Reviewerly/exHarmony huggingface dataset:
Efficient oblivious transfer construction via multiple bits dual-mode cryptosystem for secure selection in the cloud

Cloud computing is a promising paradigm to provide flexible and reliable services. Nevertheless, concern and anxiety about data security and tenants’ privacy are still a major barrier for the popularity of cloud computing. Oblivious transfer (OT) is a cryptographic primitive that enables a client to selectively retrieve data in a privacy-preserving manner. It can be used to construct high-level protocols enabling applications in the cloud-computing paradigm. Technically, it has been proven that OT protocols can be constructed using a CPA-secure public-key encryption scheme. Along these lines, Peikert et al. formulated a dual-mode cryptosystem on which they constructed a single-bit OT scheme. Obviously, it has deficiencies in performance, which makes it unqualified for use in cloud-computing scenarios. This paper aims at providing an efficient multi-bit OT scheme for secure cloud applications. We first propose a multi-bit dual-mode cryptosystem based on the learning with errors (LWE) problem. Based on this multi-bit dual-mode cryptosystem, we construct a multi-bit OT. We present details of the OT construction together with security analysis of our scheme, and finally provide an example of how it can be used in specific application scenarios.
Ontological Review of Persuasion Support Systems (PSS) for Health Behavior Change through Physical Activity

Persuasion Support Systems (PSS) for health behavior change can play an important role in promoting health and well-being through physical activity. It is an emerging application at the crossroad between information systems, persuasion, and healthcare. We propose an ontology to systematically and systemically describe the construct of PSS for health behavior change. The ontology deconstructs the construct into its constituent dimensions and elements, and assembles them into a complete, parsimonious description of the same. We then map the corpus of literature on PSS for health behavior change through physical activity onto the ontology. The resulting ontological map highlights the research topics that are highly- and lightly-emphasized, as well as those with little or no emphasis. It illuminates the landscape of research in the corpus; it highlights biases in emphases that can help and hinder the advancement of the corpus. It can be used to develop a roadmap for future research.
Hybrid Real-Time Protection System for Online Social Networks

The impact of Online Social Networks (OSNs) on human lives is foreseen to be very large with unprecedented amount of data and users. OSN users share their ideas, photos, daily life events, feelings and news. Since OSNs’ security and privacy challenges are more potential than ever before, it is necessary to enhance the protection and filtering approaches of OSNs contents. This paper explores OSNs’ threats and challenges, and categorize them into: account-based, URL-based and content-based threats. In addition, we analyze the existing protection methods and highlight their limitations and weaknesses. Based on that, we propose a comprehensive, user-level, proactive and real-time OSNs’ protection system, called Hybrid Real-time Social Networks Protector (HRSP). HRSP has three components; a user-level security protocol and two classification models. The protocol defines a structure for OSN’s cryptographic services, including encryption, access control and users’ authentication. The classification models employ machine learning, black lists, white lists and users’ feedback, in order to classify URLs into: Benign, Risk and Inappropriate classes, and contents into: Benign, Hate speech and Inappropriate classes. We constructed two data sets of 150,000 URLs and 22,000 tweets to build and test the two classification models. Results show an overall accuracy of 93.2% for the URL model and 84.4% for the content model, while the protocol implementation produces compatible size and time overhead. The components of HRSP are integrated and have compatible design with OSN platforms.
Experimental Demonstration of End-to-End Key on Demand Service Provisioning over Quantum Key Distribution Networks with Software Defined Networking

We experimentally demonstrate end-to-end key on demand (KoD) service provisioning over quantum key distribution networks with software defined networking (SDN), achieving efficient and flexible on-demand secret-key rate allocation for end-to-end data security.
Edge-Based RNN Anomaly Detection Platform in Machine Tools

