Reviewerly/exHarmony huggingface dataset:
Tensile testing of carbon fiber multifilament using an advanced video extensometer assisted by dual-reflector imaging

Tensile properties of fibers are of high significance for composite materials, thus making their testing methods also highly concerned. In this work, an advanced dual-reflector-assisted video extensometer is established for accurate determination of tensile properties of carbon fiber multifilament. To validate the practicality and effectiveness of the video extensometer, three different types of carbon fiber multifilament specimens manufactured by Toray were tested. The stress-strain curves of these specimens were firstly measured for different gauge lengths, verifying that the test results are not affected by the gauge length of the video extensometer. Then, the dual-reflector-assisted and regular video extensometers were simultaneously applied to measure the tensile strains of the specimens, confirming the higher accuracy and better stability of the dual-reflector-assisted video extensometer. Moreover, the measured tensile modulus and elongation at break show good agreement with the reference values, further validating the practicality and accuracy of the technique. Due to its prominent advantages of easy-implementation, non-contact, high-accuracy, and real-time strain measurements, the proposed dual-reflector-assisted video extensometer demonstrates broad prospects in material testing of various materials.
Goggles in the lab: Economic experiments in immersive virtual environments

This review outlines the potential of virtual reality for creating naturalistic and interactive high-immersive environments in experimental economics. After explanation of essential terminology and technical equipment, the advantages are discussed by describing the available high-immersive VR experiments concerning economic topics to give an idea of the possibilities of VR for economic experiments. Furthermore, possible drawbacks are examined, including simulator sickness, the costs of VR equipment and specialist skills. By carefully controlling a naturalistic experimental context, virtual reality brings some field into the lab. Besides, it allows for testing contexts that would otherwise be unethical or impossible. It is a promising new tool in the experimental economics toolkit.
Subunit sign modeling framework for continuous sign language recognition

A new framework named three subunit sign modeling is introduced for automatic sign language recognition. This works on continuous video sequences consisting of isolated words, signed sentences under different signer variations and illuminations. Three major issues of automatic sign language recognition is addressed namely: (i) importance of discriminative feature extraction and selection (ii) handling epenthesis movements and segmentation ambiguities (iii) automatic recognition of large vocabulary sign sentences and signer adaptation in a single subunit sign modeling framework. The proposed work has been evaluated and experimented subjectively and quantitatively with real-time signing videos gathered from different corpora and different sign languages. The results of the experiments have proven that the proposed subunit sign modeling framework remains scalable while increasing the sign vocabulary. Further, the approach is more reliable and efficient enough to adapt to real-time constraints and signer independence.
Visible objects of concern: Issues and challenges for workplace ethnographies in complex environments

Over the past few decades, we have witnessed the widespread deployment of technologies that enable real-time interaction between co-located and remote participants. These technologies and their accompanying organisational arrangements have created new forms of cooperation and collaboration. They also present challenges for ethnographers seeking to understand the practices, the ‘lived work’ of the participants. In particular, they demand a concern with the physical, the material and the embodied, in other words with what has been termed multimodality. We argue that it is through detailed analysis of specific instances, the circumstances of their use, that we can begin to discover the competencies, skills, the ‘know-how’ that enable practice. In this article, we consider one particular setting that is both distinctive because of its scale but also characteristic of many technology-saturated contemporary workplaces. We aim to show how in this case, as in others, the interactional and the sequential is an inextricable aspect of practice. To uncover these practices requires particular attention to the multimodal but that this presents challenges for ethnographies, even those that draw on complex arrays of resources such as video-recordings. We suggest that this resonates with recent debates regarding how we conceive of materiality, the roles of technologies and practice.
Non-Lipschitz Models for Image Restoration with Impulse Noise Removal

Impulse noise is a special type of noise which corrupts a portion of the image pixels while keeping the others unaffected. L$_1$TV, L$_1$Nonconvex, and NonconvexTV are three common variational models for impulse noise removal. However, non-Lipschitz cases were ruled out for L$_1$Nonconvex and NonconvexTV in former works. In this paper, we consider L$_1$non-Lipschitz and non-LipschitzTV. Uniform lower bound theories for these two models are obtained, which yield the models' characteristics. Based on the lower bound theories, we propose two new algorithms for the two models. Instead of introducing auxiliary parameters to convert the non-Lipschitz models into Lipschitz ones in usual approximation methods for non-Lipschitz optimization, the new algorithms overcome the non-Lipschitzian by iteratively adding constraints on the supports of non-Lipschitz terms. The new algorithms can be easily implemented, and the global convergence is also established. Numerical examples are given to show good performance of the algorithm and the rationality of the theories. The advantageous models for difference cases are also discussed.
Diversity enhanced particle swarm optimization algorithm and its application in vehicle lightweight design

