Reviewerly/exHarmony huggingface dataset:
Information Security Policy Compliance: Leadership, Trust, Role Values, and Awareness

The purpose of this study was to find out which one of the four selected predictor variables, i.e., leadership, trusting beliefs, role values, and information security policy awareness are most influential in predicting employees’ intention to comply with organizational information security policy. An instrument with five constructs was administered to subjects from a higher-education university in the USA. Collected data were methodically examined through multiple regression analysis. Results indicated that all four predictor variables were influential in predicting employees’ intention to comply with organizational information security policy requirements. Implications of the findings are discussed and recommendations for future research are made.
Boosting ICD multi-label classification of health records with contextual embeddings and label-granularity

Background and objective:This work deals with clinical text mining, a field of Natural Language Processing applied to biomedical informatics. The aim is to classify Electronic Health Records with respect to the International Classification of Diseases, which is the foundation for the identification of international health statistics, and the standard for reporting diseases and health conditions. Within the framework of data mining, the goal is the multi-label classification, as each health record has assigned multiple International Classification of Diseases codes. We investigate five Deep Learning architectures with a dataset obtained from the Basque Country Health System, and six different perspectives derived from shifts in the input and the output. Methods:We evaluate a Feed Forward Neural Network as the baseline and several Recurrent models based on the Bidirectional GRU architecture, putting our research focus on the text representation layer and testing three variants, from standard word embeddings to meta word embeddings techniques and contextual embeddings. Results:The results showed that the recurrent models overcome the non-recurrent model. The meta word embeddings techniques are capable of beating the standard word embeddings, but the contextual embeddings exhibit as the most robust for the downstream task overall. Additionally, the label-granularity alone has an impact on the classification performance. Conclusions:The contributions of this work are a) a comparison among five classification approaches based on Deep Learning on a Spanish dataset to cope with the multi-label health text classification problem; b) the study of the impact of document length and label-set size and granularity in the multi-label context; and c) the study of measures to mitigate multi-label text classification problems related to label-set size and sparseness.
Customer Perceived Value- and Risk-Aware Multiserver Configuration for Profit Maximization

Along with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively.
Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment

3D face alignment of monocular images is a crucial process in the recognition of faces with disguise.3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference.This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework.We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism. In order to enhance the ability of the network model to extract the spatial features of the face region, we adopt Spatial Group-wise Feature enhancement module to improve the representation ability of the network. Different loss functions are applied jointly to constrain the 3D parameters of a 3D Morphable Model (3DMM) and its 3D vertices. We use a variety of data enhancement methods and generate large virtual pose face data sets to solve the data imbalance problem. The experiments on the challenging AFLW, AFLW2000-3D datasets show that our algorithm significantly improves the accuracy of 3D face alignment. Our experiments using the field DFW dataset show that DAMDNet exhibits excellent performance in the 3D alignment and reconstruction of challenging disguised faces.The model parameters and the complexity of the proposed method are also reduced significantly.
Rank-metric codes, linear sets, and their duality

In this paper we investigate connections between linear sets and subspaces of linear maps. We give a geometric interpretation of the results of Sheekey (Adv Math Commun 10:475–488, 2016, Sect. 5) on linear sets on a projective line. We extend this to linear sets in arbitrary dimension, giving the connection between two constructions for linear sets defined in Lunardon (J Comb Theory Ser A 149:1–20, 2017). Finally, we then exploit this connection by using the MacWilliams identities to obtain information about the possible weight distribution of a linear set of rank n on a projective line $$\mathrm {PG}(1,q^n)$$.
Multi-Attention Generative Adversarial Network for image captioning

