Reviewerly/exHarmony huggingface dataset:
Blockchain’s Future Role in Cybersecurity. Analysis of Defensive and Offensive Potential Leveraging Blockchain-Based Platforms

Blockchain and cybersecurity are two current themes in Information Technology that have evolved and gained tremendous attention over the past few years. In this paper authors aim to structure existing and potential use of blockchain-based cybersecurity solutions from the perspective of attackers and defenders to assess whether the intersection will tip the scales in favor of one of these groups. Historically blockchain has been mainly associated with innovative financial services (including crypto-currencies) but it has other emerging use cases within e-government, supply chain management as well as security (e.g. encryption, identity and authentication, data security). On the other hand, blockchain-based platforms are increasingly a target of malicious actors and could be used as an initial attack vector. Therefore, there is a case to be made weather further adoption of blockchain-based solutions should be encouraged or not from a security standpoint and if the overall potential for change could be net positive.
Investigating the Impact of Spatial Augmented Reality on Communication between Design Session Participants - A Pilot Study

Abstract SAR provides an unobtrusive implementation of AR and enables multiple stakeholders to observe and interact with an augmented physical model. This is synonymous with co-design activities and hence, there is a potential for SAR to have a significant impact in the way design teams may set-up and run their co-design activities in the future. Whilst there are a growing number of studies which apply SAR to design activities, few studies exist that examine a particular element of a design activity in a controlled manner. This paper will begin to fill this gap through the controlled study of SAR and its effects on the communication between participants of a co-design activity. To do so the paper compares a controlled design session, using more traditional methods of design representations (3D models on a screen), to sessions run using SAR. The sessions are then analysed to gather information on the gestures used by the participants as well as the overall efficiency of the participants at completing the set design task. The paper concludes that the data gathered tentatively supports a link between the use of SAR and improved communication between design session participants.
Floods Prediction Using Radial Basis Function (RBF) Based on Internet of Things (IoT)

Massive and continuously rainfall will cause floods. Floods can cause people's activities in the area to be hampered. With the technology that grows rapidly, people can get information easily. This Final Project is made to give information about the result of floods prediction using a technology called Internet of Things (IoT). This floods prediction is using Radial Basis Function. The data will be received from Citarum River Hall. The Information that used from Citarum River Hall is rainfall and river water debit. The result from Radial Basis Function Neural Network will be sent to an android application that displays the opportunity of flooding. Using epoch as much as 700 giving error value of TMA equal to 0.027 and error value of CH equal to 0.002, a learning rate of 0.00007 giving error value of TMA equal to 0.286 and error value CH equal to 0.002, and a hidden neuron of 2 giving error value of TMA equal to 0.6483 and error value of CH equal to 15.999 can be used to predict the flooding.
Using Neural Networks for Fake Colorized Image Detection

Modern colorization techniques can create artificially-colorized images that are indistinguishable from natural color images. As a result, the detection of fake colorized images is attracting the interest of the digital forensics research community. This chapter tackles the challenge by introducing a detection approach that leverages neural networks. It analyzes the statistical differences between fake colorized images and their corresponding natural images, and shows that significant differences exist. A simple, but effective, feature extraction technique is proposed that utilizes cosine similarity to measure the overall similarity of normalized histogram distributions of various channels for natural and fake images. A special neural network with a simple structure but good performance is trained to detect fake colorized images. Experiments with datasets containing fake colorized images generated by three state-of-the-art colorization techniques demonstrate the performance and robustness of the proposed approach.
Data-Driven Insights from Vulnerability Discovery Metrics

Software metrics help developers discover and fix mistakes. However, despite promising empirical evidence, vulnerability discovery metrics are seldom relied upon in practice. In prior research, the effectiveness of these metrics has typically been expressed using precision and recall of a prediction model that uses the metrics as explanatory variables. These prediction models, being black boxes, may not be perceived as useful by developers. However, by systematically interpreting the models and metrics, we can provide developers with nuanced insights about factors that have led to security mistakes in the past. In this paper, we present a preliminary approach to using vulnerability discovery metrics to provide insightful feedback to developers as they engineer software. We collected ten metrics (churn, collaboration centrality, complexity, contribution centrality, nesting, known offender, source lines of code, # inputs, # outputs, and # paths) from six open-source projects. We assessed the generalizability of the metrics across two contextual dimensions (application domain and programming language) and between projects within a domain, computed thresholds for the metrics using an unsupervised approach from literature, and assessed the ability of these unsupervised thresholds to classify risk from historical vulnerabilities in the Chromium project. The observations from this study feeds into our ongoing research to automatically aggregate insights from the various analyses to generate natural language feedback on security. We hope that our approach to generate automated feedback will accelerate the adoption of research in vulnerability discovery metrics.
Efficient and robust similarity measure for denoising ultrasound images in non-local framework

