Reviewerly/exHarmony huggingface dataset:
Classification of various attacks and their defence mechanism in online social networks: a survey

Due to the popularity and user friendliness of the Internet, numbers of users of online social networks (OSNs) and social media have grown significantly. However, globally utilised, social networks are the consequence of the lack of understanding of secrecy and protection on OSN and media has increased. Secrecy and surety of OSNs need to be inquired from various positions. According to recent studies, OSN users expose their private information such as email address, phone number etc. In this paper, we have presented a high-level classification of recent OSN attacks for recognising the problem and analysing the blow of such attacks on World Wide Web. We have also discussed OSN attacks on different social networking web applications by citing certain recent reports such as Kaspersky security network and Sophos security threat report. We also offer some simple-to-implement user practice tips to protect the system and user’s information. In addition to this, we have discussed a comprehensive analysis of numerous defensive approaches on OSN security. Lastly, based on the acknowledged strength and faults of these defensive approaches, we have explained open research issues.
Group Sparse Representation With WaveNet Vocoder Adaptation for Spectrum and Prosody Conversion

The statistical approach to voice conversion typically consists of a feature conversion module followed by a vocoder. So far, the feature conversion studies are mainly focused on the conversion of spectrum. However, speaker identity is also characterized by prosodic features, such as fundamental frequency F0 and energy contour among others. In this paper, we study the transformation of speaker characteristics both in terms of spectrum and prosody. We propose two novel techniques that effectively use a limited amount of source-target training data and leverage a large general speech corpus to improve the voice conversion quality. First, we study the phonetic sparse representation under the group sparsity mathematical formulation. We use phonetic posteriorgrams PPGs together with spectral and prosody features to form tandem feature in the phonetic dictionary. The tandem feature allow us to estimate an activation matrix that is less dependent on source speakers, thus providing a better voice conversion quality. Second, we study the use of WaveNet vocoder that can be trained on general speech corpus from multiple speakers and adapted on target speaker data to improve the vocoding quality. We benefit from the large general speech databases that are used to train the PPG generator, and the WaveNet vocoder. The experiments show that the proposed conversion framework outperforms the traditional spectrum and prosody conversion techniques in both objective and subjective evaluations.
Submanifold-Preserving Discriminant Analysis With an Auto-Optimized Graph

Due to the multimodality of non-Gaussian data, traditional globality-preserved dimensionality reduction (DR) methods, such as linear discriminant analysis (LDA) and principal component analysis (PCA) are difficult to deal with. In this paper, we present a novel local DR framework via auto-optimized graph embedding to extract the intrinsic submanifold structure of multimodal data. Specifically, the proposed model seeks to learn an embedding space which can preserve the local neighborhood structure by constructing a k -nearest neighbors ( k NNs) graph on data points. Different than previous works, our model employs the l0 -norm constraint and binary constraint on the similarity matrix to impose that there only be a k nonzero value in each row of the similarity matrix, which can ensure the k -connectivity in graph. More important, as the high-dimensional data probably contains some noises and redundant features, calculating the similarity matrix in the original space by using a kernel function is inaccurate. As a result, a mechanism of an auto-optimized graph is derived in the proposed model. Concretely, we learn the embedding space and similarity matrix simultaneously. In other words, the selection of neighbors is automatically executed in the optimal subspace rather than in the original space when the algorithm reaches convergence, which can alleviate the affect of noises and improve the robustness of the proposed model. In addition, four supervised and semisupervised local DR methods are derived by the proposed framework which can extract the discriminative features while preserving the submanifold structure of data. Last but not least, since two variables need to be optimized simultaneously in the proposed methods, and the constraints on the similarity matrix are difficult to satisfy, which is an NP-hard problem. Consequently, an efficient iterative optimization algorithm is introduced to solve the proposed problems. Extensive experiments conducted on synthetic data and several real-world datasets have demonstrated the advantages of the proposed methods in robustness and recognition accuracy.
ExerCube vs. Personal Trainer