Recently, it has been shown that generative-adversarial-nets (GANs) can be directly utilized as an extension of traditional reinforcement-learning in image captioning tasks. However, the GANs-based methods generate captions as a function of only local points in the feature map without capturing non-local information. In this paper, a Multi-Attention mechanism is first proposed by utilizing both of the local and non-local evidence for more effective feature representation and reasoning in image captioning. Based on the mechanism, a Multi-Attention Generative Adversarial Image Captioning Network (MAGAN) is also proposed which contains a Multi-Attention generator and a Multi-Attention discriminator. The proposed generator is designed to generate more accurate sentences, while the proposed discriminator is employed to determine whether generated sentences are human described or machine generated. Extensive experiments are conducted to validate the proposed framework on MSCOCO benchmark dataset, and it achieves very competitive results evaluated by the evaluation server of MS COCO captioning challenge.
Threat and Abusive Language Detection on Social Media in Bengali Language

Threat and abusive languages spread quickly through social media which can be controlled if we can detect and remove them. Since there exist many social media like Facebook, Twitter, Instagram etc and a huge number of social media users, we need a robust and effective automatic system to identify threat and abusive languages. In our proposed system Machine Learning and Natural Language Processing techniques have been implemented to build an automatic system. Previous research on Bengali abusive language detection used Multinomial Näıve Bayes (MNB), Support Vector Machine(SVM) algorithms and considered Bengali Unicode characters to build their system. We considered both Unicode emoticons and Unicode Bengali characters as valid input in our proposed system. Besides MNB and SVM algorithm, we implemented Convolutional Neural Network (CNN) with Long Short Term Memory(LSTM). Among three algorithms, SVM with linear kernel performed best with 78% accuracy.
The seductions of cybercrime: Adolescence and the thrills of digital transgression

This article offers a socio-technical framework for better understanding youthful attraction to, and engagement in, online transgressions and delinquencies. Specifically, it takes the concept of ‘seduction’ from the work of Katz, as well as ‘affordance theory’ and insights from software and human–computer interaction studies, to analyse the affordances of the Internet that tempt and invite youthful transgressions such as digital piracy, viewing illegal pornography and hacking. We argue that Internet affordances not only enable transgressions to occur but can also precipitate them. The implications for youth crime policy are briefly addressed. Policy needs to reckon with the power of these factors in adolescent lives and thus minimize reliance on punitive responses. The article also contributes to the development of the concept of digital drift, by showing how Internet features and affordances foster drift into cyber delinquency.
Design and Development of Hand Gesture Based Virtual Mouse

The technique of establishing a process of interaction between human and computer is evolving since the invention of computer technology. The mouse is an excellent invention in HCI (Human-Computer Interaction) technology. Though wireless or Bluetooth mouse technology is invented still, that technology is not completely device free. A Bluetooth mouse has the requirement of battery power and connecting dongle. Presence of extra devices in a mouse increases the difficulty to use it. The proposed mouse system is beyond this limitation. This paper proposes a virtual mouse system based on HCI using computer vision and hand gestures. Gestures captured with a built-in camera or webcam and processed with color segmentation & detection technique. The user will be allowed to control some of the computer cursor functions with their hands which bear colored caps on fingertips. Primarily, a user can perform left clicks, right clicks, and double clicks, scrolling up or down using their hand in different gestures. This system captures frames using a webcam or built-in cam and processes the frames to make them track-able and after that recognizes different gestures made by users and perform the mouse function. So the proposed mouse system eliminates device dependency in order to use a mouse. Therefore it can be proved beneficial in order to develop HCI technology.
A Novel Bat Algorithm based on Cross Boundary Learning and Uniform Explosion Strategy

