Reviewerly/exHarmony huggingface dataset:
Exploration of challenges to deployment of blockchain in small construction projects

Blockchain technology provides a decentralized way of handling the data and supports the construction project's sustainability and performance. Small construction projects in Malaysia always need help adopting new technology like blockchain. This research aimed to investigate the challenges affecting the implementation of blockchain technology in small construction projects in Malaysia. The study examined Malaysian minor construction project blockchain implementation issues. A total of 17 challenges were found significant in affecting the implementation of Blockchain technology in small construction projects in Malaysia. Five constructs were seen: technological barriers, work environment barriers, economic and planning barriers, operational barriers, and privacy and regulation barriers. Structural equation modeling and these structures create a blockchain implementation model for modest building projects. The model's validity, dependability, and forecast accuracy for all obstacles and their potential influence on blockchain deployment in small construction projects in Malaysia are substantial. The model also has good convergent and discriminant validity, supporting it. Blockchain use in Malaysian small building projects could be improved mainly through economic and planning issues. The present study needs to highlight the Malaysian small construction sector's essential obstacles in integrating blocks and technology that are the basis for more creative technologies. This research will help the Malaysian construction sector concentrate on improvements and assess the effect of blockchain on modest projects.
Perceived conversational ability of task-based chatbots – Which conversational elements influence the success of text-based dialogues?

The use of text-based chatbots offering individual support to customers has increased steadily in recent years. However, thus far, research has focused on comparing text-based chatbots with either each other or with humans, whilst the investigation of task-based dialogues has been scarce. This paper aims to identify the characteristics of dialogues – that is, conversational elements – that lead to a successful task-based conversation. For this purpose, the chatbot, KIM, by MAGGI Kochstudio was used. It was designed to help customers find a recipe tailored to their individual needs. In order to investigate which conversational elements contribute to successful communication between the user and the chatbot KIM, a usability study collecting 123 unstructured dialogues and a scenario-based experiment using four dialogues with 627 respondents was conducted. The quantitative analysis demonstrates that task completion is characterized by a higher perception of the chatbot's conversational ability and user satisfaction. The chatbot should propose correct recipe suggestions following a short dialogue, without the user needing to provide too much input. Based on these findings, we recommended equipping the skillset of task-based chatbots with elements that will complement their assistive qualities – for example, improved use of standard phrases, and reactions to similar domains and non-requests. Gender-specific differences in task completion should be considered.
JND-aware Two-pass Per-title Encoding Scheme for Adaptive Live Streaming

Adaptive live video streaming applications utilize a predefined collection of bitrate-resolution pairs, known as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bitrate ladder</i> , for simplicity and efficiency, eliminating the need for additional run-time to determine the optimal pairs during the live streaming session. These applications do not incorporate two-pass encoding methods due to increased latency. However, an optimized bitrate ladder could result in lower storage and delivery costs and improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Quality of Experience</i> (QoE). This paper presents a Just Noticeable Difference (JND)-aware constrained Variable Bitrate (cVBR) Two-pass Per-title encoding Scheme (JTPS) designed specifically for live video streaming. JTPS predicts a content- and JND-aware bitrate ladder using low-complexity features based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discrete Cosine Transform</i> (DCT) energy and optimizes the constant rate factor (CRF) for each representation using random forest-based models. The effectiveness of JTPS is demonstrated using the open source video encoder x265, with an average bitrate reduction of 18.80% and 32.59% for the same PSNR and VMAF, respectively, compared to the standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HTTP Live Streaming</i> (HLS) bitrate ladder using Constant Bitrate (CBR) encoding. The implementation of JTPS also resulted in a 68.96% reduction in storage space and an 18.58% reduction in encoding time for a JND of six VMAF points.
When Deep Learning-Based Soft Sensors Encounter Reliability Challenges: A Practical Knowledge-Guided Adversarial Attack and Its Defense

Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly improved sensing accuracy; however, their vulnerability to adversarial attacks affects their reliability, thus hindering their widespread application. To improve the reliability of DLSSs, in this article, we conducted a systematic investigation of the adversarial attack and defense of DLSSs. By considering the task requirements of DLSSs and the actual scenarios that attackers may encounter, a framework based on black-box attack and proactive defense was proposed to realize the adversarial attack and defense of soft sensors. The adversarial attack was implemented through the proposed knowledge-guided adversarial attack (KGAA) method. By reconstructing the optimization model and introducing the mechanism knowledge into the objective function, the KGAA method could overcome the ill-posed problem of adversarial attack optimization when attacking a regression model. Moreover, based on the KGAA, a corresponding KGAA adversarial training defense method was proposed to achieve proactive defense. The attack and defense methods were verified in terms of the thermal deformation sensing of an air preheater rotor. Compared to other attacks, the KGAA exhibited higher imperceptibility, rationality, and stability; it can thus be considered a practical attack. The implementation of KGAA adversarial training enhances the adversarial robustness of DLSSs, thus aiding the defense of DLSSs to various attacks and improving their reliability.
Privacy-Preserving TPE-Based JPEG Image Retrieval in Cloud-Assisted Internet of Things

