Reviewerly/exHarmony huggingface dataset:
A dynamic holographic modelling method of digital twin scenes for bridge construction

Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene. However, pre-rendering methods in the existing research work are usually used to implement holographic visualization, which is static display. The above-mentioned methods for static display have many shortcomings, such as poor adaptability, low rendering efficiency and lack of real-time. A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction. Firstly, a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes. Secondly, a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics. Finally, a prototype system was developed, and the corresponding experimental analysis was completed. The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction. The amount of scene data can be reduced to more than 30%, which significantly improves rendering efficiency and reduces glare.
Quantum simulation of thermodynamics in an integrated quantum photonic processor

One of the core questions of quantum physics is how to reconcile the unitary evolution of quantum states, which is information-preserving and time-reversible, with evolution following the second law of thermodynamics, which, in general, is neither. The resolution to this paradox is to recognize that global unitary evolution of a multi-partite quantum state causes the state of local subsystems to evolve towards maximum-entropy states. In this work, we experimentally demonstrate this effect in linear quantum optics by simultaneously showing the convergence of local quantum states to a generalized Gibbs ensemble constituting a maximum-entropy state under precisely controlled conditions, while introducing an efficient certification method to demonstrate that the state retains global purity. Our quantum states are manipulated by a programmable integrated quantum photonic processor, which simulates arbitrary non-interacting Hamiltonians, demonstrating the universality of this phenomenon. Our results show the potential of photonic devices for quantum simulations involving non-Gaussian states.
Load balancing for heterogeneous traffic in datacenter networks

In modern datacenter networks (DCNs), the overwhelming heterogeneous flows have various stringent demands, ranging from delay-sensitive short flows, throughput-sensitive long flows to best-effort flows without deadline. Recently, many load balancing schemes are proposed to deliver good performance for datacenter applications. However, the existing solutions cannot meet all the above requirements simultaneously. Especially, the short flows experience head-of-line blocking due to queued behind the long and best-effort flows. The long flows often suffer from throughput degradation due to bursty congestion. To solve these issues, we present LBT, a traffic-differentiated load balancer. The key design point of LBT is to adaptively adjust the switching granularity of long flows and best-effort flows according to the two calculated switching thresholds based on the traffic strength. Specifically, under heavy load, the switching granularity is increased to guarantee required bandwidth capacity for delay-sensitive short flows to finish quickly. In contrary, the switching granularity is reduced to enable the long flows to make full of parallel equal-cost paths. Moreover, we adopt different routing strategies for the three different categories flows. We conduct NS-2 simulations to evaluate the effectiveness of LBT. The experimental results show that LBT significantly reduces the average flow completion time of short flows by up to 55.9% ∼ 65.4% compared to the state-of-the-art solutions and achieves high throughput for long flows concurrently.
Why do consumers adopt smart voice assistants for shopping purposes? A perspective from complexity theory

The widespread appeal of Smart Voice Assistants (SVAs) stems from their ability to enhance the everyday lives of consumers in a practical, enjoyable, and meaningful manner. Despite their popularity, the factors that shape consumer adoption of SVAs remain largely unexplored. To address this research gap, we utilized complexity theory to construct an integrated model that sheds light on the determinants of consumer decision-making in regard to SVA adoption. Furthermore, we applied fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine the proposed model and uncover the causal recipes associated with SVA adoption. Our necessary condition analysis highlights that perceived ease of use, perceived usefulness, perceived humanness, and perceived social presence are necessary predictors for consumers' intentions to adopt SVA. This study constitutes a significant addition to the existing literature by providing a comprehensive and nuanced understanding of the drivers of SVA adoption. Moreover, it offers crucial implications for online service provider managers to improve the adoption of SVAs among their customers.
A comprehensive methodological workflow to maximize solar energy in low-voltage grids: A case study of vertical bifacial panels in Nordic conditions