Particle swarm optimization, a widely used metaheuristic algorithm, mimics the cooperation behavior among species. The PSO algorithm has become a new trend owing to its simplicity and strong optimization capacity. However, premature convergence problem is also a serious issue for PSO comparable with other evolutionary algorithms. Diversity loss is generally known as one of the major causes. For enhancing the diversity of swarms during optimization procedure, an improved PSO algorithm named OLAR-PSO-d is proposed, which incorporates design of experiment technique as well as adaptive reset operator into standard PSO. The OLAR-PSO-d algorithm is compared with other 10 heuristic algorithms. The numerical experiments’ results demonstrate the priority of OLAR-PSO-d both in optimization ability and algorithm stability. The proposed algorithm is also used in a vehicle lightweight design problem. The auto-body achieves 20.25 kg weight reduction with meeting all the performance requirements of crashworthiness.
Learning Bayesian networks using the constrained maximum a posteriori probability method

Purely data-driven methods often fail to learn accurate conditional probability table (CPT) parameters of discrete Bayesian networks (BNs) when training data are scarce or incomplete. A practical and efficient means of overcoming this problem is to introduce qualitative parameter constraints derived from expert judgments. To exploit such knowledge, in this paper, we provide a constrained maximum a posteriori (CMAP) method to learn CPT parameters by incorporating convex constraints. To further improve the CMAP method, we present a type of constrained Bayesian Dirichlet priors that is compatible with the given constraints. Combined with the CMAP method, we propose an improved expectation maximum algorithm to process incomplete data. Experiments are conducted on learning standard BNs from complete and incomplete data. The results show that the proposed method outperforms existing methods, especially when data are extremely limited or incomplete. This finding suggests the potential effective application of CMAP to real-world problems. Moreover, a real facial action unit (AU) recognition case with incomplete data is conducted by applying different parameter learning methods. The results show that the recognition accuracy of respective recognition methods can be improved by the AU BN, which is trained by the proposed method.
Depth-Aware Motion Deblurring Using Loopy Belief Propagation

Most motion-blurred images captured in the real world have spatially-varying point-spread functions, and some are caused by different positions and depth values, which cannot be handled by most state-of-the-art deblurring methods based on deconvolution. To overcome this problem, we propose a depth-aware motion blur model that treats a blurred image as an integration of a sequence of clear images. To restore the clear latent image, we extend the Richardson-Lucy method to incorporate our blur model with a given depth image. The empty holes in the depth image, caused by occlusion or device limitations, are fixed by PatchMatch-based depth filling. We regard the depth image as a Markov random field and select candidate labels by using belief propagation to set and smooth depth values for empty areas. Deblurring and depth filling are performed iteratively to refine the results. Our method can also be applied to real-world images with the assistance of motion estimation. The deblurring process is shown to be convergent; moreover, the number of iterations and the level of noise amplification are acceptable. The experimental results show that our method can not only handle depth-variant motion blur but also refine depth images.
An identity-based encryption technique using subtree for fuzzy user data sharing under cloud computing environment

The advancement of the cloud storage technology opens up a wide range of possibilities for adaptable data sharing. When sharing data to an extensive number of users with fuzzy identities, the data proprietor must use an appropriate identity-based encryption technique that satisfies both efficiency and security prerequisites. Identity-based encryption is a promising possibility to ensure fuzzy user data sharing while meeting the security essentials; however, it may encounter efficiency trouble in multi-receiver settings. Recently, identity-based encryption has received much attention, and most of the research has aimed to apply the technique in real-world systems. A major concern about using identity-based encryption is the safety of the private keys, as disclosure of secret keys requires the reissuing of encryptions already doled out. The capability to minimize the risks associated with key disclosure is particularly important due to the increased use of mobile and unprotected devices. In this article, we shall propose a forward-secure identity-based encryption technique based on subtree for fuzzy user data sharing under cloud computing environment, and we shall demonstrate that the technique is semantically secure against a chosen subtree and chosen ciphertext attack (IND-CST–CCA). In addition, we will show the superiority of our new technique over the currently existing methods in terms of security and the length of public key. Then, we will also discuss the potential of our new technique to be deployed in pay TV systems and grid security.
Multi‐criteria workflow scheduling on clouds under deadline and budget constraints

