Reviewerly/exHarmony huggingface dataset:
Identification of bearing failure in composite-aluminium bolted joints using digital image correlation

The quasistatic failure of composite-aluminium bolted joints is investigated experimentally using the stereo Digital Image Correlation (DIC) technique. Single-shear, two-bolt lap joints made of carbon fibre reinforced polymer (CFRP) laminates and aluminium plates are subjected to tensile load while being monitored using stereo-DIC. The measured displacements are processed by removing outliers using the Grubbs test and reducing noise by exploiting symmetries of the specimen geometry. The development of the Gauss curvature across the CFRP is computed from DIC displacement data, and a sudden onset of curvature in the vicinity of the bolts is observed as a precursor to bearing failure. Fractographs of cross-sections of partially failed CFRP near the bolts confirm compressive damage of laminates as the source of this localised curvature. We conclude that stereo-DIC is useful as a non-contact measurement of bearing failure initiation.
Sustainable Engineering higher education in Oman-lessons learned from the pandemic (COVID-19), improvements, and suggestions in the teaching, learning and administrative framework.

Abstract: This research study has investigated the challenges faced due to the pandemic (COVID-19). This paper further provides recommendations that can be adopted by academics, learners, and administrators to make the education system more robust and sustainable. The impact of the COVID-19 pandemic can be felt in various fields across the world including higher education. The closure of face-to-face (FtF) learning in educational institutions worldwide has impacted over 95% of the world's student population. Therefore, in wake of this, many institutions have quickly adopted to offer complete online teaching and learning in a very short period. However, such a quick transition has raised several challenges. 1) What are the challenges encountered by academics and their readiness to adapt to the rapid remote learning transition? 2) What are the challenges encountered by learners (students), and their readiness to adapt to the rapid remote learning transition? 3) What are the recommendations for strategic planners or high-level administrators in institutions to tackle such pandemic risks effectively in the future? To address research questions mixed methods are used. A qualitativequestionnaire survey is framed by an extensive literature review to understand the perceptions of academics and learners. A total of (n=525) academician samples and (n= 1460) student samples have been collected. The academic and learner's perceptions are analyzed by estimating the Pearson correlation coefficients. The mean and SD values based on academic rank stood at 3.01±0.96, and by experience stood at 2.96±0.98. Similarly, learner's perceptions stood at 2.67±0.95. Keywords: FtF (Face to Face); problem-oriented and project-based learning (POPBL); LMS (Learning management system); emergency remote teaching (ERT); emergency risk (COVID-19): Standard deviation (SD).
SSAT: Self-Supervised Associating Network for Multiobject Tracking

Multi-object tracking (MOT), which is crucial for computer vision and video processing, has immense potential for improvement. Traditional tracking-by-detection approaches include feature-based object re-identification methods that use trained features, but these methods suffer from a lack of suitable training data. In training datasets used for MOT, every object in a video sequence must have its own location and ID. However, assigning IDs to each object in every sequence is considerably labor-intensive, and hence current MOT datasets are unsuitable for training re-identification networks. To resolve this issue, this paper proposes a novel self-supervised learning method using several short videos that contain no human-added labels, based on the idea that each video is a set of temporally corresponding image frames. We then describe how to improve tracking performance using a re-identification network trained in a self-supervised manner. In addition, ablation studies were conducted in order to define the optimal parameters, such as number of clips, data augmentation, and appropriate matching algorithms. The proposed approach achieved competitive performance compared with current best-practice methods including supervised methods, achieving MOT accuracy = 62.0% and ID F1-score = 62.7% on the MOT17 benchmark.
AIR-FI: Leaking Data from Air-Gapped Computers Using Wi-Fi Frequencies

This paper presents a new attack allowing attackers to exfiltrate data from isolated, air-gapped computers via Wi-Fi frequencies. We show that malware in a compromised air-gapped computer can generate signals in the Wi-Fi frequency bands. The signals are generated through the memory buses - no special hardware is required. Sensitive data can be modulated and secretly exfiltrated on top of the signals. We show that nearby Wi-Fi-capable devices (e.g., smartphones, laptops, and IoT devices) can intercept these signals, decode them, and send them to the attacker over the Internet. We utilized the physical layer information exposed by the Wi-Fi chips to extract the signals. We further implemented the transmitter and receiver and discussed design considerations and implementation details. We evaluated this covert channel in terms of bandwidth and distance and presented a set of countermeasures. Our evaluation shows that data can be exfiltrated from air-gapped computers to nearby Wi-Fi receivers located meters away at bit rates of 16&#x00A0;b/sec.
A Privacy-Preserving Outsourcing Computing Scheme based on Secure Trusted Environment

