Reviewerly/exHarmony huggingface dataset:
Polyhedral conic kernel-like functions for SVMs

In this study, we propose a new approach that can be used as a kernel-like function for support vector machines (SVMs) in order to get nonlinear classification surfaces. We combined polyhedral conic functions (PCFs) with the SVM method. To get nonlinear classification surfaces, kernel functions are used with SVMs. However, the parameter selection of the kernel function affects the classification accuracy. Generally, in order to get successful classifiers which can predict unknown data accurately, best parameters are explored with the grid search method which is computationally expensive. We solved this problem with the proposed method. There is no need to optimize any parameter in the proposed method. We tested the proposed method on three publicly available datasets. Next, the classification accuracies of the proposed method were compared with the linear, radial basis function (RBF), Pearson universal kernel (PUK), and polynomial kernel SVMs. The results are competitive with those of the other methods.
Mobile Session Fixation Attack in Micropayment Systems

The rapid spread of micropayment systems, together with some peculiarity of their typical use, have attracted computer criminals and dishonest companies aiming at exploiting the systems' weaknesses to steal from users both personal data and money.This paper considers and analyzes some security risks associated with a particular form of micropayment, operator centric micropayment (OCM).A new technique of attack, aimed at an OCM system used by millions of users and named mobile session fixation, is described.By its use, a criminal can obtain the payer's phone number and even arrange the theft of some money.The paper proposes possible countermeasures and further hints for potential threats which might be the subject of analysis.
Control of Unmanned Underwater Vehicle as a Member of Vehicles Team Performing a Given Task

The article presents the control system of a single underwater vehicle which is a member of the underwater vehicles team. The application of the multi-agent system concept for modelling and controlling a team of homogeneous underwater vehicles is discussed. Issues relating to cooperation actions in the team of underwater vehicles are described and solved using the theory of games and vector quality index. The process of negotiations between the vehicles is modelled as a multiplayer cooperative game. Finally, the water region search tasks performed by a team of cooperating underwater vehicles are presented and discussed taking into account possible vehicle failures happening during task realization.
Augmented Reality in Education, AR Globe Project Assessment in Actual Teaching-Learning Environment

Over the years, technology has revolutionized our world and the utilization of advanced technologies in any industry has recorded better results. The usability of cutting-edge technologies in the teaching-learning process is redefining the role of educators and reshaping learning experiences. Augmented Reality (AR), a technology that augments the real world with realistic virtual information, in various form of multimedia content, provides new possibilities for the educational domain. The information delivery with Augmented Reality (AR) engages most of the sensory channels that intensifies the effectiveness of teaching-learning process and creates a more appealing learning environment. This paper, with the discussion of the AR Globe project, aims to provide an introduction to the Augmented Reality (AR) technologyâ€™s implementation into real-life environment, to assess the potential of the technology in educational settings by outlining the attitude of learners towards the use of immersive multimedia content in their learning mediums. https://doi.org/10.26803/ijlter.18.3.1
Image Super-resolution via Deep Aggregation Network

Deep convolutional neural networks (CNNs) have recently made a considerable achievement in the single-image super-resolution (SISR) problem. Most CNN architectures for SIS-R incorporate skip connections to integrate features, and treat them equally. However, this neglects the discrimination of features, and consequently, achieving relatively poor performance. To address this problem, we introduce a deep aggregation network that merging extraction and aggregation nodes in a tree structure, which can aggregate features progressively. In particular, we rescale the information in the aggregation node by modelling the interaction between channels, which shares the same insight on the attention mechanism for improving the discriminative ability of network. In the extraction node, we introduce an mlpconv layer into a dense unit that is parallel to the convolutional layer and can improve the nonlinear mapping capability, where the residual learning is utilized to accelerate the training process. Extensive experiments conducted on several publicly available datasets have demonstrated the superiority of our model over state-of-the-art in objective metrics and visual impressions.
An improved long-term correlation tracking method with occlusion handling