Summary Cloud Computing is increasingly favored by scientists for executing large‐scale workflow applications due to its appealing features such as availability of huge infrastructure, low operating costs, easy accessibility, and high scalability. Moreover, the resources in cloud are provisioned as per user's requirement following pay‐per‐use model. Scheduling workflows in cloud requires orchestration of tasks onto heterogeneous resources while complying with the task dependencies as well as user's Quality of Service (QoS) demands. The problem becomes challenging if there are multiple QoS requirements which conflict with each other. Recent studies attempted to minimize the makespan and execution cost under deadline and/or budget constraints. However, the failure of resources in cloud poses a serious threat to successful execution of these applications. Hence, it becomes essential to consider reliability of resources in addition to other parameters. This paper presents a workflow scheduling policy which minimizes makespan and execution cost while maximizing the reliability of executing workflows under user specified deadline and budget restraints. We have devised a hybrid of Intelligent Water Drops algorithm and Genetic Algorithm (IWD‐GA) to accomplish the desired goal. It provides a set of non‐dominated solutions with different makespan, cost, and reliability to offer users more flexibility in selecting a solution as per their preferences. The evaluation of the proposed algorithm has been carried based on two performance metrics, ie, hypervolume and two set coverage. The simulation results thus obtained using four diverse scientific workflows confirm that IWD‐GA outperforms non‐dominated sort genetic algorithm (NSGA‐II) and hybrid particle swarm optimization (HPSO) with regard to accuracy and diversity.
Let’s draw and talk about urban change: Deploying digital technology to encourage citizen participation in urban planning

Enhancing the role of citizens in shaping places has been a longstanding objective for governments, communities and the academy. Although a range of techniques has been developed by the state to give people an opportunity to get involved, these methods often struggle to create a meaningful way to communicate aspirations for places on citizens' terms. In this paper, we document the design, deployments and evaluation of a new technological device that enabled participants to share place views and aspirations beyond more traditional government engagement methods. The device, called JigsAudio, is an open-source device fabricated by the authors that encourages people to express themselves creatively through drawing and talking. The research contributes to our understanding of how accessible and free technologies can reduce barriers to participation, whilst encouraging creativity and expression when talking about the future of places. It goes on to discuss the potential of devices such as JigsAudio conceptually and practically within urban and regional change, and considers the balance that needs to be struck between utilising smart technology whilst creating accessible and meaningful opportunities that inspire citizens.
Model-Based Adaptation for Robotics Software

We developed model-based adaptation, an approach that leverages models of software and its environment to enable automated adaptation. The goal of our approach is to build long-lasting software systems that can effectively adapt to changes in their environment.
A Literature Review and Comparison of Three Feature Location Techniques using ArgoUML-SPL

Over the last decades, the adoption of Software Product Line (SPL) engineering for supporting software reuse has increased. An SPL can be extracted from one single product or from a family of related software products, and feature location strategies are widely used for variability mining. Several feature location strategies have been proposed in the literature and they usually aim to map a feature to its source code implementation. In this paper, we present a systematic literature review that identifies and characterizes existing feature location strategies. We also evaluated three different strategies based on textual information retrieval in the context of the ArgoUML-SPL feature location case study. In this evaluation, we compare the strategies based on their ability to correctly identify the source code of several features from ArgoUML-SPL ground truth. We then discuss the strengths and weaknesses of each feature location strategy.
Network Embedding via Community Based Variational Autoencoder

In recent years, network embedding has attracted more and more attention due to its effectiveness and convenience to compress the network structured data. In this paper, we propose a community-based variational autoencoder (ComVAE) model to learn network embedding, which consists of a community detection module and a deep learning module. In the proposed model, both community information and deep learning techniques are utilized to learn low-dimensional vertex representations. First, community information reveals an implicit relationship between vertices from a global view, which can be a supplement to local information and help to improve the embedding quality. To obtain the community information, community detection algorithms are utilized as a module and the modularization design makes the model more flexible. Second, deep learning techniques can not only integrate and preserve the information from both local and global views efficiently but also strengthen the robustness of vertex representations. To demonstrate the performance of our model, extensive experiments are conducted in four downstream tasks, namely, network reconstruction, node classification, link prediction, and visualization. The experimental results show that our model outperforms the state-of-the-art approaches to real-world datasets.
Feature selection for multi-label learning with missing labels

