Reviewerly/exHarmony huggingface dataset:
TELL, CALL, and MALL

Language learning has become a key objective for providing new generations with competences that allow them to cope with the challenges of the 21st century. However, the socio-economic changes affecting the world have provoked the development of what has been called “language gap,” which implies that not everybody has the means to access language learning. In this situation, new educational approaches characterized by the use of technology have emerged making language learning more accessible than ever before. Among the possibilities, TELL (technology-enhanced language learning) and more particularly CALL (computer-assisted language learning) and MALL (mobile-assisted language learning), offer a great potential for this purpose. Thus, this chapter aims to revise the concepts of TELL, CALL, and MALL and explore their possibilities for language learning and, ultimately, examine their potential for bridging the language gap.
Feasibility of Stellar as a Blockchain-Based Micropayment System

The advent of Bitcoin was heralded as an innovation in the global monetary system, that could bring down transaction fees by circumventing the need for third parties and conduct transactions in real time. The divisibility of a blockchain cryptocurrency to even fractions of a cent, caused microtransactions to become feasible to formerly non-existent denominations. These microtransactions have spurred the development of novel ways of monetizing online resources and hold the potential to aid in alleviation of poverty. The paper conducts a feasibility study on Stellar as a blockchain-based micropayment system. It highlights the limitations in Stellar that impedes its progress and utilizes a characterization model for micropayment systems to evaluate the efficacy of the Stellar platform. The paper conducts a comparison with the micropayment solutions from Bitcoin, Ethereum and PayPal. The paper analyzes a subset of transactions from the Stellar blockchain to aid in drawing a conclusion on the undertaken study and elaborates on the mitigation tools to enable fraud prevention in online monetary transactions.
Vascular Biometric Graph Comparison: Theory and Performance

Vascular biometric templates are gaining increasing popularity due to simple and contact free capture and resilience to presentation attacks. We present the state of the art in Biometric Graph ComparisonGraph comparison, a technique to register and compare vascular biometric templates by representing them as formal graphs. Such graphs consist of a set of vertices, representing the branch, termination and crossover points in the vascular pattern, and a set of edges. An edge represents the relationship between a pair of feature points that are directly connected by a vessel segment in a vascular biometric image. We summarise how this information has been successfully used over the past 8 years to improve registration and recognition performance for the vasculature under the palm, wrist, hand and retinaRetina. The structural properties of biometric graphs from these modalities differ, with retinaRetina graphs having the largest number of vertices on average and the most complex structure, and hand graphs having the smallest number of vertices on average and being the least connected. All vascular graphs have similarities to trees, with the ratio of edges to vertices being close to 1. We describe our most recent algorithms for biometric graph registrationGraph registration and comparison, and our performance results. We are interested in the possibility of using biometric graphs in a template protection scheme based on the paradigm of dissimilarity vectorsDissimilarity vectors. As a first step, we wish to improve registration. Certain modalities like retinaRetina have an intrinsic reference frame that makes registration more straightforward. Other modalities may not have an intrinsic reference frame. To overcome this, we introduce the notion of anchorsAnchors—subgraphs of a biometric graph, having between 5 and 10 vertices, that occur consistently in samples from the same individual—that would enable the dissimilarity vectorDissimilarity vectors scheme to be applied to any vascular modality. Experiments on palm and wrist databases show that all individuals had at least some sets of 6 captures which could be used to identify an anchor, and anchorsAnchors were identified in $$94\%$$ and $$88\%$$ for the palm and wrist databases, respectively.
Enforcing Crash Consistency of Evolving Network Analytics in Non-Volatile Main Memory Systems

Evolving graph processing has enabled the modeling of many complex network systems, e.g., online social networks and gene networks. Existing in-memory graph data structures cannot effectively exploit the current and ongoing adoption of emerging non-volatile main memory (NVMM) for two reasons. (1) Ephemeral graph data structures are not crash-consistent nor durable for NVMM. Corruption is likely when updating its correlated application-defined data and runtime states in the face of hardware or software failures. (2) NVMM writes and reads may incur higher latency than DRAM. Placing the data structures in NVMM may result in a significant loss of performance. In this paper, we propose a novel persistent evolving graph data structure, named NVGRAPH, for both computing and in-memory storage of evolving graphs in NVMM. We devise NVGRAPH as a multi-version data structure, wherein a minimum of one version of its data is stored in NVMM to provide the desired durability at runtime for failure recovery, and another version is stored in both DRAM and NVMM to reduce the NVMM-induced memory latency. We dynamically transform the layout of NVGRAPH including changing the size of its partition in DRAM and the position of its base snapshot exploiting network properties and data access patterns of workloads. For the evaluation of NVGRAPH, we implement four representative real-world graph applications: pagerank, BFS, influence maximization, and rumor source detection. The experimental results show that the performance of NVGRAPH is comparable to other in-memory data structure (e.g., CSR and LLAMA) while using 70% less DRAM. It scales well up to 10 billion edges and 201 snapshots and supports crash consistency. It offers up to the 21X speedup of execution time compared to the scale-up graph computation approaches (e.g., GraphChi and X-stream).
A Geohash Based Place2vec Model

