Reviewerly/exHarmony huggingface dataset:
The application of blockchain technology in the recycling chain: a state-of-the-art literature review and conceptual framework

More practitioners are embracing blockchain technology to improve recycling performance. However, current research on the use of blockchain is mostly confined to general discussions on sustainable supply chains and circular economy, with limited studies specifically focusing on the recycling chain. This paper conducts a thorough content-based review of the literature related to the application of blockchain technology in the recycling chain with the objective to identify a conceptual framework. Although descriptive and thematic analyses show limited developments, blockchain technology can offer four distinct functions in the recycling chain: transparency, integration, behaviour channelisation, and service transformation. The adoption of blockchain in this area has technical, organisational, and environmental limitations that have not been overcome yet. In terms of benefits, blockchain technology improves recycling performance in terms of cost, dependability, flexibility, sustainability, and risk reduction. To summarise the findings, the paper proposes a novel conceptual framework for blockchain implementations in the recycling chain to support practitioners and guide scholars in further blockchain technology exploration.
A chaotic map with trigonometric functions: Dynamical analysis and its application in image encryption based on sparse representation and convolutional filters

A chaotic map with two sine functions is studied here, and its chaotic dynamics are investigated. Bifurcation diagrams and Lyapunov exponents of the map by different initiation methods show the multistability in some intervals of the bifurcation parameter. Then an encryption method based on sparse representation and the chaotic map is proposed. In the approach, the wavelet domain is used to obtain sparse representation. The sparsity is enhanced by a sparsification operation. A convolutional layer is considered before the compression by the measurement matrices to extract the local information. Experimental results show that the method improves the image reconstruction performance and security level compared to previous methods.
Immersive experiences in digital exhibitions: The application and extension of the service theater model

This study aims at examining the structural relationships between the dimensions of service theaters, the dimensions of immersive experience, mental imagery, and sense of presence. An onsite survey was conducted at the exhibition venue of teamLab SuperNature in Macau using convenience sampling. A total of 418 event attendees participated in this study. The research results will benefit digital exhibition organizers and managers in developing and implementing marketing strategies based on the service theater theory to improve event attendees’ immersive experience, and mental imagery to further enhance the sense of presence.
A trusted IoT data sharing and secure oracle based access for agricultural production risk management

Agricultural risks associated with weather events, soil conditions, diseases, and pests have risen in recent years due to climate change. These risks have burdened farmers with the shock of financial stress and endangered the region's food security. Despite increasing risks, notable risk management tools like agricultural insurance have not effectively reached marginal farmers in developing economies. This can be attributed to the fact that existing centralized agricultural information systems lack trust factors in sharing and accessing agricultural risk data, which is accompanied by processing delays in insurance payouts. To this end, the emergence of promising technologies like blockchain helps establish trust and automates insurance-based payments with smart contracts. This work proposes a framework called AgriSSIOracle with two key contributions. The first is to have a trusted agricultural Internet of Things (IoT) data sharing that employs blockchain-based Self-Sovereign Identity (SSI) and provable credentials. Second is the decentralized oracle-based access control mechanism for smart contracts in agricultural insurance. A method for privacy-preserved authentication and data provenance for agricultural IoT devices is developed with SSI-based Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Next, the IoT data sharing scheme is illustrated using publish–subscribe and request–response communication patterns based on the device data credentials. Finally, the decentralized oracle mechanism for smart contracts is built based on multiparty computation for risk-related data access. The implementation of the framework is realized using a permissioned identity blockchain called Hyperledger Indy and Ethereum, which is a public blockchain for smart contracts. The security and privacy analysis confirms that the AgriSSIOracle framework ensures trust factors, particularly authenticity, privacy, data provenance, data integrity, and access control. The framework is evaluated with respect to the transaction throughput, latency, credential issue-verify-revoke time, and resource utilization metrics. The evaluation results prove that the AgriSSIOracle solution is scalable and efficient and meets the requirements of real-time deployment.
Helmet Wearing Detection of Motorcycle Drivers Using Deep Learning Network with Residual Transformer-Spatial Attention