In multi-label learning, feature selection is a non-ignorable preprocessing step which can alleviate the negative effect of high-dimensionality. To address this problem, a number of effective information theory based feature selection algorithms for multi-label learning are proposed. However, these existing algorithms assume that the label space of multi-label training data is complete. In practice, the standpoint does not always hold true, due to the ambiguity among class labels or the cost effort to fully annotate instances. In this paper, we first define the new concepts of multi-label information entropy and multi-label mutual information. Then, feature redundancy, feature independence, and feature interaction are defined, respectively. In which, feature interaction is used to select more valuable features which may be ignored due to the incomplete label space. Moreover, a multi-label feature selection method with missing labels is proposed. Finally, extensive experiments conducted on eight publicly available data sets verify the effectiveness of the proposed algorithm via comparing it with state-of-the-art methods.
Blockchain anchored supply chain automation

Increasing globalization, e-commerce usage, and social awareness are leading to increased consumer demand for variety, value, convenience, immediacy, verifiable authenticity and provenance, ethical materials sourcing and manufacturing, regulatory compliance, and services after sales. Fulfilling this increased complexity of consumer demand has required supply chains to evolve into multienterprise networks with numerous flow paths in production, merchandising, and fulfillment involving many organizational/institutional handoffs, to effectively manage a large number of complex products with shorter life cycles and high transaction volumes. The supply chain management models of today place higher demands on automation and require a transition from the traditional paradigm of planning followed by long-loop execution for a handful of segments to a paradigm of managing a portfolio of end-to-end instrumented data-rich microsegmented supply chains that are monitored and adjusted in near real time. These essential aspects and challenges of supply chain management require the supporting information technology to also evolve. In this paper, we propose a novel reference software architecture to address the complex requirements of modern supply chains that also integrates blockchain into several layers of the stack. We present several examples where this reference architecture is applicable, and then demonstrate through a use case in production that integrating blockchain technology helps with providing visibility, documenting provenance, and allowing permissioned data access to facilitate the automation of many high-volume tasks such as reconciliations, payments, and settlements.
A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems

Collaborative filtering (CF) is one of the most successful approaches for an online store to make personalized recommendations through its recommender systems. A neighborhood-based CF method makes recommendations to a target customer based on the similar preference of the target customer and those in the database. Similarity measuring between users directly contributes to an effective recommendation. In this paper, we propose a sub-one quasi-norm-based similarity measure for collaborative filtering in a recommender system. The proposed similarity measure shows its advantages over those commonly used similarity measures in the literature by making better use of rating values and deemphasizing the dissimilarity between users. Computational experiments using various real-life datasets clearly indicate the superiority of the proposed similarity measure, no matter in fully co-rated, sparsely co-rated or cold-start scenarios.
Camera Model Identification using Deep CNN and Transfer Learning Approach

The forensic investigation on digital images is to assess the authenticity of images without the embedded security on the images. The camera model identification is the first step for image forensic investigation. The paper proposes the deep Convolutional Neural Network and transfer learning approach for extracting features from an images dataset. An open image dataset of 3900 images have been created using three camera models. Three state-of-the-art machine learning algorithms such as SVM, logistic regression and random forest based classifiers have been used for evaluating identification accuracy.
Virtualization on TrustZone-Enabled Microcontrollers? Voilà!

With predictions pointing to more than 20 billion Internet-enabled 'things' by 2020 and much more to come, smart sensor nodes are expected to be predominant in the Internet of Things (IoT) era. As these systems are connected to the Internet and tend to implement an ever-growing number of mixed-criticality features, there is huge pressure for strong isolation to guarantee a reliable, secure, and predictable infrastructure. While virtualization has been a game-changer for consolidation and isolation in mid-to high-end embedded applications, for low-end and low-cost systems it is still in its infancy, and only a limited number of solutions have been proposed so far. This work aims at developing a lightweight hypervisor which provides strong isolation on resource-constrained devices. Our approach leverages TrustZone technology available on modern Arm microcontrollers (TrustZone-M) to implement a predictable virtualization infrastructure for low-end and low-cost systems. Experiments conducted on an Arm Musca-A multi-core platform demonstrate our solution achieves low memory footprint, high efficiency, and strict timing predictability.