Reviewerly/exHarmony huggingface dataset:
Bidirectional and Malleable Proof-of-Ownership for Large File in Cloud Storage

Cloud storage is a cost-effective platform to accommodate massive data at low cost. However, advances of cloud services propel data generation, which pushes storage servers to its limit. Deduplication is a popular technique enjoyed by most current cloud servers, which detects and deletes redundant data to save storage and bandwidth. For security concerns, proof-of-ownership (PoW) can be used to guarantee ownership of data such that no malicious user could pass deduplication easily or utilize such mechanism for malicious purposes. Generally, PoW is implemented in static data archive where the data file is supposed to be read-only. However, to satisfy users’ needs for dynamical manipulation on data and support real-time data services, it is required to devise efficient PoW for dynamic archive. Inspired by malleable signature, which offers authentication even after its committed message changes, we propose the notion of bidirectional and malleable proof-of-ownership ( <inline-formula><tex-math notation="LaTeX">$\sf {BM\mbox{-}PoW}$</tex-math></inline-formula> ) for the above challenge. Our proposed <inline-formula><tex-math notation="LaTeX">$\sf {BM\mbox{-}PoW}$</tex-math></inline-formula> consists of bidirectional PoW ( <inline-formula><tex-math notation="LaTeX">${\mbox{B-PoW}}$</tex-math></inline-formula> ), malleable PoW ( <inline-formula><tex-math notation="LaTeX">${\mbox{M-PoW}}$</tex-math></inline-formula> ) and dispute arbitration protocol <inline-formula><tex-math notation="LaTeX">$\sf {DAP}$</tex-math></inline-formula> . While our <inline-formula><tex-math notation="LaTeX">${\mbox{B-PoW}}$</tex-math></inline-formula> is proposed for a static setting, the <inline-formula><tex-math notation="LaTeX">${\mbox{M-PoW}}$</tex-math></inline-formula> caters specifically for dynamic manipulation of data. In addition, our proposed arbitration protocol <inline-formula><tex-math notation="LaTeX">$\sf {DAP}$</tex-math></inline-formula> achieves accountable redaction which can arbitrate the originality of file ownership. We provide the security analysis of our proposal, and performance evaluation that suggests our proposed <inline-formula><tex-math notation="LaTeX">${\mbox{B-PoW}}$</tex-math></inline-formula> is secure and efficient for large file in static data archive. In addition, our proposed <inline-formula><tex-math notation="LaTeX">${\mbox{M-PoW}}$</tex-math></inline-formula> achieves acceptable performance under dynamic setting where data is supposed to be outsourced first and updated later in dynamic data archive.
A full‐reference stereoscopic image quality assessment index based on stable aggregation of monocular and binocular visual features

In stereoscopic image quality assessment, human visual system has been universally taken into account to detect perceptual characteristics. A novel full-reference stereoscopic image assessment metric by considering both monocular and binocular visual features of human visual system is proposed. In particular, a new region segmentation algorithm is firstly proposed to divide 3D images into occluded and non-occluded regions. The just noticeable difference model is employed on the occluded regions to formulate the monocular vision, while the binocular just noticeable difference model is applied to the non-occluded regions to reveal the binocular vision of the human visual system. In the proposed region segmentation, disparity information and Euclidean distance between stereo pairs are both adopted to solve the unstable segmentation problem of traditional methods. A new pooling strategy based on global edge features is then presented to aggregate the just noticeable difference and binocular just noticeable difference evaluation maps. In addition, some local image features as supplementary of just noticeable difference to describe visual characteristics of the human visual system are also extracted. Finally, an overall quality score is calculated based on the above-mentioned features to measure the visual quality of distorted stereo pairs. Experimental results show that the proposed metric achieves high consistency with the human visual system, and outperforms state-of-the-art algorithms on stereoscopic image quality assessment.
Design and Analysis of a Novel Authorship Verification Framework for Hijacked Social Media Accounts Compromised by a Human

Compromising the online social network account of a genuine user, by imitating the user’s writing trait for malicious purposes, is a standard method. Then, when it happens, the fast and accurate detection of intruders is an essential step to control the damage. In other words, an efficient authorship verification model is a binary classification for the investigation of the text, whether it is written by a genuine user or not. Herein, a novel authorship verification framework for hijacked social media accounts, compromised by a human, is proposed. Significant textual features are derived from a Twitter-based dataset. They are composed of 16124 tweets with 280 characters crawled and manually annotated with the authorship information. XGBoost algorithm is then used to highlight the significance of each textual feature in the dataset. Furthermore, the ELECTRE approach is utilized for feature selection, and the rank exponent weight method is applied for feature weighting. The reduced dataset is evaluated with many classifiers, and the achieved result of the F-score is 94.4%.
Parameter estimation for a class of radial basis function-based nonlinear time-series models with moving average noises