As one of the key technologies to enable the internet of things (IoT), cloud computing plays a significant role in providing huge computing and storage facilities for large-scale data. Though cloud computing brings great advantages, new issues emerge, such as data security breach and privacy disclosure. In this paper, we introduce a novel secure and privacy-preserving outsourcing computing scheme (hereafter referred to as SPOCS) to tackle this issue. In SPOCS, the effective use of Intel SGX, one of the trusted execution environment (TEE), ensures the confidence and integrity of sensitive data in cloud computing and prevents data loss from causing privacy disclosure. In order to keep malicious cloud service providers (CSPs) from illegally tampering with the outsourcing results, blockchain is employed to ensure the data immutability. Significantly, our proposed scheme achieves anonymity and traceability. In the outsourcing process, smart contracts are applied to make the whole process fully automated without any human involvement. Finally, the security of the proposed scheme is analyzed in terms of its resistance to different attacks. The experiments indicate that our scheme is effective and efficient.
drozBot: Using Ergodic Control to Draw Portraits

We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">drozBot: le robot portraitiste</i> , a robotic system that draws artistic portraits of people. The input images for the portrait are taken interactively by the robot itself. We formulate the problem of drawing portraits as a problem of coverage which is then solved by an ergodic control algorithm to compute the strokes. The ergodic computation of the strokes for the portrait gives an artistic look to them. The specific ergodic control algorithm that we chose is inspired by the heat equation. We employed a 7-axis Franka Emika robot for the physical drawings and used an optimal control strategy to generate joint angle commands. We explain the influence of the different hyperparameters and show the importance of the image processing steps. The attractiveness of the results was evaluated by conducting a survey where we asked the participants to rank the portraits produced by different algorithms.
Coordination Strategies When Working from Anywhere: A Case Study of Two Agile Teams

Abstract Effective coordination is the key to successful agile teams. They rely on frequent interactions and mutual adjustment to manage dependencies between activities, which traditionally has been solved by co-locating the team. As the world is adjusting to post-covid work-life, companies are moving towards a work-from-anywhere approach where workers can choose to what degree they want to work from home or office. However, little is known about coordination in such a context. We report findings on developers’ emerging strategies when working-from-anywhere, from an exploratory case study in Norway, including eight interviews. Our study shows that new strategies for mutual adjustment emerged as teams experimented with different tools and approaches: developers chose tasks according to location, tasks with vague requirements are performed collocated while individual tasks requiring focus are best performed at home; large meetings are virtual, preserving co-located time for collaborative tasks; using virtual rooms to maintain unscheduled meetings as they communicate mental presence to teammates, lowering the threshold for intra-team unscheduled talks. The strategies can help organizations create a productive and effective environment for developers.
Postdigital Intercreative Pedagogies: Ecopedagogical Practices for the Commons

This chapter explores intercreativity as a concept and strategy in contemporary collective knowledge production. The chapter is situated in a postdigital context where technology and nature encounter each other in a symbiotic relationship, and in a posthumanist perspective to relationships between subject, collectivity, and context. Intercreativity is an ecopedagogical process of production of ideas that benefits the development of knowledge commons. The chapter presents two examples of intercreative praxis: ‘Free culture from education. Pedagogical strategies, remixes and resources that favour the development of free culture’ and ‘Quadraginta’. The examples outline intercreativity as a space for experimentation where theory dialogues with practice, helps reflection on possible paths of action, and enriches educational contexts. Intercreativity is a crucial aspect of ecopedagogy, because it breaks the chains of individualistic neoliberal logic which reproduces the current social order and offers a way to imagine, and act towards, a better future.
Virtual Reality as a Teaching Resource Which Reinforces Emotions in the Teaching Process

