Reviewerly/exHarmony huggingface dataset:
Color refinement using deep neural networks for enhancing color recognition in a projector‐camera system

Abstract In projector‐camera systems, object recognition is essential to enable users to interact with physical objects. Among several input features used by the object classifier, color information is widely used as it is easily obtainable. However, the color of an object seen by the camera changes due to the projected light from the projector, which degrades the recognition performance. To solve this problem, we propose a method to restore the original color of an object from the observed color through camera. The color refinement method has been developed based on the deep neural network. The inputs to the neural network are the color of the projector light as well as the observed color of the object in multiple color spaces, including RGB, HSV, HIS, and HSL. The neural network is trained in a supervised manner. Through a number of experiments, we show that our refinement method reduces the difference from the original color and improves the object recognition rate implemented with a number of classification methods.
XBAC: A Unified Access Control Model for Heterogeneous Multi-Tenancy Cloud Environments

Cloud computing provides organizations with computing resources featuring easy deployment, connectivity, configuration and scalability. Multi-tenancy permits cloud providers to establish collaborative relationships among cloud users, especially at the storage service level. To satisfy security requirements among collaborating tenants, each tenant may define a set of access control policies to secure access to its shared data. However, tenants may enforce their policies using heterogeneous access control models (e.g RBAC and ABAC), which may be difficult to manage by cloud providers. Hence, There is a need of a cloud system that acts as an intermediary access control model in order to resolve the heterogeneous structure of other models. This paper presents a novel unified access control model for heterogeneous multi-tenancy architectures in the cloud environment. This model, called XBAC, can manage heterogeneous access control policies. The proposed model uses a learning automata to represent access policies and requests. XBAC is integrated in the cloud environment using a cloud broker. In addition, the paper defines a basic architecture that defines the interaction between the main components of heterogeneous cloud systems. Initial simulation results show that the proposed model meets the requirements of the heterogeneous access models environment, by preventing access of unauthorized users to the resources of multi-tenancy cloud services.
Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse noise is handled using appropriate use of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm along-with use of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm in a convex cost function. For optimization of the cost function, we propose an iteratively reweighted least-squares (IRLS) approach that is suitable for kernel substitution or kernel trick due to availability of a closed form solution. Simulations using real-world temperature data show efficacy of our proposed method, mainly for limited-size training datasets.
Bloofi Representation for Item/User in Recommender Systems

In this paper, we propose new algorithms to improve the performance of recommender systems, based on hierarchical Bloom filters. Since Bloom filters can make a tradeoff between space and time, proposing a new hierarchical Bloom filter causes a remarkable reduction in space and time complexity of recommender systems. Space reduction is due to hashing items in a Bloom filter to manage the sparsity of input vectors. Time reduction is due to the structure of hierarchical Bloom filter. To increase the accuracy of the recommender systems we use Probabilistic version of hierarchical Bloom filter. The structure of hierarchical Bloom filter is B+ tree of order d. Proposed algorithms not only decrease the time complexity but also have no significant effect on accuracy.
A Quasi-Isometric Embedding Algorithm

The Whitney embedding theorem gives an upper bound on the smallest embedding dimension of a manifold. If a data set lies on a manifold, a random projection into this reduced dimension will retain the manifold structure. Here we present an algorithm to find a projection that distorts the data as little as possible.
A Weakly-Supervised Approach for Discovering Common Objects in Airport Video Surveillance Footage

Object detection in video is a relevant task in computer vision. Standard and current detectors are typically trained in a strongly supervised way, what requires a huge amount of labelled data. In contrast, in this paper we focus on object discovery in video sequences by using sets of unlabelled data. Thus, we present an approach based on the use of two region proposal algorithms (a pretrained Region Proposal Network and an Optical Flow Proposal) to produce regions of interest that will be grouped using a clustering algorithm. Therefore, our system does not require the collaboration of a human except for assigning human understandable labels to the discovered clusters. We evaluate our approach in a set of videos recorded at apron area, where the aeroplanes park to load passengers and luggage. Our experimental results suggest that the use of an unsupervised approach is valid for automatic object discovery in video sequences, obtaining a CorLoc of 86.8 and a mAP of 0.374 compared to a CorLoc of 70.4 and mAP of 0.683 achieved by a supervised Faster R-CNN trained and tested on the same dataset.
A response to fake news as a response to <i>Citizens United</i>

How boundaries on speech could free the market for speech.
De-anonymize social network under partial overlap

