Reviewerly/exHarmony huggingface dataset:
Suitability analysis of consensus protocols for blockchain-based applications in the construction industry

Blockchain technology has gained increasing attention in the construction sector. The process of developing a blockchain-based application that fits user requirements largely depends on the selection of a suitable consensus protocol, which acts as the centerpiece of blockchain architecture. In distributed networks, consensus protocols not only help maintain data consistency among all stakeholders, but also allow users to work together and stay secure. However, limited efforts have been delivered to analyze and compare existing consensus protocols under various construction scenarios, leading to uncertainties when applying blockchain technology in the construction domain. Therefore, this paper performs a critical review extracting literatures from (1) blockchain-based applications in the construction industry, and (2) existing blockchain consensus protocols. Additionally, a mixed method that integrates qualitative and quantitative analysis is proposed to evaluate the suitability of mainstream consensus protocols for various construction-related applications. Based on the selection criteria and efficiency requirements of consensus protocols generated, a recommendation strategy is also presented for consensus protocol selection under different construction scenarios. This study contributes to the following: (1) it helps construction stakeholders to understand the consensus process in a specific construction application case, and (2) it provides lucid reference guidance in developing a robust blockchain-based system underlying an appropriate consensus protocol. • Reviewed blockchain applications in the construction industry to categorize its implementations. • Identified mainstream consensus protocols and compared their features via four dimensions. • Proposed a qualitative and quantitative analysis method for consensus evaluation. • Generated criteria and requirements for consensus selection under different construction scenarios. • Evaluated consensus suitability and proposed a recommendation strategy for blockchain-based applications.
BIM and ontology-based knowledge management for dam safety monitoring

Dam Safety Monitoring Systems (DSMSs) are crucial in evaluating the operational state of dams. However, heterogeneous monitoring data distributed from multiple sources during the dam operation lacks a unified data integration method and prohibits knowledge extraction and intelligent analysis, which currently poses a labor-intensive task. To address this issue, a solution relying on Building Information Modeling (BIM) and domain ontologies is proposed. Specifically, a DSMS domain ontology (OntoDSMS) is developed by comprehensively collecting domain knowledge and extracting context information from the dam information model. Furthermore, a rule-based reasoner and SPARQL queries are implemented. The proposed approach facilitates the effective integration of dam safety monitoring information while reducing retrieval time effectively compared with traditional databases. A case is illustrated to demonstrate the feasibility and practicality of the proposed approach. • A framework combining BIM and ontology is proposed. • A mapping between a Relational Database and an ontology is established. • A domain ontology for Dam Safety Monitoring Systems is established and SPARQL queries are implemented. • The proposed approach facilitates the effective integration of dam safety monitoring information.
Where Reinforcement Learning Meets Process Control: Review and Guidelines

This paper presents a literature review of reinforcement learning (RL) and its applications to process control and optimization. These applications were evaluated from a new perspective on simulation-based offline training and process demonstrations, policy deployment with transfer learning (TL) and the challenges of integrating it by proposing a feasible approach to online process control. The study elucidates how learning from demonstrations can be accomplished through imitation learning (IL) and reinforcement learning, and presents a hyperparameter-optimization framework to obtain a feasible algorithm and deep neural network (DNN). The study details a batch process control experiment using the deep-deterministic-policy-gradient (DDPG) algorithm modified with adversarial imitation learning.
Together or alone: Should service robots and frontline employees collaborate in retail-customer interactions at the POS?

This study investigates shopper behavior when interacting with an employee-robot team (vs. both actors in isolation), along the metrics of the POS conversion funnel. An unobtrusive field study was conducted using video observations, evenly spread over four conditions: (1) a control condition (i.e., no stimulus), (2) a frontline employee, (3) a humanoid service robot, and (4) an employee-robot team. The results indicate that the service robot was the better option to generate attention and stop passers-by, but in this condition the least amount of passers-by were lured into the store. While the frontline employee initiated the lowest amount of interactions, he could convert the highest number of passersby into actual buyers. The robot-employee team managed to encourage the highest number of passers-by to look at the store, but did not convert more of them into actual buyers than the robot on its own.
A longitudinal explanatory case study of coordination in a very large development programme: the impact of transitioning from a first- to a second-generation large-scale agile development method

