Reviewerly/exHarmony huggingface dataset:
ElectroCutscenes: Realistic Haptic Feedback in Cutscenes of Virtual Reality Games Using Electric Muscle Stimulation

Cutscenes in Virtual Reality (VR) games enhance story telling by delivering output in the form of visual, auditory, or haptic feedback (e.g., using vibrating handheld controllers). Since they lack interaction in the form of user input, cutscenes would significantly benefit from improved feedback. We introduce the concept and implementation of ElectroCutscenes, where Electric Muscle Stimulation (EMS) is leveraged to elicit physical user movements to different body parts to correspond to those of personal avatars in cutscenes of VR games while the user stays passive. Through a user study (N=22) in which users passively received kinesthetic feedback resulting in involuntarily movements, we show that ElectroCutscenes significantly increases perceived presence and realism compared to controller-based vibrotactile and no haptic feedback. Furthermore, we found preliminary evidence that combining visual and EMS feedback can evoke movements that are not actuated by either of them alone. We discuss how to enhance realism and presence of cutscenes in VR games even when EMS can partially rather than completely actuate the desired body movements.
Expanding Receptive Field YOLO for Small Object Detection

Abstract State-of-art object detection networks like YOLO, SSD and Faster R-CNN all have achieved great success in object detection. However, these algorithms have a low performance in small object detection. So, we produce the Expanding receptive field YOLO (ERF-YOLO) to deal with this problem. At first, we propose an efficient block which is called expanding receptive field block (ERF-block) to capture more information in larger areas. Base on YOLOv2, we down-sample the low-level location information by ERF-block, and up-sample feature information by deconvolution. Then we further assemble these two parts together to make the prediction. After training the network on VOC dataset, we have a good result with 82.6% mAP (mean Average Precision) which is 4.0% higher than the original YOLOv2 network. Thanks to the efficient block, it takes 62fps to detect one image when the input size is 416×416, which could keep a real-time speed. In addition, we also evaluate the model on a remote sensing dataset which contains many small targets, and it also shows that ours model has a better performance.
An Edge and Fog Computing Platform for Effective Deployment of 360 Video Applications

Immersive video applications based on 360 video streaming require high-bandwidth, high-reliability and low-latency 5G connectivity but also flexible, low-latency and cost-effective computing deployment. This paper proposes a novel solution for decomposing and distributing the end-to-end 360 video streaming service across three computing tiers, namely cloud, edge and constrained fog, in order of proximity to the end user client. The streaming service is aided with an adaptive viewport technique. The proposed solution is based on the H2020 5G-CORAL system architecture using micro-services-based design and a unified orchestration and control across all three tiers based on Fog05. Performance evaluation of the proposed solution shows noticeable reduction in bandwidth consumption, energy consumption, and deployment costs, as compared to a solution where the streaming service is all delivered out of one computing location such as the Cloud.
2DSM vs FFDM: A computeraided diagnosis based comparative study for the early detection of breast cancer

Abstract Purpose Accurate and early detection of breast cancer using effective imaging modalities is an active area of research in medical image analysis. Computeraided diagnosis (CAD) of breast cancer using digital mammograms may help in early diagnosis and can assist in maintaining patient health. The breast imaging reporting and data system (BIRADS) is widely used for risk assessment and classification grading in breast cancer screening. It contains seven different grading systems for breast cancer risk assessment. These range from grade 0 (incomplete) to grade 6 (proven malignancy). All other intermediate stages state the progression of risk. Methods The current research results have shown that two‐dimensional synthesized mammogram (2DSM) imaging and conventional full‐field digital mammography (FFDM) are two important imaging modalities which can be used for screening breast cancer. To the best of our knowledge, there is no study which has yet compared the BIRADS discrimination power of 2DSM and FFDM imaging modalities. In this paper we present a novel CAD‐based comparative study, using texton and gist for the characterization of breast cancer with 2DSM and FFDM imagery. Results The developed method achieved an average performance of 92.9% accuracy using a probabilistic neural network classifier for FFDM images with tenfold crossvalidation. Hence, our proposed model showed that FFDM images are more effective than the 2DSM imaging modality in discriminating BIRADS grades. Conclusion The obtained results confirmed that our method performed well in the early detection of breast cancer. Consequently, it can be used as a distinct system in rural hospitals.
Collaborative filtering and association rule mining‐based market basket recommendation on spark

