Reviewerly/exHarmony huggingface dataset:
Grammar of VR Storytelling: Analysis of Perceptual Cues in VR Cinema

With the advent of 360° film narratives, traditional tools and techniques used for storytelling are being reconsidered. VR cinema, as a narrative medium, provides users with the liberty to choose where to look and to change their point-of-view constantly. This freedom to frame the visual content themselves brings about challenges for the storytellers in carefully guiding the users so as to convey a narrative effectively. Thus researchers and filmmakers exploring VR cinema are evaluating new storytelling methods to create efficient user experiences. In this paper, we present, through empirical analysis, the significance of perceptual cues in VR cinema, and its impact on guiding the users’ attention to different plot-points in the narrative. The study focuses on examining the experiential fidelity using “Dragonfly”, a 360° film created using the existing guidelines for VR cinema. We posit that the insights derived would help better understand the evolving grammar of VR storytelling. We also present a set of additional guidelines for effective planning of perceptual cues in VR cinema.
Embedding a chatbot in a news article

Since the information communication conversion took place during the two last decades of the twentieth century, significant changes occurred in the media industry. The journalistic narrative has been enriched with multimedia and interactive features, but the basic media of communication - that is text, photos and videos - remain constant. The introduction of chatbots in the last 10 years has provided new possibilities for transforming the journalistic narrative by utilizing interactive dialogue that can take place even in the form of nature language. This paper discusses the usage and the actual implementation of chatbot in a news article. The outcome of the study includes a working protype of a news article that embeds a chatbot which provides an additional narrative.
How the development of Blockchain affected cybersecurity

Abstract This research gives a brief insight into one of the most widely used technologies known as Blockchain. The paper lays the groundwork of how Blockchain functions and how it has quickly become a network with millions of users throughout the world. This study discusses the most important use of Blockchain technology, which is the enhancement of the cyber-security industry. Blockchain has revolutionized the cyber-security industry by introducing a system which is not bound to any four walls and is widely distributed all around the world, seeking refuge on millions of user servers. This decentralized system helps Blockchain defend cyber networks against security threats and attacks such as malware, phishing attacks, and DDoS.
Accelerating OpenSHMEM Collectives Using In-Network Computing Approach

OpenSHMEM is one of the key programming models for High Performance Computing (HPC) applications with irregular communication patterns. Particularly, it is useful for problems that cannot be decomposed easily such as graph partitioning. The programming model supports Remote Memory Access (RMA), atomics, and collective operations. In this paper, we explore and evaluate the In-network Computing approach for accelerating the OpenSHMEM collective operations, particularly barrier, broadcast, and reduction operations. To achieve acceleration, In-network Computing leverages hardware engines on the networking elements and effective software that can efficiently use these capabilities. We explore the value of this approach for collective operations on the InfiniBand Host Channel Adapters (HCAs) and switches. Particularly, we focus on the recently introduced collective offload feature provided by the Mellanox Scalable Hierarchical Aggregation and Reduction Protocol (SHARP) TM capability, which accelerates the barriers and reduction operations; the multicast capability accelerates the broadcast collective operation. To leverage the hardware capabilities, we complement it with an effective software stack that includes Hierarchical Collectives (HCOLL) library, and SHARP layer. Our evaluation on Oak Ridge National Laboratory (ORNL)'s Summit system, which is the fastest supercomputer on the June 2019 Top 500 list, show that the hardware and software acceleration in the In-network Computing approach is key for achieving the performance and scalability required for collectives and applications. For a 5120 process OpenSHMEM job, our results show that the barrier operation is 710% faster, broadcast is 370% faster, and reduction operation is 10 times faster when compared with the implementation of collective operations with no acceleration. Further, experiments with a 2D-Heat kernel show that the In-network Computing approach is very effective for realworld applications.
Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning

Stream data processing has lately gained momentum with the arrival of new Big Data scenarios and applications dealing with continuously produced information flows. Unfortunately, traditional machine learning algorithms are not prepared to tackle the specific challenges imposed by data stream processing, such as the need for learning incrementally, limited memory and processing time requirements, and adaptation to non-stationary data, among others. To face these paradigms, Spiking Neural Networks have emerged as one of the most promising stream learning techniques, with variants such as Evolving Spiking Neural Networks capable of efficiently addressing many of these challenges. Interestingly, these networks resort to a particular population encoding scheme - Gaussian Receptive Fields - to transform the incoming stimuli into temporal spikes. The study presented in this manuscript sheds light on the predictive potential of this encoding scheme, focusing on how it can be applied as a computationally lightweight, model-agnostic preprocessing step for data stream learning. We provide informed intuition to unveil under which circumstances the aforementioned population encoding method yields effective prediction gains in data stream classification with respect to the case where no preprocessing is performed. Results obtained for a variety of stream learning models and both synthetic and real stream datasets are discussed to empirically buttress the capability of Gaussian Receptive Fields to boost the predictive performance of stream learning methods, spanning further research towards extrapolating our findings to other machine learning problems.
Activity Forecasting in Routine Tasks by Combining Local Motion Trajectories and High-Level Temporal Models

Human activity forecasting from videos in routine-based tasks is an open research problem. There are numerous applications in robotics, visual monitoring and skill assessment. Currently, activity forecasting has many challenges because human actions are not fully observable from continuous recording. Additionally, a large number of activities involve fine-grained articulated human motions that are hard to capture using frame-level representations. To overcome these challenges, we propose a method that forecasts human actions by learning the dynamics of local motion patterns. We employed dense trajectories to extract local atomic action patterns and Long Short-Term Memory (LSTM) neural networks for high level action dependencies modeling. The experiments on a public dataset validated the effectiveness of our proposed method in activity forecasting and demonstrated large improvements over the baseline two-stream end-to-end model. We learned that human activity forecasting benefits from learning both the short-range motion patterns and long-term dependencies between actions.
Neobanks Operations and Security Features

There is a steady tendency in the world to increase the level of use of the latest information technologies in the financial sphere, in particular new forms of banks -digital and neobank are gaining popularity. Such a business is promising, attractive to young people, people from remote places, so it is actively developing and is already a subject of interest of cybercriminals. Modern banks use open application programming interfaces to ensure the reception and transmission of information between information systems of different organizations through standard data transmission protocols. The use of open application interfaces affects the information security of banks, namely the security of information assets. Attackers who attack neobank information systems want to gain control of the information assets in order to further engage in illegal transactions or compromise the bank at the request of unfair competitors. Therefore, information banking systems must be protected against threats to internal and external interference and implement a comprehensive security strategy. This strategy should provide security and include technical solutions and procedures that will protect the security of all components of neobank information systems -electronic payment systems, electronic workflow, payment card services, banking software systems, remote systems, networking and more. The banking system security strategy must ensure high-performance processing of large volumes of information without loss of speed, tool collection, incident data analysis and response to security events, as well as reliability and fault tolerance.
A corpus for aspect-based sentiment analysis in Vietnamese

Recently, researchers have shown an increased interest in the aspect-based sentiment analysis problem. The goal is to extract valuable information concerning the aspects mentioned in users comments. This problem can be divided into three sub-tasks: term extraction, aspect detection, and polarity detection. In this paper, we present a new annotated corpus for studies on the two sub-tasks: aspect detection and polarity detection. Our corpus includes 7,828 restaurant reviews at document-level. We also performed a supervised learning method with rich features, achieving the F1-score of 87.13% for the aspect detection and the F1-score of 59.20% for polarity detection. Our corpus is published for research purpose <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Robust Line Hausdorff Distance for Face Recognition

Human face recognition has a lot of applications in recent life. For last 20 years, face recognition has been an attractive problem for pattern recognition researcher which a lot of method has been introduced. However, non-ideal conditions (lighting conditions, face expressions) for face recognition have been still a significant challenge to researchers. Line Hausdorff distance (LHD), which used distance between edge map of face images for calculating the different between facial images, was a fast and high accuracy method for face recognition. In this paper, a modification of LHD was proposed, Robust Line Hausdorff distance. The experimental results showed that the proposed method had a half of time of the LHD method while the accuracy of face recognition was higher than LHD method. Moreover, the accuracy of the proposed method was robust which the non-ideal condition of face images.
Intelligent In-Vehicle Safety and Security Monitoring System with Face Recognition