Learning the vector representing of Point Of Interest(POI) is a key aspect of POI recommender systems. As for shop POI embedding, in addition to the goods selling in shops, the location of shops is also an important factor that must be considered. Word2vec is a commonly used POI embedding model but it cannot be trained directly using location data. In this paper, we present a geohash based Place2vec model, geohash is a geocoding system that can encoding the location of shops in a string form, which can be treated as a spatial context of the Word2Vec model. We investigate the extent to which similar shops occur within the same products contexts and similar spatial contexts, and enrich a dataset of location, type and product lists of shops from YIWUGOU Online Shop Data <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The evaluation results shows that the shop vector trained by combined contexts outperform the vector trained by the products contexts.
Visual Quality Analysis of Judder Effect on Head Mounted Displays

The extended field of view (FoV) offered by head mounted displays (HMD) increases the immersive experience, but it also introduces new visual quality challenges to be addressed. The judder artefact is a quality degradation factor that appears during object tracking and it is caused by eye movement relative to the display. As a first attempt to investigate the negative effect of judder in wide FoV applications, we built a new dataset of omnidirectional videos at different bitrates and judder severity levels. Two subjective tests were conducted to assess the quality in terms of perceived severity of judder in compressed video sequences. The outcomes provide new findings about the effect of judder on human perception. The results also give further understanding about the interaction between presentation quality and judder that can be utilized for developing objective models (c) to predict quality degradation in presence of judder.
Visualizing Dynamic Network via Sampled Massive Sequence View

Massive Sequence View(MSV) is an important timeline-based technique for dynamic network visualization. However, it often suffers from severe visual clutter when limited screen space holds excessive network edges. Inspired by the use of graph sampling in static graph analysis, we propose to utilize graph sampling to reduce visual clutter in MSV. An edge sampling method based on accept-reject random sampling is designed for visualizing dynamic network via MSV. The method is able to improve the overall readability of MSV while preserving time varying network behaviors. It is also a preliminary attempt to apply graph-sampling technique into dynamic network analysis.
Discerning Edge Influence for Network Embedding

Network embedding, which learns the low-dimensional representations of nodes, has gained significant research attention. Despite its superior empirical success, often measured by the prediction performance of downstream tasks (e.g., multi-label classification), it is unclear \em why a given embedding algorithm outputs the specific node representations, and \em how the resulting node representations relate to the structure of the input network. In this paper, we propose to discern the edge influence as the first step towards understanding skip-gram basd network embedding methods. For this purpose, we propose an auditing framework Near, whose key part includes two algorithms (Near-add \ and Near-del ) to effectively and efficiently quantify the influence of each edge. Based on the algorithms, we further identify high-influential edges by exploiting the linkage between edge influence and the network structure. Experimental results demonstrate that the proposed algorithms (Near-add \ and Near-del ) are significantly faster (up to $2,000\times$) than straightforward methods with little quality loss. Moreover, the proposed framework can efficiently identify the most influential edges for network embedding in the context of downstream prediction task and adversarial attacking.
A sentiment analysis approach to improve authorship identification

Abstract Writing style is considered the manner in which an author expresses his thoughts, influenced by language characteristics, period, school, or nation. Often, this writing style can identify the author. One of the most famous examples comes from 1914 in Portuguese literature. With Fernando Pessoa and his heteronyms Alberto Caeiro, Álvaro de Campos, and Ricardo Reis, who had completely different writing styles, led people to believe that they were different individuals. Currently, the discussion of authorship identification is more relevant because of the considerable amount of widespread fake news in social media, in which it is hard to identify who authored a text and even a simple quote can impact the public image of an author, especially if these texts or quotes are from politicians. This paper presents a process to analyse the emotion contained in social media messages such as Facebook to identify the author's emotional profile and use it to improve the ability to predict the author of the message. Using preprocessing techniques, lexicon‐based approaches, and machine learning, we achieved an authorship identification improvement of approximately 5% in the whole dataset and more than 50% in specific authors when considering the emotional profile on the writing style, thus increasing the ability to identify the author of a text by considering only the author's emotional profile, previously detected from prior texts.
Noise robust Laws’ filters based on fuzzy filters for texture classification