Abstract Population-based algorithms have been used in many real-world problems. Bat algorithm (BA) is one of the states of the art of these approaches. Because of the super bat, on the one hand, BA can converge quickly; on the other hand, it is easy to fall into local optimum. Therefore, for typical BA algorithms, the ability of exploration and exploitation is not strong enough and it is hard to find a precise result. In this paper, we propose a novel bat algorithm based on cross boundary learning (CBL) and uniform explosion strategy (UES), namely BABLUE in short, to avoid the above contradiction and achieve both fast convergence and high quality. Different from previous opposition-based learning, the proposed CBL can expand the search area of population and then maintain the ability of global exploration in the process of fast convergence. In order to enhance the ability of local exploitation of the proposed algorithm, we propose UES, which can achieve almost the same search precise as that of firework explosion algorithm but consume less computation resource. BABLUE is tested with numerous experiments on unimodal, multimodal, one-dimensional, high-dimensional and discrete problems, and then compared with other typical intelligent optimization algorithms. The results show that the proposed algorithm outperforms other algorithms.
An Efficient Multi-Message and Multi-Receiver Signcryption Scheme for Heterogeneous Smart Mobile IoT

The Internet of Things (IoT) is developing towards smart and mobile Internet of Things (SM-IoT), which has made great progress.Due to the inherent heterogeneity, distribution, intensive communication, and resource constraints of SM-IoT, efficient security and privacy communication protocols become a particularly critical challenge.Signcryption has received considerable attention.Various signcryption schemes have been proposed to solve secure communication.However, most of them are low in efficiency, without the consideration of characteristics of the SM-IoT.In this paper, we propose a signcryption scheme to achieve efficient secure multi-message and multi-receiver communication for the heterogeneous and distributed SM-IoT.We develop Identity-based Cryptography (IBC) and Certificateless Cryptography (CLC) to solve the certificate management problem.Our scheme no longer needs wireless secure channel during key generation phase of traditional CLC system, which improves the applicability of our scheme in wireless SM-IoT environment.There is no expensive operations, such as bilinear pairing, in our scheme.In addition, our scheme outsources part of the verification overhead from the SM-IoT users to the gateway without revealing user privacy.The performance evaluation shows that the computation efficiency in both sender and receiver side is improved in our scheme.INDEX TERMS Certificateless cryptography (CLC), identity-based cryptography (IBC), heterogeneous, verification outsourcing, smart mobile Internet of Things (SM-IoT), multi-massage and multi-receiver signcryption.
Exploring Multi-Level Attention and Semantic Relationship for Remote Sensing Image Captioning

Remote sensing image captioning, which aims to understand high-level semantic information and interactions of different ground objects, is a new emerging research topic in recent years. Though image captioning has developed rapidly with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the image captioning task for remote sensing images still suffers from two main limitations. One limitation is that the scales of objects in remote sensing images vary dramatically, which makes it difficult to obtain an effective image representation. Another limitation is that the visual relationship in remote sensing images is still underused, which should have great potential to improve the final performance. In order to deal with these two limitations, an effective framework for captioning the remote sensing image is proposed in this paper. The framework is based on multi-level attention and multi-label attribute graph convolution. Specifically, the proposed multi-level attention module can adaptively focus not only on specific spatial features, but also on features of specific scales. Moreover, the designed attribute graph convolution module can employ the attribute-graph to learn more effective attribute features for image captioning. Extensive experiments are conducted and the proposed method achieves superior performance on UCM-captions, Sydney-captions and RSICD dataset.
A taxonomy of users’ active design engagement in the 21st century

People not only purchase and use products and services, but creatively appropriate, hack, redesign and even innovate in them. Typologies of active use have emerged in various disciplines, remaining piecemeal even if complementary. Together they produce a blurry depiction of active design engagement, despite active use being pivotal to many emerging design approaches. To remedy this, we synthesize a taxonomy of different aspects of active use and design engagement. Use as-is, active use, locally new designs and globally new innovations mark different intensities of engagement. These can concern the material form of design, new uses, new meanings, adjustment to local settings, or the collective endeavours to shape communities and organizations, ideologies and imaginaries, and global platforms that facilitate active use.
Distributed Fusion Estimation for Multisensor Multirate Systems With Packet Dropout Compensations and Correlated Noises