Today's spectrum of playful fitness solutions features systems that are clearly game-first or fitness-first in design; hardly any sufficiently incorporate both areas. Consequently, existing applications and evaluations often lack in focus on attractiveness and effectiveness, which should be addressed on the levels of body, controller, and game scenario following a holistic design approach. To contribute to this topic and as a proof-of-concept, we designed the ExerCube, an adaptive fitness game setup. We evaluated participants' multi-sensory and bodily experiences with a non-adaptive and an adaptive ExerCube version and compared them with personal training to reveal insights to inform the next iteration of the ExerCube. Regarding flow, enjoyment and motivation, the ExerCube is on par with personal training. Results further reveal differences in perception of exertion, types and quality of movement, social factors, feedback, and audio experiences. Finally, we derive considerations for future research and development directions in holistic fitness game setups.
Advances in Quantum Dense Coding

Abstract Quantum dense coding is one of the most important protocols in quantum communication. It derives from the idea of using quantum resources to boost the communication capacity and now serves as a key primitive across a variety of quantum information protocols. This Progress Report focuses on the basic theoretical ideas behind quantum dense coding, discussing its development history from discrete and continuous variables to quantum networks, then to its variant protocols and applications in quantum secure communication. With this basic background in hand, then the main experimental achievements are reviewed, from photonic qubits and qudits to optical modes, nuclear magnetic resonance, and atomic systems. Besides the state of the art, finally potential future steps are discussed.
HoloDoc

Prior research identified that physical paper documents have many positive attributes, for example natural tangibility and inherent physical flexibility. When documents are presented on digital devices, however, they can provide unique functionality to users, such as the ability to search, view dynamic multimedia content, and make use of indexing. This work explores the fusion of physical and digital paper documents. It first presents the results of a study that probed how users perform document-intensive analytical tasks when both physical and digital versions of documents were available. The study findings then informed the design of HoloDoc, a mixed reality system that augments physical artifacts with rich interaction and dynamic virtual content. Finally, we present the interaction techniques that HoloDoc affords, and the results of a second study that assessed HoloDoc's utility when working with digital and physical copies of academic articles.
No-Reference Quality Assessment for View Synthesis Using DoG-Based Edge Statistics and Texture Naturalness

View synthesis is a key technique in free-viewpoint video, which renders virtual views based on texture and depth images. The distortions in synthesized views come from two stages, i.e., the stage of the acquisition and processing of texture and depth images, and the rendering stage using depth-image-based-rendering (DIBR) algorithms. The existing view synthesis quality metrics are designed for the distortions caused by a single stage, which cannot accurately evaluate the quality of the entire view synthesis process. With the considerations that the distortions introduced by two stages both cause edge degradation and texture unnaturalness, and the Difference-of-Gaussian (DoG) representation is powerful in capturing image edge and texture characteristics by simulating the center-surrounding receptive fields of retinal ganglion cells of human eyes, this paper presents a no-reference quality index for Synthesized views using DoG-based Edge statistics and Texture naturalness (SET). To mimic the multi-scale property of the Human Visual System (HVS), DoG images are first calculated at multiple scales. Then the orientation selective statistics features and the texture naturalness features are calculated on the DoG images and the coarsest scale image, producing two groups of quality-aware features. Finally, the quality model is learnt from these features using the random forest regression model. Experimental results on two view synthesis image databases demonstrate that the proposed metric is advantageous over the relevant state-of-the-arts in dealing with the distortions in the whole view synthesis process.
The Magic Machine Workshops

New technologies emerge into an increasingly complex everyday life. How can we engage users further into material practices that explore ideas and notions of these new things? This paper proposes a set of qualities for short, intense, workshop-like experiences, created to generate strong individual commitments, and expose underlying personal desires as drivers for ideas. By making use of open-ended making to engage participants in the imagination of new things, we aim to allow a broad range of knowledge to materialise, focused on the making of work that is about technology, rather than of technology.
Deep Neural Network Compression Technique Towards Efficient Digital Signal Modulation Recognition in Edge Device

Digital signal modulation recognition is meaningful for military application and civilian application. In the non-cooperation communication scenario, digital signal modulation recognition will help people identify communication target and have better management over them. In order to the classification accuracy, deep learning is widely used to complete this task. However, current papers have not considered the deployment of deep learning in compute capability and storage limited edge equipment. In this paper, we utilize neural network pruning techniques to reduce the convolution parameters and floating point operations per second (FLOPs), which will pave a wide way to deploy signal classification convolution neural network (CNN) in edge equipment. We set the Average Percentage of Zeros (APoZ) criterion for convolution layers. Compared to original CNN, the experiment result shows that light CNN convolution layer could use only 1.5%~5% parameter and 33%~35% time without losing significant accuracy.
Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer

Over the last two decades, there has been an expeditious expansion in the generation and exploration of high-dimensional biomedical data. Identification of biomarkers from the genomics data poses a significant challenge in microarray data analysis. Therefore, for the methodical analysis of the genomics dataset, it is paramount to develop some effective algorithms. In this work, a multi-objective version of the emperor penguin optimization (EPO) algorithm with chaos, namely, multi-objective chaotic EPO (MOCEPO) is proposed. The suggested approach extends the original continuous single objective EPO to a competent binary multi-objective model. The objectives are to minimize the number of selected genes (NSG) and to maximize the classification accuracy (CA). In this work, Fisher score and minimum redundancy maximum relevance (mRMR) are independently used as initial filters. Further, the proposed MOCEPO is employed for the simultaneous optimal feature selection and cancer classification. The proposed algorithm is successfully experimented on seven well-known high-dimensional binary-class as well as multi-class datasets. To evaluate the effectiveness, the proposed method is compared with non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO), chaotic version of GA for multi-objective optimization (CGAMO), and chaotic MOPSO methods. The experimental results show that the proposed framework achieves better CA with minimum NSG compared to the existing schemes. The presented approach exhibits its efficacy with regard to NSG, accuracy, sensitivity, specificity, and F-measure.
Moving vehicle detection, tracking and traffic parameter estimation from a satellite video: a perspective on a smarter city

Satellite remote sensing is undergoing a revolution in terms of sensors and temporal coverage. The possibility of acquiring earth’s surface video from space provides an opportunity to investigate broader applications of remote sensing. High-resolution spaceborne videos can become a vital factor in earth observation. Temporally continuous tracking of moving objects, i.e. vehicles, vessels, or even military equipment on Earth’s surface demands high spatial resolution satellite videos. Detecting moving vehicles in the urban areas from space video can lead governments to a new era of traffic monitoring. Satellite videos will find many applications in the field of traffic monitoring. In this article, first, moving vehicles are detected using background subtraction with 94.7% accuracy. Afterwards, vehicles’ trajectories, average velocities, dynamic velocities, and space-time diagram are estimated and trajectories are classified based on velocities. Finally, the total frame traffic density is computed.
Vehicle Detection in Aerial Images Using Rotation-Invariant Cascaded Forest

Vehicle detection in aerial images has been taking great interest to researchers in recent years. It plays a crucial part in multidirectional applications, such as traffic surveillance, urban planning, and so on. However, the vehicle detection field faces many difficulties owing to the small size of the vehicles, different orientations, and the complex background. To solve this problem, this paper introduces a novel rotation-invariant vehicle detection method which is accurate, stable and has a simple structure compared with region-based convolutional network method. First, the data-driven method has been employed to generate the proposal region which will be applied for data augmentation. Second, this paper designs a method to obtain the rotation invariant descriptors by using radial gradient transform descriptors. Then, the rotation invariant descriptors are fed into the cascaded forest based on auto-context for feature learning and classification. The comprehensive experiments are conducted on the Munich vehicle dataset and UAVDT dataset. The results of experiment illustrate the satisfactory performance of the proposed method.
A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario

Due to the high population of hearing impaired and vocal disabled people in India, a sign language interpretation system is becoming highly important for minimizing their isolation in society. This paper proposes a signer independent novel vision-based gesture recognition system which is capable of recognizing single handed static and dynamic gestures, double-handed static gestures and finger spelling words of Indian Sign Language (ISL) from live video. The use of Zernike moments for key frame extraction reduces the computation speed to a large extent. It also proposes an improved method for co-articulation elimination in fingerspelling alphabets. The gesture recognition module comprises mainly three steps – Preprocessing, Feature Extraction, and Classification. In the preprocessing phase, the signs are extracted from a real-time video using skin color segmentation. An appropriate feature vector is extracted from the gesture sequence after co-articulation elimination phase. The obtained features are then used for classification using Support Vector Machine(SVM). The system successfully recognized finger spelling alphabets with 91% accuracy and single-handed dynamic words with 89% accuracy. The experimental results show that the system has a better recognition rate compared to some of the existing methods.
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models