By improving the long-term correlation tracking (LCT) algorithm, an effective object tracking method, improved LCT (ILCT), is proposed to address the issue of occlusion. If the object is judged being occluded by the designed criterion, which is based on the characteristic of response value curve, an added re-detector will perform re-detection, and the tracker is ordered to stop. Besides, a filtering and adoption strategy of re-detection results is given to choose the most reliable one for the re-initialization of the tracker. Extensive experiments are carried out under the conditions of occlusion, and the results demonstrate that ILCT outperforms some state-of-the-art methods in terms of accuracy and robustness.
Prediction-error-ordering for High-fidelity Reversible Data Hiding

Pixel-value-ordering (PVO) appears as an efficient solution for high-fidelity reversible data hiding. State-of-the-art PVO schemes split the host image into blocks, sort pixels within the blocks based on their graylevel and, finally, embed data into some differences between the sorted values. This paper investigates the use of the prediction error instead of the pixel graylevel both for ordering and embedding. The proposed prediction-error-ordering (PEO) scheme also introduces a two-stage procedure by splitting each block in two sets following a chessboard pattern and by processing set by set each block. The pixels of one set are used to classify and predict the ones of the other set and vice-versa. The proposed PEO approach outperforms the state-of-the-art PVO schemes.
Finite-Key Analysis for a Practical High-Dimensional Quantum Key Distribution System Based on Time-Phase States

High-dimensional quantum states key distribution (HD-QKD) can enable more than one bit per photon and tolerate more noise. Recently, a practical HD-QKD system based on time-phase states has provided a secret key at Mbps over metropolitan distances. For the purposes of further improving the secret key rate of a practical HD-QKD system, we make two main contributions in this work. Firstly, we present an improved parameter estimation for this system in the finite-key scenario based on the Chernoff bound and the improved Chernoff bound. Secondly, we analyze how the dimension d affects the performance of the practical HD-QKD system. We present numerical simulations about the secret key rate of the practical HD-QKD system based on different parameter estimation methods. It is found that using the improved Chernoff bound can improve the secret key rate and maximum channel loss of the practical HD-QKD system. In addition, a mixture of the 4-level and 8-level practical HD-QKD system can provide better performance in terms of the key generation rate over metropolitan distances.
Learning k-Occurrence Regular Expressions with Interleaving

Since lacking valid schemas is a critical problem for XML and present research on interleaving for XML is also quite insufficient, in this paper we focus on the inference of XML schemas with interleaving. Previous researches have shown that the essential task in schema learning is inferring regular expressions from a set of given samples. Presently, the most powerful model to learn XML schemas is the k-occurrence regular expressions (k-OREs for short). However, there have been no algorithms that can learn k-OREs with interleaving. Therefore, we propose an entire framework which can support both k-OREs and interleaving. To the best of our knowledge, our work is the first to address these two inference problems at the same time. We first defined a new subclass of regular expressions named k-OIREs, and developed an inference algorithm iKOIRE to learn k-OIRE based on genetic algorithm and maximum independent set (MIS). We further conducted a series of experiments on large-scale real datasets, and evaluated the effectiveness of our work compared with both ongoing learning algorithms in academia and industrial tools in real world. The results reveal the high practicability and outstanding performance of our work, and indicate its promising prospects in application.
Person Re-Identification via Group Symmetry Theory

In recent years, deep learning represented by convolutional neural networks (CNNs) has developed rapidly.The development of deep learning has also led to rapid progress in the field of person re-identification.Many related researchers have begun to use deep learning to solve the problem of person reidentification.The existing deep learning methods for person re-identification mainly use the convolutional neural networks to extract features.The middle layers of the convolutional neural networks contain a wealth of structural information but the previous methods have not fully exploited them.This paper proposes a network named ResGroupNet that uses group symmetry theory to constrain the middle structure of ResNet-50.In detail, we added a branch at the fourth layer of the backbone, the branch was implemented based on theory, at each tail to the backbone and branch, we use sphere loss and triplet loss, respectively.According to our survey, we are the first to introduce the group theory into the ReID task.The experiments show that the proposed method is effective and have achieved good results on the Market-1501, DukeMTMC-reID, and CUHK03-NP datasets.
Quantitative Study on the Waterlogging Resilience of Road Transportation System Based on the Validity View of System Functions

