Reviewerly/exHarmony huggingface dataset:
Improving the methodological system for the development of information competence of university students in the era of digital transformation

The study focuses on ongoing global processes in the field of education, described in a number of national projects, programs, standards, and recommendations. Subject to discussion are the issues (“Digital transformation as an object of research”, “Education in the world of digital technologies”, “Digital didactics”), which are raised in current research and affect the improvement of methodological systems in the university. Attention is drawn to the methodological system for the development of information-related competence of university students, its components and implementation conditions in the era of digital transformation. The approaches to its improvement are investigated. The leading research methods are: analysis of the fundamental provisions of national projects, programs, standards, and recommendations on the relevance of the process of developing information-related competence of university students; analysis of current research in the area of digital transformation in education; integration of the conceptual propositions of contemporary Russian scientists concerning the improvement of methodological systems at the university; integration of pedagogical experience in researching the process of developing information-related competence in future teacher students. The research materials are of practical value for master’s students, graduate students, university professors and teachers of other educational institutions conducting research in this field.
MetaCom: Profiling Meta Data to Detect Compromised Accounts in Online Social Networks

Social networks have become the center of research and its increasing popularity has also led to its misuse by a number of malicious users. In order to conduct various malicious activities on the online platforms, malevolent users rely on spam, fake or compromised accounts to disseminate their illegitimate information. This paper addresses the detection of compromised accounts so that the concerned user can take the necessary action to mitigate the effect of compromise. Unlike most of the existing techniques where text based features are used to address the problem, this research examines the efficiency of meta data information associated with each text in detecting the compromised accounts. Secondly, we have studied the problem from both unary as well as binary classification perspectives where efficiency of respective machine learning classifiers have been analyzed on the basis of different evaluation metrics. Amongst five binary classifiers, Random Forest attained highest efficiency achieving 92.66% F-score. On the other hand, with one class classifiers, OCC-SVM with rbf kernel attained maximum performance with an average F-score of 72.72%.
End-to-End Bank Card Number Recognition Algorithm Based on DenseNet

Printed bank cards number, which are usually black and not easily confused with the background, is easy to split and recognize; embossed bank card, the color of bumped card number is similar to background, characters difficult to split and identify. This paper proposes an end-to-end bank card number recognition algorithm, first uses CTPN network for text detection, then sends the test results into the DenseNet network to identify, finally the card number is output according to the text length. Experimental results show that the algorithm can achieve the automatic identification of the two types of bank card, with high recognition rate and accuracy.
Learning Graph Cut Class Prototypes for Thigh CT Tissue Identification

Perceptual grouping remains a challenging topic in the fields of image analysis and computer vision. Image segmentation is often formulated as an optimization problem that may be solved by graph partitioning, or variational approaches. The graph cut method has shown wide applicability in various segmentation and object recognition tasks. It approaches image segmentation as a graph partitioning problem. To find the optimal graph partition, graph cut methods minimize an energy function that consists of data and smoothness terms. An advantage of this method is that it can combine local and global visual information to obtain semantic segmentation of objects in the visual scene. In this work, we introduce unsupervised and supervised learning techniques for generating the class prototypes used by graph cuts. Our hypothesis is that computation of accurate statistical priors improves the accuracy of the graph cut solution. We utilize these techniques for tissue identification in the mid-thigh using CT scans. We evaluate the performance of the compared approaches against reference data. Our results show that inclusion of accurate statistical priors produces better delineation than unsupervised learning of the prototypes. In addition, these methods are suitable for tissue identification as they can model multiple tissue types to perform simultaneous segmentation and identification.
Building social networking services systems using the relational shared-nothing parallel DBMS

We propose methods to enable the relational model to meet scalability and functionality needs of a large-scale social networking services (SNS) system. NewSQL has emerged recently indicating that shared-nothing parallel relational DBMSs can be used to guarantee the ACID properties of transactions while keeping the high scalability of NoSQL. Leading commercial SNS systems, however, rely on a graph – not relational – data model with key–value storage and, for certain operations, suffer overhead of unnecessarily accessing multiple system nodes. Exploiting higher semantics with the relational data model could be the remedy. The solution we offer aims to perform a transaction as a set of independent local transactions whenever possible based on the conceptual semantics of the SNS database schema. First, it hierarchically clusters entities that are sitting on a path of frequently navigated one-to-many relationships, thereby avoiding inter-node joins. Second, when a multi-node delete transaction is performed over many-to-many relationships, it defers deletion of related references until they are accessed later, thereby amortizing the cost of multi-node updates. These solutions have been implemented in Odysseus/SNS — an SNS system using a shared nothing parallel DBMS. Performance evaluation using synthetic workload that reflects the real SNS workload demonstrates significant improvement in processing time. We also note that our work is the first to present the entity-relationship schema and its relational representation of the SNS database.
Research on the Method of Using Single-tone Interference to Jam OFDM Signal

