Reviewerly/exHarmony huggingface dataset:
Cloud-Assisted Asynchronous Key Transport with Post-Quantum Security

In cloud-based outsourced storage systems, many users wish to securely store their files for later retrieval, and additionally to share them with other users. These retrieving users may not be online at the point of the file upload, and in fact they may never come online at all. In this asynchronous environment, key transport appears to be at odds with any demands for forward secrecy. Recently, Boyd et al. (ISC 2018) presented a protocol that allows an initiator to use a modified key encapsulation primitive, denoted a blinded KEM (BKEM), to transport a file encryption key to potentially many recipients via the (untrusted) storage server, in a way that gives some guarantees of forward secrecy. Until now all known constructions of BKEMs are built using RSA and DDH, and thus are only secure in the classical setting. We further the understanding of the use of blinding in post-quantum cryptography in two aspects. First, we show how to generically build blinded KEMs from homomorphic encryption schemes with certain properties. Second, we construct the first post-quantum secure blinded KEMs, and the security of our constructions are based on hard lattice problems.
Discriminative Regularized Deep Generative Models for Semi-Supervised Learning

Deep generative models (DGMs) have shown strong performance in semi-supervised learning (SSL), which incorporate discrete class information into the learning process. Yet existing methods generally overfit to the given labeled data, for only considering the conditional probability of labels. In this paper, we propose a novel discriminative regularized deep generative method for SSL, which fully exploits the discriminative and geometric information of data to address the aforementioned issue. Our method introduces the cluster and manifold assumption that maximizes the classification margin between clusters and simultaneously smooths the predictions of the data which is close in the sub-manifold of each cluster, to regularize the learning of the classifier in DGMs. To derive the regularization based on introduced assumptions, we adopt the generated data of DGMs along with labelled and unlabelled data, to model the data manifold and yield clusters based on the Gumbel-softmax distribution. Experimental results on both text and image datasets demonstrate the effectiveness and flexibility of our method, and prove that two introduced assumptions are complementary in guiding the classification boundary, thus improving the discriminative ability of the classifier.
Integrated thermal management of a 150kW pilot Open Compute Project style data center

Data centers are core to modern digital society, however their growth and increased end-use energy demand has brought them under focus from both the electrical energy providers and governments. This paper presents operational monitoring of a publicly funded energy and cost efficient pilot data center built in the north of Sweden, which has one pod populated with legacy Open Compute Project (OCP) information technology (IT) hardware, that is cooled and powered in a similar vein to the large scale OCP data centers that are operating today, using direct air with evaporative function for both cooling and humidification and no centralised Uninterruptable Power Supply (UPS). The function of the data center POD1 is achieved via the use of a chain of opensource software that monitors and orchestrates IT workload in a predefined approach to measure and deploy its operation. The first stage of the data center development effort has concentrated on a unified approach to environmental control that effectively combines both the direct fresh air with the IT level cooling control that demonstrates a consistently low Power Usage Effectiveness (PUE) of less than 1.04.
Privacy Control In Social Networks By Trust Aware Link Prediction

The following topics are dealt with: Internet; medical signal processing; data analysis; feature extraction; learning (artificial intelligence); medical disorders; pattern classification; data mining; electroencephalography; backpropagation.
TwinsAdvNet : Adversarial Learning for Semantic Segmentation

Recently, many semantic segmentation networks provide more training samples by generative adversarial networks. In practice, the generator does not simulate the detail textures of natural images, and image semantics segmentation network often has the problem of inconsistent prediction results due to small changes in network structure. In view of the above problems and the task of understanding complex scenarios, it is considered that the existing generators are not suitable for providing "real" training samples, but should be based on providing valuable label semantic information. Therefore, a two-branch semantic segmentation network, called TwinsAdvNet, which uses two kinds of predictive probability map to adversarial learning, is proposed. Its novelty is that the prediction results of one branch of segmented network are regarded as weak labels of another branch of semantic segmentation. Experimental results on MIT Scene Parsing Benchmark dataset are included to demonstrate the effectiveness of the proposed model.
Using www-server statistical data for calculating altmetrics

