Reviewerly/exHarmony huggingface dataset:
Building Socially-Impactful Domain Knowledge Applications Using Graph Neural Networks

This paper commences by introducing two scientific applications of Graph Neural Networks (GNNs). Both applications are related to sustainability, which is a non-controversial subject matter. We will build our analysis toward developing more complex and socially-impactful applications that attract debates. Then, the paper highlights the unique capabilities of GNNs, as well as what they do not do. The challenges in applying GNNs to more complex and socially-impactful applications are discussed, along with 3 potential solutions: a) We discuss the concept of an Implied Graph, which can be used in modeling highly complex dynamics to increase our ability to solve socioeconomic problems. b) We show how Lagrangian Neural Networks (LNNs) can be applied to solve practical problems where there should be multi-level learning. c) The paper also explores the natural progression from the form of federated learning mentioned above, where cooperation is assumed, to future applications where cooperation cannot be taken for granted and where the centralized engine may need to serve certain “policing” functions.
Exploring the Drivers of Blockchain Technology Adoption in Construction Project Logistics: A Case of South Africa

The construction sector has been criticized for poor logistic planning which has resulted in lower output, material loss, and project delays, all of which contribute to increased project costs. This has been attributed to the very big processes involved in the distribution of construction materials. Inefficient planning and management of these processes can affect productivity in construction sites and lead to material waste throughout the entire supply chain. Hence, the need to adopt an approach to manage logistics in the sector efficiently. Blockchain technology has been identified as the solution to the logistics challenges faced in construction project delivery. However, as a significant technological tool, it is expedient for the driving forces of blockchain technology in logistics management to be recognized and comprehended. Thus, this study examines the drivers of blockchain technology adoption in construction project logistics in South Africa (SA). A survey design was adopted. A total number of one hundred and forty (140) questionnaires were administered to construction professionals in Guateng Province, SA, and ninety (90) were returned and found suitable for analysis. Collected data were analyzed using percentage, mean item score, standard deviation, one sample t-test, and Kruskal-Wallis. The findings reveal the drivers of blockchain technology adoption in construction project logistics in SA which includes trust, enhanced blockchain knowledge and capabilities, collaborative mindset, organizational capabilities, and stakeholders’ participation. These findings could inform the project stakeholders in the country on the drivers of blockchain technology adoption in construction project logistics. These highlighted drivers are expected to motivate relevant parties to take considerable action for the SA construction sector to profit from this technology in logistics management.
Optimisation of Automation Devices Based on IOT Big Data Algorithms

With the continuous development of communication, sensor and caching technologies, IoT technology has gained rapid opportunities for growth and a huge digital revolution has taken place at all levels of society. Blockchain technology has emerged rapidly in recent years and can be seen as a distributed, time-based ledger of distributed data. It utilises technologies such as consensus protocols, modern cryptography, P2P and smart contracts, which can provide a secure, stable, transparent, auditable and low-consumption system architecture that has a traceable, stable and efficient security management capability, and can provide a new solution to identity security for the Internet of Things. This paper absorbs the existing blockchain-based access management approach and improves it, proposing a new private chain-based security management approach that solves the problems of access dynamics, low intelligence and high overhead in the traditional access management approach. This paper designs a new control management architecture, the Novel-Capability-Based Access Control (NCBAC), which draws on the microkernel and microservice ideas of operating systems. Firstly, this paper abstracts the concept of management node to solve the problem of weak computing power and low storage performance of IoT devices that cannot meet the difficulty of direct communication between IoT devices and blockchain, and at the same time can reduce the network operation overhead; secondly, it constructs a multi-level smart contract system and designs three kinds of smart contracts, AC, ACC and AMC, to build a trusted and reliable access control entity model; finally, it adopts radial basis based (RBF) neural network and combines with access policy to dynamically generate the credit degree threshold of access nodes to build an intelligent access authority management model for IoT mass sensors. The model proposed in this paper designs a token mechanism based on the fact that IoT systems have multiple requests within a short period of time in a real production environment, which, according to experimental results, improves the performance of the system to a certain extent.
Reformulation techniques for automated planning: a systematic review

Abstract Automated planning is a prominent area of Artificial Intelligence and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesize a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.
Using active learning and an agent-based system to perform interactive knowledge extraction based on the COVID-19 corpus