This article investigates distributed fusion estimation problems for multisensor multirate (MSMR) stochastic systems with correlated noises (CNs) and packet dropouts (PDs). The state updates at the fast rate while sensors uniformly sample at positive integer multiples of the state updating period. Different sensors may have different sampling rates. The system noise and measurement noises are auto- and cross-correlated at the same instant. The phenomenon of PDs randomly occurs during data transmission from a sensor to a data processor through unreliable networks. A recent developed compensation strategy that a predictor of a lost packet is employed as a compensator is adopted to optimize the tracking process. First, an optimal linear local filter (LF) for each sensor at measurement sampling points (MSPs) is presented by using an innovation analysis approach. Then, a local estimator (LE) at state updating points (SUPs) is proposed by filtering or prediction based on the LF at MSPs. Furthermore, estimation error cross-covariance matrices (CCMs) between arbitrary two LEs at SUPs are deduced, which can recursively be calculated by three joint difference equations. Finally, a distributed fusion filter (DFF) weighted by matrices in the sense of linear unbiased minimum variance (LUMV) is addressed. Period steady-state (PSS) property of the LEs, CCMs, and DFF is proved. A simulation example verifies the effectiveness of algorithms.
From Video-Conferencing to Holoportation and Haptics: How Emerging Technologies Can Enhance Presence in Online Education?

Video-conferencing, if used effectively, can support learning and teaching in online and distance learning serving the human need to communicate, and to learn by watching, and interacting with teachers and learners from anywhere. The demand for a more human approach to online education drives technologists and software developers to investigate new ways of being present online while connected with others, thereby making the experience as real-life as possible. This chapter discusses the implications of using emerging synchronous technologies in online education and explains why educators need to develop their teaching practice and understand the role of presencePresence in online teaching in Higher Education. Drawing on the Tele-Community of Inquiry model, embodied cognition and research into ‘honest signals’Honest signals we examine the potential of emerging technologies such as holoportationHoloportation, hologramsHolograms, and hapticHaptics devices used in augmented learning environments. Innovative examples from Higher Education are presented to illustrate creative ways in which emerging technologies are beginning to be used in teaching practice. Technological advances continue to increase the potential for how synchronous communication technologies can support and improve presence online and enhance virtual and real-time interactions in online education. As these technologies are still emerging there is a great need for further educational research and some directions are highlighted.
SolAnalyser: A Framework for Analysing and Testing Smart Contracts

Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools - Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.
Combining Boolean and Multimedia Retrieval in vitrivr for Large-Scale Video Search

This paper presents the most recent additions to the vitrivr multimedia retrieval stack made in preparation for the participation to the 9$$^{th}$$ Video Browser Showdown (VBS) in 2020. In addition to refining existing functionality and adding support for classical Boolean queries and metadata filters, we also completely replaced our storage engine $$\textsf {ADAM}_{pro}$$ by a new database called Cottontail DB. Furthermore, we have added support for scoring based on the temporal ordering of multiple video segments with respect to a query formulated by the user. Finally, we have also added a new object detection module based on Faster-RCNN and use the generated features for object instance search.
An efficient unsupervised feature selection procedure through feature clustering

Due to the scarcity of readily available labels, unsupervised feature selection (UFS) methods are widely adopted in the analysis of high-dimensional data. However, most of the existing UFS methods primarily focus on the significance of features in maintaining the data structure while ignoring the redundancy among features. Moreover, the determination of the proper number of features is another challenge. In this paper, an efficient unsupervised feature selection method through feature clustering (EUFSFC) is proposed to address the redundancy among features, and to determine the size of the final feature subset. The proposed methodology is comprised of two steps: (a) feature cluster analysis, and (b) the selection of the representative features. An extended density-based clustering algorithm is proposed to separate features into an appropriate number of disjoint clusters with no requirement for predefined cluster numbers or radii. The selection of features is performed by choosing the most representative features from those feature clusters. Experiments are conducted to show the effectiveness of the proposed feature selection method.