With the large-scale deployment of the Internet of Things, lots of images are generated and outsourced to the cloud to alleviate storage burdens. Encrypted image retrieval has been widely studied as a promising technique for balancing privacy and usability. However, existing schemes have primarily focused on retrieval usability but neglected visual usability, resulting in limitations in image management, even in scenarios where preserved visual information poses no hazard to privacy. In this paper, we are thus inspired to propose a privacy-preserving JPEG image retrieval scheme that effectively enhances retrieval efficiency and accuracy while ensuring low file expansion and excellent thumbnail-preserving accuracy for ciphertext images. Specifically, the well-designed thumbnail-preserving encryption (TPE) enables accurate thumbnail-preserving and high-quality decryption of encrypted images by integrating Huffman coding and information embedding techniques. Moreover, the proposed TPE method fully considers JPEG compression to decrease the ciphertext file expansion and gain good format compatibility. Additionally, adaptive encryption key generation is designed to minimize key storage and considerably bolster security. Also, the cloud can generate preview thumbnails for uploaded TPE-encrypted images, and then extract the Hue-Saturation-Value (HSV) and uniform Local Binary Pattern (ULBP) features from thumbnails instead of encrypted images to boost retrieval efficiency and accuracy. Experimental results show that the peak signal-to-noise ratio (PSNR) of thumbnail-preserving accuracy and decrypted image quality reaches 52dB and 61dB, respectively, the file expansion is minimally limited to 6%, and the mean average precision (mAP) of retrieval attains 64%, indicating that our scheme significantly outperforms the state-of-the-art ones.
Quadratic interpolation and a new local search approach to improve particle swarm optimization: Solar photovoltaic parameter estimation

The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters.
An Efficient Differential Grouping Algorithm for Large-Scale Global Optimization

Cooperative co-evolution (CC) is a practical and efficient evolutionary framework for solving large-scale global optimization problems (LSGOPs). The performance of CC depends on how variables are being grouped and can be improved through guided variable decomposition for various optimization problems. However, achieving a proper variable decomposition is computationally expensive. This paper proposes an effective yet efficient differential grouping (EDG) method to reduce the associated computational cost. Our method exploits the historical interrelationship information of previous variable groups to examine interactions between the remnant variable groups. This allows us to spend less computing resources without compromising the accuracy of the final grouping result. Our proposal utilizes the covariance matrix adaptation evolution strategy (CMA-ES) algorithm, in conjunction with EDG, to solve LSGOPs. Further, to reduce time complexity and improve the stability of CMA-ES, we substitute the complex matrix decomposition step with simpler matrix operations to compute the square root of the covariance matrix. Results from our experiments and analysis indicate that EDG is a competitive method to solve LSGOPs and improve the performance of CC. The proposed schemes significantly enhance the searchability of CMA-ES compared to the other large-scale variants of CMA-ES and state-of-the-art large-scale optimizers. Moreover, our EDG could be integrated with evolutionary optimizers of different flavors like Differential Evolution (DE).
A Similarity Measurement Method of Normal Cloud Models for the Operational Status Perception and Computing of Urban Rail Transit

The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.
Multimodal Hierarchical Graph Collaborative Filtering for Multimedia-Based Recommendation

Multimedia-based recommendation (MMRec) is a challenging task, which goes beyond the collaborative filtering (CF) schema that only captures collaborative signals from interactions and explores multimodal user preference cues hidden in complex multimedia content. Despite the significant progress of current solutions for MMRec, we argue that they are limited by multimodal noise contamination. Specifically, a considerable amount of preference-irrelevant multimodal noise (e.g., the background, layout, and brightness in the image of the product) is incorporated into the representation learning of items, which contaminates the modeling of multimodal user preferences. Moreover, most of the latest researches are based on graph convolution networks (GCNs), which means that multimodal noise contamination is further amplified because noisy information is continuously propagated over the user–item interaction graph as recursive neighbor aggregations are performed. To address this problem, instead of the common MMRec paradigm which learns user preferences in an integrated manner, we propose a hierarchical framework to separately learn collaborative signals and multimodal preferences cues, thus preventing multimodal noise from flowing into collaborative signals. Then, to alleviate the noise contamination for multimodal user preference modeling, we propose to extract semantic entities from multimodal content that are more relevant to user interests, which can model semantic-level multimodal preferences and thus remove a large fraction of noise. Furthermore, we use the full multimodal features to model content-level multimodal preferences like the existing MMRec solutions, which ensures the sufficient utilization of multimodal information. Overall, we develop a novel model, multimodal hierarchical graph CF (MHGCF), which consists of three types of GCN modules tailored to capture collaborative signals, semantic-level preferences, and content-level preferences, respectively. We conduct extensive experiments to demonstrate the effectiveness of MHGCF and its components. The complete data and codes of MHGCF are available at.
TFC: Transformer Fused Convolution for Adversarial Domain Adaptation

