Reviewerly/exHarmony huggingface dataset:
Security of e-banking systems: modelling the process of counteracting e-banking fraud

The paper is devoted to the current issue of the counteracting cyberattacks in the banking sector, in particular in the field of e-banking. The main types of banking fraud, which are carried out in the online sphere, are considered. The authors propose a mathematical model that describes the process of counteracting e-banking fraud. Proposed model is based on the classic Lotka-Volterra model with logistic growth and the Holling-Tanner dynamic models. The fixed points of a dynamic system were calculated and analyzed. It was determined that there are 4 possible types of fixed points: saddle and the line of stable fixed points, which are unlikely may be in real life, stable node and a stable degenerate node, which are, in practice, the most likely cases. The constructed model could be used for theoretical study, different simulation experiments with changing input parameters could be done. Unfortunately, it is difficult to investigate this question on real data, since the statistics on cyberattacks are closed.
Pragmatic Characteristics of Security Conversations: An Exploratory Linguistic Analysis

Experts suggest that engineering secure software requires a defensive mindset to be ingrained in developer culture, which could be reflected in conversation. But what does a conversation about software security in a real project look like? Linguists analyze a wide array of characteristics: lexical, syntactic, semantic, and pragmatic. Pragmatics focus on identifying the style and tone of the author's language. If security requires a different mindset, then perhaps this would be reflected in the conversations' pragmatics. Our goal is to characterize the pragmatic features of conversations about security so that developers can be more informed about communication strategies regarding security concerns. We collected and annotated a corpus of conversations from 415,041 bug reports in the Chromium project. We examined five linguistic metrics related to pragmatics: formality, informativeness, implicature, politeness, and uncertainty. Our initial exploration into these data show that pragmatics plays a role, however small, in security conversations. These results indicate that the area of linguistic analysis shows promise in automatically identifying effective security communication strategies.
Troll Farm

Anonymity can create cowards. Perceptions of mistreatment can create an urge for revenge. And online social media platforms create opportunities to exact vengeance. This chapter provides an overview of online character assassination as it has evolved within a profusion of social media sites offering forums for uncensored airing of opinions. When opinions constitute political speech, they can be life-threatening. When opinions are commercial speech rating character and competence of professionals, digital defamation can threaten livelihood. In commercial arenas, victims often feel helpless to protect their reputations; however, some legal remedies may be available. This essay investigates the nature of abusive communication online, the role anonymity plays in digital attacks, and psychological characteristics associated with trolls and cyber-bullies. Case studies of individuals' efforts to defend themselves from online character assassination illustrate concepts discussed and strategies being used for online reputational self-defense.
A Group Recommender System for Academic Venue Personalization

With the increasing number of academic venues and scientific activities, it is generally difficult for researchers to choose the most appropriate conference or journal to submit their works. A recommender System (RS) may be used to suggest upcoming venues for scientists. Although standard recommender systems have shown their efficiency in supporting individual decisions, they are not appropriate for suggesting items when more than one person is involved in the recommendation process. Since a scientific paper is generally written by a group of researchers, we propose in this paper a new group recommender system that suggests for these researchers personalized conferences that fit their preferences and interests. The main idea is to recommend academic venues for a group of researchers based on the venues attended by not only their co-authors, i.e., the group members, but also on their co-citers. Our recommender system is also able to filter out irrelevant conferences that do not meet the requirements of those researchers, their preferences in terms of conferences location, publisher and ranking. Experimental results demonstrate the efficiency of our new group recommender system.
Sentiment Classification of Reviews Based on BiGRU Neural Network and Fine-grained Attention

Abstract Text sentiment analysis is part and parcel of natural language processing. The task of sentiment classification is actually the process of feature extraction through models. The comment text of commodities is very different from the ordinary text. The comment text has no fixed grammar and writing format and the sentiment feature information is scattered in various places of text. Due to these factors, model learning of sentiment classification is becoming increasingly complex. The paper aims at establishing a fine-grained feature extraction model based on BiGRU and attention. Firstly, the vocabulary is vectorized by means of the skip-gram model. Then, according to the pre-trained word vector, the sentiment words list can be reached and noise filtering would be conducted by Naive Bayes algorithm. Finally, the model extracts features using BiGRU and fine-grained attentions. Based on the hypothesis that a long review may lead to feature differentiation, a fine-grained attention model is proposed. In this model, the attention layer is design to focus on the feature in different level such as word level, sentence level and paragraph level. This paper validate the proposed model on two sentiment corpus JD reviews and IMDB. Empirical results show that the FGAtten-BiGRU model achieves state of the art results on sentiment analysis tasks.
Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pixels. We have proposed a novel image enhancement scheme based on intuitionistic hesitant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the photographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.
Art facing science: Artistic heuristics for face detection

