Reviewerly/exHarmony huggingface dataset:
Top-aware recommender distillation with deep reinforcement learning

Most existing recommenders focus on providing users with a list of recommended products. In practical, users may only pay attention to the recommendations at the top positions. Our analysis, however, shows that the correctly recommended products from existing methods are not top posited; this would result in sacrificing users’ patience and engagements. To address this issue, this paper proposes a top-aware recommender distillation (TRD) framework, viz., the rank of recommendation lists from given state-of-the-art recommendation approaches are further reinforced and refined using reinforcement learning. Different from the traditional goal of knowledge distillation to mimic the behavior of its teacher, our recommender contrastingly goes step further with the goal of surpassing its teacher recommender. More importantly, our proposed TRD can be plugged on any existed recommender; thus being generic for real-world deployment. Theoretical analysis demonstrates that TRD is guaranteed to perform at least as well as the basic recommender. Extensive experiments on five state-of-the-art recommendation algorithms across three real-world datasets further show that TRD consistently improves the recommendations at top positions.
Co-design Techniques for and with Children based on Physical Theatre Practice to promote Embodied Awareness

Research in Full-Body Interaction suggests the benefits of activities based on using embodied resources to strengthen the sensorimotor, cognitive and socio-emotional aspects of the user experience. However, scholars in this field have been often primarily concerned with the comprehension of and design for the user's mind. Little attention has been drawn on its connection to the bodily experience. The scarcity of adequate co-design methods with and for children to raise an awareness of their body during design risks of deriving interaction design decisions only from the perspective of adult designers. In this article, we present our research on novel co-design techniques to elicit children's embodied awareness. These techniques were analysed in the context of a design workshop series with a local theatre school. For the analysis, we used the Think4EmCoDe research framework, a tool to foreground key aspects of an embodied co-design technique for children. Results indicate the benefits of techniques based on physical theatre practice that (1) help children understand their body and space as mediators of ideas and meaning making processes; (2) allow them to incorporate the specific features of Full-Body Interaction in their co-design.
High-accuracy model-based reinforcement learning, a survey

Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems from game playing and robotics have been solved with deep model-free methods. Unfortunately, the sample complexity of model-free methods is often high. Model-based reinforcement learning, in contrast, can reduce the number of environment samples, by learning an explicit internal model of the environment dynamics. However, achieving good model accuracy in high dimensional problems is challenging. In recent years, a diverse landscape of model-based methods has been introduced to improve model accuracy, using methods such as probabilistic inference, model-predictive control, latent models, and end-to-end learning and planning. Some of these methods succeed in achieving high accuracy at low sample complexity in typical benchmark applications. In this paper, we survey these methods; we explain how they work and what their strengths and weaknesses are. We conclude with a research agenda for future work to make the methods more robust and applicable to a wider range of applications.
A deep auto-encoder satellite anomaly advance warning framework

Purpose The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses. Design/methodology/approach This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds. Findings Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data. Originality/value This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors’ knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.
Facing airborne attacks on ADS-B data with autoencoders

The automatic dependent surveillance-broadcast (ADS-B) represents a major change in flight tracking and it is one of the key components in building the next generation of air transportation systems. However, several concerns have been raised regarding its vulnerabilities to cyber attacks. In recent years, a new and promising approach of utilizing large-scale and publicly available flight recordings for training machine learning models that can detect anomalous flight patterns has been demonstrated as a valid countermeasure for several ADS-B attacks. The new approach differs significantly from previously proposed methods in the simplicity of its integration with the current ADS-B system. It also provides a valid countermeasure against highly sophisticated airborne attackers. However, previously proposed machine learning methods require training a different model for each flight route or geographic location to give acceptable results. This requirement limits the current solution to flights with a sufficient amount of historical data, which is unavailable in many cases such as business aviation, instructional flying, aerial work, and more. In this research, we address this limitation of previous work, by applying a differencing time-series transformation on the ADS-B data and utilizing a non-recurrent autoencoder classifier. The effectiveness of our method is compared to existing methods on several simulated trajectory modification attacks. The results of our experiments show that the proposed method achieves a ROC AUC value of 0.935-0.951, in comparison to 0.627 from existing methods when evaluated on flights that are absent from training data.
GT-Chain: A Fair Blockchain for Intelligent Industrial IoT Applications

