Reviewerly/exHarmony huggingface dataset:
A policy-based containerized filter for secure information sharing in organizational environments

In organizational environments, sensitive information is unintentionally exposed and sent to the cloud without encryption by insiders that even were previously informed about cloud risks. To mitigate the effects of this information privacy paradox, we propose the design, development and implementation of SecFilter, a security filter that enables organizations to implement security policies for information sharing. SecFilter automatically performs the following tasks: (a) intercepts files before sending them to the cloud; (b) searches for sensitive criteria in the context and content of the intercepted files by using mining techniques; (c) calculates the risk level for each identified criterion; (d) assigns a security level to each file based on the detected risk in its content and context; and (e) encrypts each file by using a multi-level security engine, based on digital envelopes from symmetric encryption, attribute-based encryption and digital signatures to guarantee the security services of confidentiality, integrity and authentication on each file at the same time that access control mechanisms are enforced before sending the secured file versions to cloud storage. A prototype of SecFilter was implemented for a real-world file sharing application that has been deployed on a private cloud. Fine-tuning of SecFilter components is described and a case study has been conducted based on document sharing of a well-known repository (MedLine corpus). The experimental evaluation revealed the feasibility and efficiency of applying a security filter to share information in organizational environments.
Enabling Privacy-Preserving Sharing of Genomic Data for GWASs in Decentralized Networks

The human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In this work, we propose a preventive approach for privacy-preserving sharing of genomic data in decentralized networks for Genome-wide association studies (GWASs), which have been widely used in discovering the association between genotypes and phenotypes. The key components of this work are: a decentralized secure network, with a privacy- preserving sharing protocol, and a gene fragmentation framework that is trainable in an end-to-end manner. Our experiments on real datasets show the effectiveness of our privacy-preserving approaches as well as significant improvements in efficiency when compared with recent, related algorithms.
Progressive generative adversarial networks with reliable sample identification

Generative Adversarial Networks (GANs) are deep neural network architectures comprising of two neural networks, namely discriminator and generator, which contest with each other in a zero-sum game. In the past years, although original GANs and their variations have achieved impressive success, there are some challenges still remain, especially unstable training progress leading to gradient vanishing or saturation. We can show by inspection that the reliable samples with smaller errors are beneficial to achieve a better generator, while the unreliable one might disturb the training procedure. Enlightened from this observation, we introduce an indicator for each sample to indicate its reliability in this paper. Based on this, we exploit a new objective function to learn the generator/discriminator and infer the indicator for each sample simultaneously. In such a way, the unreliable samples that might result in the opposite side are discarded in training stage. Meanwhile, when the training errors become smaller, more and more samples are included in the reliable set of samples, until no more reliable one are produced. It is noteworthy that the proposed method is adapted to both the original GANs and its variations. Experiments on CIFAR-10, STL-10 and LSUN datasets demonstrate the state-of-the-art performance of the proposed framework with respect to GANs and its variations.
Multiple Laser Stripe Scanning Profilometry Based on Microelectromechanical Systems Scanning Mirror Projection

In traditional laser-based 3D measurement technology, the width of the laser stripe is uncontrollable and uneven. In addition, speckle noise in the image and the noise caused by mechanical movement may reduce the accuracy of the scanning results. This work proposes a new multiple laser stripe scanning profilometry (MLSSP) based on microelectromechanical systems (MEMS) scanning mirror which can project high quality movable laser stripe. It can implement full-field scanning in a short time and does not need to move the measured object or camera. Compared with the traditional laser stripe, the brightness, width and position of the new multiple laser stripes projected by MEMS scanning mirror can be controlled by programming. In addition, the new laser strip can generate high-quality images and the noise caused by mechanical movement is completely eliminated. The experimental results show that the speckle noise is less and the light intensity distribution is more even. Furthermore, the number of pictures needed to be captured is significantly reduced to 1 / N ( N is the number of multiple laser stripes projected by MEMS scanning mirror) and the measurement efficiency is increased by N times, improving the efficiency and accuracy of 3D measurement.
Online User Distribution-Aware Virtual Machine Re-Deployment and Live Migration in SDN-Based Data Centers