Automatic Area Of Interest (AOI) demarcation of facial regions is not yet commonplace in applied eye-tracking research, partially because automatic AOI labeling is prone to error. Most previous eye-tracking studies relied on manual frame-by-frame labeling of facial AOIs. We present a fully automatic approach for facial AOI labeling (i.e., eyes, nose, mouth) and gaze registration within those AOIs, based on modern computer vision techniques combined with heuristics drawn from art. We discuss details in computing gaze analytics, provide proof-of-concept, and a short validation against what we consider ground truth. Relative dwell time over expected AOIs exceeded 98% showing efficacy of the approach.
Reasoning and Decision Making under Uncertainty and Risk for Situation Management

This paper addresses the problem of situation management in an uncertain and ambiguous environment. It presents an approach to reasoning and decision making for identifying asymmetric threats and preventing or mitigating possible catastrophic consequences under resource constraints. Decision making for action selection incorporates decision makers' attitude toward risk and uncertainty in the situations involving regular as well as high consequence rare events. The presented approach is based on the OODA loop and implemented within the framework of the Transferable Belief Model, Choquet Expected Utility, Multiple Quantile Model, abductive reasoning utilizing the Belief-based argumentation, and the Anytime decision making. A quality control component is added to the OODA loop to deal with possible insufficient quality of input information and results of processes involved. An example illustrating the approach is presented in this paper.
A Sketch Classifier Technique with Deep Learning Models Realized in an Embedded System

Since 2011, due to the growth in the amount of information, the innovation of learning algorithms and the improvement of computer technology make the application of artificial intelligence feasible in a wide range of fields. This paper presents a sketch classifier technique with deep learning models. We use the depth-wise convolution layer to lighten the deep neural network. The result shows the improvement in approximately 1/5 of computation. We use Google Quick Draw dataset to train and evaluate the network, which can have 98% accuracy in 10 categories and 85% accuracy in 100 categories. Finally, we realize it on STM32F469I Discovery development board for demonstration. The system can achieve real-time implementation of sketch classification.
Phase unwrapping based on transport-of-intensity equation with two wavelengths

The phase obtained in the interferogram is generally not continuous real phase information. Therefore, a phase larger than 2π will have a phase jump called wrapped phase. The traditional phase unwrapping algorithm usually requires a large amount of computation, and in reality, phase unwrapping is often a complex and morbid inverse problem due to factors such as speckle noise, random perturbations, low modulation, and phase discontinuity, and the processes of unwrapping of some algorithms depend on the path chosen. Therefore, it is difficult to obtain an accurate absolute phase under actual conditions. To improve the accuracy of the absolute phase, a phase unwrapping algorithm based on the transport-of-intensity equation with two-wavelength illumination is proposed. The experimental results illustrate that the proposed method not only improves the accuracy of absolute phase but also holds a good antinoise performance.
Implementation of a fixed-point quantum search by duality computer

The quantum algorithms where the operations are unitary can be implemented by an ordinary quantum computer, and nonunitary operations can be realized in the duality computer. The operations in the fixed-point quantum search proposed by Mizel (Phys. Rev. Lett., 102 (2009) 150501) are not unitary, and in this paper it is shown that this algorithm can effectively be implemented in duality quantum computer.
Hybrid Ant Fuzzy Algorithm for MRI Images Segmentation