The large-scale deployment of solar photovoltaic (PV) panels in residential and commercial buildings affects the local distribution grid. Voltage rises during times when PV production exceeds consumption can limit the hosting capacity of the distribution grid. This study presents a comprehensive methodological workflow that moves from the solar analysis of an ideal district to the identification of the PV hosting capacity of a distribution grid. The workflow aims to be highly flexible: the input parameters (i.e., PV technology, PV orientation, global horizontal solar irradiation, and grid properties) can be varied to simulate different cases and compare the corresponding hosting capacities. This workflow enables the analysis of the influences of PV systems’ properties and orientation, electricity consumption, and grid characteristics on a grid’s hosting capacity. A case study was conducted in which the IEEE European Low Voltage Test Grid was used, and conventionally mounted monofacial photovoltaic (MPV) panels were progressively replaced with east–west oriented, vertically mounted bifacial photovoltaic (VBPV) panels that can provide a better match with the electricity load and a higher daily total to daily peak electricity production ratio. The benefits of VBPV are highlighted at high-latitude locations, such as Nordic countries. Therefore, Turku, Finland, which is at a representative Nordic latitude considering the locations of the most important population centers, was chosen as the location for the case study. The results showed that applying VBPV increased a grid’s PV hosting capacity significantly, up to 46% with the optimal system (30% MPV and 70% VBPV).
“Blockchain in government: toward an evaluation framework”

The adoption of a new technology such as Distributed Ledger Technology (DLT) in government is a complex process with numerous potential benefits, but also costs and risks. Early pilots introducing DLT into the public sector show that its potential impact will likely vary depending on the context, including, the type of public service. Even within the same public service, the impact of DLT might be distinct for each of the stakeholders involved (the government, civil servants and citizens, among others). As the public sector is diverse, it is critical to get a proper analysis and understanding of the process of introduction of this technology, which encompasses the different dimensions that play a role in the process. This paper presents an original and multi-dimensional evaluation framework to analyze and compare the benefits, costs and risks of the introduction of DLT in the public sector. It considers a comprehensive set of factors, identified and extracted after conducting a systematic review of the literature, representing potential benefits, costs and risks of DLT in the public sector. These are categorized into four separate dimensions: technological, socio-economic, organizational-cultural, and institutional (legal and political). This evaluation framework has been designed to be used by policy-makers interested in analyzing and comparing the benefits and risks of the introduction of DLT in real-world applications of this technology in the public sector.
BE-GWO: Binary extremum-based grey wolf optimizer for discrete optimization problems

Since most metaheuristic algorithms for continuous search space have been developed, a number of transfer functions have been proposed including S-shaped, V-shaped, linear, U-shaped, and X-shaped to convert the continuous solution to the binary one. However, most existing transfer functions do not provide exploration and exploitation required to solve complex discrete problems. Thus, in this study, an improved binary GWO named extremum-based GWO (BE-GWO) algorithm is introduced. The proposed algorithm proposes a new cosine transfer function (CTF) to convert the continuous GWO to the binary form and then introduces an extremum (Ex) search strategy to improve the efficiency of converted binary solutions. The performance of the BE-GWO was evaluated through solving two binary optimization problems, the feature selection and the 0-1 multidimensional knapsack problem (MKP). The results of feature selection problems were compared with several well-known binary metaheuristic algorithms such as BPSO, BGSA, BitABC, bALO, bGWO, BDA, BSSA, and BinABC. Moreover, the results were compared with four versions of the binary GWO, the binary PSO, and the binary ABC. In addition, the BE-GWO algorithm was evaluated to solve the 0-1 MKP with difficult and very difficult benchmark instances and the results were compared with several binary GWO variants. The results of two binary problems were statistically analyzed by the Friedman test. The experimental results showed that the proposed BE-GWO algorithm enhances the performance of binary GWO in terms of solution accuracy, convergence speed, exploration, and balancing between exploration and exploitation.
Meta-heuristic search algorithms in truss optimization: Research on stability and complexity analyses

Although they are among the most researched real world engineering design problems, it is encountered with significant problems in the optimization of structural truss bar problems (TPs). The main reasons for these problems are (i) the studies on the optimization of TPs being carried out with different experimental settings, (ii) the competitor algorithms used in the optimization process being insufficient, and (iii) the stability analysis and computational complexity information of the algorithms are not investigated. It is designed a simulation environment with defined standards and a benchmarking suite consisting of nine TPs of three different types in order to eliminate these problems. It is found optimum solutions for all problems in the benchmark suite and it is introduced feasible solutions for the first time. According to the statistical analysis results, among the seventy-seven competing algorithms, the best ten algorithms showed competitive performance in the optimization of TPs are LSHADE-EpSin, LSHADE-CnEpSin, SHADE, LSHADE, GSK, FDB-AGDE, MPA, LRFDB-COA and BES. According to the results of the stability analysis carried out on feasible solutions, it has been determined the algorithms with the best success rate for planar type; LSHADE, LSHADE-CnEpSin, LSHADE-EpSin and GSK with 100% success rate, LSHADE-CnEpSin, LSHADE-EpSin, LSHADE and SHADE for Space type with 100% success rate, and LSHADE-EpSin with 69% success rate for frequency constrained type. When the performances of the algorithms are evaluated regardless of the problem type, LSHADE-EpSin has been the most stable algorithm with an overall success rate of 90% on the TPs. Click here for the simulation environment containing the source codes of the ten competitive algorithms and the TP benchmark suite proposed in the article: https://www.mathworks.com/matlabcentral/fileexchange/131668-structural-truss-problems-benchmark-suite.
Visual Detection of Waste using YOLOv8