Summary Traditional market basket recommendation approaches normally cannot well recommend unpopular commodities in big data environment. To address such problem and deal with large datasets of practical supermarkets, this paper presents a market basket recommendation framework and proposes an Extended algorithm based on Collaborative Filtering and Association Rule mining, named ECFAR. The ECFAR covers two sub‐algorithms. First, a parallel FP‐Growth algorithm is used for mining association rules on Spark, which is designed to increase the efficiency of processing big data. Then, a parallel similar commodity discovery method based on matrix factorization is proposed. By analyzing a real‐world sales dataset collected from a local supermarket group, extensive experiments are conducted to verify its effectiveness.
MPEC: Distributed Matrix Multiplication Performance Modeling on a Scale-Out Cloud Environment for Data Mining Jobs

Many data mining workloads are being analyzed in large-scale distributed cloud computing environments which provide nearly infinite resources with diverse hardware configurations. To maintain cost-efficiency in such environments, understanding the characteristics and estimating the overheads of a distributed matrix multiplication task that is a core computation kernel in many machine learning algorithms are essential. This article aims to propose a Matrix Multiplication Performance Estimator on Cloud (MPEC) algorithm. The proposed algorithm predicts the latency incurred when executing distributed matrix multiplication tasks of various input sizes and shapes with diverse instance types and a different number of worker nodes on cloud computing environments. To achieve this goal, we first analyze the characteristics of distributed matrix multiplication tasks. With characteristics generated from qualitative analysis, we propose to apply an ensemble of non-linear regression algorithm to predict the execution time of arbitrary matrix multiplication tasks. Thorough experimental results reveal that the proposed algorithm demonstrates higher accuracy than a state-of-the-art machine learning task performance estimation engine, Ernest, by decreasing the Mean Absolute Percentage Error (MAPE) in half.
Data Poisoning Attacks on Cross-domain Recommendation

Cross-domain recommendation has attracted growing interests given their simplicity and effectiveness. In the cross-domain scenarios, we may improve predictive accuracy in one domain by transferring knowledge from the other, which alleviates the data sparsity issue. However, the relatedness of these domains can be exploited by a malicious party to launch data poisoning attacks. Here we study the vulnerability of cross-domain recommendation under data poisoning attacks. We show that data poisoning attacks can be formulated as a bilevel optimization problem. Our experimental results show that cross-domain system can be compromised under attacks, highlighting the need for countermeasures against data poisoning attacks in cross-domain recommendation.
Data-Hiding Scheme Using Multidirectional Pixel-Value Differencing on Colour Images

Data hiding is a technique that hides the existence of secret data from malicious attackers. In this paper, we propose a new data-hiding scheme using multidirectional pixel-value differencing, which can embed secret data in two directions or three directions on colour images. The cover colour image is divided into nonoverlapping blocks, and the pixels of each block are decomposed into R, G, and B channels. The pixels of each block perform regrouping, and then the minimum pixel value within each block is selected. The secret data can be embedded into two directions or three directions based on the minimum pixel value by using the difference value for the block. The pixel pairs with the embedded secret data are put separately into two stego images for secret data extraction on receiver sides. In the extraction process, the secret data can be extracted using the difference value of the two stego images. Experimental results show that the proposed scheme has the highest embedding capacity when the secret data are embedded into three directions. Experimental results also show that the proposed scheme has a high embedding capacity while maintaining the degree of distortion that cannot be perceived by human vision system for two directions.
Deep neural networks design and analysis for automatic phase pickers from three-component microseismic recordings

SUMMARY It is essential to pick P-wave and S-wave arrival times rapidly and accurately for the microseismic monitoring systems. Meanwhile, it is not easy to identify the arrivals at a true phase automatically using traditional picking method. This is one of the reasons that many researchers are trying to introduce deep neural networks to solve these problems. Convolutional neural networks (CNNs) are very attractive for designing automatic phase pickers especially after introducing the fundamental network structure from semantic segmentation field, which can give the probability outputs for every labelled phase at every sample in the recordings. The typical segmentation architecture consists of two main parts: (1) an encoder part trained to extracting coarse semantic features; (2) a decoder part responsible not only for recovering the input resolution at the output but also for obtaining sparse representation of the objects. The fundamental segmentation structure performs well; however, the influence of the parameters in the structure on the pickers has not been investigated. It means that the structure design just depends on experience and tests. In this paper, we solve two main questions to give some guidance on network design. First, we show what sparse features will learn from the three-component microseismic recordings using CNNs. Second, the influence of two key parameters in the network on pickers, namely, the depth of decoder and activation functions, is analysed. Increasing the number of levels for a certain layer in the decoder will increase the burden of demand on trainable parameters, but it is beneficial to the accuracy of the model. Reasonable depth of the decoder can balance prediction accuracy and the demand of labelled data, which is important for microseismic systems because manual labelling process will decrease the real-time performance in monitoring tasks. Standard rectified linear unit (ReLU) and leaky rectified linear unit (Leaky ReLU) with different negative slopes are compared for the analysis. Leaky ReLU with a small negative slope can improve the performance of a given model than ReLU activation function by keeping some information about the negative parts.
Design of a Quantum Projection Filter