Denoising of medical image modalities is one among the foremost basic issues in medical image process. Medical modalities such as ultrasound images suffer from multiplicative speckle noise. This noise consequently reduces the contrast of ultrasound images and adversely affects the other medical ima ge processing tasks such as medical image registration, image super-resolution, and image segmentation. Therefore, one of the important objectives of any denoising algorithm is to attenuate the speckle noise effectively and also preserve the significant medical details in the denoised image. The main focus of this paper is the reduction of speckle noise for ultrasound images using various similarity measures in non-local framework. Through exhaustive experiments conducted on real ultrasound images, B-mode and simulated synthetic images demonstrate that, the Square chord and Chi-square distance-based similarity measures are the most effective similarity measure used in non-local framework for denoising of ultrasound images.
Online learning of contexts for detecting suspicious behaviors in surveillance videos

One of the main incentives for implementing video-based surveillance systems is the urban security. During the last years, several approaches for automatic detection of suspicious events have been proposed. Those methods usually require a training stage before starting their operation. This means that previous to run time a representative dataset of interest events, that may occur in the future, must be available. Nevertheless, most real surveillance systems lack of that information, so many of those proposals results impractical. In this paper, a context online learning scheme for detecting suspicious behaviors on surveillance videos is proposed. Contextual information, which is inferred from videos of people in a scenario, allows detecting suspicious behaviors before an eventual criminal's final attack occur. The main attribute of the proposed approach is the capacity to start up its operation with a reduced training dataset. By an incremental learning process, which uses new data obtained during the online operation, the proposed scheme improves the performance over time achieving a better adaptation to conditions of each scenario. The proposed scheme was validated on two datasets. The first of them includes threats against a parked truck and its driver. The second testing dataset is composed of night assault scenes recorded in an urban environment. The experimental results demonstrate that the proposed method is able to learn incrementally from a reduced initial dataset, achieving a performance similar to batch-type systems trained with all data simultaneously and outperforming five state-of-the-art algorithms over violence detection.
The Application of Artificial Intelligence in Music Education

This article comprehensively reviewed the development history and evolution of the Artificial Intelligence Technology (AIT), systematically demonstrated the convenience, practicality and limitations of Artificial Intelligence (AI) in music education, compared and studied the advantages and disadvantages of both traditional and AI-applied music education mode, statistically analyzed results of the study through questionnaire survey, and deduced that the combination of music education and AI will become the new trend of future music education.
Revisit Surround-view Camera System Calibration

The surround-view system is an essential component of an advanced driver assistance system especially when the vehicle runs in tight parking space or on a narrow road. To ensure successful maneuvering, a panoramic bird's-eye image with no blind spots is necessarily called for. Hence, a typical surround-view system consists of several cameras mounted around the vehicle capturing images from a top-down viewpoint, and an accurate extrinsic calibration for such system is prerequisite for providing a seamless surround-view image. To achieve this goal, this paper presents a novel extrinsic calibration pipeline which is both easy-to-use and reliable to operate on multiple cameras. Instead of taking the vehicle to a fixed position in a specific calibration site, a single chessboard is the only demand. We adopt a novel refinement procedure that jointly optimizes camera poses in a closed-loop manner. The effectiveness and efficiency of the proposed pipeline to calibrate a surround-view camera system has been corroborated by experiments.
Touchless Navigation in a Multimedia Application: The Effects Perceived in an Educational Context

Natural user interfaces have been widely adopted in several application fields. In particular, they are employed in learning environments to increase the motivation and the engagement of students. In this paper we evaluate the user experience with a touchless application interface. The reference scenario is an ipermedia learning application based on the Kinect touchless interface: it describes the machines designed by Leonardo Da Vinci, an Italian Renaissance inventor. The user can interact with the graphical interface by means of hand gestures and poses, which allow selecting items from the menus, scrolling text areas, zooming images and so on. We employed the well-known SUS and USE questionnaires to assess the perceived usability. We prepared also some questions about users’ impressions and opinions on the application itself. In this way, we studied how the users were influenced by their experience with the Kinect device in the learning activity.
Context and Short Term User Intention Aware Hybrid Session Based Recommendation System

