Reviewerly/exHarmony huggingface dataset:
A scalable saliency-based feature selection method with instance-level information

Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most such techniques operate on the whole dataset, but are unable to provide the user with useful information when only instance-level information is required; in other words, classic feature selection algorithms do not identify the most relevant information in a sample. We have developed a novel feature selection method, called saliency-based feature selection (SFS), based on deep-learning saliency techniques. Our algorithm works under any architecture that is trained by using gradient descent techniques (Neural Networks, SVMs, …), and can be used for classification or regression problems. Experimental results show our algorithm is robust, as it allows to transfer the feature ranking result between different architectures, achieving remarkable results. The versatility of our algorithm has been also demonstrated, as it can work either in big data environments as well as with small datasets.
Analysis of Detection and Prevention of Malware in Cloud Computing Environment

In cloud environment there are many unique and different on demand services available for users. Those services allocate workable and easy accessibility to use various web applications. In this environment malicious attacks and threats can occur anytime and can destroy the useful files and applications. So cloud needs to be well secure and highly maintained. In this paper we will discuss various malicious attacks which can dismantle our cloud environment and how we can make our cloud more powerful by using strong and robust cloud antivirus.
Exploring Configuration of Mixed Reality Spaces for Communication

Mixed Reality (MR) enables users to explore scenarios not realizable in the physical world. This allows users to communicate with the help of digital content. We investigate how different configurations of participants and content affect communication in a shared immersive environment. We designed and implemented side-by-side, mirrored face-to-face and eyes-free configurations in our multi-user MR environment and conducted a preliminary user study for our mirrored face-to-face configuration, evaluating with respect to one-to-one interaction, smooth focus shifts and eye contact within a 3D presentation using the interactive Chalktalk system. We provide experimental results and interview responses.
Effects of virtual-avatar motion-synchrony levels on full-body interaction

A full-body interaction is one of the key aspects for human-computer interaction. However, we are unaware of any rigorous studies examining the effects of motion synchrony levels on cybersickness, presence, and body ownership. To address this issue, we investigated the effect of a virtual avatar with different motion synchrony levels, using six-camera motion capture, a Kinect, and pre-recorded interactions. The results suggest that the different motion synchronization levels affect all current dependency measures (cybersickness, presence, and body ownership). Higher motion synchronization level induce a lower degree of cybersickness, higher presence, and greater body ownership. These dependent measures are related differently to the motion synchrony levels. We discuss the potential implications of the current results and the prospects for achieving a full-body interaction.
On-site calibration of underwater stereo vision based on light field

In underwater stereo vision calibration, the refractive index of water varies with the depth, temperature and salinity, and the position relationship between the two cameras varies with the deformation of the equipment in the water, so these two parameters need to be calibrated according to the scene. In this paper, an on-site calibration of underwater stereo vision is proposed to adapt to the changes of refractive index of water and the relative position between the two cameras. A method based on coplanar constraint in view of light field is proposed to determine the initial value of the underwater refractive index. After obtaining the initial value, an optimal objective function is constructed to optimize the whole underwater stereo vision system, which can reduce the error caused by the change of relative position between two cameras due to deformation of the device. The proposed method is easy to operate and the calibration can be completed by taking a calibration plate only once in the measured water area.
Thematic Correlation of Human Cognition and Artificial Intelligence

Artificial intelligence formally started in 1956 and the world today has seen one change that is from manual worked frameworks to computerized frameworks and it is still during the time spent change. This is the consequence of Artificial Intelligence where gigantic research is going on in creating insight in generally minor guidance based machines. Man-made consciousness is tied in with creating keen machines that can apply information like the human do. The objective of the study is (a) to understand the realms and dynamics of Human Cognition; (b) To comprehend the dynamics and impacts of Artificial intelligence and (c) To assess the correlation between Human Cognition and Artificial intelligence in contemporary world. The methodology followed is focus group interview with 15 experts from Dubai, followed by a thematic analysis of the data. The results highlight that AI and psychological science keep on preparing one another and Perception is one of the significant aspects of the cognition process and bases for Artificial intelligence in action.
A novel approach to automatic query reformulation for IR-based bug localization

