Reviewerly/exHarmony huggingface dataset:
An improved gravitational search algorithm for global optimization

Gravitational search algorithm (GSA) is inspired by swarm behaviors in nature and physical law based on Newtonian gravity and the laws of motion. There are two key parameters including the number of applied agents (Kbest) and gravitational coefficient (G) to control the search progress in the algor ithm. In the conventional GSA, the acceleration of the agents is mainly determined by Kbest and G. Kbest and G are calculated by a monotonically decreasing function, which is not a good schedule for solving complex problems. In order to solve the problem and accelerate the convergence of algorithm, an adaptive GSA is proposed, in which Kbest and G calculation method for strengthening exploitation capability are implemented to achieve better optimization results. Extensive experimental results based on benchmark functions are provided to show the effectiveness of the proposed method. The obtained results have been compared with the results of the original GSA, CGSA, and CLPSO. The comparison results have revealed that the proposed method has good performances.
Reduced-Reference Image Quality Assessment for Single-Image Super-Resolution Based on Wavelet Domain

Image quality metric is a critical factor to evaluate the quality assessment of single-image super-resolution (SISR) methods. The existing image quality assessment methods can be classified into full-reference and no-reference methods. However, full-reference methods require ground-truth images which are not available in practice, no-reference methods heavily rely on datasets. In this paper, we propose a reduced-reference image quality assessment method for SISR. A single low-resolution image is used as the reference image. First, small patches are taken from images and then extract features by the wavelet transform. Next, the features are fitted into the generalized Gaussian model. Finally, the distance between the fitting parameters of the LR and SISR images is used as the quality measure of SISR. Compared with the no-reference methods, training is not needed in the proposed method which has low dependence on the size of datasets, and more efficient and robust.
CAN-TM

The increasing proliferation of Cloud Services (CSs) has made the reliable CS selection problem a major challenge. To tackle this problem, this article introduces a new trust model called Chain Augmented Naïve Bayes-based Trust Model (CAN-TM). This model leverages the correlation that may exist among QoS attributes to solve many issues in reliable CS selection challenge, such as predicting missing assessments and improving accuracy of trust computing. This is achieved by combining both the n-gram Markov model and the Naïve Bayes model. Experiments are conducted to validate that our proposed CAN-TM outperforms state-of-the-art approaches.
A Middleware Framework to Address Security Issues in Integrated Multisystem Applications

Integrating multiple programmable components and subsystems developed by different manufacturers into a final system (a system of systems) can create some security concerns. While there are many efforts for developing interoperability approaches to enable smooth, reliable and safe integration among different types of components to build final systems for different applications, less attention is usually given for the security aspects of this integration. This may leave the final systems exposed and vulnerable to potential security attacks. The issues elevate further when such systems are also connected to other networks such as the Internet or systems like fog and cloud computing. This issue can be found in important industrial applications like smart medical, smart manufacturing and smart city systems. As a result, along with performance, safety and reliability; multisystem integration must also be highly secure. This paper discusses the security issues instigated by such integration. In addition, it proposes a middleware framework to address the security issues for integrated multisystem applications.
Mapping a Virtual View to the Physical World to Guide the Completion of Complex Task Sequences

The upkeep of systems that are designed to operate over long periods of time requires the precise execution of complex sequences of tasks. Such maintenance activities are expensive and require large investments in training costs. We present an approach that leverages Augmented Reality (AR) to guide the completion of such complex task sequences. This system presents real-time system-status and guidance to operators to complete the task sequences. One of the critical requirements in such a system is the ability to annotate objects and artifacts of the physical world when they are viewed in an AR device. Instead of using computationally intensive approaches based on computer vision, our novel approach relies on a CAD model of the engineered system. In our approach, we transform the gaze point of the user in the virtual world to corresponding coordinates in a 3D CAD model. The high-resolution details of in the 3D CAD model allows us to extract and present appropriate annotations. In the future, this approach can be significantly enhanced by designed intelligent methods to automatically generate the AR content necessary to guide the completion of complex task sequences.
Optimal Path Planning in Environments with Static Obstacles by Harmony Search Algorithm