De-anonymizing social networks has become a direct threat to people's privacy . Actually, It can be boiled down to graph matching problems. The attackers steal users' real information in anonymized social networks by mapping them to secondary cross-domain networks. In particular, when partial node identity in the anonymized network is known, such attacks will become more powerful.
Discriminative supplementary representation learning for novel-category classification

In this paper, we propose a new representation learning method by learning the discriminative supplementary features to implement semantic attribute augmentation for novel-category image classification. Most of previous methods based on semantic attributes explore the manually defined attributes, which limits their performance and applications. Because the number of manually defined attributes is limited in practice. Besides, these attributes are incomplete and may be not discriminative for image classification. To address the problems, one intuitive solution is to automatically expand the semantic attribute representation. However, it is challenging to guarantee that the supplementary features are more effective and discriminative. Towards this end, we propose a new Discriminative Supplementary Feature Learning (DSFL) method to expand the manually defined attributes by learning discriminative supplementary features. To make the learned features discriminative, the supplementary features and the classifiers for the novel-categories are jointly learned with the column sparsity constraints for the optimal compatibility of the representations and classifiers. To demonstrate the effectiveness of the proposed method, extensive experiments are conducted on two widely-used datasets. The experimental results show that the proposed method achieves encouraging performance.
The complexity of computing all subfields of an algebraic number field

For a finite separable field extension K/k, all subfields can be obtained by intersecting so-called principal subfields of K/k. In this work we present a way to quickly compute these intersections. If the number of subfields is high, then this leads to faster run times and an improved complexity.
Four-Directional Detection-Based Gaussian Noise Removal

Detection of gaussian noise is an exigent task, especially at high noise density levels. This paper proposes a four-directional noise detection scheme towards the noise removal process. A calculated threshold value is undertaken in the detection scheme. Moreover, a four-stage segmentation scheme is applied to check the noise contamination level of the pixel using the calculated threshold value. A binary flag image is generated after identifying the noise contamination and ultimately sequentially combined mean–median filter (SCMMF) is used for the removal purpose. Results of the computer simulations demonstrate the effectiveness of the proposed methods even at high noise density levels.
Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion

In this work, we address fusion of heterogeneous sensor data using wavelet-based summaries of fused self-similarity information from each sensor. The technique we develop is quite general, does not require domain specific knowledge or physical models, and requires no training. Nonetheless, it can perform surprisingly well at the general task of differentiating classes of time-ordered behavior sequences which are sensed by more than one modality. As a demonstration of our capabilities in the audio to video context, we focus on the differentiation of speech sequences. Data from two or more modalities first are represented using self-similarity matrices(SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality. A fused similarity template is then derived from the modality-specific SSMs using a technique called similarity network fusion (SNF). We investigate pipelines using SNF as both an upstream (feature-level) and a downstream (ranking-level) fusion technique. Multiscale geometric features of this template are then extracted using a recently-developed technique called the scattering transform, and these features are then used to differentiate speech sequences. This method outperforms unsupervised techniques which operate directly on the raw data, and it also outperforms stovepiped methods which operate on SSMs separately derived from the distinct modalities. The benefits of this method become even more apparent as the simulated peak signal to noise ratio decreases.
Security Validation of Cloud Based Storage

In the era of big data and IoT, rate of data generation is very high which needs lot of space. So it is not feasible to store such huge data locally. Hence, to cater the large storage need cloud based storage models has evolved. With the increasing demand of cloud storage, occurrence frequency of security threats has increased exponentially. The steep rise in threat incidences cannot be ignored, as cloud storage model is being used for storing confidential data. Increased threat rate cause decline in the acceptance rate of cloud-based storage models besides it numerous benefits. In this paper, we are evaluating our existing proposal by applying it on an open source cloud based system. Evaluation is done by comparing the threats identified using our approach and threats reported on it by CVE.
Hardware-Oriented Algorithm and Architecture for Generative Adversarial Networks

The most successful generative tasks, such as image completion, generally rely upon generative adversarial networks (GANs). The hardware implementation of GANs has important requirements of low power and acceleration, but differ from a usual neural network by requiring a training phase. We developed a hardware-oriented training algorithm using a quantized stochastic gradient descent method without the use of a hardware-oriented training algorithm. From this result, we devised a GANs architecture requiring 7 bits for inference and 26 bits for the training phase, when using a resized MNIST dataset with a three layer perceptron for each network. We can achieve real-time processing when the architecture functions ideally; however, the bit width and process speed are affected by the network model.
Concept-Aware Web Image Compression Based on Crowdsourced Salient Object Detection