Dangerous situations such as children are left in vehicles, are dropped off at wrong stops, or take on wrong school buses usually caused by the negligence of drivers. This paper presents a real-time intelligent in-vehicle monitoring system that can count and recognize people as well as alert drivers if such improprieties or potential dangers happen. The system uses HOG-based face detector from Dlib library to obtain face counting function. Face recognition is achieved through two steps, facial feature extraction and face identification. The ResNet is used in facial feature extraction. It transforms an aligned face into a 256-dimensional vector, a Euclidean facial embedding. In face identification, labeled faces will be transformed to facial embeddings first. Then k-nearest neighbor classifier (kNN) is adopted to identify people using such facial embeddings. The simulation on ChokePoint dataset is tested and the average accuracy is 93 percent.
Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms

Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms.
A Novel Text Classification Approach Based on Word2vec and TextRank Keyword Extraction

In the era of big data, a practical challenge is how to extract the keywords and classify the content of the massive text data efficiently. To address this challenge, through extensive research of existing approaches, we propose a novel text classification approach based on TextRank and Word2vec. In our approach, we integrate the external influence of the Word2vec training model and the location-aware weighting influence of the keywords into the Textrank module. Furthermore, we propose a novel text re-classification algorithm based on the combination of the keywords and KNN. The experiment results show that our approach achieves better accuracy and performance compared to TF-IDF and TextRank algorithm.
Code-Based Schemes for Post-Quantum Digital Signatures

The paper deals with the design and principles of functioning of code-based schemes for formation and verification of electronic digital signature. Comparative studies of the effectiveness of the known CFS scheme and the proposed scheme have been carried out, as well as their possibilities, disadvantages and prospects for use in the post-quantum period.
Time Varying Predominance Tag Maps

Visually conveying time-dependent changes in tag maps is insufficiently addressed by current approaches. Typically, for each time range a tag map is determined, and the change between tag maps of subsequent time ranges is progressively visualized. Our method compares tag maps locally in order to enable a continuous display of geographical topic changes among subsequent time ranges. We further provide an alternate tag map variant focusing on frequency changes instead of relative frequency values to visualize the geospatial-temporal rise and fall of topics.
Using Docker to Assist Q&amp;A Forum Users

Q&A forums are today a valuable tool to assist developers in programming tasks. Unfortunately, contributions to these forums are often unclear and incomplete. Docker is a container solution that enables software developers to encapsulate an operating environment and could help address reproducibility issues. This artile reports on a feasibility study to evaluate if Docker can help improve reproducibility in Stack Overflow. We started surveying Stack Overflow users to understand their perceptions on the proposal of using Docker to reproduce Stack Overflow posts. Participants were critical and mentioned two important aspects: cost and need. To validate their criticism, we conducted an exploratory study focused on understanding how costly the task of creating containers for posts is for developers. Overall, results indicate that the cost of creating containers is not high, especially due to the fact that dockerfiles are highly similar and small. Based on these findings we developed a tool, dubbed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frisk</small> , to assist developers in creating containers for those posts. We then conducted a user study to evaluate interest of Stack Overflow developers on the tool. We found that, on average, users spent nearly ten minutes interacting with <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frisk</small> and that 45.3% of the 563 <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frisk</small> sessions we created for existing posts resulted in a successful access to the corresponding web service by the owners of the post. Overall, this artile provides early evidence that the use of Docker in Q&A forums should be encouraged for configuration-related posts.
Development of the Dynamic Bayesian Network to Evaluate the National Law Enforcement Agencies' Work

The article is devoted to the information technology of using dynamic Bayesian networks (DBN) for assessing and modeling the quality of law enforcement agencies' work. A DBN model has been developed for analyzing and predicting the criminal situation in the region. The model was designed in collaboration with law enforcement officers, as well as forensic experts in the selection and quantification of input and output variables. The model was validated on a data set for the period from 2009 to 2019.
Estimation methods of nonlinear regression models

Nonlinear regression models have been a subject of an intensive investigation in recent years and they have wide uses in applied sciences namely Medicine, Forensic Science, Food Science, Information Science, Applied Ecology, Agronomy, Sports Science and Space Science. This research article mainly focuses on some important and innovative nonlinear estimation techniques of parameters of nonlinear regression models based on principles in matrix differentiation. The methods depicted here are principle of least squares, linear approximation method, and MLE estimation method. Oral Capps, Jr, (see [1]), in his research paper presented a theoretical discussion and some empirical results using maximum likely-hood (ML) method and iterative version of Zellner’s seemingly unrelated regression (IZEF) method in the estimation of a non linear system of demand equations when the disturbance terms are both contemporaneously and serially correlated. S. Neal, (see [3]), in his research article considered a discrete real-time nonlinear estimation problem using a least square criterion and derived a sequential algorithm which follows consideration of second order nonlinearities in system measurements. Besides some alternative nonlinear estimation techniques were discussed and examples were given which compare the various estimation algorithms. In 2003, Thomas Schon (see [4]), in his thesis, made a discussion on how to use convex optimization for solving the estimation problem.
Optimal Streaming and Tracking Distinct Elements with High Probability

