Reviewerly/exHarmony huggingface dataset:
Unlimited Corridor: A Visuo-haptic Redirection System

The Unlimited Corridor is a virtual reality system that enables users to walk in an ostensibly straight direction around a virtual corridor within a small tracked space. Unlike other redirected walking systems, the Unlimited Corridor allows users to keep walking around without interruptions or resetting phases. This is made possible by combining a redirected walking technique with visuo-haptic interaction and a path planning algorithm. The Unlimited Corridor produces passive haptic feedback using semi-circular handrails; that is, when users grip a straight handrail in the virtual environment, they simultaneously grip a corresponding curved handrail in the physical world. These stimuli enable visuo-haptic interaction, with the user perceiving the gripped handrail as straight, and this sensation enhances the effects of redirected walking. Furthermore, we developed an algorithm that dynamically modifies the amount of distortion to allow a user to walk ostensibly straight and turn at intersections in any direction. We evaluated the Unlimited Corridor using a virtual space of approximately 400 m2 in a physical space of approximately 60 m2. According to a user study, the median value of the straightness sensation of walking when users grip the handrails (5.13) was significantly larger than that of the sensation felt without gripping the handrails (3.38).
Fast-deepKCF Without Boundary Effect

In recent years, correlation filter based trackers (CF trackers) have received much attention because of their top performance. Most CF trackers, however, suffer from low frame-per-second (fps) in pursuit of higher localization accuracy by relaxing the boundary effect or exploiting the high-dimensional deep features. In order to achieve real-time tracking speed while maintaining high localization accuracy, in this paper, we propose a novel CF tracker, fdKCF*, which casts aside the popular acceleration tool, i.e., fast Fourier transform, employed by all existing CF trackers, and exploits the inherent high-overlap among real (i.e., noncyclic) and dense samples to efficiently construct the kernel matrix. Our fdKCF* enjoys the following three advantages. (i) It is efficiently trained in kernel space and spatial domain without the boundary effect. (ii) Its fps is almost independent of the number of feature channels. Therefore, it is almost real-time, i.e., 24 fps on OTB-2015, even though the high-dimensional deep features are employed. (iii) Its localization accuracy is state-of-the-art. Extensive experiments on four public benchmarks, OTB-2013, OTB-2015, VOT2016, and VOT2017, show that the proposed fdKCF* achieves the state-of-the-art localization performance with remarkably faster speed than C-COT and ECO.
Employing Digital Reality Technologies in Art Exhibitions and Museums

The global AR, MR, and VR markets will reach USD$40.6 billion in 2019. As a result, digital reality technologies have become a key component of promoting art exhibition and museum industries to the general public around the world. Emerging applications such as ARCHEOGUIDE, ARCO, and 3D-MURALE have allowed museum-goers to access archeological artefacts and sites remotely without physically visiting the museums. Digital reality technologies have therefore been perceived to have the great potential to promote (creative) cultural industry contents, because of the characteristics of these platforms (e.g., interactivity, realism, and visualization). This chapter employs a case study approach to discuss the current state of digital reality technology applications in museums and art exhibitions around the world. The study provides several best practice examples to demonstrate how digital reality technologies have fundamentally transformed the art exhibitions and museums.
An Efficient Signcryption Protocol for Hop-by-Hop Data Aggregations in Smart Grids

Aggregation trees have been proposed for privacy preservation to reduce data collectors' computational cost in smart grids. In aggregation tree models, smart meter usage data should be forwarded by all its ancestors, which delays usage data transmissions. We report the design of an identity-based signcryption protocol without oracles for aggregation trees. The designed protocol significantly reduces smart meter computational costs due to its homomorphic features. (1) Compressed signatures can be verified in batches. The computational cost of verification is independent of the number of usage reports. (2) A smart meter can generate a signature on the aggregated cipher without executing signing algorithms during transmissions. Extensive simulations demonstrate that, based on the proposed protocol, the communication efficiency of our privacy preservation scheme outperforms other aggregation tree-based schemes regarding transmission delay and computational cost.
Detecting Face Morphing Attacks with Collaborative Representation of Steerable Features