Automatic query reformulation techniques for Information Retrieval based Bug Localization (IRBL) have been proposed to improve the quality of queries and IRBL performance. Recently proposed techniques determine the quality of queries via the bugs' description and reformulate them using important terms in the top-N source files retrieved by the initial query. However, the bugs' description may not contain enough information about the bugs, and the retrieved top-N files may not always provide important terms. In this paper, we propose a novel automatic query reformulation approach to improve IRBL performance beyond that of a recent technique. Our method expands bug reports using attachments and expands queries by reducing the noisy terms in them. We experimented with 1,546 bug reports. According to our results, we found that the quality of 70 reports was wrongly determined, and our method improved IRBL performance by up to 118% for these reports. Moreover, compared with a state-of-the-art technique, our method resulted in improvements of approximately 17% in Top-1, 11% in [email protected], and 10% in [email protected]
Spectral analysis for the generalized least squares phase-shifting algorithms with harmonic robustness

We introduce the frequency transfer function (FTF) formalism for generalized least squares phase-shifting algorithms (GLS-PSAs), whose phase shifts are nonuniformly spaced. The GLS-PSA&#x2019;s impulsive response is found by computing the Moore&#x2013;Penrose pseudoinverse. FTF theory allows analyzing these GLS-PSAs spectrally, as well as easily finding figures of merit such as signal-to-noise ratio (SNR) and harmonic rejection capabilities. We show simulations depicting that the SNR slightly decreases as the harmonic rejection robustness improves.
Social recommendation: A user profile clustering‐based approach

Summary The recommendation in information systems is a specific form of information filtering that aims to present the relevant information interesting the user. This technique is used in different contexts such as social networking, e‐commerce and information retrieval. Generally, existing recommender system techniques implement collaborative filtering by deducing a part of user interests from the preferences of other users with similar profiles. Many techniques can be used to implement Collaborative Filtering such as Bayesian Networks, latent semantic, and clustering. We present in this work a novel clustering approach using a modified partitional algorithm. We propose a user model that integrates the relevant user information and a clustering algorithm that generates groups of similar user profiles by implementing a profile similarity function. The proposed approach is then evaluated based on a set of user profiles data corresponding to the context of an e‐commerce website.
Modeling and Estimation of Non-functional Properties: Leveraging the Power of QoS Metrics

Non-Functional Properties (e.g., safety, dependability or resource consumption, just to name a few), play a key role in most software systems. The RoQME Integrated Technical Project, funded by the EU H2020 RobMoSys Project, aims at contributing a model-driven tool-chain for dealing with system-level non-functional properties through the specification of global quality-of-service (QoS) metrics. The estimation of these metrics at runtime, in terms of the available contextual information, can then be used for different purposes, such as dynamic software adaptation or benchmarking. This paper describes the advances achieved in RoQME and presents one of the pilot experiments developed to showcase the tool-chain developed as part of the project.
Learning Action Images Using Deep Convolutional Neural Networks For 3D Action Recognition

Recently, 3D action recognition has received more attention of research and industrial communities thanks to the popularity of depth sensors and the efficiency of skeleton estimation algorithms. Accordingly, a large number of methods have been studied by using either handcrafted features with traditional classifiers or recurrent neural networks. However, they cannot learn high-level spatial and temporal features of a whole skeleton sequence exhaustively. In this paper, we proposed a novel encoding technique to transform the pose features of joint-joint distance and joint-joint orientation to color pixels. By concatenating the features of all frames in a sequence, the spatial joint correlations and temporal pose dynamics of action appearance are depicted by a color image. For learning action models, we adopt the strategy of end-to-end fine-tuning a pre-trained deep convolutional neural networks to completely capture multiple high-level features at multi-scale action representation. The proposed method achieves the state-of-the-art performance on NTU RGB+D, the largest and most challenging 3D action recognition dataset, for both the cross-subject and cross-view evaluation protocols.
Focus+context map labeling with optimized clutter reduction

