Reviewerly/exHarmony huggingface dataset:
Exploring the Barriers against Using Cryptocurrencies in Managing Construction Supply Chain Processes

Various stakeholders are involved in managing supply chain processes in construction. Suppliers can hardly tolerate upfront costs when faced with flaws in the payment pipeline. This is a serious problem in building construction that uses a large variety of materials as opposed to civil construction that requires fewer types of materials. Alternative secure payment systems are needed, and the use of cryptocurrencies can be an option. However, cryptocurrencies are seldom used in building construction projects due to several challenges that are mostly ignored in the existing literature. To fill this gap, this study investigates the use of cryptocurrencies in construction supply chains as an alternative payment solution to improve the financial performance of the stakeholders by taking advantage of this economical and traceable financial transaction system. The study involves exploratory, descriptive, and empirical survey research. Accordingly, a literature review, focus group discussions, and statistical analyses (Friedman test, Wilcoxon test, and Mann–Whitney U test) were performed. The results imply that a lack of technical knowledge about cryptocurrencies, fluctuations in the value of cryptocurrencies, limited market opportunities, security gaps, personal information required by cryptocurrency systems, no assurance of permanent use, and government actions limiting the use of cryptocurrencies were the most significant barriers against using cryptocurrencies in construction supply chain management. The findings are expected to provide critical information to construction professionals and regulatory agencies about the potential advantages and shortcomings of cryptocurrencies, hence motivating policymakers to create strategies that minimize the concerns of construction professionals about using cryptocurrencies in the building construction industry.
A Blockchain-Empowered Arbitrable Multimedia Data Auditing Scheme in IoT Cloud Computing

As increasing clients tend to outsource massive multimedia data generated by Internet of Things (IoT) devices to the cloud, data auditing is becoming crucial, as it enables clients to verify the integrity of their outsourcing data. However, most existing data auditing schemes cannot guarantee 100% data integrity and cannot meet the security requirement of practical multimedia services. Moreover, the lack of fair arbitration leads to clients not receiving compensation in a timely manner when the outsourced data is corrupted by the cloud service provider (CSP). In this work, we propose an arbitrable data auditing scheme based on the blockchain. In our scheme, clients usually only need to conduct private audits, and public auditing by a smart contract is triggered only when verification fails in private auditing. This hybrid auditing design enables clients to save audit fees and receive compensation automatically and in a timely manner when the outsourced data are corrupted by the CSP. In addition, by applying the deterministic checking technique based on a bilinear map accumulator, our scheme can guarantee 100% data integrity. Furthermore, our scheme can prevent fraudulent claims when clients apply for compensation from the CSP. We analyze the security strengths and complete the prototype’s implementation. The experimental results show that our blockchain-based data auditing scheme is secure, efficient, and practical.
The integration of blockchain and IoT edge devices for smart agriculture: Challenges and use cases

Growth of IoT (Internet of Things) has proved its importance in the various sector. But due to some limitations of security, privacy, etc. it is not possible to use IoT devices in the field of agriculture at its fullest. To overcome these limitations Blockchain is used as it provides security, privacy and it helps in monitoring, examining, to authenticate the agriculture data. With the help of Blockchain, traditional methods of collection, rearranging and distribution of Agri-products can be replaced by more trust-worthy, decentralized, vitreous, and immutable style. In agriculture sector, Blockchain and Internet of things can be amalgamated to have better results which leads us to one level up in the field of agriculture and may control or improve the supply chain in proper manner. The consequences of using blockchain and IoT in combination will result in better understanding to supervise and managing the agriculture effectively. This chapter will illustrate the importance of using blockchain and IoT collectively to develop smart agriculture from traditional agriculture. A model is also proposed to overcome the challenges encountered in agriculture sector, based on IoT applications with the help of blockchain. Also, a review is mentioned about the main characteristics and functions of blockchain used in agriculture sector such as livestock grazing, crops and food supply chain. Finally, some of the open issues Blockchain and security challenges are elaborate.
An application of interval type-2 fuzzy model based control system for generic aircraft