Information overloading in e-commerce hinders the consumers' ability to make the right decisions. Customers visiting e-commerce websites can have specific goals in an individual session. However, using sessions that are based on the last item viewed or purchased is not enough to exploit the sessions specific intention or predict users' next actions in the sessions. In this paper, we proposed context and short term user intention aware (CSUI) framework which is based on item similarity collaborative filtering and Association Rule Session-based recommendation systems, the proposed model combines context factor of users' session and users' short term intentions. The developed model has been evaluated on two real-world datasets. Experimental results showed that using session context and users' short term intentions during the recommendation process could help in improving the accuracy of the next item prerlietion.
Emotions in Software Practice: Presentation vs. Coding

Nowadays, one of the most relevant, necessary and essential professional activities is software development. Software practitioners assume a very demanding task presenting important challenges. The aim of the present study is to find out how future software practitioners deal emotionally with two important tasks in their professional life: coding and presentation. To achieve this goal, 47 participants were asked about the emotions they experience in the selected tasks. To collect emotions, the Discrete Emotions Questionnaire was applied. The questionnaire covers eight emotions: anger, disgust, fear, anxiety, sadness, happiness, relaxation, and desire. These eight emotions are divided into four emotion words, which designate different level of intensity. All emotions are self-assessed by means of a Likert Scale. The results show differences in emotions between the two tasks, for instance: the importance of emotions like Anxiety and Nervousness in the case of presentations and Satisfaction and Enjoyment in the case of coding.
Investigation of Various Hybrid Acoustic Modeling Units via a Multitask Learning and Deep Neural Network Technique for LVCSR of the Low-Resource Language, Amharic

Multitask learning (MTL) is helpful for improving the performance of related tasks when the training dataset is limited and sparse, especially for low-resource languages. Amharic is a low-resource language and suffers from the problems of training data scarcity, sparsity, and unevenness. Consequently, fundamental acoustic units-based speech recognizers perform worse compared with the speech recognizers of technologically favored languages. This paper presents the results of our contributions to the use of various hybrid acoustic modeling units for the Amharic language. The fundamental acoustic units, namely, syllable, phone, and rounded phone units-based deep neural network (DNN) models have been developed. Various hybrid acoustic units have been investigated by jointly training the fundamental acoustic units via the MTL technique. Those hybrid units and the fundamental units are discussed and compared. The experimental results demonstrate that all the fundamental units-based DNN models outperform the Gaussian mixture models (GMM) with relative performance improvements of 14.14%-23.31%. All the hybrid units outperform the fundamental acoustic units with relative performance improvements of 1.33%-4.27%. The syllable and phone units exhibit higher performance under sufficient and limited training datasets, respectively. All the hybrid units are useful with both sufficient and limited training datasets and outperformed the fundamental units. Overall, our results show that DNN is an effective acoustic modeling technique for the Amharic language. The context-dependent (CD) syllable is the more suitable unit if a sufficient training corpus is available and the accuracy of the recognizer is prioritized. The CD phone is a superior unit if the available training dataset is limited and realizes the highest accuracy and fast recognition speed. The hybrid acoustic units perform the best under both sufficient and limited training datasets and achieve the highest accuracy.
Inferring ADU Combinations from Encrypted QUIC Stream

Video traffic has experienced rapid growth in the past and will continue to grow rapidly. It is an essential issue to monitor video streaming for both network management and network security. With the popularity of encrypted video, most of the analysis methods are based on the extraction of application data units (ADUs) from an encrypted stream. However, that QUIC streaming use multiplexing makes it impossible to extract ADU from encrypted QUIC streaming. In an effort to overcome this challenge, we proposed a method to extract application data unit combination (ADUC) from encrypted QUIC streaming instead. Taking YouTube video streaming as an example, several stream features are defined. These features are used to develop machine learning models for classifying the ADUC types in the encrypted YouTube QUIC video streaming. The feasibility and accuracy of the method are validated by the actual encrypted YouTube QUIC video streams. Our proposed method represents an initial step towards the analysis of encrypted QUIC streaming.
Digital transformation of learning processes and the development of competencies in the virtual machine-building enterprise environment