This paper focuses on the parameter estimation for radial basis function-based state-dependent autoregressive models with moving average noises (RBF-ARMA models). An extended projection algorithm is derived based on the negative gradient search. In order to reduce the sensitivity of the algorithm to noise and reduce the fluctuations of the parameter estimation errors, a modified extended stochastic gradient algorithm is proposed. By introducing a moving data window, a modified moving data window-based extended stochastic gradient algorithm is further developed to improve the parameter estimation accuracy. The simulation results show that the proposed algorithms can effectively estimate the parameters of the RBF-ARMA models.
An Integrated Blockchain Approach to Model Secure Internet of Things based Systems

Internet of Things (IoT) made a tremendous change in the field of automation technologies. Objects or Things in the real world can be converted into a `smart object' by attaching some sensors or actuators. These devices are capable of communicating each other using internet. Light weight protocols make the IoT system more user friendly. But one of the critical challenges faced by any IoT designer is to provide the data security for IoT applications. Though the `things' are connected to a network, there is a high chance of data stealing or data manipulation. Block chain can be used to ensure data security, scalability and reliability of IoT applications. We propose a secure blockchain based IoT architecture to assure data security. Blockchain is utilized for tracking and coordinating millions of connected components. Blockchain uses decentralized approach which can prevent from single points of failures thereby creates a stable interoperable secure system. Cryptographic algorithms can be used for securing the sensor data. Thus, our model enhances not only security but also the privacy concerns of an end user.
A serious VR game for acrophobia therapy in an urban environment

Much of the costs and dangers of exposure therapy in phobia treatment can be removed through virtual reality (VR). Exposing people to heights, for instance, might sound easy, but it still involves time and money investments to reach a tall building, mountain or bridge. People suffering from milder forms of acrophobia might not even be treated at all, the cost not being worth it. This paper presents a prototype that allows exposure therapy to be done in a controlled environment, in a more comfortable, quick and cheaper way. By applying acrophobia questionnaires, collecting biophysical data and developing a virtual reality game, we can expose volunteers to heights and analyze if there is any change in their fear and anxiety levels. This way, regardless of the initial anxiety level and phobia severity, we can check if there is any post-therapy improvement and verify if virtual reality is a viable alternative to real-world exposure.
Improving neural network robustness through neighborhood preserving layers

High-dimensional embeddings are often projected via fully connected layers while training neural networks. A major vulnerability that makes neural networks fail to be robust against adversarial attack is their use of overparameterized fully connected layers. We present a dimension reducing layer which preserves high-dimensional neighborhoods across the entire manifold. Atypically, our neighborhood preserving layer operates on non-static high dimensional inputs and can be trained efficiently via gradient descent. Our interest is in developing a trainable manifold representation, whose low-dimensional embeddings can be re-used for other purposes, and in investigating its robustness against adversarial attack. Our layer internally uses nearest-neighbor attractive and repulsive forces to create a low dimensional output representation. We demonstrate a novel neural network architecture which can incorporate such a layer, and also can be trained efficiently. Our theoretical results show why linear layers, which have many parameters, are innately less robust. This is corroborated by experiments on MNIST and CIFAR10 replacing the first fully-connected layer with a neighborhood preserving layer by our proposed model.
Contact-Tracing Approaches to Fight Covid-19 Pandemic: Limits and Ethical Challenges

COVID-19 pandemic is the largest worldwide sanitary and economical crisis of modern time.According to the World Health Organization (WHO), the new corona virus is spreading rapidly from person to person through contact transmission.To fight the COVID-19 outbreak, a number of strategies have been implemented at the population level, such as social distancing, testing and contact tracing.In this work, we focus on contact tracing, which is considered an effective public health measure to control the spread of this disease by identifying and tracking potential persons who may have been in contact with a COVID-19 infected person.To this end, we review various approaches and existing contact tracing applications used by various governments around the world to monitor and control the spread of this disease.Then, we discuss the limits and ethical issues of such digital contact tracing solutions.
Target Tracking Applied to Extraction of Multiple Evolving Threats From a Stream of Surveillance Data

