficient Distributed SGD with Sketching

Large-scale distributed training of neural networks is often limited by network bandwidth, wherein the communication time overwhelms the local computation time. Motivated by the success of sketching methods in sub-linear/streaming algorithms, we introduce Sketched-SGD, an algorithm for carrying out distributed SGD by communicating sketches instead of full gradients. We show that \ssgd has favorable convergence rates on several classes of functions. When considering all communication -- both of gradients and of updated model weights -- Sketched-SGD reduces the amount of communication required compared to other gradient compression methods from $\mathcal{O}(d)$ or $\mathcal{O}(W)$ to $\mathcal{O}(\log d)$, where $d$ is the number of model parameters and $W$ is the number of workers participating in training. We run experiments on a transformer model, an LSTM, and a residual network, demonstrating up to a 40x reduction in total communication cost with no loss in final model performance. We also show experimentally that Sketched-SGD scales to at least 256 workers without increasing communication cost or degrading model performance.
Predicting the price of Bitcoin by the most frequent edges of its transaction network

Research on the Bitcoin transaction network has increased rapidly in recent years, but still, little is known about the network's influence on Bitcoin prices. The goals of this paper are twofold: to determine the predictive power of the transaction network's most frequent edges on the future price of Bitcoin and to provide an efficient technique for applying this untapped dataset in day trading. To accomplish these goals, a complex method consisting of single-hidden layer feedforward neural networks (SLFNs) is used. Based on the results, the presented method achieved an accuracy of approximately 60.05% during daily price movement classifications, despite only considering a small subset of edges.
Recursive Residual Convolutional Neural Network-Based In-Loop Filtering for Intra Frames

Although the in-loop filtering incorporated in High Efficiency Video Coding (HEVC) standard improves the subjective quality of reconstructed pictures and increases the compression efficiency, it still cannot satisfy the demand for higher quality in the rapid growth of video usage. In this paper, we propose recursive residual convolution neural network (RRCNN)-based in-loop filtering to further improve the quality of reconstructed intra frames while reducing the bitrates. Specifically, RRCNN estimates the residual images between the compressed distorted images and original non-compressed ones, and there are shortcut connections that skip a few stacked layers in the structure of RRCNN to ease the training difficulty. By applying the same set of weights recursively, RRCNN achieves excellent performance while utilizing far fewer parameters. For concise in-loop filtering, we train a single model capable of handling various bitrate settings. Different networks for the filtering of luma and chroma components are designed respectively to better learn the filtering characteristics of different channels. Moreover, to fully adapt the various input videos and boost the performance, a coding tree unit (CTU) control flag is signaled to indicate the filtering method from the sense of rate-distortion optimization (RDO). Extensive experimental results show that our scheme achieves significant bitrate savings compared to HEVC, leading to on average 8.7% BD-rate reduction, with up to a 15.1% BD-rate reduction for luma, and more than 20% BD-rate reductions for chroma on average.
SUM-QE: a BERT-based Summary Quality Estimation Model

We propose SUM-QE, a novel Quality Estimation model for summarization based on BERT.The model addresses linguistic quality aspects that are only indirectly captured by content-based approaches to summary evaluation, without involving comparison with human references.SUM-QE achieves very high correlations with human ratings, outperforming simpler models addressing these linguistic aspects.Predictions of the SUM-QE model can be used for system development, and to inform users of the quality of automatically produced summaries and other types of generated text.
Learning Target-Oriented Dual Attention for Robust RGB-T Tracking

RGB-Thermal object tracking attempts to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust feature representation learning or adaptive modal weighting. However, how to integrate dual attention mechanism for visual tracking is still a subject that has not been studied yet. In this paper, we propose two visual attention mechanisms for robust RGB-T object tracking. Specifically, the local attention is implemented by exploiting the common visual attention of RGB and thermal data to train deep classifiers. We also introduce the global attention, which is a multimodal target-driven attention estimation network. It can provide global proposals for the classifier together with local proposals extracted from previous tracking result. Extensive experiments on two RGB-T benchmark datasets validated the effectiveness of our proposed algorithm.