With the increasing focus on environmental conservation, efficient and accurate waste classification has become a crucial task in the face of increasing urbanisation and industrialization. The challenges faced by waste management systems include pollution of water and air, the spread of diseases, inadequate infrastructure and resources, and the need for efficient waste segregation. Recent techniques proposed to address these challenges include imaging techniques, thermal analysis, and machine learning algorithms. However, these techniques have their limitations, such as specialized equipment and expertise to operate effectively, labour-intensive, posing risks to workers, and resulting in inadequate waste segregation. The proposed objective of this article is to evaluate the effectiveness of YOLOv8, the latest version of the YOLO series of object detection models, for automated waste sorting. The aim is to increase efficiency and safety in waste treatment processes by using YOLOv8 for automated waste sorting. The results demonstrate that YOLOv8 surpasses its state of art algorithms in detecting and classifying waste, making it a valuable tool for improving waste management practices.
Blockchain technology investment strategy for shipping companies under competition

The COVID-19 epidemic normalization challenges the operational efficiency of shipping supply chain and the service level of shipping firms. How to cope with these challenges through blockchain technology investment becomes a hot question in the shipping supply chain. Therefore, we explore the shipping firms' blockchain technology investment strategies under a competitive model and investigate the shipping firms' equilibrium outcomes, consumer surplus and social welfare under different scenarios. We demonstrate that blockchain technology investment cost and logistics service transparency combine to influence shipping firms' market demand, optimal market price and profit. Moreover, we find that shipping firms do not (will) invest in blockchain technology when the investment cost of blockchain technology is high (low). When investment costs and logistics service transparency match each other, the shipping firms reach an asymmetric equilibrium, i.e., only one shipping firm adopts blockchain technology. It is noteworthy that when neither shipping firms invest in blockchain technology, the shipping firms may slip into a prisoner's dilemma. We also show that the competition level and the transparency of logistics services increase the possibility of a prisoner's dilemma. Finally, our results show that shipping firms' blockchain technology investments are conducive to increasing social welfare and consumer surplus, but market competition and logistics service transparency constrain the increase in social welfare and consumer surplus.
Building a Secure Platform for Digital Governance Interoperability and Data Exchange Using Blockchain and Deep Learning-Based Frameworks

A secured platform is a critical component of digital governance, as it helps to ensure the privacy, security, and reliability of the electronic platforms and systems used to manage and deliver public services.Interoperability and data exchange are essential for digital governance, as they enable different government agencies and departments to share data, information, and resources seamlessly, regardless of the platforms and technologies they use.In this paper, we build a secure platform to enhance the trustworthiness of digital governance interoperability and data exchange using blockchain and deep learning-based frameworks.Initially, an optimal blockchain leveraging approach is designed using the bonobo optimization algorithm to authenticate data generated from smart city environments.Furthermore, we introduce the integration of a lightweight Feistel structure with optimal operations to enhance privacy preservation.This integration provides two levels of security and ensures interoperability and double-secured data exchange in digital governance systems.In addition, we utilize a deep reinforcement learning (DRL) model to detect and prevent intrusions such as fraud/corruption in the smart city data.This approach enhances transparency and accountability in accessing the data and shows its predominance over other cutting-edge techniques on two benchmark datasets, BoT-IoT and ToN-IoT.Furthermore, the effectiveness of the framework in real-time scenarios has been demonstrated through two case studies.Overall, our proposed framework provides a trustworthy platform for digital governance, interoperability, and data exchange, addressing the challenges of privacy, security, and reliability in managing and delivering public services.
An ultra-low power fully-programmable analog general purpose type-2 fuzzy inference system