Waterlogging disasters cause significant threats to road transportation system.Scholars are currently focusing on the manner in which disaster resilience of road transportation system can be measured to improve the disaster control capability of an entire road transportation system.However, existing studies lack of quantitative analysis on the waterlogging resilience of road transportation system.This study analyzed the validity of network system structure by combining percolation theory on the basis of the validity view of system functions proposed by Pimm to construct a waterlogging resilience measurement model of road transportation systems and recognize the weak region of waterlogging resilience during a quantitative study.A resilience measurement model of an urban road transportation system in the context of waterlogging disasters was constructed.On this basis, the lost key connections, which were used as the weak region of waterlogging resilience in road transportation system, were recognized.Results demonstrated that the constructed waterlogging resilience measurement model could quantify the waterlogging resilience of the road transportation system throughout the process and highlight the dynamic features of waterlogging disasters.In the weak resilience region recognized in the quantitative study based on percolation theory, the percolation phenomenon of system network may disappear once the key connections are broken.Conclusions offset the shortcomings of previous resilience quantitative studies in the lack of waterlogging disaster analysis.The study results of waterlogging resilience of road transport system are applied directly to optimal decision making on resilience by combining the topology of system network and the post-disaster behavioral characteristics of users.This study provides a new method and decision-making idea to evaluate the disaster resilience of road transportation system.
Comparison Matrices of Semantic RESTful APIs Technologies

Semantic RESTful APIs combine the power of the REST architectural style, the Semantic Web and Linked Data. They picture a world in which Web APIs are easier to browse and more meaningful for humans while also being machine-interpretable, turning them into platforms that developers and companies can build on. We counted 36 technologies that target building such APIs. As there is no one-size-fits-all technology, they have to be combined. This makes selecting the appropriate set of technologies to a specific context a difficult task for architects and developers. So, how the selection of such a set of technologies can be eased? In this paper we propose three comparison matrices of Semantic RESTful APIs enabling technologies. It is based on the analysis of the differences and commonalities between existing technologies. It intends to help developers and architects in making an informed decision on the technologies to use. It also highlights the limitations of state-of-the-art technologies from which open challenges are derived.
Bayesian network-based framework for exposure-response study design and interpretation

Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.
RLWE-Based ID-DIA Protocols for Cloud Storage

To ensure the integrity of cloud storage, it is necessary for data owners to launch data integrity auditing (DIA) periodically. To fit more application scenarios, the research direction is shifted to design identity-based DIA (ID-DIA) protocols without expensive public key infrastructure, which can resist the quantum computing attacks simultaneously. However, the existing ID-DIA protocols are usually based on heavy anti-quantum computing cryptographies, which might severely limit their practical deployments. To address this problem, we explore three ways to design RLWE-based ID-DIA (ID-DIA-RLWE) protocols. The first is to improve the existing LWE-based ID-DIA protocols. The second is to enhance the existing RLWE-based DIA protocols. The third is to instantiate the proposed semantic ID-DIA protocol. Among these three proposed protocols, the third one is the most efficient due to its simplest construction. We also propose a new security model of ID-DIA protocols, including correctness assurance, security guarantee, and privacy protection. Then, we provide theoretical analysis of all proposed ID-DIA protocols under the given security model. Finally, we conduct extensive performance evaluation and comparison, which further validate that the third protocol has superiority in the perspectives of computation, communication, and storage costs.
Openness and government size: A new empirical assessment

This paper provides a new empirical assessment of the relationship between openness and government size. Based on a sample of 124 countries for the period from 1980 to 2016, we estimate several panel data regressions. By the use of measures of trade openness, financial openness, and globalization, as well as samples of developing countries and high-income countries, we investigate the validity of compensation and efficiency hypotheses. The findings denote that while financial openness is not relevant to government size, trade openness and globalization cannot be neglected in the case of developing countries.
The Indian Blockchain Landscape: Regulations and Policy Measures