This article develops a quantum projection filtering approach to approximating the quantum filter equation based on It$\hat{\rm o}$ stochastic Taylor expansions and quantum information geometric techniques. The proposed approximation scheme is designed so that the truncated Taylor expansion of the difference between the true quantum trajectory, and its approximation on a lower dimensional differential submanifold is minimized, through an orthogonal projection operation in the quantum Fisher metric. In addition, a convenient design for a special class of open quantum systems is formulated. Simulation results from a numerical example demonstrate the approximation capability of the proposed quantum projection filter.
RSP Consensus Algorithm for Blockchain

Blockchain is the most popular security network technology in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century and still under active research. The consensus algorithms in Blockchain plays a major role and are crucial for maintaining the integrity and security of a distributed network. In addition, there are various problems arising from the consensus algorithms adopted in the existing Blockchain network. Therefore, it is of upmost importance to develop a consensus algorithm to address these problems. In this paper, therefore, we propose an efficient consensus algorithm in a Blockchain network environment consisting of several distributed users. First, we study the process of consensus among users to add new blocks to the existing Blockchain. Second, we introduce the other conventional consensus algorithms, analyze the problems of each algorithm, and propose Rock-Scissors-Paper (RSP) algorithm to mitigate these problems. The RSP algorithm is an algorithm that achieves consensus among distributed users using three static balanced variables, Rock (R), Scissors (S), and Paper (P) to avoid attacks by malicious participants. Furthermore, the propose RSP algorithm is compared with the existing consensus algorithms to predict the performance and its effects in a distributed mobile network environment.
A Hybrid Method for Electric Spring Control Based on Data and Knowledge Integration

Electric spring (ES) is a relatively new power electronics device for voltage regulation in a distribution system. Some existing ES control methods are system-independent; however, their performances generally can be significantly improved when accurate system models are properly utilized. Unfortunately, accurate model of the concerned distribution system is not always available in practice. This paper proposes a hybrid method for ES control, based on the integration of data-driven and analytical models. The inaccurate analytical model provides a basic policy to generate system data. Three data-driven models based on the extreme learning machine (ELM) are built using these data to replace the power flow, active power, and reactive power models respectively. An ELM-based control model is proposed to generate the final control strategies. The hybrid method is tested using a 15-node distribution network. Simulation results show that the data-driven model is more accurate than the analytical model in predicting system states, while the proposed control method outperforms the original analytical method.
Unsupervised feature selection based on bio-inspired approaches

In recent years, the scientific community has witnessed an explosion in the use of pattern recognition algorithms. However, little attention has been paid to the tasks preceding the execution of these algorithms, the preprocessing activities. One of these tasks is dimensionality reduction, in which a subset of features that improves the performance of the mining algorithm is located and algorithm's runtime is reduced. Although there are many methods that address the problems in pattern recognition algorithms, effective solutions still need to be researched and explored. Hence, this paper aims to address three of the issues surrounding these algorithms. First, we propose adapting a promising meta-heuristic called biased random-key genetic algorithm, which considers a random initial population construction. We call this algorithm as unsupervised feature selection by biased random-key genetic algorithm I. Next, we propose an approach for building the initial population partly in a deterministic way. Thus, we applied this idea in two algorithms, named unsupervised feature selection by particle swarm optimization and unsupervised feature selection by biased random-key genetic algorithm II. Finally, we simulated different datasets to study the effects of relevant and irrelevant attributes, and of noisy and missing data on the performance of the algorithms. After the Wilcoxon rank-sum test, we can state that the proposed algorithms outperform all other methods in different datasets. It was also observed that the construction of the initial population in a partially deterministic way contributed to the better performance. It should be noted that some methods are more sensitive to noisy and missing data than others, as well as to relevant and irrelevant attributes.
From Big Scholarly Data to Solution-Oriented Knowledge Repository