Laws’ mask method has achieved wide acceptance in texture analysis, however it is not robust to noise. Fuzzy filters are well known for denoising applications. This work proposes a noise-robust Laws’ mask descriptor by integrating the exiting fuzzy filters with the traditional Laws’ mask for the improvement of texture classification of noisy texture images. Images are corrupted by adding Gaussian noise of different values. These noisy images are transformed into fuzzy images through fuzzy filters of different windows. Then the texture features are extracted using Laws’ mask descriptor. To investigate the proposed techniques two texture databases i.e. Brodatz and STex are used. The proposals are assessed by comparing the performance of the traditional Laws’ mask descriptor alone and after combined with the fuzzy filters on noisy images. The k-Nearest Neighbor (k-NN) classifier is utilized in the classification task. Results indicate that the proposed approach delivers higher classification accuracy than the traditional Laws’ mask method. Hence, validate that the suggested methods significantly improve the noised texture classification.
Neutrosophic set in medical image denoising

Medical computer-aided diagnosis systems are restricted by the presence of noise, uncertainty, and fuzziness in medical images. Such limitations may affect diagnostic decisions while determining the disease type and grade. Fuzzy sets are extensively employed to reduce the uncertainty and fuzziness in several applications; however, such methods ignore the spatial framework of the pixels due to noise. To overcome such limitations of the fuzzy-based methods, the neutrosophic set is used instead. Accordingly, several image denoising approaches were explored based on the neutrosophic theory for interpreting the ambiguity, intrinsic uncertainty, and vagueness, where neutrosophic images are defined by three subsets, namely T, I, and F. This chapter establishes the role of the neutrosophic theory, especially the neutrosophic set (NS), in medical image denoising. The chapter includes mapping an image to the neutrosophic domain for denoising magnetic resonance images (MRI). The NS of median filtering (NS-median), the NS of Wiener filtering (NS-Wiener), and the nonlocal NS of Wiener filtering (NLNS-Wiener) are discussed in detail. The performances of these denoising approaches are evaluated in terms of noise removal and structure preservation by using the Brainweb database of simulated MR images as well as clinical MR images. Moreover, a comparison was conducted with other methods with respect to the quantitative/qualitative measurements. The results illustrated the superiority of the proposed NS-based denoising methods compared to traditional denoising methods, proving the role of the NS for MR image denoising.
Capturing Themed Evidence, a Hybrid Approach

The task of identifying pieces of evidence in texts is of fundamental importance in supporting qualitative studies in various domains, especially in the humanities. In this paper, we coin the expression themed evidence, to refer to (direct or indirect) traces of a fact or situation relevant to a theme of interest and study the problem of identifying them in texts. We devise a generic framework aimed at capturing themed evidence in texts based on a hybrid approach, combining statistical natural language processing, background knowledge, and Semantic Web technologies. The effectiveness of the method is demonstrated on a case study of a digital humanities database aimed at collecting and curating a repository of evidence of experiences of listening to music. Extensive experiments demonstrate that our hybrid approach outperforms alternative solutions. We also evidence its generality by testing it on a different use case in the digital humanities.
Securing personal data in public cloud

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Supercomputers in the educational process

The present paper analyzes the question of how important is the implementation of a supercomputer in the educational process, mainly in universities, as an active actor in the training of future professionals in a great variety of fields, that allow them to improve their technological skills, very appreciated in the labor market and in the research activities in general. The report shows how is the implementation of supercomputing for training high-qualified specialists with the aim of using a tool for solving complex problems through numerical simulation.
Searchable Attribute-Based Encryption Protocol with Hidden Keywords in Cloud

With the continuous development of mobile devices, the emergence of 5G networks, a large number of applications for cloud computing, the computing and storage performance of mobile devices has been greatly improved. The social network applications are platform for communication between users. Although their form is diverse, they focus on connecting different users, enabling users to communicate and interact conveniently to meet social needs. However, the rise of mobile social networks still faces many challenges, including information security, privacy preserving and access control. The predecessors did a lot work at resource consuming traditional cryptographic methods and in case of the mess data, the profile matching process is inefficient. Aiming at solving these problems, a searchable encryption scheme with hidden keywords and fine-grained access control is proposed in this paper. Profile owners can design the flexible access policy on their personal profile. With the hidden keywords, the efficient of profile matching has largely increased. Security analysis shows that the proposed scheme can prevent the leakage of privacy information and the hidden keywords. Detailed performance analysis demonstrates the efficiency and the practicability.
A study on intelligent security camera system based on sequential motion recognition by using deep learning