Graph representation learning for static graphs is a well studied topic. Recently, a few studies have focused on learning temporal information in addition to the topology of a graph. Most of these studies have relied on learning to represent nodes and substructures in dynamic graphs. However, the representation learning problem for entire graphs in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised representation learning architecture for dynamic graphs, designed to learn both the topological and temporal features of the graphs that evolve over time. The approach consists of a sequence-to-sequence encoder-decoder model embedded with gated graph neural networks (GGNNs) and long short-term memory networks (LSTMs). The GGNN is able to learn the topology of the graph at each time step, while LSTMs are leveraged to propagate the temporal information among the time steps. Moreover, an encoder learns the temporal dynamics of an evolving graph and a decoder reconstructs the dynamics over the same period of time using the encoded representation provided by the encoder. We demonstrate that our approach is capable of learning the representation of a dynamic graph through time by applying the embeddings to dynamic graph classification using a real world dataset of animal behaviour.
Human–virtual character interaction: Toward understanding the influence of haptic feedback

Abstract In this study, we compare haptic feedback and nonhaptic feedback conditions in which virtual characters bump into the participant who is immersed in a virtual environment. A questionnaire was developed to determine the influence of haptic feedback on a number of concepts (presence, embodiment, positive and negative affect, interaction realism with virtual character, and haptic feedback realism). Physiological data were also collected using galvanic skin response (GSR) to investigate the influence of haptic feedback on physiological arousal during human–virtual character interaction. Five conditions were developed (no haptic feedback, full intensity, half intensity, incorrect position, and delayed timing) to determine which aspects of haptic feedback are most important in influencing participant responses. Significant differences were found in embodiment, realism of virtual character interaction, and haptic feedback realism. In addition, significant differences were found in GSR amplitude after the first interaction with the virtual character. Implications for further research are discussed.
Color Transferred Convolutional Neural Networks for Image Dehazing

Image dehazing is a crucial image processing step for outdoor vision systems. However, images recovered through conventional image dehazing methods that use either haze-relevant priors or heuristic cues to estimate transmission maps may not lead to sufficiently accurate haze removal from single images. The most commonly observed effects are darkened and brightened artifacts on some areas of the recovered images, which cause considerable loss of fidelity, brightness, and sharpness. This paper develops a variational image dehazing method on the basis of a color-transfer image dehazing model that is superior to conventional image dehazing methods. By creating a color-transfer image dehazing model to remove haze obscuration and acquire information regarding the coefficients of the model by using the devised convolutional neural network-based deep framework as a supervised learning strategy, an image fidelity, brightness, and sharpness can be effectively restored. The experimental results verify through quantitative and qualitative evaluations of either synthesized or real haze images, and the proposed method outperforms existing single image dehazing methods.
Indistinguishability Obfuscation Without Multilinear Maps: New Methods for Bootstrapping and Instantiation

Constructing indistinguishability obfuscation ( $$\mathsf{iO}$$ ) [17] is a central open question in cryptography. We provide new methods to make progress towards this goal. Our contributions may be summarized as follows:
Multi-sensor technology for B-spline modelling and deformation analysis of composite structures

High-accuracy deformation analysis of composite structures is an important topic in the field of civil engineering and increasing attention is given to geometry-based analytical model, such as B-splines where the determination of the parameter values has a great influence on the accuracy of the B-spline approximation. In this paper, B-spline approximation is carried out with point cloud data of an arch structure obtained with terrestrial laser scanner (TLS) and the parameter control point is investigated with the aid of laser tracker (LT) and corner cube reflectors (CCRs). We focused on the discussion about the influence factors of the optimal parameters, such as CCR uncertainty, curvature of object, and order of the B-spline. The innovation of this paper is that we adopt LT to optimize the parameters of B-spline fitting, and the results are compared and verified by Bayesian information criterion.
QuickInsights

Discovering interesting data patterns is a common and important analytical need in data, with increasing user demand for automated discovery abilities. However, automatically discovering interesting patterns from multi-dimensional data remains challenging. Existing techniques focus on mining individual types of patterns. There is a lack of unified formulation for different pattern types, as well as general mining frameworks to derive them effectively and efficiently. We present a novel technique QuickInsights, which quickly and automatically discovers interesting patterns from multi-dimensional data. QuickInsights proposes a unified formulation of interesting patterns, called insights, and designs a systematic mining framework to discover high-quality insights efficiently. We demonstrate the effectiveness and efficiency of QuickInsights through our evaluation on 447 real datasets as well as user studies on both expert users and non-expert users. QuickInsights is released in Microsoft Power BI.