Reviewerly/exHarmony huggingface dataset:
Light invariant photometric stereo

An imaging setup that enables unsynchronized photometric stereo (PS) for Lambertian objects based on modulated light sources is presented. Knowing the specific frequency of the modulated light source allows to filter out any other light in the scene. This creates an image that depends only on the particular light source while ignoring the ambient light. Moreover, if the scene is illuminated by multiple modulated sources with different frequencies, repeating this process for every frequency will produce a sequence of images with the corresponding illumination. This sequence is then used as an input to the PS algorithm for 3D reconstruction. The proposed approach, named Light Invariant Photometric Stereo (LIPS), was verified on both synthetic and real-world data. LIPS eliminates the need for synchronization between the sources and the camera and significantly outperformed the classical PS method in an illuminated environment.
Virtual Reality — Substitute for a Real Experience? The Role of User Motivation, Expectations and Experience Type

Virtual reality (VR) offers the opportunity to create virtual worlds that could replace real experiences. This research investigates the influence of user motivation, temporal distance and experience type on the satisfaction with the VR experience, and the degree of acceptance of a VR experience as a substitute for a real experience. The results suggest that the degree of acceptance of a VR experience as a substitute for a real experience is higher for passive VR experiences compared to active VR experiences. Furthermore, the results support the assumption that users are more satisfied with passive VR experiences.
Accelerated Randomized Coordinate Descent for Solving Linear Systems

The randomized coordinate descent (RCD) method is a simple but powerful approach to solving inconsistent linear systems. In order to accelerate this approach, the Nesterov accelerated randomized coordinate descent method (NARCD) is proposed. The randomized coordinate descent with the momentum method (RCDm) is proposed by Nicolas Loizou, we will provide a new convergence boundary. The global convergence rates of the two methods are established in our paper. In addition, we show that the RCDm method has an accelerated convergence rate by choosing a proper momentum parameter. Finally, in numerical experiments, both the RCDm and the NARCD are faster than the RCD for uniformly distributed data. Moreover, the NARCD has a better acceleration effect than the RCDm and the Nesterov accelerated stochastic gradient descent method. When the linear correlation of matrix A is stronger, the NARCD acceleration is better.
User-driven Online Kernel Fusion for SYCL

Heterogeneous programming models are becoming increasingly popular to support the ever-evolving hardware architectures, especially for new and emerging specialized accelerators optimizing specific tasks. While such programs provide performance portability of the existing applications across various heterogeneous architectures to some extent, short-running device kernels can affect an application performance due to overheads of data transfer, synchronization, and kernel launch. While in applications with one or two short-running kernels the overhead can be negligible, it can be noticeable when these short-running kernels dominate the overall number of kernels in an application, as it is the case in graph-based neural network models, where there are several small memory-bound nodes alongside few large compute-bound nodes. To reduce the overhead, combining several kernels into a single, more optimized kernel is an active area of research. However, this task can be time-consuming and error-prone given the huge set of potential combinations. This can push programmers to seek a tradeoff between (a) task-specific kernels with low overhead but hard to maintain and (b) smaller modular kernels with higher overhead but easier to maintain. While there are DSL-based approaches, such as those provided for machine learning frameworks, which offer the possibility of such a fusion, they are limited to a particular domain and exploit specific knowledge of that domain and, as a consequence, are hard to port elsewhere. This study explores the feasibility of a user-driven kernel fusion through an extension to the SYCL API to address the automation of kernel fusion. The proposed solution requires programmers to define the subgraph regions that are potentially suitable for fusion without any modification to the kernel code or the function signature. We evaluate the performance benefit of our approach on common neural networks and study the performance improvement in detail.
Perception-Motion Coupling in Active Telepresence: Human Behavior and Teleoperation Interface Design

Teleoperation enables complex robot platforms to perform tasks beyond the scope of the current state-of-the-art robot autonomy by imparting human intelligence and critical thinking to these operations. For seamless control of robot platforms, it is essential to facilitate optimal situational awareness of the workspace for the operator through active telepresence cameras. However, the control of these active telepresence cameras adds an additional degree of complexity to the task of teleoperation. In this paper we present our results from the user study that investigates: (1) how the teleoperator learns or adapts to performing the tasks via active cameras modeled after camera placements on the TRINA humanoid robot; (2) the perception-action coupling operators implement to control active telepresence cameras, and (3) the camera preferences for performing the tasks. These findings from the human motion analysis and post-study survey will help us determine desired design features for robot teleoperation interfaces and assistive autonomy.
Linked or unlinked: A systematic review of linkable ring signature schemes