Abstract The paper presents the results of the pilot project “Virtual Machine-Building Enterprise”. It was developed for multi-level learning to meet the needs of the national digital economy and the digital industry. The profile of requirements for end-to-end learning (baccalaureate - magistracy - postgraduate) of prospective personnel in the digital development of machine-building enterprises has been substantiated. E-Portfolio model has been developed in accordance with the requirements of the standards GOST R 57720-2017 and ISO/IEC TR 20013:2015 to manage the digital competency development in the learning process and practical activities. The pilot project “Virtual Machine-Building Enterprise” was implemented at the initiative of MSUT “STANKIN”. It is a corporate technological platform for the digital machine-building development with the participation of industry, IT sector and technical universities. The architecture and functional model of a virtual enterprise are developed taking into account the principles of Industry 4.0, fundamental standards in automation systems integration (ERP, MES, CRM, CAD, CAE, CAPP, PLM, etc.) and e-learning (LMS, CMS, etc.). The educational programs are developed in accordance with the requirements of the FSES 3++, Professional Standards and qualification requirements for Chief Digital Officer. In the future, the pilot project “Virtual Machine-Building Enterprise” should be the basis for the formation of a digital technological university model.
Who Owns Bitcoin? Private Law Facing the Blockchain

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Augmented Reality to Engage Preschool Children in Foreign Language Learning

The goal of this study is to see if Augmented Reality (AR) helps to motivate preschool kids and engage them in foreign language learning activities. The study conducts an experimental study to compare traditional approaches with an AR app for teaching a foreign language to preschool kids. Data were collected from experiments performed at three daycares on students at three age groups; 4, 5, and 6 years old. Surveys were also conducted to receive input from teachers and kids. The results show that there is significant engagement increase in learning a foreign language when AR apps utilized in the classroom.
Deep‐learning‐based depth estimation from light field images

A novel deep-learning-based depth estimation method for light field images is introduced. The proposed method employs a novel neural network design to estimate the disparity of each pixel based on block patches extracted from epipolar plane images. The network output is further refined based on filtering and denoising algorithms. Experimental results demonstrate an average improvement of in root mean squared error (RMSE) and in mean squared error over machine learning-based state-of-the-art methods.
A Fourier domain acceleration framework for convolutional neural networks

Acceleration of training and inference of convolutional neural networks (CNNs) plays a significant role in deep learning efforts for large-scale datasets. However, it is difficult to accelerate the training and inference of CNNs based on traditional Fourier domain acceleration frameworks because Fourier domain training and inference are related to many complicated factors, such as the architecture of Fourier domain propagation passes, the representation of the activation function and the design of downsampling operations. A conceptually intuitive, useful and general Fourier domain acceleration framework for CNNs is proposed in this paper. Taking the proposed Fourier domain rectified linear unit (FReLU) as an activation function and the proposed Fourier domain pooling function (FPool) as a downsampling function, a Fourier domain acceleration framework is established for CNNs, and the inverse activation function (FReLU−1) and inverse downsampling function (FPool−1) are further obtained for the backward propagation pass. Furthermore, a block decomposition pipeline is integrated into the Fourier domain forward/backward propagation passes of CNNs to accelerate the training and inference of CNNs. The results show that the proposed acceleration framework can accelerate the training and inference of CNNs by a significant factor without reducing the recognition precision.
Adaptation of person re-identification models for on-boarding new camera(s)

Existing approaches for person re-identification have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where one or multiple new cameras may be temporarily on-boarded into an existing system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. First, we formulate a domain perceptive re-identification method based on geodesic flow kernel that can effectively find the best source camera (already installed) to adapt with newly introduced target camera(s), without requiring a very expensive training phase. Second, we introduce a transitive inference algorithm for re-identification that can exploit the information from best source camera to improve the accuracy across other camera pairs in a network of multiple cameras. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dynamic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute.
Consensus Algorithm for a Private Blockchain

In this paper we give an overview of the Blockchain. We explain quickly how it works thanks to the hash function and the distributed architecture. Then come the descriptions of the two most famous consensus algorithms: Proof of Work and Proof of Stake. We also present some security issues for each algorithm. This paper contains a little explanation of others consensus algorithms that are inspired of these two. Finally, a new consensus algorithm for private Blockchain is presented.