Zooming is a basic operation that many digital maps support for exploring them interactively. Especially for maps on small screen devices, this is a helpful operation to uncover the user's region of interest possibly hidden by labels, e.g. points of interest represented by icons. However, by scaling the map larger the user loses the context. As a consequent the user might need to repeatedly zoom in and out to explore the map step by step.We present an approach that reduces the necessity of zooming by providing the user with the possibility of displacing the labels of a circular focus region. To that end, we utilize techniques from focus+context maps implementing the displacement of the labels by fish-eye projections. The visual association between labels and their point features is established by connecting lines aggregated to bundles. Our approach particularly guarantees that labels move smoothly when the user continuously shifts the focus region, which reduces distracting flickering effects while exploring the map by panning the map view. Further, when the user stops moving the focus region, mathematical programming is applied to optimize positions of the displaced labels. In an evaluation on real-world data and synthetically generated data, we show that our approach substantially increases the legibility of both the focus region and the displaced labels.
Density-based label placement

We introduce a versatile density-based approach to label placement that aims to put labels in uncluttered areas of an underlying 2D visualization. Our novel, image-space algorithm constructs a density map by applying kernel density estimation to the input features, i.e., the locations of the points to be labeled. In order to find a suitable position for a label where it does not overlap any features or other labels, we move it following the gradient descent of this density map. This guides labels toward nearby areas of low feature density, resulting in a layout where labels are spread around feature-dense areas. The gradient descent trajectory can be used to draw curved leaders that connect the point features to their labels. Additionally, our approach supports prioritized label placement, user-defined label-to-label and label-to-feature margins, obstacle-constrained labeling, and arbitrarily shaped labels. The proposed method is conceptually simple and can easily be implemented using OpenCV and image-processing libraries.
Generative adversarial network based image privacy protection algorithm

For personal-oriental applications, the outstanding recognition ability has caused great concern for privacy disclosure. Existing methods mainly protect the privacy by adding occlusion or noise to the original images before uploading, which is either impractical or destroying the utility of the photos. In this paper, we propose a Generative Adversarial Network (GAN) based image privacy protection algorithm PriGAN, which generates a privacy image for each original image to fool the recognition networks. When generating the privacy image, we consider the technique of adversarial image perturbations (AIP), which could confuse recognition networks with slight perturbations. That is, the privacy image could protect the privacy information by confusing the neural network hosted by the service providers. Meanwhile, the privacy image appears unmodified compared to the original one for human observers, and thus its utility could be preserved. The advantage of PriGAN is that it provides a general privacy protection framework for personal applications, by which privacy information of images can be protected without destroying the image utility. The experiment shows that the privacy images are misclassified with approximate 82.9% higher than the original images which indicate that our approach can prevent the privacy from leakage with considerable improvement.
Association rule mining using chaotic gravitational search algorithm for discovering relations between manufacturing system capabilities and product features

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm–based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm; the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm–based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm–based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm–based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm–based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.
Single image super-resolution incorporating example-based gradient profile estimation and weighted adaptive p-norm

Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from only one observed low-resolution (LR) image. It is a severely ill-posed problem that needs image priors to ensure a reliable HR estimation. Since different priors usually emphasize different aspects of image characteristics, it’s a big challenge to impose a balanced-and-overall image property constraint on single image SR. In this paper, we cast single image SR into a maximum a posteriori optimization problem and combine two types of complementary priors to answer this challenge. One prior is a novel local gradient field prior derived from example-based gradient field estimation (EGFE) that focuses on recovering the sharpness of gradient profiles. It is good at enhancing the edge sharpness and restoring fine texture details. Whereas the other is a powerful non-local low rank prior implemented in a weighted adaptive p-norm model (WANM). By imposing lp penalties adaptive to regional saliency and weighted constraints, the WANM prior performs well in preserving edge smoothness and suppressing image noise and reconstruction artifacts. An improved Split Bregman Iteration method that adaptively attenuates the regularization strength is further developed to solve the proposed EGFE-WANM SR problem. Comprehensive experiments are conducted and the results show that the proposed EGFE-WANM SR method outperforms many state-of-the-art methods in both objective evaluations and subjective visual comparisons.
Block-Based Hough Transform for Recognition of Zebra Crossing in Natural Scene Images

