Reviewerly/exHarmony huggingface dataset:
Discriminative Streaming Network Embedding

Many real-world networks (e.g., friendship network among Facebook users) generate data (e.g., friend requests) in a stream fashion. Recently, several network embedding methods are proposed to learn embeddings on such networks incrementally. However, these methods perform incremental updates in a heuristic manner and thus fail to quantitatively restrict the differences between incremental learning and direct learning on the entire network (i.e., the batch learning). Moreover, they ignore the node labels (e.g., interests) when learning node embeddings, which undermines the performance of network embeddings for applications such as node classification. To solve this problem, in this paper we propose a novel network embedding framework, Discriminative Streaming Network Embedding (DimSim). When an edge insertion/deletion occurs, DimSim fast learns node embeddings incrementally, which is desired for many online applications such as anomaly detection. With the incremental learning method, at any time, the objective function well approximates to that of batch learning on the current snapshot. More importantly, the average amount of updating operations of DimSim for processing each newly coming edge is about O(n1−λ), where λ is constant between 1 and 2 and n is the number of nodes. As a result, DimSim can be increasingly fast as the network grows, which adheres to the intuition that an edge insertion/deletion has a smaller impact on larger networks. In addition, we also utilize node label information to learn discriminative node embeddings and we deliberately design a new model to automatically trade-off the node-specific importance of topology and label information. Extensive experiments on real-world streaming networks show that our method DimSim is up to 50 times faster and 40% more accurate than the state-of-the-arts.
Player Interaction with Procedurally Generated Game Play from Crowd-Sourced data

This paper discusses the potential for player-data interaction enabled through the medium of gameplay that is procedurally generated using crowd-sourced data. A mobile game, which is called 'Balance Trucks' procedurally generates levels containing terrains derived from data collected through the SmartRoadSense (SRS) application. SRS allows data on the quality of roads to be collected via a user's mobile device whilst driving along various routes, providing open data towards boosting traffic conditions in Europe. Data collected can then be used to unlock game levels and the subsequent terrains. The paper describes the development considerations and process, providing insights on the technical infrastructure that could be adopted and adapted for different types of data. The player-data interaction demonstrated by this game provides a new form of human-computer interaction from the perspective of games and crowd-sourced data that can inform future data-driven games and also the engagement strategy for other crowd-sourcing and sensing initiatives.
License Plate Character Segmentation Algorithm in Intelligent IoT Visual Label

One of the most important core technologies in intelligent visual Internet of Things is visual labeling technology. It identifies, understands, and classifies the content in a video or image, "tags" it, and displays the contents of the label information corresponding to the identified object. In the Intelligent Visual IoT vehicle Visual label production process, including: access to vehicle images, license plate positioning, license plate correction, character segmentation, character recognition and other processing, and finally identify the license plate number to provide the most important vehicle information for the vehicle Visual label. This paper mainly focuses on the research of character segmentation algorithm for intelligent Visual IOT vehicle visual label making, and puts forward some better improved algorithms through the experiment, comparison and analysis of three character segmentation methods of horizontal projection, template matching and c-mean clustering analysis. For example, a given type of class center method is used in clustering analysis, and the steps of upper and lower boundary detection are added to template matching and clustering analysis.
Speech strategy of anonymity in communication

The article examines Internet channels as new communicative functional systems in Telegram Messenger as it often happens that there are some anonymous channels that refuse to disclose their authorship. The analysis of anonymity in Telegram shows that there is a special communicative strategy that can be a verbal provocation aimed at activating the communicative interest of readers on the one hand and, on the other hand, it is a way of avoiding responsibility for the reliability of the reported news. It is possible to confirm that the new trend appears that includes the mixture of mass media and interpersonal communications.
Brand Recognition of Phishing Web Pages via Global Image Descriptors

Phishing attacks, which have exponentially increased in recent years, are a form of cyber attack aiming to steal sensitive credentials of innocent users.In general, the attackers attempt to deceive users by creating and submitting a fake but visually similar version of a legitimate web page, which has already been in usage.In this study, we suggest an approach for recognition of phishing web pages by utilizing two global image descriptors namely GIST and local binary patterns (LBP) which have never been employed in phishing web page recognition literature.Moreover, in order to obtain a discriminative representation, we have experimented two kinds of visual feature extraction scheme such as (1) "holistic" and ( 2) "multi-level patches".While we have only used whole web page screenshot in "holistic" scheme, screenshots were divided into equally sized smaller crops at growing number of levels during the implementation of "multi-level" patches scheme.In order to evaluate the proposed approach, we have employed a publicly available phishing web page dataset in literature including screenshots of both 14 different highly phished brands and legitimate web pages posing an open-set problem for researchers.Besides, the aforementioned dataset covers 1313 training and 1539 testing cases in total.The visual signatures extracted by use of GIST and LBP descriptors were then fed to various machine learning models such as SVM, Random Forest and XGBoost (regularized gradient tree boosting).According to the results of comprehensively conducted experiments, XGBoost has been found as the best learner.In line with this finding, we obtained 87.7% (GIST) and 83.1% (LBP) validation accuracy along with the representation of "multi-level patches".Consequently, it has been shown that preferred global image descriptors can be successfully employed for detecting and recognizing phishing web pages.In addition, average required time for processing one screenshot (around 1.12 sec.) with GIST descriptors indicates that the proposed scheme and GIST can be effectively used as a browser based plug-in for recognizing brands of phishing web pages.
Multi-criteria Group Recommender System Based on Analytical Hierarchy Process