In unsupervised domain adaptation (UDA), a classifier is applied to the target domain without or with limited labels, when the target domain has no or few labels. Recently, inspired by their capabilities of long-distance feature dependencies, vision transformer (ViT)-based methods have been used in UDA, however, they ignore the fact that ViT lacks strength in extracting local feature details. To handle the above problems, the purpose of this article is to demonstrate how to take advantage of both convolutional operations and transformer mechanisms for adversarial UDA by using a hybrid network structure called transformer fused convolution (TFC). TFC integrates local features with global features to boost the representation capacity for UDA which can enhance the discrimination between foreground and background. Moreover, to improve the robustness of the TFC, we leverage an uncertainty penalty loss to make incorrect classes have consistently lower scores. Extensive experiments validate the significant performance gains compared to conditional adversarial domain adaptation (CDAN) on all five datasets including DomainNet ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 8.5%), VisDA-2017 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 14.9%), Office-Home ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 18.9%), Office-31 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 11.5%), and ImageCLEF-DA ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\uparrow$</tex-math> </inline-formula> 5.5%).
An Overview of Advanced Deep Graph Node Clustering

Graph data have become increasingly important, and graph node clustering has emerged as a fundamental task in data analysis. In recent years, graph node clustering has gradually moved from traditional shallow methods to deep neural networks due to the powerful representation capabilities of deep learning. In this article, we review some representatives of the latest graph node clustering methods, which are classified into three categories depending on their principles. Extensive experiments are conducted on real-world graph datasets to evaluate the performance of these methods. Four mainstream evaluation performance metrics are used, including clustering accuracy, normalized mutual information, adjusted rand index, and F1-score. Based on the experimental results, several potential research challenges and directions in the field of deep graph node clustering are pointed out. This work is expected to facilitate researchers interested in this field to provide some insights and further promote the development of deep graph node clustering.
Ethical leadership and employee performance: The role of Islamic work ethics and knowledge sharing

BACKGROUND: Many recent studies recommend ethical leadership as a key antecedent to improving employee performance in organizations. OBJECTIVE: Therefore, this study aims to examine how ethical leadership affects employee performance by focusing on the role of Islamic work ethics and knowledge sharing. METHODS: Based on data obtained from 196 Muslim employees who work in the three stone milling companies located in Central Java - Indonesia, this study uses a survey in the form of a questionnaire and Structural Equation Modeling (SEM) based on Amos 24.0 software to test hypotheses in the proposed research model. RESULTS: The results of this study prove that ethical leadership has a significant effect on employee performance, Islamic work ethics, and knowledge sharing. Furthermore, Islamic work ethics and knowledge sharing significantly improve employee performance. CONCLUSION: The findings in this study further confirm that Islamic work ethics and knowledge sharing play an important role in linking ethical leadership and employee performance.
A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

We propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: OUHANDS and NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved.
Design and Practical Implementation of a High Efficiency Two-Layer Trajectory Planning Method for AGV

This paper proposes a two-layer trajectory optimization method for the autonomous ground vehicle (AGV). This twolayer strategy includes an efficient path planning layer and a fast trajectory planning layer. In the first layer, a novel target area adaptive rapidly exploring random tree algorithm (TAA-RRT*) is proposed to search the shortest path. This layer mainly includes a preprocessing and a sampling planning process. In the preprocessing process, the generalized voronoi diagram (GVD) is used to construct the environment information and find the initial path. Then, the sampled target area (TA) is constructed based on this initial path to provide non-uniform sampling. In the sampling planning process, the improved adaptive RRT* algorithm is used to carry out sampling planning in the TA, and the direct connection strategy (DCS) is combined to quickly locate the optimal solution. In the trajectory planning layer, combined with the constraints of the unmanned vehicle and the path constraints obtained in the first layer, the speed planning and the trajectory optimization are addressed by solving the optimal control problem (OCP). After performing a large number of experiments, the feasibility and effectiveness of the proposed method is verified.