The authors discuss the use of files being accumulated by library www-serves (log-files) for calculating altmetrics. The log-files comprise information on all queries therefore they can be used for calculating statistical indicators of online user activity index in compliance with the federal standard GOST 7.0.20-2014. The authors also analyze the provisions of the standard as referred to online user services control. The COUNTER format is the international format assigning the list and form for presenting statistical data on using digital resources. The current version 5, the COUNTER Code and the algorithm of master data (log-files) processing are discussed. The authors conclude that Russian libraries have not introduced the unified method for counting the requests to their digital resources so far. Meaningful combination of GOST requirements, COUNTER fundamental indicators and calculation of indicators obtained through log-files analysis enable to analyze the relevance of library digital resources to various user categories.
Indoor Robot Path Planning Based on an Improved Probabilistic Road Map Method

Aiming at indoor robot path planning, an improved probabilistic road map method is proposed to decide feasible routes for robots. Firstly, the proposed method sets nodes around the obstacles on the map randomly. By connecting starting point, nodes, and ending point, multi alternative practical routes which are not in collision with obstacles can be acquired. Secondly, this paper tries to optimize a route by eliminating unnecessary nodes and repositioning the nodes between starting point and ending point. Finally, the route with the shortest distance and the minimum number of inflection points is chosen as the best route. Experimental results show that, compared with other path planning methods, the improved probabilistic road map method can generate high quality routes rapidly and stably.
Proposal of a Visual Environment to Support Scrum

Agile methodologies are increasingly considered important in the current context because of their focus on business benefit, strong stakeholder involvement and rapid incorporation of changing requirements. The Scrum methodology presents a generic and agile model for software development, but there are some gaps regarding solution architecture and requirements modeling and traceability. Fast customer feedback and support for volatile requirements result in higher product value, however, initial requirements modelling using modelling techniques are not commonly used in agile processes such as Scum to prepare the phase of Implementation of the software project. This article presents a first proposal for a visual environment to support the product / sprint backlog, helping to prioritize, track and decide on the most valuable, aligned and quickly deployable requirements.
An analysis of children’s online activities and behaviours that expose them to cybercrimes

In this paper an analysis of children's online activities and behaviour that expose them to cybercrimes was done. Children nowadays are constantly online and as such they are exposed to cybercrimes. This research used a systematic literature review to analyse literature on online activities and the behaviour of children that exposes them to cybercriminals. The results indicate that children mostly visit social networking sites and the greatest online risk to them is sexual exploitation. However, online threats such as cyber harassment and exposure to violent Internet content can also be faced. The findings of the reviews provide guidelines for Internet child protection policy makers as well as feed into further research on how to develop child online protection (COP) solutions which educate children on safe and secure online behaviour.
Massive Open Online Courses (MOOCs) Recommendation Modeling using Deep Learning

Since knowledge in the world of internet has always been developed with updated information. Recommendation system for a Massive Open Online Courses (MOOCs) can help create endless learning opportunities. This study presents a Massive Open Online Courses Recommendation Modeling using Deep Learning with Multilayer Perceptron architecture which is suitable for enormous data analysis. The research methodology begins with the process used for the data analysis process, using the data mining technique according to the Cross-industry standard process for data mining (CRISP-DM), consisting of six steps: business understanding, understanding of data, data preparation, modeling, evaluation and deployment. We received a set of data from Harvard and MIT, published for edX learning data in 2012-2013, consisting of 16 programs, 18 features and 641138 sample items. The research found that the most appropriate model is a model with 7 hidden layers and 1e-3 learning rate, processed by GPU acceleration for 250 Epochs. The evaluation of the model's performance is evaluated by calculating the precision value using 542784 testing samples.
Research on Trust-Role Access Control Model in Cloud Computing

Abstract In order to ensure the security of data in cloud computing, a trust-role-based hybrid cloud computing access control model is proposed based on the combination of role-based and trust-based access control models. The model introduces the calculation of trust based on the role-based access control, that is, the user needs to verify the trust value in order to obtain the permission to access the data. Through the application in the local business system, it shows that the model can effectively solve the user’s legitimate access to the data in the cloud, that is, to protect the security of the data in the cloud. The trust-based access control model has good advantages in terms of system throughput and storage space. As the number of user requests increases, both access control methods increase significantly in terms of throughput, but when the number of user requests reaches 10, the system throughput of both access control methods decreases, but based on trust - The role’s access control method is always larger than the role-based access control method in terms of throughput, which roughly increases the total amount by about ten percent.
LADD: A Length-Adaptive Approach to Detecting Taxi Anomalous Detours