In Industrial Internet of Things (IIoT), a number of mutually untrusted devices with diverse computing resources and application requirements often need to work together to serve industrial production. When applying permissionless blockchain to process transactions among these devices, we are concerned with two types of fairness: mining fairness (i.e., miners should obtain commensurate rewards according to their respective invested computing power) and transaction-processing fairness (i.e., transaction packing and confirming is in a desired fair order). To address the two types of fairness simultaneously, this paper proposes GT-Chain, in which miners select transactions according to <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> eometric-distribution and propagate transactions with a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ime-to-live limit for achieving transaction-processing fairness as well as balancing the processing efficiency and fairness. Besides, GT-Chain inherits properties of a famous blockchain called FruitChain for achieving mining fairness. We then conduct a probability analysis to quantify the efficiency of transaction processing and define a Euclidean-distance-based fair degree to measure the fairness of transaction processing. Extensive simulations verify that GT-Chain can well achieve our design objectives, and our theoretical model is accurate. This paper is helpful in better designing blockchain protocols for IIoT.
Defending Touch-based Continuous Authentication Systems from Active Adversaries Using Generative Adversarial Networks

Previous studies have demonstrated that commonly studied (vanilla) touch-based continuous authentication systems (V-TCAS) are susceptible to population attack. This paper proposes a novel Generative Adversarial Network assisted TCAS (G-TCAS) framework, which showed more resilience to the population attack. G-TCAS framework was tested on a dataset of 117 users who interacted with a smartphone and tablet pair. On average, the increase in the false accept rates (FARs) for V-TCAS was much higher (22%) than G-TCAS (13%) for the smartphone. Likewise, the increase in the FARs for V-TCAS was 25% compared to G-TCAS (6%) for the tablet.
Understanding the inward emotion-focused coping strategies of individual users in response to mobile malware threats

According to coping theory, individuals cope with information system threats by adopting either problem-focused coping (PFC) or emotion-focused coping (EFC). However, little is known about EFC in the information security (ISec) literature. Moreover, there is potential confusion regarding the meaning of some EFC strategies. Hence, ISec scholars and practitioners may (i) have a narrow view of EFC or (ii) confuse it with other concepts. In this study, we offer one response to this issue. We first address the ambiguity regarding EFC before differentiating five inward EFC strategies and assessing them empirically in the mobile malware context. To the best of our knowledge, this study is the first to compare several inward EFC strategies in the ISec field.We contribute two new findings on EFC: 1) response efficacy is a crucial factor that impedes users from implementing EFC strategies; 2) avoidance and fatalism significantly impede PFC. Our study also contributes to the ISec literature by categorising EFC into active and passive forms. We showed that individuals’ use of passive inward EFC strategies was positively associated with threat vulnerability. Finally, we provide interesting insights into the complicated responses of individuals to mobile malware threats, presenting implications for ISec research and practice.
Multi-Objective Algorithms for Virtual Machine Selection and Placement in Cloud Data Center

Dynamic Virtual Machine (DVM) consolidation is one of the operational techniques to reduce energy consumption by grouping Virtual Machines (VM) in a limited number of hosts. However, the mighty Virtual Machine consolidation may incite the Service Level Agreements violation (SLAV). In this study, we proposed (a) Multi-objective Virtual Machine selection (MOVMS) algorithm to determine a migratable Virtual Machine from the overloaded host and (b) Multi-objective Modified Best Fit Decreasing (MOMBFD) algorithm for virtual machine placement. We have implemented it in cloudsim and the result shows that the proposed approaches appear to significantly reduce energy consumption by 31.36%, Service Level Agreements violation(SLAV) by 4.95%, and decrease the number of migrated Virtual Machine by 28.49 % compared to some previous studies which used Maximum Correlation (MC) algorithm, Minimum Migration Time (MMT) algorithm, Minimum Utilization (MU) algorithm and Random Selection (RS) algorithm for Virtual Machine selection and Power-Aware Best Fit Decreasing (PABFD) algorithm for Virtual Machine placement.
Pinball Twin Bounded Support Vector Clustering