Virtual machine (VM) re-deployment and migration has been proven to be a key technique for cloud data centers to implement resource optimization and load balance. Also, live VM migration aims to guarantee the continuity of the existing data flows. Though VM management has been well studied, it has so far mostly focused on optimizing resource utilization, while the important issue of users’ quality of experience has been ignored. In mobile cloud computing, user distribution changes dynamically over time and it can significantly affect both the service latency and network resource utilization. In the traditional network, VM migration may cause a flash crowd of flow changing and a long service downtime due to VM’s IP address changing. Software-defined networking is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements, which can be ubiquitously deployed in data centers and serve as an effective means for flow handling. In this paper, we design a user distribution-aware virtual machine re-deployment mechanism and propose a traffic-redirection virtual machine migration scheme to keep the active flows from being interrupted. We further provide simulations to show the effectiveness and superiority of the proposed approaches over existing schemes.
Adaptively Random Weighted Cubature Kalman Filter for Nonlinear Systems

This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear state estimation. This method adopts the concept of random weighting to address the problem that the cubature Kalman filter (CKF) performance is sensitive to system noise. It establishes random weighting theories to estimate system noise statistics and predicted state and measurement together with their associated covariances. Subsequently, it adaptively adjusts the weights of cubature points based on the random weighting estimations to improve the prediction accuracy, thus restraining the disturbances of system noises on state estimation. Simulations and comparison analysis demonstrate the improved performance of the proposed method for nonlinear state estimation.
The Effects of Problem Based Mathematics Teaching Through Mobile Applications on Success

The purpose of this study is to determine the effects of problem based mathematics teaching through mobile based applications which are used as assistive technology. It has been designed in a mixed method model that integrates research results. The participants of the experimental group were chosen from the students who continue to take mathematics courses within the traditional curriculum and were included in the process of problem based learning via mobile applications. Quantitative dimension of the study was designed using quasi-experimental design with pretest-posttest control group and it was seen that the success of the students in two different environments showed significant difference before and after the experiment. In other words, it has been found that the common effects of successive measurement factors on the success of different learning environments are significant. Within the light of these findings, it has been determined that both learning environments have different effects in increasing the success of the students and that the environment created for the experimental group is more effective in increase of the success. As a result of the qualitative analysis of the data collected by the researcher through the open-ended questionnaire, it was seen that the students reported positive opinions towards the environment (mobile technology, WhatsApp or virtual stock exchange application) and problem based teaching process. Based on these results, it can be asserted that the problem based learning process in which mobile based applications are used as an assistive technology is effective both in the increase of students’ success in maths and their positive attitudes.
Skimping on Gas – Reducing Ethereum Transaction Costs in a Blockchain Electricity Market Application

In recent years, information systems have not been largely evaluated by their operating costs, but mainly by their strategic benefit and competitive advantage.As blockchain-based decentralized applications become more commonplace, representing a shift towards fully consumption-based distributed computing, a new mode of thinking is required of developers, with meticulous attention to computational resource efficiency.This study improves on a blockchain application designed for conducting microtransactions of electricity in a nanogrid environment.By applying the design science research methodology, we improve the efficiency of the application's smart contract by 11 %, with further improvement opportunities identified.Despite the results, we find the efficiency remains inadequate for public Ethereum deployment.From the optimization process, we extrapolate a set of general guidelines for optimizing the efficiency of Ethereum smart contracts in any application.
Apoyo social percibido e implicación escolar del alumnado de educación secundaria

Secondary education is a stage in whichmany changes occur, including evolution instudents’ relationships with teachers, family,and peers. Despite adolescents’ need for independencefrom adults, these processes continueto affect students’ adjustment. Therefore,perceived social support can be an importantcontextual variable in school engagement toprevent high levels of educational failure andschool dropout. Consequently, the main aimsof this study are: to examine the relationshipbetween perceived social support (from teachers,family, and peers) and the dimensions ofschool engagement (behavioral engagement,cognitive engagement, and emotional engagement);to analyse differences in perceived socialsupport and school engagement by sex andage; and to test the predictive capacity of perceivedsocial support on school engagement. Atotal of 1468 Basque secondary school students(51% female; 49% male), aged between 12and 17, participated in the study (M=14.03;SD=1.36). The Social and Family SupportScale (AFA), the Teacher and Classmate SupportScale (TCMS), and the School EngagementMeasure (SEM) were administered.Student’s t-test, correlations and regressionanalyses were performed. The results indicatepositive relationships between perceived socialsupport and school engagement. Secondly, significantdifferences in perceived social supportand school engagement by sex and age are alsoobserved. Thirdly, perceived social supporthas explanatory power for school engagement,particularly emotional engagement. Finally,the results are discussed and directions for futureresearch are proposed.
Locally Statistical Dual-Mode Background Subtraction Approach