Passports have used face characteristics to verify and establish the identity of an individual. Face images provide high accuracy in verification and also present the opportunity of verifying the identity visually against the passport face image if the need arises. Morphed image-based identity attacks are recently shown to exploit the vulnerability of passport issuance and verification systems, where two different identities are morphed into one image to match against both images. The challenge is further increased when the properties in the digital domain are lost after the process of print and scan. This work addresses such a problem of detecting the morphing of face images such that the attacks are detected even after the print and scan process. As the first contribution of this work, we extend an existing database with 693 bonafide and 1202 morphed face images with the newly added of 579 bonafide and 1315 morphed images. We further propose a new approach based on extracting textural features across scale-space and classifying them using collaborative representation. With a set of extensive experiments and benchmarking against the traditional (non-deep-learning methods) and deep-learning methods, we illustrate the applicability of the proposed approach in detecting the morphing attacks. With an obtained Bonafide Presentation Classification Error (BPCER) of \(13.12\%\) at Attack Presentation Classification Error Rate (APCER) of \(10\%\), the use of the proposed method can be envisioned for detecting morph attacks even after print and scan process.
Temperature variation during continuous laser-assisted adjacent hoop winding of type-IV pressure vessels: An experimental analysis

Laser-assisted tape winding is an automated process to produce tubular or tube-like continuous fiber-reinforced polymer composites by winding a tape around a mandrel or liner. Placing additional layers on a previously heated substrate and variation in material and process parameters causes a variation in the bonding temperature of fiber-reinforced thermoplastic tapes which need to be understood and described well in order to have a reliable manufacturing process. In order to quantify the variation in this critical bonding temperature, a comprehensive temperature analysis of an adjacent hoop winding process of type-IV pressure vessels is performed. A total of five tanks are manufactured in which three glass/HDPE tapes are placed on an HDPE liner. The tape and substrate temperatures, roller force and tape feeding velocity are measured. The coefficient of variation for each round is characterized for the first time. According to the statistical analysis, the coefficient of variation in substrate temperature is found to be approximately 4.8–8.8% which is larger than the coefficient of variation of the tape temperature which is 2.1–7.8%. The coefficient of variations of the substrate temperatures in the third round decrease as compared with the coefficient of variations in the second round mainly due to the change in gap/overlap behavior of the deposited tapes. Fourier and thermographic analysis evince that the geometrical disturbances such as unroundness and eccentricity have a direct effect on the temperature variation. In addition to the temperature feedback control, a real-time object detection technique with deep learning algorithms can be used to mitigate the unwanted temperature variation and to have a more reliable thermal history.
A Critical Exploration of Agonistic Participatory Design

Agonism is often described as a means for designers and citizens to reveal and contest hegemonies in society. This paper explores how agonism used within participatory design processes can enable participants to question power structures embedded in projects, express diverging views and renegotiate project frames. The potential for reaching the democratic ideals of participation thereby increases. However, there are challenges with agonism that are yet to gain momentum in the design discourse. The purpose of this paper is to identify these challenges. This is done by reflecting on a community-based tourism development project in Kenya. The reflection illustrates that not everybody may feel comfortable with sharing views in democratic forums, that people have varying preconditions for engaging in agonism and that a multitude of hegemonies exists within projects. It also highlights a need to reflect on the transferability of agonism, at a time when participatory design moves across cultures.
Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing

Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown outstanding performance in various machine learning tasks. In this study, we propose using a DDAE to address a dispatching rule selection problem that represents a major problem in semiconductor manufacturing. Recently, the significance of dispatching systems for storage allocation has become more apparent because operational issues lead to transfer inefficiency, resulting in production losses. Further, recent approaches have overlooked the possibility of a class imbalance problem in predicting the best dispatching rule. The main purpose of this study is to examine DDAE-based predictive control of the storage dispatching systems to reduce idle machines and production losses. We conducted an experimental evaluation to compare the predictive performance of DDAE with those of five other novelty detection algorithms. Finally, we compared our adaptive approach with the optimization and existing heuristic approaches to demonstrate the effectiveness and efficiency of the proposed method. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of machine utilizations and throughputs.
Towards assisting developers in API usage by automated recovery of complex temporal patterns

Despite the many advantages, the use of external libraries through their APIs remains difficult because of the usage patterns and constraints that are hidden or not properly documented. Existing work provides different techniques to recover API usage patterns from client programs in order to help developers use those libraries. However, most of these techniques produce patterns that generally do not involve temporal properties. In this paper, we discuss the problem of temporal usage patterns recovery and propose an algorithm to solve it. We also discuss how the obtained patterns can be used at different stages of client development. We address the recovery of temporal API usage patterns as an optimization problem and solve it using a genetic-programming algorithm. Our evaluation on different APIs shows that the proposed algorithm allows to derive non-trivial temporal usage that are useful and generalizable to new API clients. Recovering API usage temporal patterns helps client developers to use APIs in an appropriate way. In addition to potentially improve productivity, such patterns also helps preventing errors that result from an incorrect use of the APIs.
An efficient and lightweight multi‐scroll chaos‐based hardware solution for protecting fingerprint biometric templates