This paper presents a design application of Interval Type-2 (IT2) Takagi–Sugeno (T–S)​ Fuzzy Model Based (FMB) control system for generic aircraft. The IT2 T–S FMB flight control system consists of an IT2 T–S fuzzy model and a fuzzy controller connected in a closed-loop. The IT2 T–S​ fuzzy model is obtained by linearizing the nonlinear aircraft dynamics about various representative points (equilibrium points) of the flight envelope with some fuzzy rules. The aircraft flight envelope parameters, i.e., operating altitude and aircraft speed are characterized as premise parameters and elements of stability and control derivative matrix are identified as consequent parameters of fuzzy model. Longitudinal dynamics of X-29A research aircraft is selected for design of IT2 T–S FMB control system. To achieve the optimum design flexibility, an imperfectly matched premise and membership function dependent (MFD) stability analysis is considered. In MFD stability conditions, the information of flight envelope parameters is included to capture the nonlinearity pertaining to variation in flight conditions. The closed-loop response of IT2 T–S FMB controller is presented at trim equilibrium points by taking four initial angle of attack flight conditions. The performance analysis of designed controller is also presented and discussed in comparison with Fuzzy LQR and LQR based optimal controllers. The simulation results reveal that proposed IT2 T–S FMB controller not only stabilizes the aircraft dynamics but also provides improved transient performance as compared with Fuzzy LQR and LQR based optimal controllers. This demonstrates the utility of IT2 T–S FMB control system for aircraft/ UAV’s related application.
MLCRNet: Multi-Level Context Refinement for Semantic Segmentation in Aerial Images

In this paper, we focus on the problem of contextual aggregation in the semantic segmentation of aerial images. Current contextual aggregation methods only aggregate contextual information within specific regions to improve feature representation, which may yield poorly robust contextual information. To address this problem, we propose a novel multi-level context refinement network (MLCRNet) that aggregates three levels of contextual information effectively and efficiently in an adaptive manner. First, we designed a local-level context aggregation module to capture local information around each pixel. Second, we integrate multiple levels of context, namely, local-level, image-level, and semantic-level, to aggregate contextual information from a comprehensive perspective dynamically. Third, we propose an efficient multi-level context transform (EMCT) module to address feature redundancy and to improve the efficiency of our multi-level contexts. Finally, based on the EMCT module and feature pyramid network (FPN) framework, we propose a multi-level context feature refinement (MLCR) module to enhance feature representation by leveraging multi-level contextual information. Extensive empirical evidence demonstrates that our MLCRNet achieves state-of-the-art performance on the ISPRS Potsdam and Vaihingen datasets.
Dim Target Detection Method Based on Deep Learning in Complex Traffic Environment

Although the current vehicle detection and recognition framework based on deep learning has its own characteristics and advantages, it is difficult to effectively combine multi-scale and multi category vehicle features, and there is still room for improvement in vehicle detection and recognition performance. Based on this, an improved fast R-CNN convolutional neural network is proposed to detect dim targets in complex traffic environment. The deep learning model of fast R-CNN convolutional neural network is introduced into the image recognition of complex traffic environment, and a structure optimization method is proposed, which replaces VGG16 in fast RCNN with RESNET to make it suitable for small target recognition in complex background. Max pooling is the down sampling method, and then feature pyramid network is introduced into RPN to generate target candidate box to optimize the structure of convolutional neural network. After training with 1497 images, the complex traffic environment images are identified and tested. The results show that the accuracy of the proposed method is better than other comparison methods, and the highest accuracy is 94.7%.
RDH-DES: Reversible Data Hiding over Distributed Encrypted-Image Servers Based on Secret Sharing

Reversible Data Hiding in Encrypted Image (RDHEI) schemes may redistribute the data hiding procedure to other parties and can preserve privacy of the cover image. Recently, cloud computing technology has led to the rapid growth of networked media, and many multimedia rights are owned by multiple parties, such as a film's producer and multiple distributors. Thus, the data hiding task could be distributed to multiple distributed servers. Multi-party data hiding has become an important demand for networked media. In addition, it is essential to preserve multi-server and multi-message privacy and data integrity. However, most of the RDHEI schemes involve only one data hider. That inspired us to design the secure multi-party embedding over distributed encrypted-image servers as a solution for multi-party RDHEI applications. In this article, we propose a novel Reversible Data Hiding over Distributed Encrypted-Image Servers (RDH-DES) based on secret sharing. The Chinese remainder theorem, secret sharing, and block-level scrambling are developed as a lightweight cryptography to generate the encrypted image shares. These shares are distributed to different image servers and are used to embed secret data in the proposed framework. The marked encrypted image can be constructed through the marked encrypted shares from different parties, and the decryption and extraction can be completed by the receiver. The experimental results and theoretical analysis have demonstrated that the proposed scheme is secure and effective.
Unpacking Invisible Work Practices, Constraints, and Latent Power Relationships in Child Welfare through Casenote Analysis