Abstract Large-scale agile development has gained widespread interest in the software industry, but it is a topic with few empirical studies of practice. Development projects at scale introduce a range of new challenges in managing a large number of people and teams, often with high uncertainty about product requirements and technical solutions. The coordination of teams has been identified as one of the main challenges. This study presents a rich longitudinal explanatory case study of a very large software development programme with 10 development teams. We focus on inter-team coordination in two phases: one that applies a first-generation agile development method and another that uses a second-generation one. We identified 27 coordination mechanisms in the first phase, and 14 coordination mechanisms in the second. Based on an analysis of coordination strategies and mechanisms, we develop five propositions on how the transition from a first- to a second-generation method impacts coordination. These propositions have implications for theory and practice.
Development of a Lightweight Centralized Authentication Mechanism for the Internet of Things Driven by Fog

The rapid development of technology has made the Internet of Things an integral element of modern society. Modern Internet of Things’ implementations often use Fog computing, an offshoot of the Cloud computing that offers localized processing power at the network’s periphery. The Internet of Things serves as the inspiration for the decentralized solution known as Fog computing. Features such as distributed computing, low latency, location awareness, on-premise installation, and support for heterogeneous hardware are all facilitated by Fog computing. End-to-end security in the Internet of Things is challenging due to the wide variety of use cases and the disparate resource availability of participating entities. Due to their limited resources, it is out of the question to use complex cryptographic algorithms for this class of devices. All Internet of Things devices, even those connected to servers online, have constrained resources such as power and processing speed, so they would rather not deal with strict security measures. This paper initially examines distributed Fog computing and creates a new authentication framework to support the Internet of Things environment. The following authentication architecture is recommended for various Internet of Things applications, such as healthcare systems, transportation systems, smart buildings, smart energy, etc. The total effectiveness of the method is measured by considering factors such as the cost of communication and the storage overhead incurred by the offered integrated authentication protocol. It has been proven that the proposed technique will reduce communication costs by at least 11%.
Why do hotel frontline employees use service robots in the workplace? A technology affordance theory perspective

The rapid robotization of the hotel industry faces reluctance from frontline employees. This study aims to explore frontline employees’ intentions to use service robots in the hotel workplace. Combining technology affordance theory and socio-material perspective, the study conducted four experiments pre-pandemic, amid-pandemic, and post-pandemic to test the proposed framework. The results reveal that hotel employees, especially those with low collectivism (vs. high), prefer a room service robot with physical affordance to a concierge robot with cognitive affordance because the former offers more relative advantages and higher trust. This main effect remained the same both pre- and amid-pandemic. During the pandemic, the COVID-19 compliance of guests showed a significant interaction effect on the employees’ intentions to use service robots in the workplace. The study findings provide meaningful implications for hoteliers selecting the correct type of robot for adoption and encouraging employees to use service robots. • Hotel employees prefer using a room service robot to a concierge robot. • The preference is caused by more relative advantage and higher trust a physical affordance robot offers. • Employees’ intentions to use service robots increased amid-pandemic compared to pre-pandemic. • Concierge robots receive higher usage intention when guests are not following COVID guidelines. • Employees with low collectivism show higher preferences toward a room service robot. • Employees with high collectivism have higher intentions to use both robots.
Semi-supervised learning with graph convolutional extreme learning machines

Extreme Learning Machine (ELM) has been widely used for various classification problems. However, the traditional ELMs are typically based on the regular Euclidean data, thus ignoring the intrinsic structured information among data and resulting in poor robustness. In this paper, we present a novel semi-supervised learning framework, termed Graph Convolutional Extreme Learning Machines (GCELM), based on extending the traditional ELM to the non-Euclidean domain. Technically, we recast ELM layers into a randomized graph convolutional embedding layer followed by a graph convolutional regression layer, which endows ELM with the capability to handle graphs in the non-Euclidean domain. Benefiting from the diversity of the randomized graph convolution, we further propose an enhanced GCELM, i.e., Voting-based GCELM (V-GCELM), by using a simple voting ensemble strategy. The proposed methods preserve the advantages of ELMs, thus being more efficient than the gradient-based graph convolutional networks but without loss of graph learning ability. Extensive experiments on 36 benchmark datasets demonstrate that the proposed methods significantly outperform many previous semi-supervised classification methods.
Blazing Fast PSI from Improved OKVS and Subfield VOLE