Many threats (terrorist attacks, military actions, etc.) can be modeled by someone with relevant expert knowledge. A “threat” here implies a sequence of actions that evolve over time and are intended to culminate in a goal that from the article's perspective is unfavorable. This work presents a method to model probabilistically these types of processes using hidden Markov models (HMMs). We thence present a detection scheme based on random finite set (RFS) filters-specifically a multi-Bernoulli approach-that allows for detection of multiple threat processes using a single observed data stream. Key here is that associated with threats are a list of entities that are a priori unknown and must be inferred, but once (probabilistically) identified, aid greatly in the data association step, and inference on the perceived threat.
Varying spatial orientation of photoelectric panels as method of managing graph of electric energy generation

Abstract One of the main issues in the determining layout of photovoltaic installations is choosing optimal parameters for photovoltaic panels and auxiliary equipment and duplicating energy sources, which makes it possible to justify economically the electrical energy generation. Regularity of solar radiation variation for the studied geographical point throughout the year, and the influence of atmospheric phenomena, as well as the dynamics of daily and seasonal electrical energy consumption must be considered, which will help to predict the operation mode of the plant, and match generation and consumption schedules. Variation of tilt angle of the photovoltaic module receiving surface relatively to horizon and cardinal points makes it possible to manage the schedule of power generation at solar plant and to match it with the load curve, due to the maximum possible convergence of them, as change in the spatial orientation of the photovoltaic panel surface makes it possible to shift the maximum of solar radiation flux in time. The results of the experimental research also revealed the dependence of the current produced by the photoelectric panel on its spatial orientation due to the change of the incoming solar radiation flux on it.
Single-Image Snow Removal Based on an Attention Mechanism and a Generative Adversarial Network

Bad weather, such as snowfall, can seriously decrease the quality of images and pose great challenges to computer vision algorithms. In view of the negative effect of snowfall, this paper presents a single-image snow removal method based on a generative adversarial network (GAN). Unlike previous GANs, our GAN includes an attention mechanism in the generator component. By injecting attention information, the network can pay increased attention to areas covered by snow and improve its capability to perform local repairs. At the same time, we improve the traditional U-Net network by combining it with the residual network to enhance the effect of the model when removing snowflakes from a single image. Our experiments on both synthetic and real-word images show that our method produces better results than those of other state-of-the-art methods.
Privacy Preserving Facial Recognition Against Model Inversion Attacks

Machine learning has a vast outreach in principal applications and uses large amount of data to train the models, prompting a viable and easy to use Machine Learning as a Service (MLaaS). This flexible paradigm however, could have immense privacy implications since the training data often contains sensitive features, and adversarial access to such models could pose a security risk. In adversarial attacks such as model inversion attack on a system used for face recognition, an adversary uses the output (target label) to reconstruct the input (image of the target individual from the training dataset). To avert such a vulnerability of the system, in this paper, we develop a novel approach of applying perceptual hash to parts of the given training images that leverages the functional mechanism of image hashing. The facial recognition system is then trained over this newly created dataset of perceptually hashed images and high classification accuracy is observed. Furthermore, we demonstrate a series of model inversion attacks emulating adversarial access that yield hashed images of target individuals instead of the original training dataset images; thereby preventing original image reconstruction and counteracting the inversion attack. Through rigorous empirical evaluations of applying the proposed formulation over real world dataset, we verify the effectiveness of our proposed framework in protecting the training image dataset and counteracting inversion attack.
Decision Support System Tool for Arabic Text Recognition

The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative approach in selecting the highest score using three Arabic text extraction algorithms: term frequency-inverse document frequency measure algorithm, Cortical.io tool with Retina Database, and keyword extraction using word co-occurrence algorithm. The data repository consisted of 1000 datasets built from five different faculties at King Abdul-Aziz University and King Faisal University. It was followed by a discussion of the evaluation techniques using precision and recall measurements, which indicated that the keyword extraction using word co-occurrence algorithm scored 90% for the English language and 80% for the Arabic language in terms of the F1 measure that focuses on the linguistic relation between words.
Device-Free Crowd Counting Based on the Phase Difference of Channel State Information

Estimating the number of people accurately in an area of interest plays a critical role in various applications. In this paper, we perform crowd counting from 0 to 7 persons with a closed meeting room scenario. Triple processes (linear transformation, the phase difference extraction and Savitzy-Golay filtering) are performed on the CSI raw phase so that we can successfully extract the features related to the human moving, eliminate environment interferences and phase errors effectively. We propose the support vector machine (SVM) model based on the phase difference expanded matrix of channel state information (CSI) between adjacent receiving antennas. The mean and standard deviation of the phase difference series calculated in a time window at each time spot are added into the initial phase difference matrix. The proposed phase difference expansion matrix can improve the accuracy and robustness in the subsequent SVM classification. This model achieves 98.82% counting accuracy with only a maximum of 1-count-difference.
RPCA-induced self-representation for subspace clustering