With the rapid advances in machine learning algorithms and sensing technologies, machine prognostics and health management (PHM) via data-driven approaches has become a trend in sophisticated machine tool industry. The run-to-failure data are necessary for data-driven approaches. However, the average life of the machine is two to three years, the time of collecting data is extended. It is a big challenge to collect run-to-failure data and build a PHM model. Therefore, we propose an Edge-based RNN Anomaly Detection Platform (ERADP). ERADP builds the model based on healthy data and notify anomalies two hours in advance. The true alarm rate is up to 100%. Besides, ERADP can accelerate the training time almost 120 times faster than the traditional model.
Research and Development of User Authentication using Graphical Passwords: A Prospective Methodology

Nowadays in information security user authentication is a very important task. In most of the computer, user authentication depends on the alphanumeric username and password. It means text-based password. But, this is not highly secure because of hackers can easily break the password. Brute force attack, dictionary attack, guessing attack etc. these all are some possible attacks on the password. If the user chooses a difficult password to prevent the system from the attackers which is very much harder for the user to remember such a difficult password. So, to resolve this problem introduced a new technique called graphical password authentication. This paper presents a detailed survey of user authentication techniques using a graphical password. It contains basically two type approaches. They are recognition-based and recall-based approaches. This survey discusses the different techniques about Graphical password authentication and their advantages and limitations. The survey provides a roadmap for the development of new graphical authentication scheme.
Surveying Safety-relevant AI Characteristics

FMP and JHO were supported by the EU (FEDER) and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, by Generalitat Valenciana (GVA) under grant PROME-TEOII/2015/013 and by the U.S. Air Force Office of Scientific Research under award number FA9550-17-1-0287.FMP was also supported by INCIBE (Ayudas para la excelencia de los equipos de investigacion avanzada enciberseguridad), the European Commission, JRC?s Centre for Advanced Studies, HUMAINT project (Expert Contract CT-EX2018D335821-101), and UPV PAID-06-18 Ref. SP20180210. JHO was supported by a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECDfor a research stay at the Leverhulme Centre for the Future of Intelligence (CFI), Cambridge, and a BEST grant (BEST/2017/045) from GVA for another research stay also at the CFI. JHO and SOH were supported by the Future of Life Institute (FLI) grant RFP2-152. SOH was also supported by the Leverhulme Trust Research Centre Grant RC2015-067 awarded to the Leverhulme Centre for the Future of Intelligence, and a a grant from Templeton World Charity Foundation
Black Box Quantiles for Kernel Learning

Kernel methods have been successfully used in various domains to model nonlinear patterns. However, the structure of the kernels is typically handcrafted for each dataset based on the experience of the data analyst. In this paper, we present a novel technique to learn kernels that best fit the data. We exploit the measure-theoretic view of a shift-invariant kernel given by the Bochner's theorem, and automatically learn the measure in terms of a parameterized quantile function. This flexible black box quantile function, evaluated on Quasi-Monte Carlo samples, builds up quasi-random Fourier feature maps that can approximate arbitrary kernels. The proposed method is not only general enough to be used in any kernel machine, but can also be combined with other kernel design techniques. We learn expressive kernels on a variety of datasets, verifying the methods ability to automatically discover complex patterns without being guided by human expert knowledge.
Deep Learning

The vast majority of the examination on profound neural systems so far has been centered on acquiring higher exactness levels by building progressively vast and profound structures. Preparing and assessing these models is just practical when a lot of assets; for example, handling power and memory are easy run of the mill applications that could profit by these models. The system starts handling the compelled gadget and depends on the remote part when the neighborhood part does not give a sufficiently precise outcome. The falling system takes into account a new ceasing component amid the review period of the system. This chapter empowers an entire assortment of independent frameworks where sensors, actuators, and registering hubs can cooperate and demonstrate that the falling design takes into account a free change in assessment speed on obliged gadgets while the misfortune in precision is kept to a base.
Two-step phase-shifting interferometry for illumination highly non-uniform by fitting 2D polynomials of degree <i>K</i>