The volume of scientific articles grows rapidly, producing a scientific basis for understanding and identifying research problems and state-of-the-art solutions. Despite the considerable significance of the problem-solving information, existing scholarly recommending systems lack the ability to retrieve this information from the scientific articles for generating knowledge repositories and providing problem-solving recommendations. To address this issue, this paper proposes a novel framework to build solution-oriented knowledge repositories and provide recommendations to solve given research problems. The framework consists of three modules: a semantics-based information extraction module mining research problems and solutions from massive academic papers; a knowledge assessment module based on the heterogeneous bibliometric graph and a ranking algorithm; and a knowledge repository generation module to produce solution-oriented maps with recommendations. Based on the framework, a prototype scholarly solution support system is implemented. A case study is carried out in the research field of intrusion detection, and the results demonstrate the effectiveness and efficiency of the proposed method.
Training students metacognitive skill using mobile learning

Abstract Mobile learning is a learning media for using in helping teachers in the 21 st century class to train students’ metacognitive skills. There have not been many researchers on the use of mobile learning in senior high schools of Manokwari. Metacognitive skills become the focus of research that was to be achieved in this research. A total of 126 XI MIA class students of SMA Negeri 01 Manokwari which was divided into five groups were taught used mobile learning in teaching biology. Data collected used learning outcomes test. Corebima rubric (2009) was used to measure metacognitive students. Data analysis showed that p = 0.214 &gt; 0.05, which means that there were no differences in metacognitive skills of students for the five treatment groups. The mean total overall of metacognitive skills was 70.12. It belongs to the good category. It can be concluded that the use of mobile learning in teaching can be training the metacognitive of students well in teaching biology
Automated Deep Learning for Threat Detection in Luggage from X-Ray Images

Luggage screening is a very important part of the airport security risk assessment and clearance process. Automating the threat objects detection from x-ray scans of passengers' luggage can speed-up and increase the efficiency of the whole security procedure. In this paper we investigate and compare several algorithms for detection of firearm parts in x-ray images of travellers' baggage. In particular, we focus on identifying steel barrel bores as threat objects, being the main part of the weapon needed for deflagration. For this purpose, we use a dataset of 22k double view x-ray scans, containing a mixture of benign and threat objects. In the pre-processing stage we apply standard filtering techniques to remove noisy and ambiguous images (i.e., smoothing, black and white thresholding, edge detection, etc.) and subsequently employ deep learning techniques (Convolutional Neural Networks and Stacked Autoencoders) for the classification task. For comparison purposes we also train and simulate shallow Neural Networks and Random Forests algorithms for the objects detection. Furthermore, we validate our findings on a second dataset of double view x-ray scans of courier parcels. We report and critically discuss the results of the comparison on both datasets, showing the advantages of our approach.
A Fine-grained and Noise-aware Method for Neural Relation Extraction

Distant supervision is an efficient way to generate large-scale training data for relation extraction without human efforts. However, a coin has two sides. The automatically annotated labels for training data are problematic, which can be summarized as multi-instance multi-label problem and coarse-grained (bag-level) supervised signal. To address these problems, we propose two reasonable assumptions and craft reinforcement learning to capture the expressive sentence for each relation mentioned in a bag. More specifically, we extend the original expressed-at-least-once assumption to multi-label level, and introduce a novel express-at-most-one assumption. Besides, we design a fine-grained reward function, and model the sentence selection process as an auction where different relations for a bag need to compete together to achieve the possession of a specific sentence based on its expressiveness. In this way, our model can be dynamically self-adapted, and eventually implements the accurate one-to-one mapping from a relation label to its chosen expressive sentence, which serves as training instances for the extractor. The experimental results on a public dataset demonstrate that our model constantly and substantially outperforms current state-of-the-art methods for relation extraction.
Deep supervised learning with mixture of neural networks

Deep Neural Network (DNN), as a deep architectures, has shown excellent performance in classification tasks. However, when the data has different distributions or contains some latent non-observed factors, it is difficult for DNN to train a single model to perform well on the classification tasks. In this paper, we propose mixture model based on DNNs (MoNNs), a supervised approach to perform classification tasks with a gating network and multiple local expert models. We use a neural network as a gating function and use DNNs as local expert models. The gating network split the heterogeneous data into several homogeneous components. DNNs are combined to perform classification tasks in each component. Moreover, we use EM (Expectation Maximization) as an optimization algorithm. Experiments proved that our MoNNs outperformed the other compared methods on determination of diabetes, determination of benign or malignant breast cancer, and handwriting recognition. Therefore, the MoNNs can solve the problem of data heterogeneity and have a good effect on classification tasks.