Aiming at the existing problem of unmanned aerial vehicle (UAV) aerial photography for riders’ helmet wearing detection, a novel aerial remote sensing detection paradigm is proposed by combining super-resolution reconstruction, residual transformer-spatial attention, and you only look once version 5 (YOLOv5) image classifier. Due to its small target size, significant size change, and strong motion blur in UAV aerial images, the helmet detection model for riders has weak generalization ability and low accuracy. First, a ladder-type multi-attention network (LMNet) for target detection is designed to conquer these difficulties. The LMNet enables information interaction and fusion at each stage, fully extracts image features, and minimizes information loss. Second, the Residual Transformer 3D-spatial Attention Module (RT3DsAM) is proposed in this work, which digests information from global data that is important for feature representation and final classification detection. It also builds self-attention and enhances correlation between information. Third, the rider images detected by LMNet are cropped out and reconstructed by the enhanced super-resolution generative adversarial networks (ESRGAN) to restore more realistic texture information and sharp edges. Finally, the reconstructed images of riders are classified by the YOLOv5 classifier. The results of the experiment show that, when compared with the existing methods, our method improves the detection accuracy of riders’ helmets in aerial photography scenes, with the target detection mean average precision (mAP) evaluation indicator reaching 91.67%, and the image classification top1 accuracy (TOP1 ACC) gaining 94.23%.
Disentangled Image Colorization via Global Anchors

Colorization is multimodal by nature and challenges existing frameworks to achieve colorful and structurally consistent results. Even the sophisticated autoregressive model struggles to maintain long-distance color consistency due to the fragility of sequential dependence. To overcome this challenge, we propose a novel colorization framework that disentangles color multimodality and structure consistency through global color anchors, so that both aspects could be learned effectively. Our key insight is that several carefully located anchors could approximately represent the color distribution of an image, and conditioned on the anchor colors, we can predict the image color in a deterministic manner by utilizing internal correlation. To this end, we construct a colorization model with dual branches, where the color modeler predicts the color distribution for anchor color representation, and the color generator predicts the pixel colors by referring the sampled anchor colors. Importantly, the anchors are located under two principles: color independence and global coverage, which is realized with clustering analysis on the deep color features. To simplify the computation, we creatively adopt soft superpixel segmentation to reduce the image primitives, which still nicely reserves the reversibility to pixel-wise representation. Extensive experiments show that our method achieves notable superiority over various mainstream frameworks in perceptual quality. Thanks to anchor-based color representation, our model has the flexibility to support diverse and controllable colorization as well.
LSSA-BP-based cost forecasting for onshore wind power

An LSSA-BP neural network prediction model was established for more accurate onshore wind power cost prediction. Optimise the weights and thresholds of the BP neural network using the sparrow search algorithm. Comparison of the traditional BP model, GA-BP model and LSSA-BP model to verify the superiority of the LSSA-optimised BP model. Moreover, using LSSA-BP in compared with Support Vector Regression Forecasting (SVR) and Random Forest Regression Forecasting (RFR) models. The results of model trial calculations and analysis showed that the LSSA-BP model had the highest prediction accuracy and could be used as a reference for the onshore wind power cost prediction.
Towards the Evaluation of Augmented Reality in the Metaverse: Information Presentation Modes

In the future, many activities will be carried out in the Metaverse: hybrid offices and video-based education are just some examples. The way research is carried out could change, too. In this context, this work investigates the possibility of simulating Augmented Reality (AR) user studies on information presentation in a virtual environment. Organizing an industrial setup is complex; thus, most studies are executed in laboratories. However, lab experiments present limitations, e.g., the number and variety of participants and the availability of facilities. User studies may also be carried out by exploiting simulated AR, as an initial step for the Metaverse, where people are connected regardless of their location. This alternative could be used to carry out experiments on AR information presentation to solve common issues, such as the lack of physical equipment to perform component location tasks and the long time required to collect a large sample of users. Indeed, researchers could propose CAD models with information that simulates the same visual realism achieved with true AR. Moreover, multiple tests could be conducted in parallel by not relying on a limited amount of physical equipment per user. In this work, we developed and evaluated a desktop-simulated testing environment (DSTE) to conduct AR information presentation experiments remotely. We applied it in a pilot user study, revealing that the proposed DSTE was effective for the related research goals. Furthermore, 40 participants reported a positive user experience. The evaluation confirms that using a DSTE is promising for collecting and analyzing data from a wide range of people.
A secure blockchain-enabled vehicle identity management framework for intelligent transportation systems

