Reviewerly/exHarmony huggingface dataset:
A Statistical Learning Approach to Ultra-Reliable Low Latency Communication

Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9</sup> . Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper, we treat this fundamental problem in the simplest possible communication-theoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; and (iii) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems.
New entanglement-assisted quantum codes from k-Galois dual codes

Entanglement-assisted quantum error-correcting (EAQEC, for short) codes use pre-existing entanglements between the sender and receiver to boost the rate of transmission. It is possible to construct an EAQEC code from any classical linear code, unlike standard quantum error-correcting codes, they can only be constructed from classical linear codes which contain their Hermitian dual codes. However, how to determine the parameters of ebits c in EAQEC codes is not an easy task. In this paper, let p be prime and e, k be integers, we construct six classes of EAQEC codes based on k-Galois dual codes over finite fields Fpe, where 0≤k<e. The parameter of ebits c of these EAQEC codes can be easily generated algebraically. Furthermore, the six classes of EAQEC codes are of maximal entanglement, most of which have better parameters than current EAQEC codes available.
Dynamic Feature Matching for Partial Face Recognition

Partial face recognition (PFR) in an unconstrained environment is a very important task, especially in situations where partial face images are likely to be captured due to occlusions, out-of-view, and large viewing angle, e.g., video surveillance and mobile devices. However, little attention has been paid to PFR so far and thus, the problem of recognizing an arbitrary patch of a face image remains largely unsolved. This study proposes a novel partial face recognition approach, called Dynamic Feature Matching (DFM), which combines Fully Convolutional Networks (FCNs) and Sparse Representation Classification (SRC) to address partial face recognition problem regardless of various face sizes. DFM does not require prior position information of partial faces against a holistic face. By sharing computation, the feature maps are calculated from the entire input image once, which yields a significant speedup. Experimental results demonstrate the effectiveness and advantages of DFM in comparison with state-of-the-art PFR methods on several partial face databases, including CAISA-NIR-Distance, CASIA-NIR-Mobile, and LFW databases. The performance of DFM is also impressive in partial person re-identification on Partial RE-ID and iLIDS databases. The source code of DFM can be found at https://github.com/lingxiao-he/dfm new.
Spectral Clustering by Joint Spectral Embedding and Spectral Rotation

Spectral clustering is an important clustering method widely used for pattern recognition and image segmentation. Classical spectral clustering algorithms consist of two separate stages: 1) solving a relaxed continuous optimization problem to obtain a real matrix followed by 2) applying K -means or spectral rotation to round the real matrix (i.e., continuous clustering result) into a binary matrix called the cluster indicator matrix. Such a separate scheme is not guaranteed to achieve jointly optimal result because of the loss of useful information. To obtain a better clustering result, in this paper, we propose a joint model to simultaneously compute the optimal real matrix and binary matrix. The existing joint model adopts an orthonormal real matrix to approximate the orthogonal but nonorthonormal cluster indicator matrix. It is noted that only in a very special case (i.e., all clusters have the same number of samples), the cluster indicator matrix is an orthonormal matrix multiplied by a real number. The error of approximating a nonorthonormal matrix is inevitably large. To overcome the drawback, we propose replacing the nonorthonormal cluster indicator matrix with a scaled cluster indicator matrix which is an orthonormal matrix. Our method is capable of obtaining better performance because it is easy to minimize the difference between two orthonormal matrices. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method (called JSESR).
Virtual Reality, Augmented Reality, and <i>In Vivo</i> Exposure Therapy: A Preliminary Comparison of Treatment Efficacy in Small Animal Phobia

This study aggregated data from three randomized control trials to explore the differential efficacy of three forms of exposure therapy, namely, in vivo (iVET), virtual reality (VRET), and augmented reality (ARET), in the treatment of small animal phobia. Additionally, baseline patient characteristics were used to detect subgroups of patients who showed a differential response to certain treatment modalities. Primary measures were distance covered, anxiety during the behavioral avoidance test (BAT), and overall fear of small animals. A repeated-measures analysis of variance was used to explore the overall treatment effect across the exposure modalities. A cluster analysis and an analysis of moderation were conducted to explore differential response to treatments. The main study finding was that the three treatment conditions were similarly efficacious in the treatment of small animal phobia for all study outcomes. Only for distance covered, our results revealed a tendency for iVET to be more effective than VRET and ARET in participants with worse performance on the BAT before treatment. The present study findings provide further evidence for the comparable efficacy of the three forms of exposure. Our results also suggest that, overall, treatments are likely to be similarly effective, regardless of the individual baseline characteristics (i.e., fear, anxiety, and age), whereas pretreatment scores on distance covered in the avoidance test might be used to personalize treatments (iVET may be preferable when participants perform worse at pretreatment).
Centroid and Covariance Alignment-Based Domain Adaptation for Unsupervised Classification of Remote Sensing Images