The modulation technology of OFDM (Orthogonal Frequency Division Multiplexing) is widely used in high-speed data transmission fields. In order to jam the OFDM communication more effectively, whether the single-tone interference can be used or not and how to set the jamming frequency need to be studied deeply. The strength of aligned single-tone interference and non-aligned single-tone interference on each sub-carrier of OFDM (Orthogonal Frequency Division Multiplexing) signal is deduced and calculated quantitatively with the setting parameters in this paper. With the model and simulation program based on the Matlab, it’s studied that the bit error rate of OFDM signal under single-tone interference changes with the jamming frequency and the jam-to-signal ratio. The simulation results and theoretical analysis are coherent. It’s concluded that aligned single-tone interference can only interfere with the targeted sub-carrier and non-aligned single-tone interference can leak to all sub-carriers the jamming energy which decreases with the increase of the distance between the subcarrier frequency and the jamming frequency. It is suggested that the jamming frequency should be set at the midpoint of the adjacent sub-carriers when using single-tone interference to jam OFDM signal. The single-tone jamming method of OFDM signal is studied in this paper which lays a theoretical foundation for the related research of other jamming methods, and puts forward specific suggestions of using single-tone jamming strategy.
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy

Recent developments in image acquisition literature have miniaturized the confocal laser endomicroscopes to improve usability and flexibility of the apparatus in actual clinical settings. However, miniaturized devices collect less light and have fewer optical components, resulting in pixelation artifacts and low resolution images. Owing to the strength of deep networks, many supervised methods known as super resolution have achieved considerable success in restoring low resolution images by generating the missing high frequency details. In this work, we propose a novel attention mechanism that, for the first time, combines 1st- and 2nd-order statistics for pooling operation, in the spatial and channel-wise dimensions. We compare the efficacy of our method to 10 other existing single image super resolution techniques that compensate for the reduction in image quality caused by the necessity of endomicroscope miniaturization. All evaluations are carried out on three publicly available datasets. Experimental results show that our method can produce superior results against state-of-the-art in terms of PSNR, and SSIM metrics. Additionally, our proposed method is lightweight and suitable for real-time inference.
Spectral influence analysis on sunshine duration measurement

Sunshine duration is an important variable in surface meteorological measurement. In this article, two methods for measuring sunshine duration are described, one is photovoltaic-based instrument, which ranges from 400 to 1100 nm, and the other is thermopile-based pyrheliometer, which ranges from 300 to 3000 nm. Due to the spectrum is more representative and accuracy for solar radiation, the pyrheliometer is recommended as the reference sensor for the detection of the threshold irradiance for defining the sunshine duration. The comparison between the photovoltaic- and thermopile-based instruments is carried out to uncover the spectral influence on sunshine duration measurement, analysis by the SMARTS tool is presented to calculate irradiance with different spectral ranges under different altitudes.
Searchable Encryption Using Secret-Sharing Scheme for Multiple Keyword Search Using Conjunctive and Disjunctive Searching

The main searching functions realized by searchable encryption can be divided into searching using one query and searching using multiple queries. Searchable encryption using one query has been widely studied and researched; however, few methods of searchable encryption can accommodate search using multiple queries. In addition, most of the method proposed thus far utilize the concept of index search. Therefore, a new problem exists, in which an additional process of updating or deleting an index when new documents are added or removed is required. Hence, the overall computation cost increases. Another problem is that a document that is not registered in the index cannot be searched. Therefore, herein, using a secret-sharing scheme that is known to offer a low computational cost, we propose a method that can realize both logical conjunctive (AND) and logical disjunctive (OR) search over multiple conditions, without the construction of any index. Hence, we can realize direct searching over sentences, thus achieving a more efficient search method.
Estimation of Visual Attention via Canonical Correlation between Visual and Gaze-based Features

This paper presents a method for estimating visual attention via canonical correlation between visual and gaze-based features. The proposed method estimates user-specific visual attention by comparing a test image with training images including their corresponding individual eye gaze data in a common space. Specifically, canonical correlation analysis can derive projections which enable comparison between visual and gaze-based features in the common space. Therefore, given the new test image, our method projects its visual features to the common space and can estimate visual attention. Experimental results show the effectiveness of the proposed method.
Using Design-Based Research to Develop a Virtual Human Interface for Police Nystagmus Training