We present new semi-honest and malicious secure PSI protocols that outperform all prior works by several times in both communication and running time. Our semi-honest protocol for n = 2^20 can be performed in 0.37 seconds compared to the previous best of 2 seconds (Kolesnikov et al., CCS 2016). This can be further reduced to 0.16 seconds with 4 threads. Similarly, our protocol sends 187n bits compared to 426n bits of the next most communication-efficient protocol (Rindal et al., Eurocrypt 2021). Additionally, we apply our new techniques to the circuit PSI protocol of Rindal et al. and observe a 6x improvement in running time. These performance results are obtained by two types of improvements.
Deep Learning Models for Yoga Pose Monitoring

Activity recognition is the process of continuously monitoring a person’s activity and movement. Human posture recognition can be utilized to assemble a self-guidance practice framework that permits individuals to accurately learn and rehearse yoga postures without getting help from anyone else. With the use of deep learning algorithms, we propose an approach for the efficient detection and recognition of various yoga poses. The chosen dataset consists of 85 videos with 6 yoga postures performed by 15 participants, where the keypoints of users are extracted using the Mediapipe library. A combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has been employed for yoga pose recognition through real-time monitored videos as a deep learning model. Specifically, the CNN layer is used for the extraction of features from the keypoints and the following LSTM layer understands the occurrence of sequence of frames for predictions to be implemented. In following, the poses are classified as correct or incorrect; if a correct pose is identified, then the system will provide user the corresponding feedback through text/speech. This paper combines machine learning foundations with data structures as the synergy between these two areas can be established in the sense that machine learning techniques and especially deep learning can efficiently recognize data schemas and make them interoperable.
Robust fixed-lag smoothing under model perturbations

A robust fixed-lag smoothing approach is proposed in the case there is a mismatch between the nominal model and the actual model. The resulting robust smoother is characterized by a dynamic game between two players: one player selects the least favorable model in a prescribed ambiguity set, while the other player selects the fixed-lag smoother minimizing the smoothing error with respect to least favorable model. We propose an efficient implementation of the proposed smoother. Moreover, we characterize the corresponding least favorable model over a finite time horizon. Finally, we test the robust fixed-lag smoother in two examples. The first one regards a target tracking problem, while the second one regards a parameter estimation problem.
A Confidentiality-based data Classification-as-a-Service (C2aaS) for cloud security

Rapid development and massive use of Information Technology (IT) have since produced a massive amount of electronic data. In tandem, the demand for data outsourcing and the associated data security is increasing exponentially. Small organizations are often finding it expensive to save and process their huge amount of data, and keep the data secure from unauthorized access. Cloud computing is a suitable and affordable platform to provide services on user demand. The cloud platform is preferable used by individuals, Small, and Medium Enterprises (SMEs) that cannot afford large-scale hardware, software, and security maintenance cost. Storage and processing of big data in the cloud are becoming the key appealing features to SMEs and individuals. However, the processing of big data in the cloud is facing two issues such as security of stored data and system overload due to the volume of the data. These storage methods are plain text storage and encrypted text storage. Both methods have their strengths and limitations. The fundamental issue in plain text storage is the high risk of data security breaches; whereas, in encrypted text storage, the encryption of complete file data may cause system overload. This paper propose a feasible solution to address these issues with a new service model called Confidentiality-based Classification-as-a-Service (C2aaS) that performs data processing by treating data dynamically according to the data security level in preparation for data storing in the cloud. In comparison to the conventional methods, our proposed service model is strongly showing good security for confidential data and is proficient in reducing cloud system overloading.
P-ResUnet: Segmentation of brain tissue with Purified Residual Unet

Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epilepsy, Alzheimer's, and multiple sclerosis. Recently, UNet-like architectures are widely used for medical image segmentation, which achieved promising performance by using the skip connection to fuse the low-level and high-level information. However, In the process of integrating the low-level and high-level information, the non-object information (noise) will be added, which reduces the accuracy of medical image segmentation. Likewise, the same problem also exists in the residual unit. Since the output and input of the residual unit are fused, the non-object information (noise) of the input of the residual unit will be in the integration. To address this challenging problem, in this paper we propose a Purified Residual U-net for the segmentation of brain tissue. This model encodes the image to obtain deep semantic information and purifies the information of low-level features and the residual unit from the image, and acquires the result through a decoder at last. We use the Dilated Pyramid Separate Block (DPSB) as the first block to purify the features for each layer in the encoder without the first layer, which expands the receptive field of the convolution kernel with only a few parameters added. In the first layer, we have explored the best performance achieved with DPB. We find the most non-object information (noise) in the initial image, so it is good for the accuracy to exchange the information to the max degree. We have conducted experiments with the widely used IBSR-18 dataset composed of T-1 weighted MRI volumes from 18 subjects. The results show that compared with some of the cutting-edge methods, our method enhances segmentation performance with the mean dice score reaching 91.093% and the mean Hausdorff distance decreasing to 3.2606.
Transfer learning and sentiment analysis of Bahraini dialects sequential text data using multilingual deep learning approach