A new domain adaptation algorithm based on the class centroid and covariance alignment (CCCA) is proposed for classification of remote sensing images. This approach exploits both the first- and second-order statistics to describe the data distribution and aligns the data distribution between domains on a per-class basis. Since the predicted labels of target data are used to estimate the two statistics, we applied overall centroid alignment (OCA) as a coarse domain adaptation strategy to improve the estimation accuracy. In addition, the OCA coarse adaptation in conjunction with CCCA refined adaptation can also benefit by incorporation of spatial information, resulting in a Spa_OCA_CCCA approach. The proposed approach is easy to implement, and only one parameter is required in the spatial filtering step. It does not require labeled information in the target domain and can achieve labor-free classification. The experimental results using Hyperion, National Center for Airborne Laser Mapping, and Worldview-2 remote sensing images demonstrated the effectiveness of the proposed approach.
Privacy Enhancing Technologies in the Internet of Things: Perspectives and Challenges

Internet of Things (IoT) devices have brought much efficiency and convenience to our daily life. However, the devices may collect a myriad of data from people without their consent. Controlling the large amount of data generated from the devices from being misused is critical to mitigate privacy risks. Therefore, privacy protection on personal data has become an important factor in the development of the IoT. Historically, privacy enhancing technologies (PETs) can effectively enhance the privacy and protect users' personally identifiable information. To date, many researchers have stressed the importance of PETs and proposed solutions relevant to different application fields of the IoT. However, to the best of our knowledge, none of the research has analyzed the PETs in IoT from the aspects of privacy threat issues and privacy legislation. As a result, this paper surveys on the solutions of PETs in the field of IoT, which has filtered down from the large number of published academic papers to the 120 primary studies published between 2014 and 2017. After collecting the papers, we categorized them based on the functions and the coverage of privacy protection, and analyzed them from different aspects, ranging from high-level principles of general data protection regulations and ISO/IEC 29100:2011 requirements to the actual resolution of privacy threats in IoT. Thus, we aim to identify the current state of development of the PETs in various fields and examine whether the existing PETs comply with the latest legal principles and privacy standards and reduce the threats to privacy. Finally, recommendations for future research are given based on the results.
Deep Transfer Low-Rank Coding for Cross-Domain Learning

Transfer learning has attracted great attention to facilitate the sparsely labeled or unlabeled target learning by leveraging previously well-established source domain through knowledge transfer. Recent activities on transfer learning attempt to build deep architectures to better fight off cross-domain divergences by extracting more effective features. However, its generalizability would decrease greatly due to the domain mismatch enlarges, particularly at the top layers. In this paper, we develop a novel deep transfer low-rank coding based on deep convolutional neural networks, where we investigate multilayer low-rank coding at the top task-specific layers. Specifically, multilayer common dictionaries shared across two domains are obtained to bridge the domain gap such that more enriched domain-invariant knowledge can be captured through a layerwise fashion. With rank minimization on the new codings, our model manages to preserve the global structures across source and target, and thus, similar samples of two domains tend to gather together for effective knowledge transfer. Furthermore, domain/classwise adaption terms are integrated to guide the effective coding optimization in a semisupervised manner, so the marginal and conditional disparities of two domains will be alleviated. Experimental results on three visual domain adaptation benchmarks verify the effectiveness of our proposed approach on boosting the recognition performance for the target domain, by comparing it with other state-of-the-art deep transfer learning.
Investigating and comparing the predictors of the intention towards taking security measures against malware, scams and cybercrime in general