Path planning represents an important optimization problem that need to be solved in various applications. It is a hard optimization problem thus deterministic algorithms are not usable but it can be tackled by stochastic population based metaheuristics such as swarm intelligence algorithms. In this paper we adopted and adjusted harmony search algorithm for the path planning problem in environment with static obstacles and danger zones. Objective function includes path length and safety degree. The proposed method was tested on standard benchmark examples from literature. Simulation results show that our proposed model produces better and more consistent results in spite of its simplicity.
Approaches to obtaining fingerprints of steganography tools which embed message in fixed positions

Recently, more and more steganography tools are exploited to embed malicious code into images by attackers, which bring harm to society. Thus, it has been a tricky problem, for which needs to detect the stego-images correctly. Signature detection, an effective method was proposed to solve this problem by identifying the fingerprints of steganography tool that are left in the stego-images. Unfortunately, extracting the fingerprints of steganography tools from the stego-images is a troublesome job. Few researchers pay attention to it and realize it. In this report, we discuss some approaches to obtaining fingerprints of steganography tools which embed message in fixed positions. Three main cases will be analyzed, including embedding message in the reserved positions of the image file header, embedding message in the sequential LSBs (least significant bits) of the image file data and embedding message in the redundant positions behind the image file tail. For each case, we summarize a general approach of fingerprint extraction and its effectiveness has been confirmed through the test on some typical steganography tools.
Do We Have to Look at the Mirror All the Time? Effect of Partial Visuomotor Feedback on Body Ownership of a Virtual Human Tail

Studies have shown that the sense of body ownership towards virtual humanoid avatars with additional body parts can be successfully elicited when synchronous visuomotor and/or visuotactile feedback is given. In an interactive virtual reality (VR) application, however, it is difficult for users to uninterruptedly observe the added body parts, especially when they are attached to the backs of the avatars. Thus, the embodiment of such body parts needs to be achieved using limited synchronous visuomotor feedback. Commonly, it is specifically done by looking at a virtual mirror reflection of the avatar’s movement. However, the methodology of eliciting the sense of body ownership in such conditions remains to be studied.
The Long Tail: Understanding the Discoverability of API Functionality

Almost all software development revolves around the discovery and use of application programming interfaces (APIs). Once a suitable API is selected, programmers must begin the process of determining what functionality in the API is relevant to a programmer's task and how to use it. Our work aims to understand how API functionality is discovered by programmers and where tooling may be appropriate. We employed a mixed-methods approach to investigate Apache Beam, a distributed data processing API, by mining Beam client code and running a lab study to see how people discover Beam's available functionality. We found that programmers' prior experience with similar APIs significantly impacted their ability to find relevant features in an API and attempting to form a top-down mental model of an API resulted in less discovery of features.
The Interest Rate Behaviour of Bitcoin as a Digital Asset

The objective of this study is to assess interest rate behaviour of bitcoin as a digital asset in relation to market rates. The implied bitcoin interest rate is quantified through the assumptions of uncovered interest parity theory, and implied bitcoin exchange rate determined from the triangular of USD/BTC, and EUR/BTC. The Vector Autoregressive model is regressed on implied bitcoin interest rate along with four maturity classes of LIBOR interest rates for US and Euro markets respectively. The results show that there is a uni–directional impact with bitcoin interest rate responding to shocks from market rates, while shocks emanating from bitcoin to market rates are non-existent, or not statistically significant. The findings of this study have potential value towards monetary policy and capital market investors.
Enhancing unsupervised video-based vehicle tracking and modeling for traffic data collection

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements.
Teaching Deductive Verification Through Frama-C and SPARK for Non Computer Scientists

Deductive verification of software is a formal method that is usually taught in Computer Science curriculum. But how can students with no strong background in Computer Science be exposed to such a technique? We present in this paper two experiments made at ISAE-SUPAERO, an engineering program focusing on aerospace industry. The first one is a classic lecture introducing deductive methods through the Frama-C platform or the SPARK programming language. The second one is the production by two undergraduate students of a complete guide on how to prove complex algorithms with SPARK. Both experiments showed that students with no previous knowledge of formal methods nor theoretical Computer Science may learn deductive methods efficiently with bottom-up approaches in which they are quickly confronted to tools and practical sessions.
DocEng'19 Competition on Extractive Text Summarization

The DocEng'19 Competition on Extractive Text Summarization assessed the performance of two new and fourteen previously published extractive text sumarization methods. The competitors were evaluated using the CNN-Corpus, the largest test set available today for single document extractive summarization.
Measuring Conditional Independence by Independent Residuals for Causal Discovery