Blockchain is considered to be a technology that has the potential to bring major political, social and economic benefits to developing economies such as India (Kshetri, 2017a). According to the National Association of Software and Services Companies (NASSCOM), blockchain-led increase in productivity and cost reduction can create value of up to US$5 billion in the Indian economy by 2023 (IANS, 2018). The research company ResearchAndMarkets.com's study indicated that the banking and financial services sector is expected to the leading sector to benefit from this technology (Business Wire, 2018).
Normative and performative aspects of social predictions in the context of philosophy of education

М. И. Бойченко, Е. И. Яковлева, О
Researches on the Security Protection and Inspection Method for Confidential Documents Based on Linux Operating System

With the increasing usage of electronic confidential documents on Linux operating systems, the caused potential threats are becoming more and more serious, because of the lack of relevant mature security management software for Linux operating system. In this paper, a related document monitoring strategies based on virtual file system (VFS) is studied. Then the common file search methods and string matching methods are summarized for the security inspection of the confidential computer running the Linux operating system. The study of this paper has important engineering value for improving the effect of the confidential computer security management work in the future.
Modelling perceptions on the evaluation of video summarization

Hours of video are uploaded to streaming platforms every minute, with recommender systems suggesting popular and relevant videos that can help users save time in the searching process. Recommender systems regularly require video summarization as an expert system to automatically identify suitable video entities and events. Since there is no well-established methodology to evaluate the relevance of summarized videos, some studies have made use of user annotations to gather evidence about the effectiveness of summarization methods. Aimed at modelling the user’s perceptions, which ultimately form the basis for testing video summarization systems, this paper seeks to propose: (i) A guideline to collect unrestricted user annotations, (ii) a novel metric called compression level of user annotation (CLUSA) to gauge the performance of video summarization methods, and (iii) a study on the quality of annotated video summaries collected from different assessment scales. These contributions lead to benchmarking video summarization methods with no constraints, even if user annotations are collected from different assessment scales for each method. Our experiments showed that CLUSA is less susceptible to unbalanced compression data sets in comparison to other metrics, hence achieving higher reliability estimates. CLUSA also allows to compare results from different video summarizing approaches.
Environmental Health: Evaluation of heavy metals pollution in Isfahan industrial zone from soils, well / eluent waters and waste water by microwave- electro-thermal atomic absorption spectrometry

In this study, soils, well waters, drinking waters, and waste water in the Isfahan industrial area were sampled in spring and summer 2018. In 8 points of industrial zone, important toxic heavy metals such as, lead (Pb), Cobalt (Co), Nickel (Ni), Chromium (Cr), and Cadmium (Cd) were sampled from surface soil (up to 2 m), well/drinking waters and waste water. After sample preparation with micro-wave digestion technique, the concentration of heavy metals was determined by electro-thermal atomic absorption spectrometry (ET-AAS). According to the well water analysis, the mean concentration of Cr, Ni, Co, Cd, and Pb in well water were 95 μg L-1, 146 μg L-1, 185 μg L-1, 12 μg L-1, and 11 μg L-1, respectively. In well water, the concentrations of Cd, Ni, Cr and Co were found much higher than Pb in comparison with the references of World Health Organization (WHO).
OSSR-P: Ontological Service Searching and Ranking System for PaaS Services

Cloud computing enables the use of computing resources through the web, which is delivered as software, platform and infrastructure services.With increasing demand and adoption of cloud services, cloud service user faces many issues on selecting a particular service.In this paper, we provide a system, OSSR-P, which search and rank the platform as a service type services according to user requirements.OSSR-P system is based on PaaS service ontology which is developed and populated by parsing the service description document and WSDL document of PaaS services.The user requirements are parsed and generated as SPARQL queries which then retrieve the service list from the ontology repository.The retrieved services are ranked according to the requirements matching.The performance evaluation of OSSR-P shows that it provides and maintains consistent results irrespective of the number of requirements given by the user.