Caseworkers are trained to write detailed narratives about families in Child-Welfare (CW) which informs collaborative high-stakes decision-making. Unlike other administrative data, these narratives offer a more credible source of information with respect to workers' interactions with families as well as underscore the role of systemic factors in decision-making. SIGCHI researchers have emphasized the need to understand human discretion at the street-level to be able to design human-centered algorithms for the public sector. In this study, we conducted computational text analysis of casenotes at a child-welfare agency in the midwestern United States and highlight patterns of invisible street-level discretionary work and latent power structures that have direct implications for algorithm design. Casenotes offer a unique lens for policymakers and CW leadership towards understanding the experiences of on-the-ground caseworkers. As a result of this study, we highlight how street-level discretionary work needs to be supported by sociotechnical systems developed through worker-centered design. This study offers the first computational inspection of casenotes and introduces them to the SIGCHI community as a critical data source for studying complex sociotechnical systems.
VR and AR in construction management research: bibliometric and descriptive analyses

Purpose The construction industry is affected by the technologies of Industry 4.0 as the buildings are transforming into complex productions. Integration of innovative technologies, such as virtual reality (VR) and augmented reality (AR), in construction management is an emerging area to increase efficiency and effectiveness. With an aim of determining the current situation, this study evaluates the literature on VR and AR within construction management research. Design/methodology/approach The methodology of this research is based on descriptive and bibliometric analyses to understand and state the current relations between VR-AR technologies and construction management research. According to the meta-analysis (Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA]) protocol of reviewing, 143 papers between 2010 and 2021 from Scopus, Web of Science (WoS) and Science Direct have been retrieved, and then analysis has been applied on these papers. Descriptive analysis contains increment of publications throughout the years, rates of papers according to countries and content of the publications. The bibliometric analysis involves the analysis through Excel and VOSviewer: co-occurrence of keywords, country-based, source and citation. Findings The results of this study emphasize the increasing interest in the adoption of VR-AR technologies in construction management research. As the number of publications has been continuously increased, this trend indicates the strength and growing interest in the topic. Another significant finding is VR-AR implementation studies are mostly based on building information modelling (BIM). In this study, the results of analyses have been discussed in terms of learning, knowledge areas and digital technologies as part of construction management. Originality/value This study contributes to providing a significant theoretical reference for the potentials of VR-AR in construction management research. VR-AR technologies mostly attract the interest of researchers from architectural design studies; however, there is a gap to investigate their potentials in terms of construction management. Additionally, this study focuses on the last 11 years, which indicate the latest trends.
Influence of the input signal’s phase modulation on the performance of optical delay-based reservoir computing using semiconductor lasers

In photonic reservoir computing, semiconductor lasers with delayed feedback have shown to be suited to efficiently solve difficult and time-consuming problems. The input data in this system is often optically injected into the reservoir. Based on numerical simulations, we show that the performance depends heavily on the way that information is encoded in this optical injection signal. In our simulations we compare different input configurations consisting of Mach-Zehnder modulators and phase modulators for injecting the signal. We observe far better performance on a one-step ahead time-series prediction task when modulating the phase of the injected signal rather than only modulating its amplitude.
Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

Abstract Computational fluid dynamics (CFD) simulations are useful in the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high-resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the super-resolution generative adversarial network (SRGAN) model to a multiphase turbulent fluid flow problem, specifically to reconstruct fluid phase fraction at a higher resolution. Two models were created in this work, one which incorporates a physics-informed term in the loss function and one which does not, and the results are discussed and compared. We found that both models significantly outperform non-machine learning upsampling methods and can preserve a substantial amount of detail, showing the versatility of the SRGAN model for upsampling multiphase fluid simulations. However, the difference in accuracy between the two models is minimal indicating that, in the context studied here, the additional complexity of a physics-informed approach may not be justified.
AI-enabled in silico immunohistochemical characterization for Alzheimer's disease