This paper focuses on the practical problem of detecting taxi anomalous detours. In this paper, we propose a length-adaptive detection approach called LADD to detect if a taxi conducted an anomalous detour by investigating the taxi journey's trajectory. We employ a similarity-based detection strategy, which takes trajectory length into consideration. Our approach can deal with both long and short trajectories by applying weighted similarity on trajectories and using trajectory concatenation or clipping. For quantitative performance comparison, we propose a performance metric, and compare our approach with the state-of-the-art methods on a real-world dataset. The results verify the superior performance of LADD on taxi detour detection.
Similarity Measurement Based on Author's Writing Styles for Academic Report Plagiarism Detection

The number of cases of plagiarism is increasing as it becomes easier for students to obtain well-written reports from the Internet or to copy and paste the contents of their classmates' reports into their own. Consequently, student plagiarism is becoming a primary issue interfering with fair grading by teachers. Academic reports tend to contain common expressions or academic terms. To write a good report, students try to use popular expressions for academic reports. Thus, it is important for teachers to detect plagiarism through careful attention to coincidental similarities. There is another important issue to be addressed: Plagiarism detection causes psychological burdens for both teachers and students. In this study, we introduce a plagiarism detection method for academic reports written in Japanese involving different types of characters. We train a number of Hidden Markov Models called writing models and identify authors by their writing style.
Logics of imprecise comparative probability

This paper studies connections between two alternatives to the standard probability calculus for representing and reasoning about uncertainty: imprecise probability and comparative probability. The goal is to identify complete logics for reasoning about uncertainty in a comparative probabilistic language whose semantics is given in terms of imprecise probability. Comparative probability operators are interpreted as quantifying over a set of probability measures. Modal and dynamic operators are added for reasoning about epistemic possibility and updating sets of probability measures.
The algorithm of overall optimization based on the principles of intraspecific competition of orb-web spiders

Abstract This work presents a new global optimization algorithm based on the behavior of orb-web spiders. The spider method is a heuristic competitive iterative method of random search whose main idea is to model the behavior of Garden orb-web spiders. The paper describes a solution search strategy based on the features of building a web and competitive behavior; the step-by-step algorithm for solving the problem is presented. The concept of the method is simple; the algorithm does not contain complex calculations. The positions of spiders and flies (test points) are generated randomly, herewith, due to the correct selection of the obtained values, the search for an optimum lead to a rather accurate result. The article describes all the parameters used in the method and presents recommendations for changing settings. The selection of the optimal parameters for various classes of test functions is performed. Parameter settings were performed on three classes of test functions: unimodal, ravine, and multiextremal. As part of the work, a computational experiment was conducted to study the effectiveness of the developed method as well. We compared the proposed method with other proven metaheuristic optimization algorithms. The method showed good results both when working with simple (unimodal) functions, and when finding the optimum of functions with a more complex landscape (multiextremal functions). Based on the above advantages, the spider method can be adapted to solve applied problems with relative ease.
Detection of Phishing Websites Using Machine Learning

Phishing is defined as imitating a creditable company's website aiming to take private information of a user. These phishing websites are to obtain confidential information such as usernames, passwords, banking credentials and some other personal information. Website phishing is the act of attracting unsuspecting online users into revealing private and confidential information which can be used by the phisher in fraud, blackmail or other ways to negatively affect the users involved. In this research, an approach had been proposed to detect phishing websites by applying a different kind of algorithms and filters to achieve a reliable and accurate result. The experiments were performed on four machine learning algorithms, e.g., SMO, logistic regression and Naïve Bayes. Logistic regression classifiers were found to be the best classifier for the phishing website detection. In addition, the accuracy was enhanced when the filter had been applied to logistic regression algorithm.
An Efficient off-Line Handwritten Japanese Address Recognition System