Image segmentation is the process of subdividing an image into regions that are consistent and homogeneous in some characteristics. Image segmentation is indeed a vital process in the early diagnosis of abnormalities and treatment planning. The segmentation algorithms are applied to extract the anatomical structures and anomalies from medical images. Segmentation of Magnetic Resonance Imaging (MRI) requires a lot of time when it is performed by medical specialists. The task of automation of recognition is topical in case the correct evaluation is given. The different types of segmentation algorithms are discussed in this paper. It is true that there is no universal algorithm for medical image segmentation, wherein, the choice depends upon the image modality, characteristics of region of interest and application. There is neither a single segmentation model for all medical image modalities nor all methods are efficient for a specific medical image modality. In image processing and computer vision, segmentation is still a challenging problem in many real time applications and hence more research work is required. The Hybrid Ant Fuzzy Algorithm (HAFA) for the segmentation of MRI is considered in this paper. The parameters of the HAFA are examined for different groups of MRI images. Medical images from OsiriX set and real patient pictures were used to test the algorithm. The experimental results show that the proposed algorithm has good performance and accuracy in comparison to analogues.
Analysis and Classification of University Centers for Students with Disabilities

University centers for students with disabilities are focused on providing appropriate assistance, assistive technologies, and accommodations for those students to ensure that they will receive equal access to university programs, facilities, services, and events. The concept of a smart university requires universities to equip students with disabilities with suitable software/hardware systems and assistive technologies that will help them to succeed in technological learning environments such as smart classrooms and labs, smart libraries, and smart campuses. This paper presents the outcomes of a research project at the InterLabs Research Institute, Bradley University (Peoria, IL, USA) aimed at a systematic analysis of existing university centers for students with disabilities and their classification. Using information available in public domain, we analyzed and classified 30 university centers for students with disabilities and classified 23 university/college centers for students with special needs in terms of software systems, assistive technology, and hardware available for these students. In our opinion, those technological components could significantly benefit university/college students with disabilities at a smart university.
New Era in the Supply Chain Management With Blockchain

The results show that transparency and auditability, security and indelibility, and distribution and sustainability are the key attributes of blockchain-based solutions in 56% of the articles reviewed. These three aspects represent the foundation of blockchain technologies which may contribute positively to improve supply management processes. Moreover, immutability, tracking and tracing, and smart contracts are also included in nearly a third of the cases. Moreover, efficiencies and costs through this technology would reduce the costs in payment of intermediaries, reduce paperwork, and help in the shipment of physical documents. Supply chain plays a critical role in the global trade and urgently needs to reassess its models in searching for greater efficiencies. Moreover, better results in visibility across the chain will increase trust for the customers and all interested parties. Secure transactions, strong security mechanisms that prevent fraud and illegal practices, could be achieved through the blockchain.
Information model of the economic efficiency of advertising

The development of the economy and trade has led to the widespread use of advertising and the need for its constant improvement. There is a vast field of advertising theory. This paper proposes an information model of the functioning of advertising. We introduce ideas about useful and harmful (excess) information. The concept of user’s thesaurus is also introduced. The effectiveness of advertising is determined by the mutual influence of useful and redundant information. Differential equations are formulated on this basis, the solution of which allows to establish the patterns of the influence of psychological characteristics of users and the mode of presentation of information on the effectiveness of advertising.
Deep Fusion Feature Presentations for Nonaligned Person Re-Identification

Person re-identification aims to retrieve the pedestrian across different cameras.It is still a challenging task for the intelligent visual surveillance system because of similar appearances, camera shooting angles, scene illumination, and pedestrian pose.In this paper, we propose a novel two-stream network named spatial segmentation network that learns both the global and local features in a unified framework for nonaligned person re-identification.One stream focuses on spatial feature learning using global adaptive average pooling in deep convolutional neural networks.Another stream is utilized to learn the fine local features by adopting horizontal average pooling without division that depends on the pose predictor.To assess the importance ranking of all features, we also obtain the performance of every part feature and global features.Our evaluation of the proposed method on Market-1501 acquires 94.51% Rank-1 and 90.78% mAP, that on DukeMTMC-re-ID acquires 87.52% Rank-1 and 84.82% mAP, and that on CHUK03-detected acquires 69.71% Rank-1 and 71.67% mAP; these findings verify the state-of-the-art performance of the proposed method.
Designing Software to Prevent Child Marriage Globally

Child marriage is any marriage where one or both of the participants is under the age of 18. Today, more than 12 million such marriages happen every year [11]. Giving children and the communities around them access to information about the dangers of child marriage is crucial in order to change the cultural norms that enable and perpetuate it. Mobile applications have so far seen sparse use to combat this problem. This paper covers a project that looks to evaluate the effect of different design options for mobile applications in this field. It covers seven different design approaches, as well as the testing and evaluation of these in a case study amongst youth in Malawi. During Spring 2019, an initial testing round will be held there in cooperation with Plan International. There, we will gather feedback from local youths on what they know and think about child marriage, as well as seeing how they interact with the designs we have made.
Revealing the system-bath coupling via Landau-Zener-Stückelberg interferometry in superconducting qubits