Unsupervised machine learning algorithms are extensively used for unlabelled data clustering. Twin support vector clustering (TWSVC) and twin bounded support vector clustering (TBSVC), plane-based clustering algorithms introduced recently, work on twin support vector machine (TWSVM) principles and are used in widespread clustering problems. However, both TWSVC and TBSVC are sensitive to noise and suffers from low re-sampling stability due to usage of hinge loss. The pinball loss, an alternative loss function first introduced in the pinball twin support vector clustering (pinTSVC) which makes the algorithm noise insensitive and more stable for re-sampling of data. In this paper, we propose an efficient plane-based clustering method called twin bounded support vector clustering using pinball-loss (pinTBSVC). The proposed pinTBSVC inherits various attributes from previous plane-based clustering algorithms (like the regularisation term or the structural risk minimisation principle) and improves upon them by implementing pinball loss for its optimisation. Unlike TWSVC and pinTSVC, the matrix appear in the dual formulation of the proposed pinTB-SVC is nonsingular. Experimental results performed on several benchmark UCI datasets indicate that the proposed pinTBSVC outperforms TWSVC, TBSVC and pinTSVC. Furthermore, we also discuss the application of the proposed pinTBSVC in biomedical datasets. Numerical experiments show that the proposed pinTBSVC has shown better generalization performance on both synthetic and real-world benchmark datasets. The MATLAB implementation of the proposed pinTBSVC is available on https://github.com/mtanveer1.
Coverage-Based Summaries for RDF KBs

As more and more data become available as linked data, the need for efficient and effective methods for their exploration becomes apparent. Semantic summaries try to extract meaning from data, while reducing its size. State of the art structural semantic summaries, focus primarily on the graph structure of the data, trying to maximize the summary’s utility for query answering, i.e. the query coverage. In this poster paper, we present an algorithm, trying to maximize the aforementioned query coverage, using ideas borrowed from result diversification. The key idea of our algorithm is that, instead of focusing only to the “central” nodes, to push node selection also to the perimeter of the graph. Our experiments show the potential of our algorithm and demonstrate the considerable advantages gained for answering larger fragments of user queries.
Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

A Grid-Local Probability Road Map (PRM) method was proposed for the path planning of manipulators in dynamic environments. Based on the idea of boundary discretization, a double-grid model was built to obtain a mapping from dynamic obstacles to configuration space. The collision detection was simplified as a data indexing process to improve its efficiency. Times of collision detections were reduced by employing local programming strategies and the stratified sampling method. Moreover, the validity of sampling was increased. Taking the PUMA560 manipulator as a research object, the simulation experiments show that the time consumption of the proposed simplified collision-detection algorithm is about 14% of that of the standard one, and the stratified sampling is beneficial to the generation of probability maps compared with simple random sampling method. The simulation experiment of the static path planning shows that the proposed algorithm consumes an average of 10ms, which is superior to the comparison algorithm and has high efficiency and real-time performance. The simulation experiment of the dynamic path planning shows that the proposed algorithm consumes an average of 7ms per step, which is better than the comparison algorithm. The proposed algorithm can adjust the global path in real time to avoid obstacles as the environment changes. The algorithm mentioned has been proved to be efficient.
Facial Expression Recognition using Multi-Feature Concatenation of Local Face Components and Hierarchical SVM

To prepare an efficient and reliable automatic Facial Expression Recognition (FER) system, 1) face and active regions detection, 2) feature extraction, and 3) feature classification have to be robustly employed. In this paper, we propose a novel approach with three contributions. First, we have used facial local active regions such as Mouth, Eyes, and Eyebrows because they are more affected by emotions. Second, very efficient joint feature descriptors, HOG (Histogram of Oriented Gradients) and LDP (Local Directional Pattern) are employed to extract features. Finally, a hierarchical multi-class classification SVMs model has been designed for classification. The results show that our approach can efficiently recognize human facial expressions from facial images where it can outperform the related work. Moreover, we show that our approach is stable on illumination variation, which could be useful for evaluating the facial with variant skin tone or facial images in different intensity conditions.
SenseTrust: A Sentiment Based Trust Model in Social Network