Due to the variety of background model in the real world, detecting changes in a video cannot be addressed exhaustively by a simple background subtraction method, especially with several motion detection challenges, such as dynamic background, camera jitter, intermittent object motion, and so on. In this paper, we propose an efficient background subtraction method, namely locally statistical dual-mode (LSD), for detecting moving objects in video-based surveillance systems. The method includes a local intensity pattern comparison algorithm for foreground segmentation by analyzing the homogeneity of intensity patterns of the input frame and the background model, in which the homogeneity is calculated by the mean and standard deviation of pixel intensity. Besides that, a dual-mode scheme is developed to temporally update the background model for the short- and long-term scenarios corresponding to sudden and gradual changes in the background. The advantage of this scheme is the allowance of updating the model in both pixel- and frame-wise manners simultaneously. The parameters used in both the local intensity pattern comparison algorithm and the dual-mode background model updating scheme are estimated for every input frame consecutively based on local and global statistical information of segmentation result. In experiments, the proposed LSD method is extensively evaluated on the Wallflower and CDnet2014 datasets; and remarkable performance demonstrates its preeminence to the many state-of-the-art background subtraction approaches in terms of segmentation accuracy and computational complexity.
Effect of kernel size on Wiener and Gaussian image filtering

In this paper, the effect of the kernel size of Wiener and Gaussian filters on their image restoration qualities has been studied and analyzed. Four sizes of such kernels, namely 3x3, 5x5, 7x7 and 9x9 were simulated. Two different types of noise with zero mean and several variances have been used: Gaussian noise and speckle noise. Several image quality measuring indices have been applied in the computer simulations. In particular, mean absolute error (MAE), mean square error (MSE) and structural similarity (SSIM) index were used. Many images were tested in the simulations; however the results of three of them are shown in this paper. The results show that the Gaussian filter has a superior performance over the Wiener filter for all values of Gaussian and speckle noise variances mainly as it uses the smallest kernel size. To obtain a similar performance in Wiener filtering, a larger kernel size is required which produces much more blur in the output mage. The Wiener filter shows poor performance using the smallest kernel size (3x3) while the Gaussian filter shows the best results in such case. With the Gaussian filter being used, similar results of those obtained with low noise could be obtained in the case of high noise variance but with a higher kernel size.
Direct phase unwrapping method based on a local third-order polynomial fit

When recovering smooth phases by phase unwrapping algorithms, many noniterative algorithms are available. However, normally those algorithms offer approximations of the current phase that cannot be accurate enough. This is because the majority of them are based on global approaches instead of local ones. Although smooth estimations are not often expected in phase reconstructions for real applications, a smooth initial guess could be useful for robust iterative techniques. Therefore, based on the most recent local polynomial approaches, we propose a simple least-squares fitting of the partial derivatives of the phase, normally estimated from the wrapped operator, by considering local polynomial models of the phase up to the third order. Synthetic and real data of wrapped phases are considered in our work.
Politeness and Combination Methods for Theories with Bridging Functions

The Nelson–Oppen combination method is ubiquitous in Satisfiability Modulo Theories solvers. However, one of its major drawbacks is to be restricted to disjoint unions of theories. We investigate the problem of extending this combination method to particular non-disjoint unions of theories defined by connecting disjoint theories via bridging functions. A possible application is to solve verification problems expressed in a combination of data structures connected to arithmetic with bridging functions such as the length of lists and the size of trees. We present a sound and complete combination method à la Nelson–Oppen for the theory of absolutely free data structures, including lists and trees. This combination procedure is then refined for standard interpretations. The resulting theory has a nice politeness property, enabling combinations with arbitrary decidable theories of elements. In addition, we have identified a class of polite data structure theories for which the combination method remains sound and complete. This class includes all the subtheories of absolutely free data structures (e.g, the empty theory, injectivity, projection). Again, the politeness property holds for any theory in this class, which can thus be combined with bridging functions and arbitrary decidable theories of elements. This illustrates the significance of politeness in the context of non-disjoint combinations of theories.
Dual Neural Personalized Ranking