Summary This paper presents a new efficient and lightweight approach for enhancing the security of biometric models, namely, fingerprint templates, against possible attacks. The proposed design is based on Vernam stream cipher in which the key generator is designed in the hardware manner. The designed cryptosystem consists of using multi‐scroll chaotic system that is characterized by a large key space and can be generated N × N grid multi‐scroll attractors, with a good behavior of chaotic dynamic. The hardware approach is carried out through describing Euler method by VHDL. Field‐programmable gate array (FPGA) experimental results validate the developed architecture while still providing a good compromise between hardware resources and performance. Indeed, security analysis also shows that the designed encryption algorithm is robust against statistical, brute force, and entropy attacks. Therefore, it can be considered as a lightweight security solution, which could be very useful in many embedded applications namely securing biometric authentication systems.
Convolutional Neural Networks for Online Arabic Characters Recognition with Beta-Elliptic Knowledge Domain

Handwriting recognition is challenging research filed in spite of the progress of techniques used on its recognition. Deeper neural networks have achieved good results in this field. Current neural networks especially deep convolutional networks, neglect spatial and temporal information of script and deal only with it as an image. Features can be a crucial fact for separating between handwriting scripts. In this paper, we propose a CNN based on Beta-elliptic parameters and Fuzzy Elementary Perceptual Codes for Online Arabic Characters Recognition. Experimental results on two databases, LMCA and MAYASTROUN, indicate that our novel system based on CNN is possible on an online script and gives good accuracy of 98.90% compared to recent works in the state of the art.
Relationship Between Digital Creativity, Parenting Style, and Adolescent Performance

Introduction: Today's adolescents live immersed in the digital world and are much more familiarized with the use of electronic devices. At the same time, the new technologies have become established as a powerful resource in teaching and learning, providing new texts where the limits of time and space are overcome. Digital creativity is part of people's daily lives and must be developed from the school and family context. Objective: The objective of this study was to analyze the relationship among digital creativity, parenting style, and academic performance. Method: This analysis was carried out in a sample of 742 adolescents in Middle School and High School aged 13-19. Digital creativity was evaluated using the Creative Behavior Questionnaire: Digital (CBQD). The Parenting Style Scale was used to evaluate the perception teenage boys and girls who have the various dimensions of their parents' educational style. Results: Parenting styles were established as a mediating variable in the relationship between digital creativity and academic performance. Conclusion: The roles of digital creativity, which is proposed as a facilitating tool in teaching, and parenting styles in academic performance for improving the family-school relationship are discussed.
Signal identification system for developing rehabilitative device using deep learning algorithms

Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to migrate. To overcome from their problem they need some assistive technology with the help of bio signals. Electrooculogram (EOG) based Human Computer Interaction (HCI) is one of the technique used in recent days to overcome such problem. In this paper we clearly check the possibilities of creating nine states HCI by our proposed method. Signals were captured through five electrodes placed on the subjects face around the eyes. These signals were amplified with ADT26 bio amplifier, filtered with notch filter, and processed with reference power and band power techniques to extract features to detect the eye movements and mapped with Time Delay Neural Network to classify the eye movements to generate control signal to control external hardware devices. Our experimental study reports that maximum average classification of 91.09% for reference power feature and 91.55%-for band power feature respectively. The obtained result confirms that band power features with TDNN network models shows better performance than reference features for all subjects. From this outcome we conclude that band power features with TDNN network models was more suitable for classifying the eleven difference eye movements for individual subjects. To validate the result obtained from this method we categorize the subjects in age wise to check the accuracy of the system. Single trail analysis was conducted in offline to identify the recognizing accuracy of the proposed system. The result summarize that band power features with TDNN network models exceed the reference power with TDNN network model used in this study. Through the outcome we conclude that that band power features with TDNN network was more suitable for designing EOG based HCI in offline mode.
Revisiting hydrocarbon phase mobilization of Au in the Au–Hg McLaughlin Mine, Geysers/Clear Lake area, California