In the educational context, virtual reality (VR) can be defined as a set of diverse hardware and software technologies that can be used to provide the experience of immersion in the learning process. This study aimed to investigate the connection between VR and emotions that can motivate students to be more active in the didactic process. A group of 99 participants was involved and watched three short individual VR sessions using Oculus glasses. We monitored participants’ moods (using the SUPIN/PANAS questionnaire) and we also gauged their self-esteem (SES questionnaire). Participation in VR sessions increases the intensity in learners’ emotions. The increase or decrease in emotions depends on whether they are positive or negative. First and foremost, we observed changes in the intensity of positive emotions which, after VR intervention, increased significantly in relation to the initial measurement, compared with the intensity of negative emotions during the post-test. The results show that VR significantly modifies learners’ emotions, contributing to the reinforcement of positive emotional states, and in the case of those with low self-esteem, it also decreases negative emotional states. In conclusion, VR can be used as a didactic tool to facilitate the teaching-learning process at various levels. It makes it possible not only to practice skills in simulated, safer conditions and illustrate presented material, but also to make the didactic process more attractive, thereby influencing learners’ emotions positively.
IADA: A dynamic interference-aware cloud scheduling architecture for latency-sensitive workloads

Cloud computing allows several applications to share physical resources, yielding rapid provisioning and improving hardware utilization. However, multiple applications contending for shared resources are susceptible to interference, which might lead to significant performance degradation and consequently an increase in Service Level Agreements violations. In previous work, we started to analyze resource contention and its impact on performance degradation and hardware utilization. Then, we created an interference-aware application classifier based on machine learning techniques and evaluated it comparing two classification strategies: (i) unique, when a single classification is performed over the entire applications’ execution; and (ii) segmented, when the classification is carried out over multiple static-defined intervals. Moving towards a dynamic scheduling solution, we combine and improve on previous work findings and, in this work, we present IADA, a full-fledged dynamic interference-aware cloud scheduling architecture for latency-sensitive workloads. Our approach consists in improving on a segmented interference classification of applications to a dynamic classification scheme based on workload variations. Aiming at using the available resource more efficiently and respecting Quality of Services requirements, the proposed architecture was developed supported by machine learning techniques, heuristics, and a bayesian changepoint detection algorithm for online inference. We conducted a set of real and simulated experiments, utilizing a developed extension of CloudSim Toolkit to analyze and compare the proposed architecture efficiency with related studies. Results evidenced that IADA reduces by 25%, on average, the overall performance degradation. • A novel dynamic interference-aware scheduling architecture. • Experiments with an extension of a cloud simulator platform. • Comparison with related schedulers found in the literature. • Evaluation of the general architecture overhead to validate its usability.
Optimized Distributed Generalized Reed-Solomon Coding with Space-Time Block Coded Spatial Modulation

We present a well-known generalized Reed-Solomon (GRS) code incorporated with space-time block coded spatial modulation (STBC-SM) for wireless networks, which is capable of enjoying coded cooperation between the source and the relay. In the proposed distributed GRS-coded STBC-SM (DGRSC-STBC-SM) scheme, the source and relay nodes use distinct GRS codes. At the relay, we employ the concept of information selection to choose the message symbols from the source for further encoding. Thus, the codewords jointly constructed by the source and relay are generated at the destination. For achieving the best codeword set at the destination, we propose an optimal algorithm at the relay to select partial symbols from the source. To reduce the computational complexity, we propose a more practical algorithm with low complexity. Monte Carlo simulation results show that the proposed scheme using the low-complexity algorithm can achieve near-optimal error performance. Furthermore, our proposed scheme provides better error performance than its corresponding coded non-cooperative counterpart and the existing Reed-Solomon coded cooperative SM (RSCC-SM) scheme under identical conditions.
TIFF: Tokenized Incentive for Federated Learning

In federated learning (FL), clients collectively train a global machine learning model with their own local data. Without sharing sensitive raw data, each client in FL only sends updated weights to consider privacy and security concerns. Most of existing FL works focus mainly on improving model accuracy and training time, but only a few works focus on FL incentive mechanisms. To build a high performance model after FL training, clients need to provide high quality and large amounts of data. However, in real FL scenarios, high-quality clients are reluctant to participate in FL process without reasonable compensation, because clients are self-interested and other clients can be business competitors. Even participation incurs some cost for contributing to the FL model with their local dataset. To address this problem, we propose TIFF, a novel tokenized incentive mechanism, where tokens are used as a means of paying for the services of providing participants and the training infrastructure. Without payment delays, participation can be monetized as both providers and consumers, which promotes continued long-term participation of high-quality data parties. Additionally, paid tokens are reimbursed to each client as consumers according to our newly proposed metrics (such as token reduction ratio and utility improvement ratio), which keeps clients engaged in FL process as consumers. To measure data quality, accuracy is calculated in training without additional overheads. We leverage historical accuracy records and random exploration to select high-utility participants and to prevent overfitting. Results show that TIFF provides more tokens to normal providers by up to 6.9% and less tokens to malicious providers by up to 18.1%, achieving improvement of the final model accuracy by up to 7.4%, compared to the default approach.
Blockchain-Based Administration of Access in Smart Home IoT