As a prominent cryptographic primitive underlying anonymous communication, privacy-preserving cryptocurrencies, and other blockchain-based applications, ring signatures have garnered much attention in both research and industry over a decade with diverse guarantees of which linkability is an integral constituent. The linkability property enables any verifier to ascertain the fact that two ring signatures were generated by the same signer under a specified condition at the same time preserving signer anonymity. In this paper, we present a systematic investigation into linkable ring signature (LRS) schemes by reviewing 31 articles from 2006 to the first quarter of the year 2022 thereby providing readers with a broad synthesis of state-of-the-art information on LRS, current approaches to their constructions, security model, security guarantees, instantiation and applications. Our findings reveal non-uniformity in link tag constructions, diversity in underlying computational hard problems, and signature sizes as well as application areas. We point out the ramifications of construction paradigms as well as future research prospects aimed at providing guidance and reference for future research along this promising and vital cryptographic primitive.
Context-awareness for information correction and reasoning in evidence theory

The notion of context awareness and the challenge of reasoning with partially reliable sources are two important aspects within Information Fusion. Context is the information relevant to, but not directly affecting, the problem at hand and can be broadly categorised into either context-for or context-of, referring either to the information related to some situation or to the environment induced by some situation, respectively. In evidence theory, the Behaviour-Based Correction (BBC) model generalises reasoning with partially reliable sources as well as contextual belief correction. In this paper, we propose a model for contextual reasoning framed into evidence theory, which captures both the notions of context-for and context-of. We rephrase the BBC model to explicitly account for variation of metaknowledge regarding source behaviour, and subsequently include within it the variables defining the context-for the problem and the context-for the source. The benefit is two-fold: on the one hand, the explicit inclusion of context in the reasoning provides a better insight into the problem and on the other hand, it can improve the expressiveness of the model. This is illustrated on a case of maritime surveillance involving a missing vessel, where it is shown that this model is not only more expressive than the simple fusion of sources model but also more interpretable.
Are blockchain and cryptocurrency M&amp;As harder to close?

We investigate whether distinctive features of the blockchain/cryptocurrency industry led to different completion rates of the announced mergers and acquisitions (M&As), relative to other industries, during the years 2013–2022. Despite having a significantly lower deal closure rate on average, we find that this effect is contained only in a few years and does not span the entire decade. We also find that bitcoin prices are an important determinant of the industry's deal completion rate. Our findings may help give a glimpse into the eventual fate of the most recent deals announced during the crypto crash that started in early 2022.
Method name recommendation based on source code metrics

Method naming is a critical factor in program comprehension, affecting software quality. State-of-the-art naming techniques use deep learning to compute the methods’ similarity considering their textual contents. They highly depend on identifiers’ names and do not compute semantical interrelations among methods’ instructions. Source code metrics compute such semantical interrelations. This article proposes using source code metrics to measure semantical and structural cross-project similarities. The metrics constitute features of a KNN model, determining the k-most similar methods to a given method. Experiments with 4000000 Java methods on the proposed model demonstrate improvements in precision and recall of state-of-the-arts with 4.25 and 12.08%.
A hierarchical decentralized architecture to enable adaptive scalable virtual machine migration

Abstract Cloud computing is an established paradigm for end users to access resources. Cloud infrastructure providers seek to maximize accepted requests, meet Service Level Agreements (SLAs), and reduce operational costs by dynamically allocating Virtual Machines (VMs) to physical nodes. Many solutions have been presented to manage cloud infrastructure, however, these tend to be centralized and suffer in their ability to maintain Quality of Service (QOS) and support data centers with thousands of nodes. Decentralized approaches, with no central management, can manage large data centers. However, these tend to reduce the ability to obtain an optimal resource allocation across the data center. To address this, we propose a hybrid hierarchical decentralized architecture that achieves lower SLA violations and lowers network traffic. We used simulation to evaluate our proposal in practice with a variety of existing VM placement policies.
EventDNA: a dataset for Dutch news event extraction as a basis for news diversification

News organizations increasingly tailor their news offering to the reader through personalized recommendation algorithms. However, automated recommendation algorithms reflect a commercial logic based on calculated relevance to the user, rather than aiming at a well-informed citizenry. In this paper, we introduce the EventDNA corpus, a dataset of 1773 Dutch-language news articles annotated with information on entities, news events and IPTC Media Topic codes, with the ultimate goal to outline a recommendation algorithm that uses news event diversity rather than previous reading behaviour as a key driver for personalized news recommendation. We describe the EventDNA annotation guidelines, which are inspired by the well-known ERE framework and conclude that it is not practical to apply a fixed event typology such as used in ERE to an unrestricted data context. The corpus and related source code is made available at https://github.com/NewsDNA-LT3/.github.
Collaborative Fuzzy Controlled Obstacle Avoidance in a Vibration-Driven Mobile Robot