Abstract Efficient knowledge extraction from Big Data is quite a challenging topic. Recognizing relevant concepts from unannotated data while considering both context and domain knowledge is critical to implementing successful knowledge extraction. In this research, we provide a novel platform we call Active Learning Integrated with Knowledge Extraction (ALIKE) that overcomes the challenges of context awareness and concept extraction, which have impeded knowledge extraction in Big Data. We propose a method to extract related concepts from unorganized data with different contexts using multiple agents, synergy, reinforcement learning, and active learning. We test ALIKE on the datasets of the COVID-19 Open Research Dataset Challenge. The experiment result suggests that the ALIKE platform can more efficiently distinguish inherent concepts from different papers than a non-agent-based method (without active learning) and that our proposed approach has a better chance to address the challenges of knowledge extraction with heterogeneous datasets. Moreover, the techniques used in ALIKE are transferable across any domain with multidisciplinary activity.
Fast Fourier Transforms for Nonequispaced Data

In this chapter, we describe fast algorithms for the computation of the DFT for d-variate nonequispaced data, since in a variety of applications the restriction to equispaced data is a serious drawback. These algorithms are called nonequispaced fast Fourier transforms (NFFT). In Sect. 7.1, we present a unified approach to the NFFT for nonequispaced data either in space or frequency domain.
<tt>PASTA</tt>: A Dataset for Modeling PArticipant STAtes in Narratives

Abstract The events in a narrative are understood as a coherent whole via the underlying states of their participants. Often, these participant states are not explicitly mentioned, instead left to be inferred by the reader. A model that understands narratives should likewise infer these implicit states, and even reason about the impact of changes to these states on the narrative. To facilitate this goal, we introduce a new crowdsourced English-language, Participant States dataset, PASTA. This dataset contains inferable participant states; a counterfactual perturbation to each state; and the changes to the story that would be necessary if the counterfactual were true. We introduce three state-based reasoning tasks that test for the ability to infer when a state is entailed by a story, to revise a story conditioned on a counterfactual state, and to explain the most likely state change given a revised story. Experiments show that today’s LLMs can reason about states to some degree, but there is large room for improvement, especially in problems requiring access and ability to reason with diverse types of knowledge (e.g., physical, numerical, factual).1
Perception and Range Measurement of Sweeping Machinery Based on Enhanced YOLOv8 and Binocular Vision

As the "Green Concept" gains momentum, the state of road infrastructure has emerged as a topic of global concern. In parallel, the demand for road cleaning services provided by sweepers has experienced a dramatically increase. Consequently, the efficiency of road cleaning heavily depends on the capabilities of the sweeper, particularly its capacity for environmental perception and ranging. At present, the perception and ranging of road environments by sweepers is still largely rely on artificial observation, inefficient sensors, and traditional binocular ranging methods. These conventional techniques fall short in ensuring both cleanliness and driving safety of sweepers. This study introduces an enhancement to the YOLOv8 network, aiming to achieve precise environmental perception and ranging by integrating predictive frame resolution measurement with binocular stereo vision. Compared with the traditional binocular ranging method, the improve of binocular ranging via the YOLOv8 network effectively avoids the inaccuracies and misinterpretations stemming from incomplete parallax maps in traditional binocular ranging. This enhancement leads to heightened levels of accuracy and safety. Experimental results confirm that the enhanced ranging algorithm achieves an error rate of less than 0.5% under static testing conditions. Furthermore, the average error rate can be reduced to 0.78% during dynamic testing scenarios. Our ranging methodology significantly improves the precision of environmental road and distance data provided to sweepers in comparison to the pre-improvement binocular ranging detection. Post-improvement, the model retains its portability and versatility, making it well-suited for permanent integration. This model demonstrates notable migratability and generalisability.
Confirmation Bias and Time Pressure: A Family of Experiments in Software Testing