In this work we propose a way to unveil the type of environmental noise in strongly driven superconducting flux qubits through the analysis of the Landau-Zener-St\"uckelberg (LZS) interferometry. We study both the two-level and the multilevel dynamics of the flux qubit driven by a dc+ac magnetic field. We found that the LZS interference patterns exhibit well defined multiphoton resonances whose shape strongly depend on the time scale and the type of coupling to a quantum bath. For the case of transverse system-bath coupling, the n-photon resonances are narrow and nearly symmetric with respect to the dc magnetic field for almost all time scales, whilst in the case of longitudinal coupling they exhibit a change from a wide symmetric to an antisymmetric shape for times of the order of the relaxation time. We find this dynamic behavior relevant for the interpretation of several LZS interferometry experiments in which the stationary regime is not completely reached.
A Unified Method for Digital Super-Resolution and Restoration in Infrared Microscopy Imaging

Infrared (IR) imaging systems are known to have a range of sensor and optical limitations that result in degraded imagery.Fixed pattern noise (FPN), resulting from pixel-to-pixel response nonuniformity, is a dominant source of error that manifests in collected imagery through the appearance of temporally and spatially correlated noise patterns that are mixed with each image.Furthermore, finite detector size coupled with imperfect system optics can introduce blurring effects and aliasing, ultimately reducing resolution in acquired images.Here, we propose a unified method to reduce FPN and recover high-frequency image content in IR microscopy images.The proposed method uses regularized nonlocal means to highlight spatial features in the scene while maintaining fine textural image details.We derive an iterative optimization method based upon a gradient descent minimization strategy that applies a Wiener deconvolution in each iteration to estimate the blur artifacts.The method is implemented within an embedded mid-wave IR imaging system for microscopy applications.We demonstrate a reduction in FPN and blurring artifacts, achieving improved image resolution in the reconstructed images that are apparent in recovered details on scene objects.
Gesture-based target acquisition in virtual and augmented reality

Background Gesture is a basic interaction channel that is frequently used by humans to communicate in daily life. In this paper, we explore to use gesture-based approaches for target acquisition in virtual and augmented reality. A typical process of gesture-based target acquisition is: when a user intends to acquire a target, she performs a gesture with her hands, head or other parts of the body, the computer senses and recognizes the gesture and infers the most possible target. Methods We build mental model and behavior model of the user to study two key parts of the interaction process. Mental model describes how user thinks up a gesture for acquiring a target, and can be the intuitive mapping between gestures and targets. Behavior model describes how user moves the body parts to perform the gestures, and the relationship between the gesture that user intends to perform and signals that computer senses. Results In this paper, we present and discuss three pieces of research that focus on the mental model and behavior model of gesture-based target acquisition in VR and AR. Conclusions We show that leveraging these two models, interaction experience and performance can be improved in VR and AR environments.
Image-based tracking technique assessment and application to a fluid–structure interaction experiment

This work analyses the accuracy and capabilities of two image-based tracking techniques related to digital image correlation and the Lucas–Kanade optical flow method, with the subsequent quantification of body motion in a fluid–structure interaction experiment. A computer-controlled shaker was used as a benchmark case to create a one-dimensional oscillatory target motion. Three target frequencies were recorded. The measurements obtained with a low-cost digital camera were compared to a high-precision motion tracking system. The comparison was performed under changes in image resolution, target motion and sampling frequency. The results show that, with a correct selection of the processing parameters, both tracking techniques were able to track the main motion and frequency of the target even after a reduction of four and five times the sampling frequency and image resolution, respectively. Within this good agreement, the Lucas–Kanade technique shows better accuracy under tested conditions, achieving up to 15.6% of lower tracking error. Nevertheless, the achievement of this higher accuracy is highly dependent on the position of the selected initial target point. These considerations are addressed to satisfactorily track the response of a wall-mounted cylinder subjected to a range of turbulent flows using a single camera as the measuring device.