It is a popular belief that humans are the weakest link in cyber-security. However, there has been little research devoted to why people do (or do not) protect themselves and what explains their behaviors. Instead, more attention has been paid to victimization and prevalence of cybercrime. Therefore, we investigate and compare the motivations to protect oneself against scams, malware and cybercrime in general using an adjusted model of the protection motivation theory (PMT), which includes subjective norm, threat and coping awareness. To test this model, different structural equation models (SEM) were used and compared, with each focusing on a specific cybercrime. Our non-random (convenience) sample (n = 1181) is representative for age, residence and gender for the Belgian population. The results show the extended PMT model is a good predictive model to explain people's protection against cybercrime. The findings suggest significant differences when protecting oneself against 'technical' cybercrimes (malware), compared to more 'social' cybercrimes (scams). The differences are situated on the predictive role of coping, threat awareness and perceived vulnerability. Furthermore, we found subjective norm to be an important predictor in the intention toward protecting against all cybercrimes. Recommendations for further research and awareness campaigns are provided in the discussion.
Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases

In recent years, fog computing emerges as a proactive solution for healthcare service as it facilitates continuous monitoring of remote patient health and early detection of mosquito-borne diseases. In addition, fog computing reduces the latency and communication cost that is normally an immense concern of cloud computing. The key objective of the proposed intelligent system is to detect and control the mosquito-borne diseases at the early stage. For this purpose, wearable and IoT sensors are used to gather the required information and fog computing is used to analyze, categorize and share medical information among the user and healthcare service providers. We utilize similarity coefficient to differentiate the various mosquito-borne diseases based on patient's symptoms, and the fuzzy k-nearest neighbor approach is employed to categorize the user into infected or uninfected class. Further, on the cloud layer, Social Network Analysis (SNA) is employed to represent the outbreak of mosquito-borne diseases. The likelihood of the registered user to receive or spread the disease is measured by computing PDO (Probability of Disease Outbreak) which is used to provide the location-based awareness to avert the outbreak. The experimental evaluation reveals the improved performance of the proposed F-HMRAS with 95.9% classification accuracy.
Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China

Day of the year-based (DYB) models can achieve great accuracy in daily global solar radiation estimation without specific meteorological elements. Many empirical models (EMs) and machine learning (ML) methods have been proposed for DYB models. However, the number of their comparative studies based on diverse climates is limited. In this study, a grand total of 14 DYB models are established to estimate daily global solar radiation based on measured data from 1994 to 2015 at 35 meteorological stations in six climate zones of China. Detailed tasks are as follows: (1) Seven EMs and seven ML models are trained for solar radiation estimation. (2) A new EM and two novel ML models are proposed, i.e. hybrid 3rd order polynomial and sine wave model, adaptive neuro-fuzzy inference system (ANFIS) optimized by chaotic firefly algorithm (CFA) and ANFIS optimized by whale optimization algorithm with simulated annealing and roulette wheel selection (WOASAR). (3) Four statistical indicators are utilized to compare those models, and the best model for each station is decided. (4) We discuss the model parameters and climate variances of six specific stations in different climate zones. The comparison results demonstrate superb estimation precision and climate adaptability of the newly proposed models.
Light Field Image Compression Using Generative Adversarial Network-Based View Synthesis

Light field (LF) has become an attractive representation of immersive multimedia content for simultaneously capturing both the spatial and angular information of the light rays. In this paper, we present a LF image compression framework driven by a generative adversarial network (GAN)-based sub-aperture image (SAI) generation and a cascaded hierarchical coding structure. Specifically, we sparsely sample the SAIs in LF and propose the GAN of LF (LF-GAN) to generate the unsampled SAIs by analogy with adversarial learning conditioned on its surrounding contexts. In particular, the LF-GAN learns to interpret both the angular and spatial context of the LF structure and, meanwhile, generates intermediate hypothesis for the unsampled SAIs in a certain position. Subsequently, the sampled SAIs and the residues of the generated-unsampled SAIs are re-organized as pseudo-sequences and compressed by standard video codecs. Finally, the hierarchical coding structure is adopted for the sampled SAI to effectively remove the inter-view redundancies. During the training process of LF-GAN, the pixel-wise Euclidean loss and the adversarial loss are chosen as the optimization objective, such that sharp textures with less blurring in details can be produced. Extensive experimental results show that the proposed LF-GAN-based LF image compression framework outperforms the state-of-the-art learning-based LF image compression approach with on average 4.9% BD-rate reductions over multiple LF datasets.
Cognitive-inspired class-statistic matching with triple-constrain for camera free 3D object retrieval