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background</b> : Software testers manifest confirmation bias (the cognitive tendency) when they design relatively more specification consistent test cases than specification inconsistent test cases. Time pressure may influence confirmation bias of testers per the research in the psychology discipline. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</b> : We examine the manifestation of confirmation bias of software testers while designing functional test cases, and the effect of time pressure on confirmation bias in the same context. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method</b> : We executed one internal and two external experimental replications concerning the original experimentation in Oulu. We analyse individual replications and meta-analyse our family of experiments (the original and replications) for joint results on the phenomena. Results: Our findings indicate a significant manifestation of confirmation bias by software testers during the designing of functional test cases. Time pressure significantly promoted confirmation bias among testers per the joint results of the family. The different experimental sites affected the results; however, we did not detect any effects of site-specific variables. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion</b> : Software testers should develop an outside-of-the-box thinking attitude to counter the manifestation of confirmation bias. Time pressure can be manoeuvred by centring manual suites on the designing and consequently the execution of inconsistent test cases, while automated testing focuses on consistent ones.
SUST and RUST: Two Datasets for Uyghur Scene Text Recognition

The main objective of scene text recognition is to recognize text in complex images and convert it into editable text. However, scene text recognition research has long been focused on English, and there is a lack of research on other small languages, such as Uyghur language. This paper conducts a series of studies on Uyghur text recognition in natural scenes, with the following main contributions:(1) To address the lack of Uyghur scene text recognition datasets, we established the Synthetic Uyghur Scene Text dataset SUST and the Real Uyghur Scene Text dataset RUST, and we augmented RUST with STR-Aug. (2) The contemporary existing STR models are selected to conduct experiments on the dataset proposed in this paper, through which we search for the model structure suitable for Uyghur scene text recognition. (3) According to the characteristics of Uyghur text, this paper designs a lightweight Uyghur text recognition model named LUSTR, which takes into account both lightweight and accuracy, and achieves good performance in the Uyghur scene text recognition dataset proposed in this paper. The SUST and RUST are now publicly available at https://github.com/kongfnajie/SUST-and-RUST-datasets-for-Uyghur-STR.
Improving FMEA Comprehensibility via Common-Sense Knowledge Graph Completion Techniques

The Failure Mode Effect Analysis process (FMEA) is widely used in industry for risk assessment, as it effectively captures and documents domain-specific knowledge. This process is mainly concerned with causal domain knowledge. In practical applications, FMEAs encounter challenges in terms of comprehensibility, particularly related to inadequate coverage of listed failure modes and their corresponding effects and causes. This can be attributed to the limitations of traditional brainstorming approaches typically employed in the FMEA process. Depending on the size and diversity in terms of disciplines of the team conducting the analysis, these approaches may not adequately capture a comprehensive range of failure modes, leading to gaps in coverage. To this end, methods for improving FMEA knowledge comprehensibility are highly needed. A potential approach to address this gap is rooted in recent advances in common-sense knowledge graph completion have illustrated effectiveness of text-aware graph embedding methods. However, the applicability of such methods in an industrial setting is limited. This paper addresses this issue on FMEA documents in an industrial environment. Here, the application of common-sense knowledge graph completion methods on FMEA documents from semiconductor manufacturing is studied. These methods achieve over 20% MRR on the test set and 70% of the top 10 predictions were manually assessed to be plausible by domain experts. Based on the evaluation, this paper confirms that text-aware knowledge graph embedding for common-sense knowledge graph completion are more effective than structure-only knowledge graph embedding for improving FMEA knowledge comprehensibility. Additionally we found that language model in domain fine-tuning is beneficial for extracting more meaningful embedding, thus improving the overall model performance.
Machine Learning in Chaos-Based Encryption: Theory, Implementations, and Applications

Chaos-based encryption is a promising approach to secure communication due to its complexity and unpredictability. However, various challenges lie in the design and implementation of efficient, low-power, attack-resistant chaos-based encryption schemes with high encryption and decryption rates. In addition, Machine learning (ML) has emerged as a promising tool for enhancing the growing security and efficiency concerns and maximizing the potential of emerging computing platforms across diverse domains. With the rapid advancements in technology and the increasing complexity of computing systems, ML offers a unique approach to addressing security challenges and optimizing performance. This paper presents a comprehensive study on the application of ML techniques to secure chaotic communication for wearable devices, with an emphasis on chaos-based encryption. The theoretical foundations of ML for secure chaotic communication are discussed, including the use of ML algorithms for signal synchronization, noise reduction, and encryption. Various ML algorithms, such as deep neural networks, support vector machines, decision trees, and ensemble learning methods, are explored for designing chaos-based encryption algorithms. This paper places a greater emphasis on methodological aspects, metrics, and performance evaluation of machine learning algorithms. In addition, the paper presents an in-depth investigation into state-of-the-art ML-assisted defense and attacks on chaos-based encryption schemes, covering their theoretical foundations and practical implementations. Furthermore, a review of the potential advantages and limitations associated with the utilization of ML techniques in secure communication systems and encryption is provided. The study extends to exploring the diverse range of applications that can benefit from ML-assisted encryption, such as secure communication in the Internet of Things (IoTs), cloud computing, and wireless networks. Overall, we provide insights into the applications of ML for secure chaotic communication in wearable devices, its challenges, and opportunities, offering a foundation for further research and development and facilitating advancements in the field of secure chaotic communication.
Fast Dual-Feature Extraction Based on Tightly Coupled Lightweight Network for Visual Place Recognition