Reduced output quality and being unaware of content are among major issues with traditional image compression techniques. Such issues cause some critical problems when it comes to quality-intensive applications, including object/face detection and recognition, Web-based image viewers and management systems, etc. On the other side, efficiency of Web-based image search engines and retrieval systems in terms of user experience and usability could be affected. In order to cope with these challenges, a novel image compression method is proposed that takes advantages of collective human cognitive intelligence to detect the salient object(s) based on the recognized key concept(s). Then, other less-important regions/objects will be subject to the safe compression. Such an approach, besides preserving semantic aspects of the images that will result in smart (concept-aware) compression, could provide some crowdsourced labels for more efficient indexing and annotating of images. In this regard, two birds could be beaten with one stone: compressing Web images with respect to their content/concept and annotating them with crowd-suggested labels. The experimental results as well as user acceptance evaluation proved the efficacy of the introduced method.
ReFall: Real-Time Fall Detection of Continuous Depth Maps with RFD-Net

With the growth in the elderly population, fall detection methods for the elderly are of great significance. In this paper, we propose a deep learning-based method for real-time fall detection continuous depth maps with Residual Fall Detection Network (RFD-Net). Our method incorporates feature extraction with fall detection. In feature extraction part, seven important features that accurately represent the body posture are extracted from the depth maps to reduce the computation load. In the fall detection part, a novel RFD-Net is proposed to recognize body posture for fall detection. Meanwhile, two other networks are developed to compare with RFD-Net. The experimental results show that the extracted features are good representative of the body posture, and our method delivers performance with a fall detection accuracy of 98.51%, which is higher than other related methods.
A new approach in seismic behavior using fuzzy methods

In this work we revisit the subject of a seismic sequence but we use new innovative tools and so the conclusions extracted are more precise and more close to reality. Assuming that the major earthquake in a specified region has a certain intensity level, we study all the earthquakes, which follow, according to their magnitude and to the time difference from the main one. Therefore, taking a sample and collecting our data about the main incident and the seismic activity for the next days, we compute the deviation from the observations of each fuzzy implication of a set of certain implications in order to compare them and choose the most appropriate, using the compositional rule of inference, which is based on the generalized modus ponens.
Minimum entangling power is close to its maximum

Given a quantum gate U acting on a bipartite quantum system, its maximum (average, minimum) entangling power is the maximum (average, minimum) entanglement generation with respect to certain entanglement measures when the inputs are restricted to be product states. In this paper, we mainly focus on the 'weakest' one, i.e. the minimum entangling power, among all these entangling powers. We show that, by choosing the entropy of entanglement or Schmidt rank as entanglement measure, even the 'weakest' entangling power is generically very close to the maximum possible value of the entanglement measure. In other words, maximum, average and minimum entangling powers are generically close. We then study minimum entangling power with respect to other Lipschitiz-continuous (for the Hilbert space distance) entanglement measures and generalize our results to multipartite quantum systems.
Detection of Planted Solutions for Flat Satisfiability Problems

We study the detection problem of finding planted solutions in random instances of flat satisfiability problems, a generalization of boolean satisfiability formulas. We describe the properties of random instances of flat satisfiability, as well of the optimal rates of detection of the associated hypothesis testing problem. We also study the performance of an algorithmically efficient testing procedure. We introduce a modification of our model, the light planting of solutions, and show that it is as hard as the problem of learning parity with noise. This hints strongly at the difficulty of detecting planted flat satisfiability for a wide class of tests.
Parallel architecture design of genetic algorithms on HPC platform

In the genetic algorithm for compute-intensive task, the execution time of fitness function increases rapidly. As a result, when the population size or evolution population increases, the convergence speed of the algorithm is very slow. Based on the supercomputer platform, this paper designs and implements a parallel genetic algorithm architecture with dynamic migration strategy. A hybrid parallel genetic algorithm based on "coarse-grained and master-slave" two-level parallel architecture is proposed. The proposed architecture is composed of hybrid parallel programming models. Compared with the traditional genetic algorithm implemented on the simple MPI architecture, the proposed structure achieves better performance. Experiments show that the convergence speed of the algorithm is significantly improved.
Surgical tool trajectory optimization in brain tumour resection