Online social networks, as popular media and communications tools with their own extensive uses, play key roles in public opinion polls, politics, economy, and even governance. An important issue regarding these networks is the use of multiple sources of publishing or re-publishing news and propositions that can influence audiences depending on the level of trust in these sources between users. Therefore, estimating the level of trust in social networks between users can predict the extent of social networks’ impact on news and different publication and re-publication sources, and correspondingly provide effective strategies in news dissemination, advertisements, and other diverse contents for trustees. Therefore, trust is introduced and interpreted in the present study. A large portion of interactions in social networks is based on sending and receiving texts employing natural language processing techniques. A Hidden Markov Model (HMM) was designed via an efficient model, namely SenseTrust, to estimate the level of trust between users in social networks.
Residual Refinement Network with Attribute Guidance for Precise Saliency Detection

As an important topic in the multimedia and computer vision fields, salient object detection has been researched for years. Recently, state-of-the-art performance has been witnessed with the aid of the fully convolutional networks (FCNs) and the various pyramid-like encoder-decoder frameworks. Starting from a common encoder-decoder architecture, we enhance a residual refinement network with feature purification for better saliency estimation. To this end, we improve the global knowledge streams with intermediate supervisions for global saliency estimation and design a specific feature subtraction module for residual learning, respectively. On the basis of the strengthened network, we also introduce an attribute encoding sub-network (AENet) with a grid aggregation block (GAB) to guide the final saliency predictor to obtain more accurate saliency maps. Furthermore, the network is trained with a novel constraint loss besides the traditional cross-entropy loss to yield the finer results. Extensive experiments on five public benchmarks show our method achieves better or comparable performance compared with previous state-of-the-art methods.
Lifting Monocular Events to 3D Human Poses

This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the first learning-based method for 3D human pose from a single stream of events. Our method consists of two steps. First, we process the event-camera stream to predict three orthogonal heatmaps per joint; each heatmap is the projection of of the joint onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D localisation of the body joints. As a further contribution, we make available a new, challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. Experiments demonstrate that our method achieves solid accuracy, narrowing the performance gap between standard RGB and event-based vision. The code is freely available at https://iit-pavis.github.io/lifting_events_to_3d_hpe.
Multi-Stage Liver Segmentation in CT Scans Using Gaussian Pseudo Variance Level Set

No single technology can be rich enough to segment accurately due to the challenges of liver segmentation, which include low contrast with neighboring organs and the presence of pathology as well as highly varied shapes between subjects.This paper presents a Multi-stage framework for location and segmentation.First, Faster RCNN is employed to locate the liver region.Then, the Gaussian mixture model-based signed distance function is proposed to increase the flexibility of shape prior models.To reach the long and narrow ravine liver regions, the Gaussian pseudo variance level set is applied.Experimental results demonstrate the efficiency of the proposed method.More specifically, the proposed method is evaluated on 40 CT scan images, which are publicly available on three databases: SLIVER07, 3Dircadb, and LiTS.Our method has a slightly superior performance compared with other newly published methods.
Ensemble-Based Instance Relevance Estimation in Multiple-Instance Learning

The objective of Multiple-instance learning (MIL) is to learn a mapping function from weakly labeled training data, the training data in MIL is arranged in the form of labeled bags, and every bag holds several instances. The label of the bag depends upon the characteristics of unlabeled instances. This formulation has been used in decision-making applications, such as medical image classification and molecular activity prediction. This data formulation leads to a complex hypothesis, and many existing MIL algorithms are not robust to complex hypothesis space. To deal with this limitation, this paper proposes a Fisher vector-based stacking ensemble design with an instance relevance estimation process, called relevance-based multiple-instance Fisher vector encoding (RMI-FV). The ensemble design builds on top of the instance relevance estimation mechanism. The instance relevancy calculation process employs a Gaussian mixture-based subspace clustering approach, which helps to identify instances with higher relevance to the bag label. The experiments show that the proposed RMI-FV achieves better performance than state-of-the-art MIL approaches.