Visual place recognition (VPR) is a task that aims to predict the location of an image based on the existing images. Because image data can often be massive, extracting features efficiently is critical. To solve the problems of model redundancy and poor time efficiency in feature extraction, this study proposes a fast dual-feature extraction method based on a tightly coupled lightweight network. The tightly coupled network extracts local and global features in a unified model which has a lightweight backbone. Learned step size quantization is then performed to reduce the computational overhead in the inference stage. Additionally, an efficient channel attention module ensures feature representation ability. Efficiency and performance experiments on different hardware platforms showed that the proposed algorithm incurred significant runtime savings for feature extraction, and the inference was 2.9–4.0 times faster than that in the general model. The experimental results confirmed that the proposed method can significantly improve VPR efficiency while ensuring accuracy.
Improved Traffic Sign Detection Model Based on YOLOv7-Tiny

Traffic sign detection is a critical task in the autonomous driving. Ordinary networks cannot obtain satisfactory results in traffic sign detection because the size distribution of traffic signs are extremely unbalanced. To overcome this challenge, this paper proposed an improved YOLOv7-Tiny object detection model. Firstly, a path connection strategy was proposed to enhance small-scale feature representation. Compared to the original FPN connection strategy, it adds a path that leads out of the backbone and connects into the Feature Pyramid Network(FPN). Secondly, we proposed a new down-sampling module----Slice-Sample. By slicing, the size of the feature map is reduced and subsequently, the weights of the sliced feature map channels are assigned using the channel attention mechanism. It can reduce the loss of feature information. Additionally, a module for detecting attention was proposed to address the aliasing effect found in the fusion of different scales. This channel attention mechanism not only focuses on the correlation of neighboring channels, but also employs two branches to increase the model’s ability to extract information from the feature map. Experiments on the German Traffic Sign Detection Benchmark (GTSDB) showed that the improved model can achieve more remarkable performance than yolov7-tiny. Our method achieved 93.47% mean average precision (mAP) surpassing the yolov7-tiny’s 7.48%, and the frames per second (FPS) value is maintained at 67.5. Besides, our method is superior to other lightweight models on the GTSDB. To demonstrate the generalizability of our approach, we tested it on the Tsinghua-Tencent 100K dataset (TT100K) without tuning and obtained 66.29% mAp surpassing the yolov7-tiny’s 7.59%. In addition, the number of parameters of improved YOLOv7-Tiny is about 23.29 M.
Complements or Confounders? A Study of Effects of Target and Non-target Features on Online Fraudulent Reviewer Detection

The online review fraud process is an event in which a reviewer posts fraudulent reviews on an e-commerce platform with respect to a review target, such as a commodity or service. Extant studies on detecting fraudulent reviewers rely on reviewer behavior patterns and the textual content of reviews while ignoring the targets being reviewed. Based on the Goals Plans Action theory, we examine the relative importance of target features for fraudulent re-viewer detection in comparison to that of non-target features. Target features refer to objective clues about firms’ business status and competitive position, while non-target features refer to subjective clues about linguistic manifestations of review content and reviewer posting behavior. The results of an empirical evaluation of the Yelp.com dataset of restaurant reviews suggest that the combination of target features with non-target features can improve the performance of online fraudulent reviewer detection in comparison with using non-target features alone. Moreover, it will further improve the performance of online fraudulent reviewer detection when we consider the non-target features as conditioned on the review target features rather than regarding them as independent of each other.