We investigate the relationship between conditional independence (CI) x ⫫ y | Z and the independence of two residuals x −E( x | Z )⫫ y −E( y | Z ), where x and y are two random variables and Z is a set of random variables. We show that if x , y , and Z are generated by following linear structural equation models and all external influences follow joint Gaussian distribution, then x ⫫ y | Z if and only if x −E( x | Z )⫫ y −E( y | Z ). That is, the test of x ⫫ y | Z can be relaxed to a simpler unconditional independence test of x −E( x | Z )⫫ y −E( y | Z ). Furthermore, testing x −E( x | Z )⫫ y −E( y | Z ) can be simplified by testing x −E( x | Z )⫫ y or y −E( y | Z )⫫ x . On the other side, if all these external influences follow non-Gaussian distributions and the model satisfies structural faithfulness condition, then we have x ⫫ y | Z ⇔ x −E( x | Z )⫫ y −E( y | Z ). We apply the results above to the causal discovery problem, where the causal directions are generally determined by a set of V -structures and their consistent propagations, so CI test-based methods can return a set of Markov equivalence classes. We show that in the linear non-Gaussian context, in many cases x −E( x | Z )⫫ z or y −E( y | Z )⫫ z (∀ z ∈ Z and Z is a minimal d -separator) is satisfied when x −E( x | Z )⫫ y −E( y | Z ), which implies z causes x (or y ) if z directly connects to x (or y ). Therefore, we conclude that CIs have useful information for distinguishing Markov equivalence classes. In summary, comparing with the existing discretization-based and kernel-based CI testing methods, the proposed method provides a simpler way to measure CI, which needs only one unconditional independence test and two regression operations. When being applied to causal discovery, it can find more causal relationships, which is extensively validated by experiments.
Improving Maintenance Efficiency with an Adaptive AR-assisted Maintenance System

The lack of adaptability of existing Augmented Reality (AR) assisted maintenance systems has prevented the implementation of many existing AR systems in real industrial maintenance scenarios. This paper presents an adaptive Augmented Reality human-machine interface (AR-HMI) framework that can provide suitable sets of maintenance information and guidance to an operator during maintenance to enhance efficiency and safety. A human-centric framework has been developed to determine the most suitable types of information to be augmented and presented to the user. During maintenance, the AR-assisted system allows a user to request for a change in the types of augmentation via an explicit request or an implicit mechanism, which is based on the head-gaze of the user. To demonstrate the viability of the AR-HMI framework and AR-assisted system, a case study based on an industrial robot has been conducted.
A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem

In the recent years, it has been shown that real world-problems are often comprised of two, interdependent subproblems.Often, solving them independently does not lead to the solution to the entire problem.In this article, a Travelling Thief Problem is considered, which combines a Travelling Salesman Problem with a Knapsack Problem.A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is investigated, along with its recent modification -a Non-Dominated Tournament Genetic Algorithm (NTGA).Each method is investigated in two configurations.One, with generic representation, and genetic operators.The other, specialized to the given problem, to show how the specialization of genetic operators leads to better results.The impact of the modifications introduced by NTGA is verified.A set of Quality Measures is used to verify the convergence, and diversity of the resulting PF approximations, and efficiency of the method.A set of experiments is carried out.It is shown that both methods work almost the same when generic representation is used.However, NTGA outperforms classical NSGA-II in the specialized results.
Effects on user perception of a 'modified' speed experience through in-vehicle virtual reality

In order to make the experience of traveling in automated vehicles more enjoyable, Virtual Reality (VR) experiences based on the real-world journey have been proposed. Presenting users with VR content synched to the car's actual movement decreases the motion sickness, but it also sharply limits the possible range of VR content. In this paper, we investigate whether the user's subjective perception of speed can be 'modified' by presenting VR content at a different speed than the actual speed of the vehicle, and whether users feel this experience is strange. Study participants viewed VR content occurring at a faster or slower speed than their actual travel speed in an electric wheelchair. Our results show that the participants were able to do this without experiencing a feeling of "strangeness". However, the participants did report higher "strangeness" scores when the speed in the VR content was slower than their actual speed.
RGBD Semantic Segmentation Based on Global Convolutional Network