The distinct elements problem is one of the fundamental problems in streaming algorithms—given a stream of integers in the range { 1,… , n }, we wish to provide a (1+ε) approximation to the number of distinct elements in the input. After a long line of research an optimal solution for this problem with constant probability of success, using O (1/ε 2 +lg n ) bits of space, was given by Kane, Nelson, and Woodruff in 2010. The standard approach used to achieve low failure probability δ is to take the median of lg δ −1 parallel repetitions of the original algorithm. We show that such a multiplicative space blow-up is unnecessary: We provide an optimal algorithm using O (lg δ −1 /ε 2 + lg n ) bits of space—matching known lower bounds for this problem. That is, the lg δ −1 ; factor does not multiply the lg n term. This settles completely the space complexity of the distinct elements problem with respect to all standard parameters. We consider also the strong tracking (or continuous monitoring ) variant of the distinct elements problem, where we want an algorithm that provides an approximation of the number of distinct elements seen so far, at all times of the stream. We show that this variant can be solved using O (lg lg n + lg δ −1 /ε 2 + lg n ) bits of space, which we show to be optimal.
Test4Deep: an Effective White-Box Testing for Deep Neural Networks

Current testing for Deep Neural Networks (DNNs) focuses on quantity of test cases but ignores diversity. To the best of our knowledge, DeepXplore is the first white-box framework for Deep Learning testing by triggering differential behaviors between multiple DNNs and increasing neuron coverage to improve diversity. Since it is based on multiple DNNs facing problems that (1) the framework is not friendly to a single DNN, (2) if incorrect predictions made by all DNNs simultaneously, DeepXplore cannot generate test cases. This paper presents Test4Deep, a white-box testing framework based on a single DNN. Test4Deep avoids mistakes of multiple DNNs by inducing inconsistencies between predicted labels of original inputs and that of generated test inputs. Meanwhile, Test4Deep improves neuron coverage to capture more diversity by attempting to activate more inactivated neurons. The proposed method was evaluated on three popular datasets with nine DNNs. Compared to DeepXplore, Test4Deep produced average 4.59% (maximum 10.49%) more test cases that all found errors and faults of DNNs. These test cases got 19.57% more diversity increment and 25.88% increment of neuron coverage. Test4Deep can further be used to improve the accuracy of DNNs by average up to 5.72% (maximum 7.0%).
Comparing the Effectiveness of Visualizations of Different Data Distributions

The effectiveness of a data visualization depends on several factors, such as the visual encoding of its elements, the analytical task it aims to support, the dimensionality of the data, and even the data distribution. In this work, we report an empirical evaluation of common data visualizations, focusing on how different data distributions affect their effectiveness and the level of confidence users have when answering questions related to certain analytical task types. The study allowed us to assess to what extent some data visualizations are more effective than others, regardless of data distribution. We conclude that data visualization creators and algorithms need to consider the data distribution when generating data visualizations. From the results, we also propose recommendations for choosing a visualization when dealing with data distribution issues.
The impact of digital technology on the study of languages and the development of digital education

Abstract This research is based on an analysis of the impact of information technology on education. It also examined the transformation of traditional education into a digital format. The paper presents the arguments of outstanding specialists in the field of education of various countries and clippings from their popular works. This paper explores the symbiosis of pedagogical and technical principles. It also shows the place of digital technology in teacher education. As special cases, here are considered the use of technology in the study of English language and modern theories of language learning.
Improve Image Captioning by Self-attention

The common attention mechanism has been widely adopted in prevalent image captioning frameworks. In most of the prior work, attention weights were only determined by visual features as well as the hidden states of Recurrent Neural Network (RNN), while the interaction of visual features was not modelled. In this paper, we introduce the self-attention into the current image captioning framework to leverage the nonlocal correlation among visual features. Moreover, we propose three distinctive methods to fuse the self-attention and the conventional attention mechanism. Extensive experiments on MSCOCO dataset show that the self-attention can empower the captioning model to achieve competitive performance with the state-of-the-art methods.