Special care must be taken when considering robots designed to operate collaboratively, such as a swarm, to prevent these agents from being damaged due to unwanted collisions. This work proposes integrating techniques used to move robots, using the Robot Operating System (ROS) and Python’s Scikit-Fuzzy module. Thus, this work developed a fuzzy-controlled collaborative obstacle avoidance system for a type of robot whose dynamics are based on motors’ vibration. Thus, these robots were designed to participate in a swarm, and the collision must be avoided. In the search for navigation stability, optimal values were sought for the engines’ pulse width modulation (PWM).
The Effect of Organizational Virtuousness on the Organizational Citizenship Behavior: Evidence from Jordanian Health Sector.

The purpose of this study was to determine the influence of organizational virtuousness, as defined by optimism, trust, empathy, integrity, and forgiveness on employees’ organizational citizenship behaviors in Jordan’s health sector. We test our proposed models in two big hospitals in Jordan’s capital Amman. We analyze data using structural equation modeling (SEM) methods. The research sampled 338 personnel from a variety of medical, nursing, and ancillary medical specialties. The findings indicate that organizational virtuousness has a favorable effect on the health sector’s organizational citizenship behavior.
Chatbots for Creating Smart Things with Children: Preliminary Investigations and Several Challenges

Smart things are pervasive in children's life. However, children do not know how to create them. Chatbots for children can help them learn how to create their own smart things and reflect around them. This paper reviews current research in the area of chatbots for children with educational aims. It presents a preliminary contextual inquiry with children using the SNaP online game for creating simple smart things. It concludes with reflections on the results of the review and inquiry, in relation to challenges for the design of chatbots for guiding children to create and reflect around technology-driven solutions for them.
<i>SENSIBLE</i>: SEquestered aNd SynergIstic BLockchain Ecosystem

Abstract Health care interoperability unfolds the way for personalized health care services at a reduced cost. Furthermore, a decentralized system holds the promise to prevent compromises such as cyber‐attacks due to data breaches. Hence, there is a need for a framework that seamlessly integrates and shares data across the system stakeholders. We propose SEquestered aNd SynergIstic BLockchain Ecosystem (SENSIBLE), a blockchain‐powered, knowledge‐driven data‐sharing framework that gives patients complete control of their medical history and can extract rich information hidden in it using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for secure data sharing among stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We present a Proof‐of‐Concept of the SENSIBLE network with Ethereum to share dynamic knowledge across stakeholders. Dynamic knowledge generation on the blockchain provides a two‐fold advantage of cooperation and communication amongst the stakeholders in the health care ecosystem. This leads to operational ease through sharing relevant portions of complex information while also ensuring the isolation of sensitive medical data.
Using Multi-Label Classification Methods to Analyze Complaints Against Cargo Services During the COVID-19 Outbreak: Comparing Survey-Based and Word-Based Labeling

This study investigates how cargo companies, with a significant market share in Turkey's service sector, managed their last-mile activities during the Covid-19 outbreak and suggests the solution to the adverse outcomes. The data used in the study included complaints made for cargo companies from an online complaint management website called sikayetvar.com from the start of the pandemic to the date of the research, which contained words related to the pandemic and was collected using Python language and the Scrapy module web scraping methods. Multilabel classification algorithms were used to categorize complaints based on assessments of training data obtained according to the topics. Results showed that parcel delivery-related themes were the most often complained about, and a considerable portion were delay issues.
Superresolution Reconstruction Algorithm of Ultrasonic Logging Images Based on High-Frequency Enhancement

High-resolution logging images with glaring detail information are useful for analysing geological features in the field of ultrasonic logging. The resolution of logging images is, however, severely constrained by the complexity of the borehole and the frequency restriction of the ultrasonic transducer. In order to improve the image superresolution reconstruction algorithm, this paper proposes a type of ultrasonic logging based on high-frequency characteristics, with multiscale dilated convolution to feature as the basis of network-learning blocks, training in the fusion of different scale texture feature. The outcomes of other superresolution reconstruction algorithms are then compared to the outcomes of the two-, four-, and eightfold reconstruction. The proposed algorithm enhances subjective vision while also enhancing PSNR and SSIM evaluation indexes, according to a large number of experiments.
Carousel: Improving the Accuracy of Virtual Reality Assessments for Inspection Training Tasks