Convolutional neural networks have gradually dominated the field of image semantic segmentation, and have achieved good results in 2D image semantic segmentation tasks. However, the 2D semantic segmentation algorithm based on CNN is still unsatisfactory in many complex scenarios, such as indoor scenes. Fortunately, advances in depth sensor technology have made it easy to obtain depth information, which carries rich geometric structure information. In order to effectively embed the depth map into the convolutional neural network, this paper introduces the dual encoder fusion network framework to fully exploit the geometric features. For the problem of weakening the local pixel classification ability of the dual encoder fusion network, this paper introduces global convolutional network (GCN), which is based on the large kernel idea, to improve the performance of dual encoder fusion network. Extensive experiments on the NYU v2 dataset demonstrate that the two-encoder fusion network based on global convolution network has much better precision than the original fusion network, and the classification ability for local pixels is stronger.
eCLASS: Edge-Cloud-Log Assuring-Secrecy Scheme for Digital Forensics

User activity logs are important pieces of evidence in digital forensic investigations. In cloud forensics, it is difficult to collect user activity logs due to the fact of virtualization technologies and the multitenancy environment, which can infringe upon user privacy when collecting logs. Furthermore, the computing paradigm is shifting from conventional cloud computing toward edge computing, employing the advances of 5G network technology. This change in the computing paradigm has also brought about new challenges for digital forensics. Edge nodes that are close to users are exposed to security threats, and the collection of logs with limited computing resources is difficult. Therefore, this study proposes a logging scheme that considers log segmentation and distributed storage to collect logs from distributed edge nodes and to protect log confidentiality by taking into account edge-cloud characteristics. This scheme protects the integrity of log data collected by a multi-index chain network. To demonstrate the performance of the proposed scheme, edge nodes with three different capacity types were used, and the proposed log-segmentation method performed 29.4% to 64.2% faster than the Cloud-Log Assuring-Secrecy Scheme (CLASS) using 2048 bit Rivest-Shamir-Adleman (RSA) in three types of edge nodes for log-confidentiality protection. The log segmentation of edge CLASS (eCLASS) reduced the log size to approximately 58% less than CLASS log encryption, and edge-node CPU usage was also reduced from 14% to 28%.
Remote Multimodal Biometric Authentication using Visual Cryptography

This work proposes an architecture for multimodal biometric recognition systems where user, recognition system, and template database are remotely located over a network. As the number of biometrics are limited and once lost they are compromised forever, it becomes imperative to design systems that optimize recognition rates and also address security and privacy issues for biometric-enabled authentication schemes. The proposed architecture provides revocability to multimodal biometric templates and secures their storage and transmission over a remote network with the help of visual cryptography technique. The proposed architecture gives a good matching performance and also fulfills four template protection criteria, i.e., security, diversity, revocability, and performance. Various attack scenarios such as phishing, replay, database, man-in-middle, and attack via record multiplicity are also addressed.
Text Localization in Scientific Figures using Fully Convolutional Neural Networks on Limited Training Data

Text extraction from scientific figures has been addressed in the past by different unsupervised approaches due to the limited amount of training data. Motivated by the recent advances in Deep Learning, we propose a two-step neural-network-based pipeline to localize and extract text using Fully Convolutional Networks. We improve the localization of the text bounding boxes by applying a novel combination of a Residual Network with the Region Proposal Network based on Faster R-CNN. The predicted bounding boxes are further pre-processed and used as input to the of-the-shelf optical character recognition engine Tesseract 4.0. We evaluate our improved text localization method on five different datasets of scientific figures and compare it with the best unsupervised pipeline. Since only limited training data is available, we further experiment with different data augmentation techniques for increasing the size of the training datasets and demonstrate their positive impact. We use Average Precision and F1 measure to assess the text localization results. In addition, we apply Gestalt Pattern Matching and Levenshtein Distance for evaluating the quality of the recognized text. Our extensive experiments show that our new pipeline based on neural networks outperforms the best unsupervised approach by a large margin of 19-20%.
Motion-planning Using RRTs for a Swarm of Robots Controlled by Global Inputs

Small-scale robots have great potential in medicine, micro-assembly and many other areas. For example, robots containing iron can be steered using the magnetic gradient generated by MRI scanners. Since the gradient is approximately the same everywhere inside the scanner, each robot receives the same input and therefore they all are subjected to the same force. A similar technique can be used with rotating magnetic fields. Each robot receives the same inputs, making motion planning challenging. This paper uses a Rapidly Exploring Random Tree (RRT) to plan paths that deliver multiple robots to goal positions by using obstacles to break the actuation symmetry.