Sentiment analysis is a crucial Natural Language Processing task to analyze the user’s emotions and opinions towards events, entities, services, or products. Arabic NLP faces numerous challenges, some of which include: (1) the scarcity of resources, especially in modern standard Arabic and Arabic dialects, particularly the Bahraini one; (2) lack of multilingual deep learning models; and (3) insufficient transfer learning studies on Arabic dialects in general and Bahraini dialects specifically. This research aims to create a balanced dataset of Bahraini dialects that covers product reviews by translating English Amazon product reviews to modern standard Arabic, which were then converted to Bahraini dialects. Another aim of this research is to provide a reusable multilingual deep learning long short term memory model to analyze the parallel dataset of English, modern standard Arabic, and Bahraini dialects, which differ in linguistic properties. Many experiments were conducted using train-validate-test split and k-fold cross-validation to evaluate the model performance using accuracy, F1 score, and AUC metrics. The model runs average accuracy on all datasets ranging from 96.72% to 97.04%, 97.91% to 97.93% in F1 score, while in AUC was 98.46% to 98.7% when utilizing an augmentation technique. Moreover, a pre-trained Long Short Term Memory model was created to exploit and transfer the knowledge gained from analyzing the product reviews in Bahraini dialects to perform sentiment analysis on a small dataset of movie comments in the same dialects. The Pre-trained model performance was 96.97% accuracy, 96.65% F1 score, and 97.94% AUC.
History archive assisted niching differential evolution with variable neighborhood for multimodal optimization

Multimodal optimization problems (MMOPs) require the algorithms to find multiple global or local optima in a single run, which is considered a difficult task. In recent years, niching techniques have received widespread attention from researchers and extensively recognized as efficient methods for MMOPs. However, most niching techniques have the disadvantage of parameters settings. Moreover, the information of historical individuals is not fully used for evolution. In this paper, a history archive assisted niching differential evolution with variable neighborhood (HANDE/VN) is proposed. The main features are as follows: (i) a variable neighborhood strategy dynamically controls the neighborhood size of individual during the evolution, which can balance exploration and exploitation to some extent; (ii) a new mutation operation based on history archive can fully exchange the information between history archive and current population to enhance the search ability and help the algorithm locate multiple global solutions more accurately; (iii) a local optima processing based on history archive deal with the individuals trapped in the local optima. To demonstrate the performance of HANDE/VN, 20 MMOPs from CEC2013 were selected as test suite. Compared with state-of-the-art multimodal algorithms, the proposed approach obtains better results in terms of peak ratio and success rate.
Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future

Machine and deep learning techniques are prevalent in the medical discipline due to their high level of accuracy in disease diagnosis. One such disease is malaria caused by Plasmodium falciparum and transmitted by the female anopheles mosquito. According to the World Health Organisation (WHO), millions of people are infected annually, leading to inevitable deaths in the infected population. Statistical records show that early detection of malaria parasites could prevent deaths and machine learning (ML) has proved helpful in the early detection of malarial parasites. Human error is identified to be a major cause of inaccurate diagnostics in the traditional microscopy malaria diagnosis method. Therefore, the method would be more reliable if human expert dependency is restricted or entirely removed, and thus, the motivation of this paper. This study presents a systematic review to understand the prevalent machine learning algorithms applied to a low-cost, portable optical microscope in the automation of blood film interpretation for malaria parasite detection. Peer-reviewed papers were downloaded from selected reputable databases eg. Elsevier, IEEExplore, Pubmed, Scopus, Web of Science, etc. The extant literature suggests that convolutional neural network (CNN) and its variants (deep learning) account for 41.9% of the microscopy malaria diagnosis using machine learning with a prediction accuracy of 99.23%. Thus, the findings suggest that early detection of the malaria parasite has improved through the application of CNN and other ML algorithms on microscopic malaria parasite detection.