During resection of tumours, which are located inside the brain, normal tissues are also destroyed. It may be reason of permanent brain damage and loss or lesion of some bodily functions. Therefore damages minimization during tumour removing is such important. Knowledge of the tumour localization gives opportunity to planning of path to the tumour and method of its resection. In this paper surgical tool trajectory optimization for brain tumour resection was presented. The computed trajectory is an element of surgical plan. It may be used to automatic brain tumour with usage of the surgical tool, which is mounted on the surgical robot arm. A mathematical description of brain tumour method was described in this paper. A quality indicator was formulated and an optimal trajectory of surgical tool was computed. Results of experiments were analysed and conclusions were presented.
Solving the Complexity of Geometry Friends by Using Artificial Intelligence

Artificial Intelligence is the most widely used in many applications. Especially, applications such as pattern recognition, gender classification and other types of applications. Geometry friends are characters used in the puzzle games and these are in various shapes such as circle or rectangle. Designing the agent based games with geometry friends and AI adopted techniques gives the better result. Many existing systems have the agent mis-understanding problem which may cause confusion between the geometry friends. In this paper, an amalgamative artificial intelligence and adopted with convolution neural networks technique developed a puzzle game to increase the tedious to the player.
The Role of Previous Discourse in Identifying Public Textual Cyberbullying

&lt;div data-canvas-width="619.2967614992452"&gt;In this paper we investigate the contribution of previous discourse in identifying elements that are key to detecting public textual cyberbullying. Based on the analysis of our dataset, we first discuss the missing cyberbullying elements and the grammatical structures representative of discourse-dependent cyberbullying discourse. Then we identify four types of discourse dependent cyberbullying constructions: (1) fully inferable constructions, (2) personal marker and cyberbullying link inferable constructions, (3) dysphemistic element and cyberbullying link inferable constructions, and (4) dysphemistic element inferable constructions. Finally, we formalise a framework to resolve the missing cyberbullying elements that proposes several resolution algorithms. The resolution algorithms target the following discourse dependent message types: (1) polarity answers, (2) contradictory statements, (3) explicit ellipsis, (4) implicit affirmative answers, and (5) statements that use indefinite pronouns as placeholders for the&lt;/div&gt;&lt;div data-canvas-width="146.57988069516077"&gt;dysphemistic element.&lt;/div&gt;
Product-review Classification Combining Multiple Clustering Algorithms

As product reviews accumulate more and more at online shopping sites, customers begin to have an increasing demand for analyzing reviews automatically. In some previous studies, clustering algorithms have been proved to be effective in grouping reviews. However, most of the existing systems are built based on a single clustering algorithm which might make the system fragile. In this paper, we have proposed a method to combine multiple clustering algorithms. Evaluation experiments have shown the effectiveness of our approach.
Making sense of the dark web

At the end of April, one of the world's biggest dark websites, Dream Market, was shut down. Whatever the reasons, it's unlikely to make a serious long-term dent in what is estimated to be a trillion-dollar industry. The dark web and the cybercrime economy it facilitates are a phenomenon of our times, offering all the tools and resources that cyber criminals need to launch attacks on consumers and organisations, and a ready-made platform on which to sell their wares.
Sports Coaching in Impoverished Communities Through the Use of Virtual Reality

The paper explores the potential of virtual reality to aid sports coaching within poverty-stricken communities in South Africa. The use of virtual reality has been tested in education and in the mining industry but not in sports coaching in South Africa. The paper tests the learning experience posed by virtual reality on a sample to review knowledge acquisition and retention of information. The results show that the virtual reality simulation created an immersive environment and enhanced the learning experience and is therefore a viable option to address the transformation of sports coaching in impoverished communities.
Improving One-Time Pad Algorithm on Shamir’s Three-Pass Protocol Scheme by Using RSA and ElGamal Algorithms

Abstract This study aims to cover the shortcomings of the one-time pad algorithm used in the Shamir’s three-pass protocol scheme. In this study, the technique used to cover the weakness of one-time pad algorithm is by changing each ciphertext generated from the three paths in the three pass protocol scheme. Ciphertext modification is performed by encrypting the cipher-text by using the RSA and Elgamal algorithms to generate super ciphertext. The first line and the third line are encrypted by using the RSA algorithm and the second line is using ElGamal algorithm. Both algorithms are aimed at keeping no secret key exchange which is a major requirement of the three pass protocol scheme. The evaluation results show that the weakness of one-time pad algorithm can be overcome. Although the processing time of three pass protocol increases, but the longer the plaintext character the closer the processing time ratio to one. The research showsThe only way for plaintext to be formed by cryptoanalysis is they must succeed to decrypt the three super ciphertexts back into the three initial ciphertexts first. But it is not easy if RSA and ElGamal algorithms use the big prime number.