The need for secure and reliable identity management and authentication scheme for vehicles is becoming increasingly prominent with the digitization of the Intelligent Transportation System (ITS) in smart cities. Today, ITS is extensively acknowledged and employed in a variety of nations. Several existing ITS solutions in smart cities have observant limitations such as trust, security, threats, and centralization platform that can be vulnerable to the authentication and validation of vehicles. The differentiation in compatibility and requirement of IoT-based applications/services produced different challenges for establishing a uniform standard for data exchange, data usage, encoding mechanisms, and data storage for the identity management of vehicles. In this paper, identity generation and management methods are proposed using Blockchain to address existing challenges in ITS applications prevailing with the security of existing identity management and authentication of vehicles. The proposed solution focuses mainly on assurance of the validity of Personal Identification Information (PII) used for producing identity and usability of the application to make the solution suitable to raise the vehicle administration in the ITS. The experiment results and performance analysis represent that the proposed method ensures the correctness and validity of the PII and ensures the reliability and trustworthiness of such identity by securing the correct mapping of a user.
DPF-S2S: A novel dual-pathway-fusion-based sequence-to-sequence text recognition model

In this paper, a novel dual-pathway-fusion-based sequence-to-sequence learning model (DPF-S2S) is proposed for text recognition in the wild, which mainly focuses on enriching the spatial information and extracting high-dimensional representation features to assist decoding. In particular, a double alignment module is developed to solve the problem of text misalignment, where both position and vision information are well considered. Moreover, a global fusion module is deployed to enrich 2D information in the aligned attention maps, which benefits accurate recognition from complicated scenes with arbitrary text shapes and poor imaging conditions. Benchmark evaluations on seven datasets have demonstrated the superiority of proposed DPF-S2S model in comparison to other state-of-the-art text recognition methods, which presents great competitiveness on identifying texts in both regular and irregular scenes. In addition, extensive ablation studies have been carried out, which validate the effectiveness of applied strategies in proposed DPF-S2S.
A K-means clustering model for analyzing the Bitcoin extreme value returns

Bitcoin prices are highly volatile and have extreme upper tails of the return distributions. One important component of Bitcoin price jumps is that it does not follow a normal distribution. This present study aims to reduce the extreme value data available on Bitcoin into simple clusters based on extreme value returns. The study first measures the excessive volatility and then estimates the extreme value returns of Bitcoin between November 2013 and August 2022 to achieve this objective. For robustness checks, extreme value returns are estimated using both the Rogers and Satchell (RS) and the Variance Ratio (VRatio) estimators that embed jumps in the model. Further, K-means clustering is used to form clusters based on the estimated Bitcoin's extreme value returns as the probable good days (extreme days), medium days, and bad days. The study observes that K-means clustering can explain 65 percent point return variability. The study findings will be highly useful for crypto investors, policymakers, and future studies in data mining.
Data independent warmup scheme for non-IID federated learning

Machine Learning’s influence increases daily, and its application prevails in different, critical, and life-changing fields. However, collecting the needed data is challenging due to the increasing concerns about clients’ privacy. In this context, federated learning addresses privacy issues by adopting an on-device model training strategy while communicating only the model parameters rather than raw data. Such an approach allows for preserving users’ information and shielding them from harm following malicious or suspicious parties’ dissemination of their private information. Nevertheless, the distributed nature of federated learning makes it vulnerable to the weight divergence problem caused by the Non-IID (Non-Independent And Identically Distributed) clients. As a result, we can observe a substantial accuracy reduction and convergence time increase. In this paper, we propose a scheme for federated learning addressing the Non-IID clients while also handling the dynamic evolution of the learning context without sacrificing the clients’ privacy. Our scheme takes advantage of the client’s weights to select a compatible subset with the minimum weight divergence to aggregate the initial global model. Our experiments show that the proposed method can boost the federated learning performance while reducing the convergence time.
A comparison of chatbot platforms with the state-of-the-art sentence BERT for answering online student FAQs

Online learning enables academic institutions to accommodate increased student numbers at scale. With this scale comes high demands on support staff for help in dealing with general questions relating to qualifications and registration. Chatbots that implement Frequently Asked Questions (FAQs) can be a valuable part in this support process. A chatbot can provide constant availability in answering common questions, allowing support staff to engage on higher value one-to-one communication with prospective students. A variety of approaches can be used to create these chatbots including vertical platforms, frameworks, and direct model implementation. A comparative analysis is required to establish which approach provides the most accuracy for an existing, available dataset. This paper compares intent classification results of two popular chatbot frameworks to a state-of-the-art Sentence BERT (SBERT) model that can be used to build a robust chatbot. A methodology is outlined which includes the preparation of a university FAQ dataset into a chatbot friendly format for upload and training of each implementation. Results obtained from the framework-based implementations are generated using their published Application Programming Interfaces (APIs). This enables intent classification using testing phrases and finally comparison of F1 scores. Using ten intents comprising 284 training phrases and 85 testing phrases it was found that a SBERT model outperformed all others with an F1-score of 0.99. Initial comparison with the literature suggests that the F1-scores obtained for Google Dialogflow (0.96) and Microsoft QnA Maker (0.95) are very similar to other benchmarking exercises where NLU (Natural Language Understanding) has been compared.
Multi-label weak-label learning via semantic reconstruction and label correlations