Training simulations in virtual reality (VR) have become a focal point of both research and development due to allowing users to familiarize themselves with procedures and tasks without needing physical objects to interact with or needing to be physically present. However, the increasing popularity of VR training paradigms raises the question: Are VR-based training assessments accurate? Many VR training programs, particularly those focused on inspection tasks, employ simple pass or fail assessments. However, these types of assessments do not necessarily reflect the user’s knowledge.
3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models

Acquiring the virtual equivalent of exhibits, such as sculptures, in virtual reality (VR) museums, can be labour-intensive and sometimes infeasible. Deep learning based 3D reconstruction approaches allow us to recover 3D shapes from 2D observations, among which single-view-based approaches can reduce the need for human intervention and specialised equipment in acquiring 3D sculptures for VR museums. However, there exist two challenges when attempting to use the well-researched human reconstruction methods: limited data availability and domain shift. Considering sculptures are usually related to humans, we propose our unsupervised 3D domain adaptation method for adapting a single-view 3D implicit reconstruction model from the source (real-world humans) to the target (sculptures) domain. We have compared the generated shapes with other methods and conducted ablation studies as well as a user study to demonstrate the effectiveness of our adaptation method. We also deploy our results in a VR application.
Automated Blendshape Personalization for Faithful Face Animations Using Commodity Smartphones

Digital reconstruction of humans has various interesting use-cases. Animated virtual humans, avatars and agents alike, are the central entities in virtual embodied human-computer and human-human encounters in social XR. Here, a faithful reconstruction of facial expressions becomes paramount due to their prominent role in non-verbal behavior and social interaction. Current XR-platforms, like Unity 3D or the Unreal Engine, integrate recent smartphone technologies to animate faces of virtual humans by facial motion capturing. Using the same technology, this article presents an optimization-based approach to generate personalized blendshapes as animation targets for facial expressions. The proposed method combines a position-based optimization with a seamless partial deformation transfer, necessary for a faithful reconstruction. Our method is fully automated and considerably outperforms existing solutions based on example-based facial rigging or deformation transfer, and overall results in a much lower reconstruction error. It also neatly integrates with recent smartphone-based reconstruction pipelines for mesh generation and automated rigging, further paving the way to a widespread application of human-like and personalized avatars and agents in various use-cases.
Trends in… Adoption of Twitter by Health Sciences Librarians

There is substantial research on librarians' engagement with various social media platforms as part of their professional obligations. We were interested in examining librarians' use of Twitter outside of the context of a job-related, but still professional, context. To find out more, we invited health sciences librarians via Twitter to discuss the impact that the platform has had on their professional lives, offering this column as an opportunity to share their experiences. The case reports support the premise that Twitter can be an impactful communications tool and can benefit librarians in meaningful ways, both professionally and personally.
Blockchain Security as “People Security”: Applying Sociotechnical Security to Blockchain Technology

The notion that blockchains offer decentralized, "trustless" guarantees of security through technology is a fundamental misconception held by many advocates. This misconception hampers participants from understanding the security differences between public and private blockchains and adopting blockchain technology in suitable contexts. This chapter introduces the notion of "people security" to argue that blockchains hold inherent limitations in offering accurate security guarantees to people as participants in blockchain-based infrastructure, due to the differing nature of the threats to participants reliant on blockchain as secure digital infrastructure, as well as the technical limitations between different types of blockchain architecture. This chapter applies a sociotechnical security framework to assess the social, software, and infrastructural layers of blockchain applications to reconceptualize "blockchain security" as "people security." A sociotechnical security analysis of existing macrosocial-level blockchain systems surfaces discrepancies between the social, technical, and infrastructural layers of a blockchain network, the technical and governance decisions that characterize the network, and the expectations of, and threats to, participants using the network. The results identify a number of security and trust assumptions against various blockchain architectures, participants, and applications. Findings indicate that private blockchains have serious limitations for securing the interests of users in macrosocial contexts due to their centralized nature. In contrast, public blockchains reveal trust and security shortcomings at the micro- and meso-organizational levels, yet there is a lack of suitable desktop case studies by which to analyze sociotechnical security at the macrosocial level. The theoretical basis of this piece provides a foundation for blockchain security promises to be further investigated in practice and addressed in order for blockchain security to more accurately provide "people security" for those that use it.