Neuro-fuzzy systems is a soft computing technique for developing intelligent systems that solve complex real-world problems in a way that closely resembles human reasoning. In this work, a novel ultra-low power pure analog integrated type-2 fuzzy inference system architecture is proposed. The system consists of Gaussian membership function and MIN/MAX operator circuits and a center of gravity defuzzification block. The architecture is easily scalable in the design-level, regarding various system’s hyperparameters, resulting in a general-purpose system that can be modified to suit a wide variety of applications. Moreover, the system is fully programmable and electronically tunable even post-fabrication. Finally, a novel fuzzy classifier is implemented in a 90nm CMOS process using the Cadence IC Suite for the electrical and physical design utilizing the proposed architecture. The classifier is evaluated on the breast cancer Wisconsin (original) dataset and is compared with the equivalent software model and other analog classifiers. Post-layout simulation results confirm the proper operation of the design while demonstrating the optimal combination of ultra-low power and high accuracy performance relative to the other analog classifiers.
Enhancing aspect-based sentiment analysis using a dual-gated graph convolutional network via contextual affective knowledge

The primary goal of aspect-based sentiment analysis is to identify sentiment polarity concerning the given aspect in a sentence. Recent investigations have demonstrated the superior performance of graph convolutional neural network (GCN) on dependency parsing tree. However, these GCN-based models fail to take the given aspect into account when calculating the hidden node representation vector, as well as lack exploration of contextual commonsense knowledge. On the contrary, the gating mechanism enables the interaction of the context and the given aspect to enhance the impact of the given aspect on the context. Nevertheless, such interactions are frequently inadequate resulting in insufficient extraction of sentiment information. This paper proposes a dual-gated graph convolutional network via contextual affective knowledge (DGGCN) to address these issues. The core idea is to incorporate GCN into the gating mechanism to enhance GCN to fully aggregate node information while strengthening the concentration on the given aspect. Simultaneously, the incorporation of contextual affective knowledge into graph networks can refine the perception of affective features. Experimental findings on five benchmark datasets reveal that our proposed DGGCN surpasses state-of-the-art methods.
Does blockchain-based traceability system guarantee information authenticity? An evolutionary game approach

While blockchain-based traceability systems in agricultural product supply chains (APSCs) have the potential to improve operational efficiency and food safety, little literature examined the risk of traceability information being falsified before it is recorded on the blockchain. Therefore, using an evolutionary game model, this paper studies the potential falsification risk in the blockchain-based traceability system for agricultural products. The model focuses on the evolutionary path of two key stakeholders: farmers who may falsify information and blockchain platforms that may verify the authenticity of the information. Factors including government supervision and market demand are also considered. The results demonstrate that: (1) the evolutionary stable strategy of the game model depends on whether both stakeholders can benefit from information falsification after taking into account related costs and penalties; (2) when the purchasers' preference for product freshness and well-known places of origin is sufficiently high, farmers will consistently engage in the information falsification, with the platform allowing such behavior to persist; (3) if the downstream stakeholders attach importance to the authenticity of traceability information recorded on the blockchain, this will reduce farmers' willingness to falsify information and increase blockchain platforms’ willingness to verify authenticity; these two effects can lead to situations where the optimal evolutionary stable strategy exists. Based on the results, we discuss the details of blockchain-enabled audits to curb information falsification. The findings of this paper call attention to preventing falsification and developing guidelines for blockchain technology adoptions in APSCs.
Generative AI and the end of corpus-assisted data-driven learning? Not so fast!

This article explores the potential advantages of corpora over generative artificial intelligence (GenAI) in understanding language patterns and usage, while also acknowledging the potential of GenAI to address some of the main shortcomings of corpus-based data-driven learning (DDL). One of the main advantages of corpora is that we know exactly the domain of texts from which the corpus data is derived, something that we cannot track from current large language models underlying applications like ChatGPT. We know the texts that make up large general corpora such as BNC2014 and BAWE, and can even extract full texts from these corpora if needed. Corpora also allow for more nuanced analysis of language patterns, including the statistics behind multi-word units and collocations, which can be difficult for GenAI to handle. However, it is important to note that GenAI has its own strengths in advancing our understanding of language-in-use that corpora, to date, have struggled with. We therefore argue that by combining corpus and GenAI approaches, language learners can gain a more comprehensive understanding of how language works in different contexts than is currently possible using only a single approach.
Applying Ant Colony Optimisation When Choosing an Individual Learning Trajectory

The effectiveness of learning depends in many ways on the organization of the educational process. These days, the educational environment is becoming more flexible and responsive to the needs of students. The traditional form of learning is expanding through the use of new approaches and teaching methods, learning systems, and information technology. Individual learning trajectory allows the learner to regulate the order of studying course modules and the pace of mastering the subject material. Decision-making in choosing the learning trajectory can be supported by specialized methods and tools. This paper proposes the use of ant colony optimisation to support decision making on the choice of an individual learning trajectory. #CSOC1120.