In this paper, a block-based Hough transform is proposed to recognize the zebra crossing in natural scene images. Overlapping blocks are laid on the region of interest (ROI) in each image. For each patch in the block, two processes are performed successively. First, preprocessing is adopted for edge detection, whereas the adaptive thresholding is used to minimize the effect of various shadows. Second, parallel lines detection is adapted to recognize the zebra crossing, whereas the Hough transform is used for straight lines detection. When all the blocks are processed, the angles of parallel lines are averaged to provide the direction of the zebra crossing, and the accumulative scores are synthesized to provide the position of the zebra crossing. The performance of the proposed method is evaluated by testing results based on numerous images.
Air-to-ground multimodal object detection algorithm based on feature association learning

Detecting objects on unmanned aerial vehicles is a hard task, due to the long visual distance and the subsequent small size and lack of view. Besides, the traditional ground observation manners based on visible light camera are sensitive to brightness. This article aims to improve the target detection accuracy in various weather conditions, by using both visible light camera and infrared camera simultaneously. In this article, an association network of multimodal feature maps on the same scene is used to design an object detection algorithm, which is the so-called feature association learning method. In addition, this article collects a new cross-modal detection data set and proposes a cross-modal object detection algorithm based on visible light and infrared observations. The experimental results show that the algorithm improves the detection accuracy of small objects in the air-to-ground view. The multimodal joint detection network can overcome the influence of illumination in different weather conditions, which provides a new detection means and ideas for the space-based unmanned platform to the small object detection task.
Cryptanalysis of He’s quantum private comparison protocol and a new protocol

Recently, He proposed a novel quantum private comparison protocol without a third party (G. P. He, Int. J. Quantum Inf. 15(2) (2016) 1750014). This paper points out that two security loopholes in He’s protocol are existent. And a new QPC protocol which can avoid these loopholes is proposed without the help of a third party in this paper.
Smoothing policies and safe policy gradients

Abstract Policy gradient (PG) algorithms are among the best candidates for the much-anticipated applications of reinforcement learning to real-world control tasks, such as robotics. However, the trial-and-error nature of these methods poses safety issues whenever the learning process itself must be performed on a physical system or involves any form of human-computer interaction. In this paper, we address a specific safety formulation, where both goals and dangers are encoded in a scalar reward signal and the learning agent is constrained to never worsen its performance, measured as the expected sum of rewards. By studying actor-only PG from a stochastic optimization perspective, we establish improvement guarantees for a wide class of parametric policies, generalizing existing results on Gaussian policies. This, together with novel upper bounds on the variance of PG estimators, allows us to identify meta-parameter schedules that guarantee monotonic improvement with high probability. The two key meta-parameters are the step size of the parameter updates and the batch size of the gradient estimates. Through a joint, adaptive selection of these meta-parameters, we obtain a PG algorithm with monotonic improvement guarantees.
I/O Is Faster Than the CPU

I/O is getting faster in servers that have fast programmable NICs and non-volatile main memory operating close to the speed of DRAM, but single-threaded CPU speeds have stagnated. Applications cannot take advantage of modern hardware capabilities when using interfaces built around abstractions that assume I/O to be slow. We therefore propose a structure for an OS called parakernel, which eliminates most OS abstractions and provides interfaces for applications to leverage the full potential of the underlying hardware. The parakernel facilitates application-level parallelism by securely partitioning the resources and multiplexing only those resources that are not partitioned.