We develop a deep learning approach, in silico immunohistochemistry (IHC), which takes routinely collected histochemical-stained samples as input and computationally generates virtual IHC slide images. We apply in silico IHC to Alzheimer's disease samples, where several hallmark changes are conventionally identified using IHC staining across many regions of the brain. In silico IHC computationally identifies neurofibrillary tangles, β-amyloid plaques, and neuritic plaques at a high spatial resolution directly from the histochemical images, with areas under the receiver operating characteristic curve of between 0.88 and 0.92. In silico IHC learns to identify subtle cellular morphologies associated with these lesions and can generate in silico IHC slides that capture key features of the actual IHC.
Searching the Literature: An Analysis of an Exploratory Search Task

Exploratory search is an intuitive concept in interactive information retrieval. While many definitions for Exploratory Search have been proposed, the main dimensions involve high uncertainty with respect to the problem context, the user expertise, and the search process. In this paper, we draw together the different characteristics relating to the three main exploratory dimensions to provide a conceptual model of exploratory search. We build an exploratory search questionnaire using this model. We then use the questionnaire instrument to examine how literature searches are exploratory and what factors influence the exploratory dimensions and characteristics. We provide one of the first detailed investigations into the nature of exploratory literature review searches. Our analysis of the 368 responses reveals that about 84% of the participants described their literature review task as somewhat exploratory or very exploratory in nature. Also, the analysis points to another dimension of the exploratory search, the knowledge gain/change dimension. Furthermore, we investigated how users’ experience influences how people rate the exploratory search characteristics (e.g., whether they find surprising information, learn new concepts and keywords).
A conceptual framework for designing blockchain technology enabled supply chains

Despite the growing popularity of blockchain technology (BT) applications in a supply chain (SC), the academic literature has failed to offer a complete picture of BT embedded in SC, hereafter referred to as BT-enabled SC, while focusing on BT's specifics. Against this backdrop, this paper aims to develop a conceptual framework by identifying BT's multifaceted features and clarifying their roles in designing BT-enabled SCs. Specifically, we delve into BT's extant definitions in business disciplines as a basis for identifying BT's key attributes and elucidate how these attributes can be incorporated into the design of BT-enabled SCs by introducing the framework that encompasses the strategic, configural, and operational spheres. This paper contributes to clarifying BT's conceptual boundary by framing BT as a stack of technologies and applications – seven dimensions. Based on seven dimensions, the proposed framework offers guidance on how to design BT-enabled SCs.
A Systematic Literature Review on the Characteristics and Effectiveness of Web Application Vulnerability Scanners

Web applications have been a significant target for successful security breaches in the last few years.They are currently secured, as a primary method, by searching for their vulnerabilities with specialized tools referred to as Web Application Vulnerability Scanners (WVS's).Although, these dynamic approaches of testing have some advantages, there is still a scarcity of studies that explore their features and detection capabilities in a systematic way.This article reports findings from a Systematic Literature Review (SLR) to look into the characteristics and effectiveness of the most frequently used WVS's.A total of 90 research papers were carefully evaluated.Thirty (30) WVS's were collected and reported, with only 12 having at least one quantitative assessment of effectiveness.These 12 WVS's were evaluated by 15 original evaluation studies.We found that these evaluations tested mostly only two of the Open Web Application Security Project (OWASP) Top Ten vulnerability types: SQL injection (SQLi) (13/15) and Cross-Site Scripting (XSS) (8/15).Additionally, only one work evaluated six of the OWASP Top Ten vulnerability types and for only one scanner.We also found that the reported detection rates were highly dissimilar between these 15 evaluations.Based on these surprising results we suggest avenues for future directions.
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or other factors, making it difficult to learn effective features for HAR tasks, especially when with inadequate data. To address this issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD) framework, which can disentangle the behavior patterns from the irrelevant noises such as personal styles or environmental noises, etc. Based on a disentanglement network, we designed several loss functions and used an adversarial training strategy for optimization, which can disentangle activity signals from the irrelevant noises with the least dependency (between them) in the feature space. Our BPD framework is flexible, and it can be used on top of existing deep learning (DL) approaches for feature refinement. Extensive experiments were conducted on four public HAR datasets, and the promising results of our proposed BPD scheme suggest its flexibility and effectiveness. This is an open-source project, and the code can be found at http://github.com/Jie-su/BPD