This paper presents a robust and non-iterative algorithm for phase retrieval from two interferograms changed by an arbitrary unknown phase-step. First, the object phase is algebraically eliminated; second, the background and modulation light are approximated to 2D polynomials of degree K; third, an error function is algebraically derived; fourth, the least squares method is applied for estimating the phase-step, background, and modulation light, and finally the object phase is retrieved. With this approach, it is not necessary to do additional measurements. The important advantages of this method are its simple computer implementation and its capacity to support high spatial variations in the illumination. This approach is described theoretically, and verified numerically and experimentally.
Dimension-Free Bounds for Low-Precision Training

Low-precision training is a promising way of decreasing the time and energy cost of training machine learning models. Previous work has analyzed low-precision training algorithms, such as low-precision stochastic gradient descent, and derived theoretical bounds on their convergence rates. These bounds tend to depend on the dimension of the model $d$ in that the number of bits needed to achieve a particular error bound increases as $d$ increases. In this paper, we derive new bounds for low-precision training algorithms that do not contain the dimension $d$ , which lets us better understand what affects the convergence of these algorithms as parameters scale. Our methods also generalize naturally to let us prove new convergence bounds on low-precision training with other quantization schemes, such as low-precision floating-point computation and logarithmic quantization.
Wearable Devices for Human Activity Recognition and User Detection

Human activity recognition is attracting interest from researchers and developers in recent years due to its immense applications in wide area of human endeavors. The main issue in human behaviour modeling is represented by the diverse nature of human activities and the nature in which they are performed by the individual makes them challenging to recognize. In this paper we propose a method aimed to recognize human activities and detect users using features gathered from accelerometer sensors widespread in wearable and mobile devices. We exploit machine learning aimed to build models with the ability to discriminate between a set of user activities: sitting, sitting down, standing, standing up and walking. Furthermore, we demonstrate that the proposed method is able to distinguish between different users and to identify the user genre. Real-world experiment shows the effectiveness of the proposed solution.
Talking about digital textbooks. The teacher perspective

Purpose An increasing amount of students’ and teachers’ work in the classroom involves digital technologies such as tablets and laptop computers. The purpose of this paper is to gain insight into the possibilities and challenges related to teachers’ use of digital textbooks and, through this work, the conditions for technology-enhanced learning (TEL). Design/methodology/approach This study was based on interviews with two teachers. In the analysis, The Ecology of Resources Model (Luckin, 2010) was used. Findings The findings of this study show that teachers see challenges in finding time to review digital textbooks and support the work with the textbooks in the classroom. However, the teachers in this study prioritize this work, seeing it as providing structure and improved accessibility for students. Practical implications The findings of this study point toward the need to support teachers in their work with reviewing and using digital textbooks, as well as other digitalized resources. How schools as organizations can support teachers’ activities, both individually and collaboratively, will be important. If this work is supported it may, in turn, have impact on TEL and promoting students’ use of digital textbooks and improving student outcomes. Originality/value This small study provides interesting insights into how teachers prioritize their work with digital textbooks to, despite challenges related to time, individualize and support students’ work with digital textbooks and other digital resources. Research on teachers’ use of digital textbooks in practice is limited.
ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019.
Creative Competence And Reflexive Competence As Required Characteristics Of A Modern Student

The article describes development of competitiveness, creative and reflective competence in students as required characteristics in the modern constantly changing society. The authors suggest developing these characteristics through various disciplines, using opportunities for additional and distance education, extracurricular activities, research activities, using reflexive methods and forms. As a part of the comprehensive study, the author presents a model for developing competitiveness, creative and reflective competence in students. The model involves the following components: a purpose determined by the government and the educational system to develop the personality of a modern student; methodological approaches (system-activity, competence, hermeneutic) and principles (openness, continuity, flexibility, understanding, dialogue and reflexivity); a substantial component reflected in various activities (contests, grants, conferences, competitions, etc.); an organizational component which involves development of competitiveness, creative and reflective competencies and use of complex methods, forms and means; a modular component involving implementation of a program consisting of four modules (disciplinary, training, extracurricular and coursework). The article describes the results of diagnostics of competitiveness, creative and reflexive competences of students of the experimental and control groups at different model implementation stages. The experimental and control groups were compared using the Pearson χ2 test and the Spearman test.
A new spectral estimation-based feature extraction method for vehicle classification in distributed sensor networks

Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic sensors and showed that the classification performance varies with the type of road due to the distinct spectral characteristics of the medium. Our proposed classifier fuses time and frequency-domain features and acoustic and seismic modalities to achieve the highest classification rate of 98.6% using a relatively small number of features.
Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures.
Blockchain in Smart City Development—The Knowledge Governance Framework in Dynamic Alliance

Smart city, as a developing strategy for a global economy, has been initiated in many countries and presented the booming future. There are various researches suggesting that a blockchain is an indispensable tool for smart city construction, but how to adopt it to smart city strategy is not certainly solved. The study here aims to propose a scheme of employing the blockchain in smart city strategy with the theory of Knowledge Governance and Dynamic Alliance, of which the former is focusing on reducing the organization of dynamic alliance, deciding the information transmission in blockchain, and the latter is organizing the smart services by promoting the knowledge innovation and competition and quickening the smart solution with blockchain in city. The scheme is analyzed and its function is detailed at last in order to help explain how it works for guiding and planning the smart city model.
A fast two-stage active contour model for intensity inhomogeneous image segmentation

This paper presents a fast two-stage image segmentation method for intensity inhomogeneous image using an energy function based on a local region-based active contour model with exponential family. In the first stage, we preliminary segment the down-sampled images by the local correntropy-based K-means clustering model with exponential family, which can fast obtain a coarse result with low computational complexity. Subsequently, by taking the up-sampled contour of the first stage as initialization, we precisely segment the original images by the improved local correntropy-based K-means clustering model with exponential family in the second stage. This stage can achieve accurate result rapidly as the result of the proper initialization. Meanwhile, we converge the energy function of two-stage by the Riemannian steepest descent method. Comparing with other statistical numerically methods, which are used to solve the partial differential equations(PDEs), this method can obtain the global minima with less iterations. Moreover, to promote regularity of energy function, we use a popular regular method which is an inner product and applies spatial smoothing to the gradient flow. Extensive experiments on synthetic and real images demonstrate that the proposed method is more efficient than the other state-of-art methods on intensity inhomogeneous images.
A multi-GPU accelerated virtual-reality interaction simulation framework

In this paper, we put forward a real-time multiple GPUs (multi-GPU) accelerated virtual-reality interaction simulation framework where the reconstructed objects from camera images interact with virtual deformable objects. Firstly, based on an extended voxel-based visual hull (VbVH) algorithm, we design an image-based 3D reconstruction platform for real objects. Then, an improved hybrid deformation model, which couples the geometry constrained fast lattice shape matching method (FLSM) and total Lagrangian explicit dynamics (TLED) algorithm, is proposed to achieve efficient and stable simulation of the virtual objects’ elastic deformations. Finally, one-way virtual-reality interactions including soft tissues’ virtual cutting with bleeding effects are successfully simulated. Moreover, with the purpose of significantly improving the computational efficiency of each time step, we propose an entire multi-GPU implementation method of the framework using compute unified device architecture (CUDA). The experiment results demonstrate that our multi-GPU accelerated virtual-reality interaction framework achieves real-time performance under the moderate calculation scale, which is a new effective 3D interaction technique for virtual reality applications.
Team Policy Learning for Multi-agent Reinforcement Learning

This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which $K$ players interact simultaneously with an environment and with each other to attain a common goal. In the algorithm, agents only communicate with other agents in their immediate neighborhood and choose their actions independently of one another based only on local information. Learning is done off-policy, which results in high data efficiency. The proposed algorithm is of the stochastic primal-dual kind and can be shown to converge even when used in conjunction with a wide class of function approximators.