There is a rising concern about authorization in IoT environments to be appropriately designed and applied, due to smart things surge to be part of people's daily lives on one hand, and the amount of personal/private information they utilize, on the other hand. Different access control systems have been proposed for different IoT environments, many are remaining only at a conceptual level. In this paper, we propose a decentralized, ledger-based, publish-subscribe based architecture for the administration of access in a smart home IoT environment to preside at the assignments of underlying operational authorizations. Proposed architecture is endorsed by a proof-of-concept implementation, which utilizes smart contracts to ensure the integrity of administration supplemented by intrinsic benefits of blockchain to be distributed and transparent. Despite the rising hype around the blockchain technology that stokes its utilization in different domains, utilizing it for access control purposes is not yet promising. Our implementation results assure using blockchain for administrative access control is propitious, while is not yet appropriate for operational access control, which have been mainly the focus of previously proposed blockchain-based access control works.
Blockchain Applicability for the Internet of Things: Performance and Scalability Challenges and Solutions

Blockchain has recently drawn wide attention in the research community. Since its emergence, the world has seen the expansion of this new technology, which was initially developed as a digital currency more than a decade ago. A self-administering ledger that ensures extensive data immutability over a peer-to-peer network has made it attractive for cybersecurity applications, including sensor-enabled Internet of Things (IoT) systems. Brand new challenges and questions now demand solutions, as IoT devices are now online in a distributed fashion to simplify our everyday lives. Motivated by those challenges, the work here has detailed issues from which an IoT infrastructure can suffer if the wrong blockchain technology is chosen. Unlike a typical review, this paper focuses on security challenges of the blockchain-IoT ecosystem through critical findings and applicable use cases. The contribution directs Blockchain architects, designers, and researchers in the domain to select an unblemished combination of Blockchain-powered IoT applications. In addition, the paper provides insight into the state-of-the-art Blockchain platforms, namely Ethereum, Hyperledger, and IOTA, to exhibit their respective challenges, their constraints, and their prospects in terms of performance and scalability.
A feature extraction method for person re-identification based on a two-branch CNN

Abstract A two-branch convolutional neural network (CNN) architecture for feature extraction in person re-identification (re-ID) based on video surveillance is proposed. Highly discriminative person features are obtained by extracting both global and local features. Moreover, an adaptive triplet loss function based on the original triplet loss function is proposed and is used in the network training process, resulting in a significantly improved learning efficiency. The experimental results on open datasets demonstrate the effectiveness of the proposed method.
Self-Supervised Monocular Depth Estimation With 3-D Displacement Module for Laparoscopic Images

We present a novel self-supervised training framework with 3D displacement (3DD) module for accurately estimating per-pixel depth maps from single laparoscopic images. Recently, several self-supervised learning based monocular depth estimation models have achieved good results on the KITTI dataset, under the hypothesis that the camera is dynamic and the objects are stationary, however this hypothesis is often reversed in the surgical setting (laparoscope is stationary, the surgical instruments and tissues are dynamic). Therefore, a 3DD module is proposed to establish the relation between frames instead of ego-motion estimation. In the 3DD module, a convolutional neural network (CNN) analyses source and target frames to predict the 3D displacement of a 3D point cloud from a target frame to a source frame in the coordinates of the camera. Since it is difficult to constrain the depth displacement from two 2D images, a novel depth consistency module is proposed to maintain depth consistency between displacement-updated depth and model-estimated depth to constrain 3D displacement effectively. Our proposed method achieves remarkable performance for monocular depth estimation on the Hamlyn surgical dataset and acquired ground truth depth maps, outperforming monodepth, monodepth2 and packnet models.