Implicit user feedback is a fundamental dataset for personalized recommendation models. Because of its inherent characteristics of sparse one-class values, it is challenging to uncover meaningful user/item representations. In this paper, we propose dual neural personalized ranking (DualNPR), which fully exploits both user- and item-side pairwise rankings in a unified manner. The key novelties of the proposed model are three-fold: (1) DualNPR discovers mutual correlation among users and items by utilizing both user- and item-side pairwise rankings, alleviating the data sparsity problem. We stress that, unlike existing models that require extra information, DualNPR naturally augments both user- and item-side pairwise rankings from a user-item interaction matrix. (2) DualNPR is built upon deep matrix factorization to capture the variability of user/item representations. In particular, it chooses raw user/item vectors as an input and learns latent user/item representations effectively. (3) DualNPR employs a dynamic negative sampling method using an exponential function, further improving the accuracy of top-N recommendation. In experimental results over three benchmark datasets, DualNPR outperforms baseline models by 21.9-86.7% in hit rate, 14.5-105.8% in normalized discounted cumulative gain, and 5.1-23.3% in the area under the ROC curve.
Quantum Key Distribution (QKD) over Software-Defined Optical Networks

Optical network security is attracting increasing research interest. Currently, software-defined optical network (SDON) has been proposed to increase network intelligence (e.g., flexibility and programmability) which is gradually moving toward industrialization. However, a variety of new threats are emerging in SDONs. Data encryption is an effective way to secure communications in SDONs. However, classical key distribution methods based on the mathematical complexity will suffer from increasing computational power and attack algorithms in the near future. Noticeably, quantum key distribution (QKD) is now being considered as a secure mechanism to provision information-theoretically secure secret keys for data encryption, which is a potential technique to protect communications from security attacks in SDONs. This chapter introduces the basic principles and enabling technologies of QKD. Based on the QKD enabling technologies, an architecture of QKD over SDONs is presented. Resource allocation problem is elaborated in detail and is classified into wavelength allocation, time-slot allocation, and secret key allocation problems in QKD over SDONs. Some open issues and challenges such as survivability, cost optimization, and key on demand (KoD) for QKD over SDONs are discussed.
A Smart Many-Core Implementation of a Motion Planning Framework along a Reference Path for Autonomous Cars

Research on autonomous cars, early intensified in the 1990s, is becoming one of the main research paths in automotive industry. Recent works use Rapidly-exploring Random Trees to explore the state space along a given reference path, and to compute the minimum time collision-free path in real time. Those methods do not require good approximations of the reference path, they are able to cope with discontinuous routes, they are capable of navigating in realistic traffic scenarios, and they derive their power from an extensive computational effort directed to improve the quality of the trajectory from step to step. In this paper, we focus on re-engineering an existing state-of-the-art sequential algorithm to obtain a CUDA-based GPGPU (General Purpose Graphics Processing Units) implementation. To do that, we show how to partition the original algorithm among several working threads running on the GPU, how to propagate information among threads, and how to synchronize those threads. We also give detailed evidence on how to organize memory transfers between the CPU and the GPU (and among different CUDA kernels) such that planning times are optimized and the available memory is not exceeded while storing massive amounts of fuse data. To sum up, in our application the GPU is used for all main operations, the entire application is developed in the CUDA language, and specific attention is paid to concurrency, synchronization, and data communication. We run experiments on several real scenarios, comparing the GPU implementation with the CPU one in terms of the quality of the generated paths and in terms of computation (wall-clock) times. The results of our experiments show that embedded GPUs can be used as an enabler for real-time applications of computationally expensive planning approaches.
Improving Early Cancer Detection Knowledge through Mobile Learning Application

&lt;p class="0abstract"&gt;The study aims to build a mobile-based application prototype with an Android platform for learning early cancer detection. This prototype design is emerged from data collection about the challenges faced by health workers to gain knowledge about cancer. It requires mobile-based learning technology to assist in their daily work. Based on the data gathered, an Android-based application is developed to be used to simplify the knowledge dissemination method and enhance the material quality by digitizing content into multimedia formats.&lt;/p&gt;&lt;p class="0abstract"&gt; &lt;/p&gt;