In the multi-label classification task, an instance is simultaneously associated with multiple semantic labels. Due to the high complexity of the semantic space in practical applications, obtaining instances with full labels is difficult, leading to the weak-label problem. Existing methods focus on the low-rank and instance manifold regularization assumptions of the label matrix to recover the ground-truth label matrix but ignore the influence that the above assumptions may not hold due to the semantic noise caused by missing labels. To address the problem, this paper proposes a simple and effective method to recover the label space by reconstructing the label semantic space through joint label correlation to solve the multi-label weak-label classification task. Specifically, we leverage the label information consistency and feature-label dependency assumptions to reconstruct the semantic space and consider label correlations to enhance the information of semantic views. Moreover, l2,1-norm is utilized to mitigate the effect of missing label space noise. Additionally, the linear model of the proposed method is expanded to a nonlinear version of the kernel method to address the problem of the inseparability of linear data. Extensive experiments on several real-world tasks show that the proposed method outperforms some state-of-the-art methods.
A Multilayered Audio Signal Encryption Approach for Secure Voice Communication

In this paper, multilayer cryptosystems for encrypting audio communications are proposed. These cryptosystems combine audio signals with other active concealing signals, such as speech signals, by continuously fusing the audio signal with a speech signal without silent periods. The goal of these cryptosystems is to prevent unauthorized parties from listening to encrypted audio communications. Preprocessing is performed on both the speech signal and the audio signal before they are combined, as this is necessary to get the signals ready for fusion. Instead of encoding and decoding methods, the cryptosystems rely on the values of audio samples, which allows for saving time while increasing their resistance to hackers and environments with a noisy background. The main feature of the proposed approach is to consider three levels of encryption namely fusion, substitution, and permutation where various combinations are considered. The resulting cryptosystems are compared to the one-dimensional logistic map-based encryption techniques and other state-of-the-art methods. The performance of the suggested cryptosystems is evaluated by the use of the histogram, structural similarity index, signal-to-noise ratio (SNR), log-likelihood ratio, spectrum distortion, and correlation coefficient in simulated testing. A comparative analysis in relation to the encryption of logistic maps is given. This research demonstrates that increasing the level of encryption results in increased security. It is obvious that the proposed salting-based encryption method and the multilayer DCT/DST cryptosystem offer better levels of security as they attain the lowest SNR values, −25 dB and −2.5 dB, respectively. In terms of the used evaluation metrics, the proposed multilayer cryptosystem achieved the best results in discrete cosine transform and discrete sine transform, demonstrating a very promising performance.
A survey on complex factual question answering

Answering complex factual questions has drawn a lot of attention. Researchers leverage various data sources to support complex QA, such as unstructured texts, structured knowledge graphs and relational databases, semi-structured web tables, or even hybrid data sources. However, although the ideas behind these approaches show similarity to some extent, there is not yet a consistent strategy to deal with various data sources. In this survey, we carefully examine how complex factual question answering has evolved across various data sources. We list the similarities among these approaches and group them into the analysis–extend–reason framework, despite the various question types and data sources that they focus on. We also address future directions for difficult factual question answering as well as the relevant benchmarks.
Quantum Neural Network Compression

Model compression, such as pruning and quantization, has been widely applied to optimize neural networks on resource-limited classical devices. Recently, there are growing interest in variational quantum circuits (VQC), that is, a type of neural network on quantum computers (a.k.a., quantum neural networks). It is well known that the near-term quantum devices have high noise and limited resources (i.e., quantum bits, qubits); yet, how to compress quantum neural networks has not been thoroughly studied. One might think it is straightforward to apply the classical compression techniques to quantum scenarios. However, this paper reveals that there exist differences between the compression of quantum and classical neural networks. Based on our observations, we claim that the compilation/traspilation has to be involved in the compression process. On top of this, we propose the very first systematical framework, namely CompVQC, to compress quantum neural networks (QNNs). In CompVQC, the key component is a novel compression algorithm, which is based on the alternating direction method of multipliers (ADMM) approach. Experiments demonstrate the advantage of the CompVQC, reducing the circuit depth (almost over 2.5×) with a negligible accuracy drop (<1%), which outperforms other competitors. Another promising truth is our CompVQC can indeed promote the robustness of the QNN on the near-term noisy quantum devices.