The Individual Nystagmus Training Simulation Experience, or INSITE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> , is a virtual human simulation program to help train police officers in identifying one of the strongest clues of alcohol impairment in drivers - nystagmus, or involuntary rapid movement of the eyeball. In this paper we talk about the design-based research principles that helped us iteratively design, develop, test and implement this police training simulation as part of the Texas' Advanced Roadside Impaired Driving Enforcement (ARIDE) program. We describe the simulation, the educational implementation context, the learning activities, and the identified needs of the various users -- including trainees, trainers, researchers and program administrators. Most importantly, we discuss the evolution of the user experience over time in response to feedback. This paper focuses on: 1) design considerations for modeling physiologic symptoms of nystagmus in a virtual human; 2) the strategy for implementing INSITE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> into ARIDE police training sessions; 3) detail on the numerous iterations of the multi-leveled user interface and experience based on qualitative and quantitative feedback from trainees, Standard Field Sobriety Test (SFST) instructors, and subject matter experts; and (4) an overall summary of our experience on this design to date.
Geology and petrography of Sabon Garin Kara and environs part of Malumfashi sheet 79 NE Nigeria

The study area is about 40 km2 that is (8 km &times; 5 km) which is located in Gwarzo Local Government area of Kano State part of&nbsp; Malumfashi sheet 79NE which lies within Longitude E07&deg;57&rsquo;19.5&rdquo; and E08&deg;00&rsquo;00&rdquo; and Latitude N11&deg; 51&rsquo;30&rdquo;andN11&deg;47&lsquo;00. Major rock types identified include medium grained biotite-muscovite granite, coarse grained muscovite-biotite granites, quartz-syenite, and quartz-monzonites. Most of the crystals show subhedral to anhedral form and a perfect cleavage is somehow visible. The interference colours observed under cross-polarized light include blue, dark grey, light grey, some pinkish colour and brown colour which disappear upon the stage rotation. Albite and Carlsbad twinning are seen and used to identify the feldspars. Various structural features were observed with NE- SW trend which represents the final imprint of the Pan-African orogeny. Key words: Biotite, granite, cross-polarized, twinning. &nbsp
A stratified reservoir sampling algorithm in streams and large datasets

In data stream mining, a stream is a dataset of unknown size with continuously incoming elements, which is typically large enough so that a computer processing it does not have enough memory to hold it in its entirety and each element can be read only once and only in order. Classical sampling methods such as simple random sampling (SRS), stratified sampling and cluster sampling cannot be used on the stream data since the entire set is not available all at once and data cannot be reread. Vitter’s (1985 Vitter, J. S. 1985. Random sampling with a reservoir. ACM Transactions on Mathematical Software 11 (1):37–57. ISSN 0098-3500. doi:10.1145/3147.3165.[Crossref], [Web of Science ®] , [Google Scholar]) Algorithm R is a reservoir sampling method which can be used to select an SRS from a data stream. In this article, we propose Algorithm SR which extends Algorithm R to a stratified reservoir sampling method with optimal allocation. We prove that the proposed method is asymptotically equivalent to classical stratified random sampling with optimal allocation. Implementation results show that the proposed method is efficient and can outperform Algorithm R.
Evaluation der User Experience eines AR-Systems

Virtual techniques are becoming increasingly important in the context of Industry 4.0. Augmented reality via tablet is used in industrial manufacturing to increase process reliability. The user experience plays a decisive role in user-acceptance in the iterative development process. It is a holistic evaluation method that can be used to uncover weaknesses in the augmented reality system and derive optimization recommendations.
Privacy-Preserving Deep Learning Models for Law Big Data Feature Learning

Nowadays, a massive number of data, referred as big data, are being collected from social networks and Internet of Things (IoT), which are of tremendous value. Many deep learning-based methods made great progress in the extraction of knowledge of those data. However, the knowledge extraction of the law data poses vast challenges on the deep learning, since the law data usually contain the privacy information. In addition, the amount of law data of an institution is not large enough to well train a deep model. To solve these challenges, some privacy-preserving deep learning are proposed to capture knowledge of privacy data. In this paper, we review the emerging topics of deep learning for the feature learning of the privacy data. Then, we discuss the problems and the future trend in deep learning for privacy-preserving feature learning on law data.
KID Model Realization Using Memory Networks for Text-Based Q/A Analyses and Learning