3D object retrieval has attracted much research attention in recent years, while most of the state-of-the-art approaches highly depend on the camera array settings for capturing 3D object views. We also note that the generality among objects in the same category has not been exploited in existing works. To this end, we propose a cognitive-inspired class-statistics matching method with triple-constraint (CSTC) for camera free 3D object retrieval. In this method, each object in the gallery set is represented by a free set of views without camera constraint. Inspired by representativeness heuristic, the category-independent distribution of each feature is calculated and Gaussian probabilistic models are generated with corresponding weights. Meanwhile, the distances between positive-to-positive examples are statistically measured based on pre-chosen matched views, and then the pairwise matching model is constructed in an off-line manner. In the retrieval procedure, for each query, the view-based distance measure is firstly converted into the object-based distance measure, and then the trained class-statistics matching models are employed to calculate the similarity between different objects, meanwhile, the constrains of the pairwise matching model are combined by CSTC model which can balances the performance and retrieval speed. In this model, since the object-based distance measure is firstly utilized, which is very helpful to speed up the retrieval, and then class-statistics matching model between the query object and gallery object, which can explore the generality among objects, is employed to improve the performance, moreover, the pairwise matching model is further used to filter the retrieval results, finally, in order to boost the retrieval speed and combine their complementary characteristic, their results are fused only by simple and effective linear combination (supervising good). We have conducted experiments on ETH, NTU-60, MVRED and PSB 3D datasets, and experimental results show that our performance outperforms or is comparable with the-state-of-the-art algorithms, but our retrieval speed obviously outperforms others.
Ensemble feature selection using election methods and ranker clustering

Feature selection (FS) has become a significant part of the data processing pipeline. Recently, ensemble FS has emerged as a new methodology that promises to improve FS robustness and performance. In this paper, we propose several ensemble FS methods built on voting aggregation schemes such as plurality vote, single transferable vote, Borda count, and novel weighted Borda count. Additionally, we present the new concept of clustering FS methods prior to building ensembles using a mean-shift clustering algorithm. The proposed methods are examined using three accuracy measures: the ability to correctly identify relevant features, FS stability, and influence on classification. The ensembles and clustered ensembles based on a weighted Borda count show very balanced performance, achieving quality results in all investigated measures and outperforming the other methods examined.
Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm

The ultimate goal of the Recommender System (RS) is to offer a proposal that is very close to the user's real opinion. Data clustering can be effective in increasing the accuracy of production proposals by the RS. In this paper, single-objective hybrid evolutionary approach is proposed for clustering items in the offline collaborative filtering RS. This method, after generating a population of randomized solutions, at each iteration, improves the population of solutions first by Genetic Algorithm (GA) and then by using the Gravitational Emulation Local Search (GELS) algorithm. Simulation results on standard datasets indicate that although the proposed hybrid meta-heuristic algorithm requires a relatively high run time, it can lead to more appropriate clustering of existing data and thus improvement of the Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Coverage criteria.
Survey on Virtual Assistant: Google Assistant, Siri, Cortana, Alexa

Virtual assistant is boon for everyone in this new era of 21st century. It has paved way for a new technology where we can ask questions to machine and can interact with IVAs as people do with humans. This new technology attracted almost whole world in many ways like smart phones, laptops, computers etc. Some of the significant VPs are like Siri, Google Assistant, Cortana, and Alexa. Voice recognition, contextual understanding and human interaction are the issues which are not solved yet in this IVAs. So, to solve those issues 100 users participated a survey for this research and shared their experiences. All users' task was to ask questions from the survey to all personal assistants and from their experiences this research paper came up with the actual results. According to that results many services were covered by these assistants but still there are some improvements required in voice recognition, contextual understanding and hand free interaction. After addressing these improvements in IVAs will definitely increased its use is the main goal for this research paper.
Regularizing Deep Neural Networks by Enhancing Diversity in Feature Extraction

This paper proposes a new and efficient technique to regularize the neural network in the context of deep learning using correlations among features. Previous studies have shown that oversized deep neural network models tend to produce a lot of redundant features that are either the shifted version of one another or are very similar and show little or no variations, thus resulting in redundant filtering. We propose a way to address this problem and show that such redundancy can be avoided using regularization and adaptive feature dropout mechanism. We show that regularizing both negative and positive correlated features according to their differentiation and based on their relative cosine distances yields network extracting dissimilar features with less overfitting and better generalization. This concept is illustrated with deep multilayer perceptron, convolutional neural network, sparse autoencoder, gated recurrent unit, and long short-term memory on MNIST digits recognition, CIFAR-10, ImageNet, and Stanford Natural Language Inference data sets.