The self-expressiveness property of the data, i.e., each sample is linearly represented as a combination of other samples, has recently aroused much attention in the community of data mining and machine learning and shown great promising for subspace clustering. However, real-world data are usually contaminated by noise and outliers. The true similarity between samples directly learned from the original data may deviate from the intrinsic structure of the data, and subsequent clustering results will be severely affected. Hence naively taking a corrupted dictionary, i.e., data itself, will not always obtain the desired clustering performance. To address the above issues, this paper proposes a robust principal component analysis induced self-representation clustering method via adaptively learning the similarity graph from the clean data in the self-representation framework, where the original data are recovered, and the representation coefficients are obtained simultaneously in a unified framework. Specifically, we jointly integrate a nonnegative constraint and a distance regularization into the proposed framework, which guarantees the learned affinity matrix can simultaneously capture the global and local structure of data. Experimental results on both synthetic data and several famous multimedia datasets demonstrate that the proposed method performs much better against state-of-the-arts.
A two-way photonic quantum entanglement transfer interface

Abstract A quantum interface for two-way entanglement transfer, e.g., between orbital angular momentum degree of freedom in free space and time-energy degree of freedom in optical fibers, provides a way toward establishing entanglement between remote heterogeneous quantum nodes. Here, we experimentally demonstrate this kind of transfer interface by using two interferometric cyclic gates. By using this quantum interface, we perform two-way entanglement transfer for the two degrees of freedom. The results show that the quantum entangled state can be switched back and forth between orbital angular momentum and time-energy degrees of freedom, and the fidelity of the state before and after switching is higher than 90%. Our work demonstrates the feasibility and high performance of our proposed transfer interface, and paves a route toward building a large-scale quantum communication network.
HsClone Genetic Algorithm Implementation on a Combinational Circuit

Digital circuits play a major role in the design of future Space Vehicles. The future planetary and deep Space exploration requires the spacecraft components to have robust system architectures and the components to tackle the unpredictable environment such as Van Allen Belts to avoid Single Event Upset (SEU). So there is a pressing need to tackle the effects of radiation, temperature on the on-board satellites. The best Solution is the use of Evolutionary algorithm (EA). EAs allow the designer to obtain the desired circuit through the process of randomization. Genetic Algorithm (GA) is the one that employs the Evolvable Hardware (EHW) technique that operates in the space of possible circuits and determines the solution that specifies the Imposed specification. HsClone GA is a half-sibling-clone technique that manages the fitness probabilities to the chromosomes.
Correlation feature and instance weights transfer learning for cross project software defect prediction

Due to the differentiation between training and testing data in the feature space, cross-project defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot-spot for building classifiers in the target domain using the data from the related source domains. To implement better CPDP models, recent studies focus on either feature transferring or instance transferring to weaken the impact of irrelevant cross-project data. Instead, this work proposes a dual weighting mechanism to aid the learning process, considering both feature transferring and instance transferring. In our method, a local data gravitation between source and target domains determines instance weight, while features that are highly correlated with the learning task, uncorrelated with other features and minimizing the difference between the domains are rewarded with a higher feature weight. Experiments on 25 real-world datasets indicate that the proposed approach outperforms the existing CPDP methods in most cases. By assigning weights based on the different contribution of features and instances to the predictor, the proposed approach is able to build a better CPDP model and demonstrates substantial improvements over the state-of-the-art CPDP models.
Quantum Circuits for Binary Convolution

Deep learning is used in different research topics and draws attention with its successful results. Today, the resource need of deep learning, which requires much data and resources, is tried to be met by technologies such as parallel processors and graphic processors. In this study, the convolution process, which is one of the main causes of resource need in deep learning, is performed with quantum circuits. Thanks to the convolution process run on quantum computers, whose computation speeds are many times higher than supercomputers, the training step of deep learning can be performed much faster. In this way, the need for performance, which is one of the biggest obstacles in front of deep learning and forces researchers to work on pre-trained networks, will be met. With running deep learning networks on quantum computers, it is predicted by the scientific world that a new era for deep learning will start. Although we propose quantum circuits for the convolution process with 2, 3, and 4 window sizes, it can be used for only one iteration today. However, with obtaining structures such as quantum ram and developing sequential processing architecture for quantum computers, the proposed circuits can be used for a complete convolution process.