KID (Knowledge-Information-Data) model as a cognitive model was initially proposed for business intelligence by Huang's research team. It aims to mimic human-like cognitive learning by abstracting human information processing into the following three stages: data interpretation, information assimilation and knowledge instantiation. It is a model with highlevel abstraction and a generic framework which can accommodate a variety of realistic cognitive learning applications. "Memory networks" referring to an architecture for memorizing what learned in networks and performing training jointly with other machine learning methods. It consists of four components: input, generalization, output and response components and longterm memory. In this study, we put efforts on closely looking at both the KID model and components of "memory networks" architecture so as to understand their relations with which they jointly work for some potential applications. It is found that "memory networks" can provide a partial realization for the KID model and this paper describes how the "memory networks", in particular, the enhanced version called "end-to-end memory" is adopted for realizing the KID model for text-based question answering learning.
Scalable Laplacian Regularized Least Squares Classification on Anchor Graph

In this paper, we address the scalability issue of graph-based semi-supervised learnings. In recent years, graph-based semi-supervised learnings using anchor graphs and hierarchical anchor graphs have been proposed as high scalability approaches. However, these techniques utilize linear predictor and cannot infer to unknown data. We propose a Laplacian regularized least squares classification to assume a hierarchical anchor graph (LapRLSC-HAG). The proposed method reduces a memory pressure by the property of the anchor graph or hierarchical anchor graph. Furthermore, we present two strategies optimizing the primal LapRLSC-HAG problems to reduce the computational complexity. To demonstrate the effectiveness of the proposed method, we make experimental evaluations on data sets varying the size from thousands to one million.
First Degree Entailment with Group Attitudes and Information Updates

We extend the epistemic logic with De Morgan negation by Fagin et al. (Artif. Intell. 79, 203–240, 1995) by adding operators for universal and common knowledge in a group of agents, and with a formalization of information update using a generalized version of the left division connective of the non-associative Lambek calculus. We provide sound and complete axiomatizations of the basic logic with the group operators and the basic logic with group operators and updates. Both logics are shown to be decidable.
A Neural-Symbolic Architecture for Inverse Graphics Improved by Lifelong Meta-learning

We follow the idea of formulating vision as inverse graphics and propose a new type of element for this task, a neural-symbolic capsule. It is capable of de-rendering a scene into semantic information feed-forward, as well as rendering it feed-backward. An initial set of capsules for graphical primitives is obtained from a generative grammar and connected into a full capsule network. Lifelong meta-learning continuously improves this network’s detection capabilities by adding capsules for new and more complex objects it detects in a scene using few-shot learning. Preliminary results demonstrate the potential of our novel approach.
Subspace k-anonymity algorithm for location-privacy preservation based on locality-sensitive hashing

Existing location-privacy-preserving methods primarily focus on solving the problem of location-privacy preservation in the global space. This not only increases the response time of the location service, it also degrades the data quality. In this paper, a k-anonymity algorithm based on locality-se nsitive hashing is proposed to solve the problem of location-privacy preservation in the subspace. In the proposed algorithm, higher efficiency and higher quality of service are achieved by applying a bottom-up grid-search method. Further, reasonable division is obtained based on locality-sensitive hashing by retaining position characteristics. The results of experiments conducted to evaluate the proposed algorithm indicate that the proposed algorithm provides a smaller anonymous spatial region, higher data quality, and lower time cost than methods with no subspace.
A Performance Modeling Approach for Mobile Cloud System

In recent years, by using mobile cloud system (MCS) that integrates the cloud radio access network (C-RAN) with the mobile cloud computing (MCC) technology, mobile service provider (MSP) can efficiently enhance the capabilities of mobile users devices and handle the increasing mobile traffic to provide better quality of service (QoS). In a realistic scenario, the QoS of the MCS, i.e., service performance are extremely affected by resources allocation (such as the number of Virtual Machine (VM) and the CPU power of each VM). Therefore, the service performance evaluating of MCS is very necessary, which can benefit for optimizing resource allocation and task scheduling strategy. However, the studies of the performance evaluation for the MCS are still lacking in previous works. In this paper, we propose a performance modeling approach to evaluate the service performance of the MCS. A multi-queue model is first used to model the request execution process of the MCS. To the best as we know, we are the first one who uses the multi-queue model modeling the MCS. In the experiment part, two simulations are designed to verify this modeling approach. Then, we change the value of each performance-related factors to reveal their impact on the service performance of the MCS. The results show that when resources are lacking or just enough in the MCS, increasing the computing resources and the available machine in the C-RAN or the MCC is the fastest way to improve the service performance of the MCS.