Current researches have demonstrated that the significance of Multi-Criteria Decision-Making (MCDM) methods in Group Recommender Systems (GRSs) has yet to be thoroughly discovered. Thus, we have proposed a Multi-criteria GRS (MCGRS) to provide recommendations for group of users based on multi-criteria optimization. The idea behind our approach is that, each member in a group have different opinions about each criterion and he/she would try to make the best use of multi-criteria to fulfill his/her own preference in decision-making process. Therefore, we have employed Analytical Hierarchy Process (AHP) to learn the priority of each criterion to maximize the utility for each criterion. Then, MCGRS generate the most appropriate recommendation for the group. Experiments are performed on Yahoo! Movies dataset and the results of comparative analysis of proposed MCGRS with baseline GRSs techniques clearly demonstrate the supremacy of our proposed model.
A simple approach to design patterns identification in programming code

The identification of Design Patterns in the code can help program maintenance and evolution processes: this is a way to understand the design and implementation of the program; this is a way for informed restructuring, improving the structure, and making changes to the code. The specialized literature presents quite a lot of approaches to identification, which in most cases are rather complex and very often ineffective. We offer in this paper a simple and practical approach to design patterns identification in programming code. It is an approach that is used for educational purposes within the framework of the "Software Design Patterns" course in TU-Varna. Although tested to identify a particular GoF Pattern - Singleton, the approach can be easily adapted to identify any other GoF pattern. The use of this approach in the educational practice has had its positive results, led to raising students' interest in software maintenance processes and to improving the efficiency of the learning process.
Absolute Depth Measurement Using Multiphase Normalized Cross-Correlation for Precise Optical Profilometry

In a multifrequency phase-shifting (MFPS) algorithm, the temporal phase unwrapping algorithm can extend the unambiguous phase range by transforming the measurement range from a short fringe pitch into an extended synthetic pitch of two different frequencies. However, this undesirably amplifies the uncertainty of measurement, with each single-frequency phase map retaining its measurement uncertainty, which is carried over to the final unwrapped phase maps in fringe-order calculations. This article analyzes possible causes and proposes a new absolute depth measurement algorithm to minimize the propagation of measurement uncertainty. Developed from normalized cross-correlation (NCC), the proposed algorithm can minimize wrong fringe-order calculations in the MFPS algorithm. The experimental results demonstrated that the proposed measurement method could effectively calibrate the wrong fringe order. Moreover, some extremely low signal-to-noise ratio (SNR) regions of a captured image could be correctly reconstructed (for surface profiles). The present findings confirmed measurement precision at one standard deviation below 5.4 µm, with an absolute distance measurement of 16 mm. The measurement accuracy of the absolute depth could be significantly improved from an unacceptable level of measured errors down to 0.5% of the overall measuring range. Additionally, the proposed algorithm was capable of extracting the absolute phase map in other optical measurement applications, such as distance measurements using interferometry.
Cold-Start Representation Learning

Video relevance computation is one of the most important tasks for the personalized online streaming service. Given the relevance of videos and viewer feedbacks, the system can provide personalized recommendations, which helps viewers discover more contents of interest in most online services. However, the computation of a video relevance table is based on viewers' implicit feedbacks such as watch and search history, which perform poorly for newly added "cold-start'' videos. Facing the cold start problem, we introduce a recommendation method with Bidirectional Encoder Representations from Transformers, which considers the continuity of ordered watching plan and trained the sequence of path from start to end named Bert4Movie. What's more, we propose a method named Movie2Vec to represent the videos in a different way. Our method has been used in our solutions of Content-based Video Relevance Prediction Challenge and got a significant improvement in the AUC.
Novel Reliability Methodology for Virtual Solutions