Carbonaceous material (CM)-enriched silica is co-located with gold (Au) mineralization at the Au–Hg McLaughlin deposit, Geysers/Clear Lake area, California, U.S.A. The co-location suggests that hydrocarbons (HC) may be involved in mineralization and metal concentration processes, but little is known about the role of HC in the formation of ore deposits. Previous studies noted liquid oil inclusions in samples from the McLaughlin deposit, and proposed that the HC were liquid at the time of ore deposition. Hydrocarbon materials in the McLaughlin deposit occur as solid and liquid. Textural evidence suggests that hydrocarbon-rich and aqueous, silica-rich fluids were present simultaneously, as well as separately in alternating pulses. Synchrotron X-ray fluorescence microscopy of microscopic silica-free carbonaceous material reveals that the CM contains abundant ore metals e.g., Au, Ag, Hg, and Pb. The CM could have become metal-enriched by scavenging metals from other ore fluids, or it could have transported metals when the CM was still liquid, with subsequent in-situ degradation due to hydrothermal heat. Gold concentrations of up to 18 ppm were measured via acid digestion of solid and liquid HC material and subsequent inductively coupled plasma–mass spectrometry (ICP–MS) analyses. Hydrocarbon material with liquid to medium viscous properties bearing 10.8 ppm Au provides evidence that Au in liquid HC in the McLaughlin Au-Hg deposit is still mobile and that remobilization and/or transport of metals to the deposit by HC liquids cannot be ruled out.
Neural Review Rating Prediction with User and Product Memory

Neural network methods have achieved great success in sentiment classification. Recent studies have found that incorporating user and product information can effectively improve the performance of review sentiment classification. However, most of these studies only concentrate on the influence of users and products, ignoring the inherent correlation between users or products. This information is important for users or products since they can obtain more information from similar users or products. In this paper, we propose a novel framework for review rating prediction with user and product memory. First, besides the original user or product representations, we construct inferred representations from representative users or products which are stored in memory slots. These memory units can be viewed as refined knowledge representations of users or products learned from the data. Then, we employ two hierarchical networks with user attention and product attention using both the original and inferred representations. Experiments on benchmark datasets show that our method can achieve state-of-the-art performance. Besides, our approach performs much more better in cold-start scenarios where the training data is scarce.
Moving target defense for embedded deep visual sensing against adversarial examples

Deep learning-based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial example attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results. Deployable adversarial examples such as small stickers pasted on the road signs and lanes have been shown effective in misleading advanced driver-assistance systems. Many existing countermeasures against adversarial examples build their security on the attackers' ignorance of the defense mechanisms. Thus, they fall short of following Kerckhoffs's principle and can be subverted once the attackers know the details of the defense. This paper applies the strategy of moving target defense (MTD) to generate multiple new deep models after system deployment, that will collaboratively detect and thwart adversarial examples. Our MTD design is based on the adversarial examples' minor transferability across different models. The post-deployment dynamically generated models significantly increase the bar of successful attacks. We also apply serial data fusion with early stopping to reduce the inference time by a factor of up to 5. Evaluation based on four datasets including a road sign dataset and two GPU-equipped Jetson embedded computing platforms shows the effectiveness of our approach.
Cognitive cryptography for data security in cloud computing

Summary This paper presents new approach for creation of advanced multilevel user authentication protocols using hybrid CAPTCHA codes. Such codes will define a new class of cognitive CAPTCHAs, which, during verification, require from users special skills and knowledge, which is necessary for proper authentication. This way of authentication may be oriented for providing data access only for a special group of experts or trusted users who represent particular expertise areas and have special perception abilities and knowledge. For presented authentication protocols, possible examples of applications will be presented, and security features will be described. Presented innovative verification codes will considerably expand the existed security protocols toward the creation of cognitive‐based approaches oriented for user authentication. Such approaches define novel classes of cryptographic procedures, which can use personal features and perception skills.
The Impact of Remote User’s Role in a Mixed Reality Mixed Presence System

Research has shown that Mixed Presence (MP) systems are a valuable collaboration tool. However current research into MP systems is limited to a handful of tabletop and Virtual Reality (VR) systems with no exploration of Head-Mounted Display (HMD) based Mixed Reality (MR) solutions. In this paper, we present a prototype HMD based MR Mixed Presence system we designed and developed. We conducted a user study to investigate how different role assignment affects coordination and engagement in a group task with two local users using MR HMDs, and one remote user with a desktop-based Augmented Reality (AR) interface. The results indicated that the role of coordinator significantly increased the remote user's engagement with increased usage of visual communication cues. This is further supported by the mental effort and perceived dominance reported by users. Feedback from users also indicated visual communication cues are valuable for remote users in MP systems.