Abstract In this paper, we propose a new intelligent security camera system, which is named COMDES, for automated detection of snatching incidents on streets during the night by using long short‐term memory (LSTM) network. Although over a half of all snatching incidents occur at night, this has not been considered in past studies. Thus, we have proposed an intelligent security camera system using a deep neural network and snapshot of a video frame to detect snatching incidents in the night by our previous paper. The COMDES can perform more efficient detection of snatching than our previous paper by using sequential frames observed in the criminal scene and LSTM. It can classify the situations into criminal or noncriminal scenes precisely. The experimental results show that the system, COMDES, can effectively detect snatching incidents with an accuracy of 98.89%.
The development of methodical approaches to the implementation of the propedeutic course of informatics in primary school by means of Kodu Game Lab

The article describes the results of the study of methodological approaches to teaching programming in primary school using the Kodu Game Lab environment. Existing trends in teaching programming are described, as well as the connection of training with the development of computational thinking and the formation of “new literacy”. Seven methodological aspects of using the Kodu Game Lab are highlighted (quick entry of students into the topic; mastering the method of action instead of remembering the sequence of actions; mastering the method of action instead of listing; differentiation of results in the implementation of formalized tasks; implementation of project activity as part of the lesson; conducting research on the patterns of the virtual environment; development of teamwork skills on a digital project) and examples of the implementation of each of them are provided. The lack of sufficient statistical support for the analysis of the effectiveness of the implementation of the developed approaches has been taken into account, accordingly, further research paths have been formulated. There are also arguments in favor of using the stated methodological approaches.
POSTER: Precise Capacity Planning for Database Public Clouds

Database platform-as-a-service (dbPaaS) is developing rapidly and a large number of databases have been migrated to run on the Clouds for the low cost and flexibility. Emerging Clouds rely on the tenants to provide the resource specification for their database workloads. However, they tend to over-estimate the resource requirement of their databases, resulting in the unnecessarily high cost and low Cloud utilization. A methodology that automatically suggests the "just-enough" resource specification that fulfills the performance requirement of every database workload is profitable. To this end, we propose URSA, a capacity planning system for dbPaaS Clouds. Our real system experimental results show that URSA can accurately plan the capacity for dbPaaS.
VISON: An Ontology-Based Approach for Software Visualization Tool Discoverability

The following topics are dealt with: software maintenance; program visualisation; data visualisation; graph theory; program diagnostics; software metrics; source code (software); software tools; program debugging; configuration management.
ABACUS

Home Voice Assistants (HVAs), like the Amazon Echo, are increasingly being used to control devices in a smarthome. This paper presents ABACUS, an audio based access control utility for smarthomes. ABACUS is designed to be run on a HVA, and makes use of the sounds in the home to provide additional context information to support richer access control rules. ABACUS also includes a pictorial utility to help non-expert users configure these rules.
Research on Text Classification Method Based on Word2vec and Improved TF-IDF

TF-IDF is widely used as the most common feature weight calculation method. The traditional TF-IDF feature extraction method lacks the representation of the distribution difference between classes in the text classification task and the feature matrix generated by the TF-IDF is huge and sparse. Based on this situation, this paper proposes a method of using the feature extraction algorithm of chi-square statistics to compensate for the distribution difference between classes and generating a fixed-dimensional real matrix through word2vec. The experimental results show that the new method is significantly better than the traditional feature extraction methods in the evaluation results such as precision, recall, F1 and ROC_AUC.
PAKE: a supervised approach for Persian automatic keyword extraction using statistical features

Keywords are a collection of important words in a document that are the core topic of the discussion. This paper proposes a hybrid method for automatically extracting keywords from Persian documents and web pages. In the proposed method, firstly, based on linguistic knowledge, processing was performed at word and letter levels to optimize of the analysis. Then a new statistical features set is defined and extracted at the word level. At the final stage, keywords are determined using the SVM algorithm. Also, in this paper, due to the lack of a corpus for evaluating the methods of automatic extraction of Persian keywords, a large-scale corpus has been developed and introduced. The achieved F-measure for keywords and non-keywords are 99.89% and 99.99% respectively.