Handwritten address recognition is very practical and popular in ICR (Intelligent Character Recognition) application field, especially for postal and express service. The structure of Japanese address has three components: high level, middle level and low level address. The high level and middle level address is very useful in postal applications since they indicate the destination of the express freight car. However, it's not easy to achieve high recognition accuracy on handwritten address merely by general ICR engine. In this paper, an efficient handwritten Japanese address recognition system is proposed that combines general ICR engine and special processing functions. Rough recognition result is obtained from a general ICR engine based on over-segmentation strategy. Special processing for precise address correction contains three main functions: FCN-based middle level address detection and recognition, BK-Tree-based high level address candidates searching and DTW-based accurate high level address matching. In the experiments on 670 handwritten Japanese address images, the proposed method successfully divided the total address into three levels, and achieved string-level correct rates of 81.6% and 72.5% in high level and middle level address respectively.
Optimized Database Using Crowdsourcing

RDBMS is the most normal and antiquated way to deal with database arrangements. The data is kept in an extremely organized methodology in style of tables or relations. With appearance of Big Data in any case, the organized methodology misses the mark to serve the needs of monstrous data frameworks that are basically unstructured in nature. Expanding ability of SQL, however, allows huge amount of data to be overseen, and it does not generally make a difference as a response to Big Data frameworks that anticipates brisk reaction and quick speedy adaptability. To comprehend this disadvantage, a sensibly new database framework suggested to as NoSQL was presented. NoSQL framework is acquainted which give the fast adaptability and unstructured stage for Big Data application.
A Study on Comparison of Lung Cancer Prediction Using Ensemble Machine Learning

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.
A Model for Describing and Maximising Security Knowledge Sharing to Enhance Security Awareness

Employees play a crucial role in enhancing information security in the workplace, and this requires everyone having the requisite security knowledge and know-how. To maximise knowledge levels, organisations should encourage and facilitate Security Knowledge Sharing (SKS) between employees. To maximise sharing, we need first to understand the mechanisms whereby such sharing takes place and then to encourage and engender such sharing. A study was carried out to test the applicability of Transactive Memory Systems Theory in describing knowledge sharing in this context, which confirmed its applicability in this domain. To encourage security knowledge sharing, the harnessing of Self-Determination Theory was proposed— satisfying employee autonomy, relatedness and competence needs to maximise sharing. Such sharing is required to improve and enhance employee security awareness across organisations. We propose a model to describe the mechanisms for such sharing as well as the means by which it can be encouraged.
Implementation of Differential Privacy Using Diffie–Hellman and AES Algorithm

Differential privacy is a method adopted to check for any privacy breach that occurs during communication for the exchange of confidential information. Here, in this work, differential privacy is being implemented in the context of vehicular ad hoc networks (VANETs). In this paper, we implement the concept of differential privacy using the Diffie–Hellman key exchange algorithm and the advanced encryption standards (AES) algorithms that are very powerful in terms of their performance. Algorithms like Laplace and Gaussian algorithms, which are currently the most commonly implemented algorithms, have been used for verification. The algorithms were analyzed by considering a situation where an initial location and final location have been defined and these have been encrypted using the mentioned algorithms and the privacy has been preserved.
An Implementation of Motorized Wheelchair for Handicapped Persons

Researchers and scientists have contributed a lot for handicapped or physically disabled people to adopt techniques that may help to smooth their mobility in daily life reducing their painful effort, instead of being dependent on others specially while using traditional tools like wheelchair. Many people continuously need help of someone while moving somewhere with the wheelchair. By having an automated control system incorporated with the wheelchair, they would become more independent. The main goal of this project is to fabricate an android controlled wheelchair using Arduino, which can be navigated easily by disabled people with own effort. The inputs will have to be provided through android application which has navigation control and other features, also this application is integrated with home automation system for controlling home appliances. This wheelchair is able to detect any obstacle or crack on the path towards the direction of motion and alerts the user with the help of sonar sensor and IR sensor. The enriched results of this project fabricated a path for further advancement of this technology and finally being manufactured.
Logo Design Analysis by Ranking

In this paper, we analyze logo designs by using machine learning, as a promising trial of graphic design analysis. Specifically, we will focus on favicon images, which are tiny logos used as company icons on web browsers, and analyze them to understand their trends in individual industry classes. For example, if we can catch the subtle trends in favicons of financial companies, they will suggest to us how professional designers express the atmosphere of financial companies graphically. For the purpose, we will use top-rank learning, which is one of the recent machine learning methods for ranking and very suitable for revealing the subtle trends in graphic designs.