Reliability is crucial in the fiercely-competitive mobile network service provider environment. 5G application virtualization and cloud deployment introduce an entirely new dimension to the vendor requirements process. The ability to separate software from hardware, and select different vendors for each, creates the need to rationally allocate the quantifiable reliability requirements between these layers of separation in a multi-vendor virtualized environment. In this work, we define a comprehensive set of service-level reliability metrics, and develop a novel methodology to allocate these metrics to the service delivery platform layers using natural tunable parameters.
The Eye-out-Device Multi-Camera Expansion for Mobile Robot Control

In visual based control (VBC), a single eye-out-device camera provides a limited viewing area. The multi-camera configuration can be used to overcome this problem. However, simultaneous multi-image processing is a challenging task. In this study; a mobile robot control is performed with Gaussian and adaptive potential field based methods under the eye-out device multi-camera configuration. Images taken from the cameras are stitched according to common features. The color based object detection is operated to detect the robot, target and obstacles in this image. To acquire a suitable path between robot and target, adaptive potential field algorithm is executed. The Gaussian based mobile robot controller is used to drive on the robot according to the path plan. In this way, configuration space can be expanded via cameras. At the same time, systematic and unsystematic errors are avoided through VBC. Simulation and real-world experiments show that the system demonstrates a good performance and efficiency.
4‐bit Boolean functions in generation and cryptanalysis of secure 4‐bit crypto S‐boxes

Abstract In modern ciphers of commercial computer cryptography, 4‐bit crypto substitution boxes or 4‐bit crypto S‐boxes have been of utmost importance since the late 1960s. Since then, the 4‐bit Boolean functions (BFs) have been proved to be the best tool to generate the said 4‐bit crypto S‐boxes. In this article, the crypto‐related properties of the 4‐bit BFs, such as the algebraic normal form (ANF) of the 4‐bit BFs, the balancedness, the linearity, the nonlinearity, the affinity and nonaffinity of the 4‐bit BFs, and the strict avalanche criterion (SAC) of 4‐bit BFs, are studied in detail. An exhaustive study of 4‐bit BFs with some new observations and algorithms on SAC of 4‐bit BFs is also reported in this article. In the end of 1990s, the Galois field polynomials over Galois field GF(2 8 ) are in use to generate the 8‐bit crypto S‐box of the Advance Encryption Standard. A detailed study on generation of the 4‐bit crypto S‐boxes with such Galois field polynomials over the binary as well as nonbinary extended Galois fields is also given in this article. The generated 4‐bit crypto S‐boxes are analyzed with four cryptanalysis techniques and the well‐defined SAC algorithms of 4‐bit crypto S‐boxes to search for the best possible 4‐bit crypto S‐boxes. Some existing 4‐bit crypto S‐boxes such as the thirty‐two 4‐bit crypto S‐boxes of the data encryption standard (DES) and the four 4‐bit crypto S‐boxes of the two variants of the Lucifer are analyzed to report the weakness of such S‐boxes. A comparative study of the ancient as well as the modern 4‐bit crypto S‐boxes with the generated 4‐bit crypto S‐boxes proves the said generated 4‐bit crypto S‐boxes to be the best possible one.
Defending Against Adversarial Examples via Soft Decision Trees Embedding

Convolutional neural networks (CNNs) have shown vulnerable to adversarial examples which contain imperceptible perturbations. In this paper, we propose an approach to defend against adversarial examples with soft decision trees embedding. Firstly, we extract the semantic features of adversarial examples with a feature extraction network. Then, a specific soft decision tree is trained and embedded to select the key semantic features for each feature map from convolutional layers and the selected features are fed to a light-weight classification network. To this end, we use the probability distributions of each tree node to quantify the semantic features. In this way, some small perturbations can be effectively removed and the selected features are more discriminative in identifying adversarial examples. Moreover, the influence of adversarial perturbations on classification can be reduced by migrating the interpretability of soft decision trees into the black-box neural networks. We conduct experiments to defend the state-of-the-art adversarial attacks. The experimental results demonstrate that our proposed approach can effectively defend against these attacks and improve the robustness of deep neural networks.
An approach of refining RC4 with performance analysis on new variants

Many years of research on the RC4 stream cipher proves it to be strong enough, but there are claims that its swap function is responsible for essential biases in the output. There are suggestions to discard some initial bytes from the key-stream, to get rid of this, before the actual encryption starts, though no optimum value has been defined. In this paper, by analysing different variants of RC4, the authors have attempted to find out whether this cipher becomes more secure by discarding initial bytes and, if so, what is its optimum limit. Also, multiple S-boxes generated by different logics and a unique key-mixing procedure have been implemented, which made k RC4 more robust.
Modeling Adaptive Video Streaming Using Discrete-Time Analysis

HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet: video clips are split into segments and for each segment, several quality levels are provided. Based on client-centric parameters like the video buffer state or the throughput, an adaptive bitrate (ABR) algorithm is used to decide the quality of the segment to be downloaded. Besides the applied ABR algorithm, network and video characteristics, as well as the setting of buffer thresholds, the number of provided quality levels, and the choice of segment durations influence the Quality-of-Experience (QoE). Thus, the QoE is the result of the highly complex interaction of these parameters and the interdependence of these parameters has not been explored in a holistic manner yet. In this paper, a generic performance model for rate-based and buffer-based ABR algorithms is proposed. Using discrete-time analysis, the model allows computing relevant HAS metrics such as video interruptions and playback quality. To highlight the practical applicability of the model, an extensive evaluation is presented, in which the model's probabilistic results are compared with actual measurements of buffer-based and rate-based ABR algorithms. The results indicate that the model is sufficiently expressive to capture the HAS behavior across various settings, while still remaining computationally tractable.
Virtual Reality Technologies

Virtual reality (VR) shows great potential for use in serious games as indicated by experiences in fields ranging from healthcare to therapy and education. Still, VR is not without challenges. This chapter introduces the concept of VR and presents current technological developments. It further illustrates VR use for serious games in case studies and discusses important challenges of VR-based serious games.
Convolutional neural network based vehicle classification

Deep neural network classifier is proposed as a solution to the problem of vehicle detection and classification based on vehicle's inductive loop signature.
VRParaSet: A Virtual Reality Model for Visualizing Multidimensional Data

Visual analytics relies heavily on two-dimensional graphical representations. Going beyond 2D is useful as we can utilize additional dimensions to present complex data sets but requires more user efforts to navigate/rotate to the appropriate views. Moreover, multidimensional visual analytics in immersed environments has not been fully explored. In this paper, we present a virtual reality model for visualizing multidimensional data in parallel coordinates, called VRParaSet. Our approach stacks polylines traveling in the similar paths between two consecutive dimensions. We anchor the lines at the vertices to reduce occlusions, offer esthetic appearance, and reveal data-based structures not evident in 2D panels or contours. The proposed model is compared to its 2D and 3D equivalences through a user study of 19 participants. The results show that the virtual reality model, while potentially useful, requires a lot of cares, especially on navigation and interaction within the virtual space.
Open Set Deep Learning with A Bayesian Nonparametric Generative Model

Being a widely studied model in machine learning and multimedia community, Deep Neural Network (DNN) has achieved an encouraging success in various applications. However, conventional DNN suffers the difficulty when handling the open set learning problem, in which the true class number is unknown, and the predication label in the testing dataset usually has unseen classes which are not contained in the training set. In this paper, we aim to tackle this problem by unifying deep neural network and Dirichlet process mixture model. Firstly, to learn the deep feature and enable the incorporation of DNN and the Bayesian nonparametric model, we extend deep metric learning to a semi-supervised framework. Secondly, with the learned deep feature, we construct our open set classification method by expanding the Dirichlet process mixture model to a semi-supervised framework. To infer our semi-supervised Bayesian model, the corresponding variational inference algorithm has also been derived. Experiment on synthetic and real world datasets validates our theory analysis and demonstrates the state-of-the-art performance.
Hermite Polynomial Representation of Qubit States in Quantum Suprematism Picture

We consider theMan’ko, Margarita A. Hermite polynomial representationMan’ko, Vladimir (H-representation) of spin states for qubits and qudits in quantum suprematism picture, where the state geometry is illustrated by Triadas of Malevich’s squares. We obtain an explicit connection of the density matrices of qubit states with the wave functions of two-dimensional harmonic oscillators and the probabilities identified with the states. We establish the connection of optical tomographic-probability distributions describing the oscillator states with qudit state tomograms.
Subset Spaces for Conditional Norms

We introduce two notions of conditionals, forward conditional for deductive implication and backward conditional for abductive implication. The former is in regard to Lewis [16]’s conditional, while the latter is treated as a binary window modality. We introduce logics of forward and backward conditionals, interpreted over a point-set semantics (with explicit likelihood) from the logic of subset spaces. These conditionals and their logics have applications in the studies of conditional norms.
Historical Evolutions of Cybercrime: From Computer Crime to Cybercrime

This chapter aims to examine cybercrime viz-à-viz technological developments by identifying key research studies in the field and crime resulting from technological changes. The industrial revolution technological process and innovations are discussed to illustrate their significant role in shaping and creating cybercrime research. In the age of the fourth industrial revolution, existing cybercrimes will coexist with newer types and versions of cybercrimes while combining traditional crimes in hybrid forms. This chapter concludes with future challenges for cybercrime research.