Reviewerly/exHarmony huggingface dataset:
A Green Supply Chain Management Strategy for E-Commerce Based on Multiple Blockchain Technology

Abstract In this paper, firstly, the e-commerce green supply chain management strategy of multivariate blockchain is explored, and digital assets are traded on the e-commerce supply chain platform through the information anchoring model. Secondly, the Diffie-Hellman algorithm is used to generate the key, the AES algorithm is used to encrypt the information, and the account data, transaction data and IoT data, are stored in three independent chains. Finally, the selection strategies of different e-commerce sales models and the green supply chain management strategies of e-commerce platforms are visually analyzed through numerical arithmetic examples. The results show that when E 0 = 10, it is optimized by the ecological R&amp;D efforts of supply chain members. When E 0 = 50, the greenness of the product is decreasing. When it finally tends to the steady state, E ∞ C &gt; E ∞ BR &gt; E ∞ MR . The three green supply chain management models proposed in this study are conducive to enhancing consumer utility and in line with the sustainable development strategy.
Creating a voice-activated AI responder in Python

How do you solve a problem like a séance? With Python and GPT3, is my answer. I’ve spent this weekend working on the technology for a forthcoming Artangel exhibition in Leeds that features an AI séance.
Development of a Safety Assistance System Based on Computer Vision for Mobility Scooters

The authors have been developing an assistance system for mobility scooters. The users of mobility scooters are usually elderly people or visually impaired people. Thus, an additional warning system can assist them when driving. The goal of this warning assistance system is not to achieve autonomous driving, but to assist human-driven vehicles with sound or vibration. Realizing autonomous driving not only requires high cost but also has the problem of attribution of accident responsibility. A more feasible solution is to provide a warning system to assist driving. This paper describes the design of our system, where the perception of various dangerous information is performed via vision algorithms and visual sensors. Finally, we demonstrate some preliminary recognition results through practical experiments.
Probabilistic robotic logic programming with hybrid Boolean and Bayesian inference

Abstract Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic.
An Empirical Study of Blockchain-Driven Transparency in a Consumer Marketplace

Marketplaces have been reshaped continually by technological advancements ranging from railroads to the mobile Internet and search. Now, marketplace transparency is set to be transformed by blockchain technology, which offers a uniquely credible platform for tracing the flow of products through firms' supply chains. The popular belief is that blockchain will imminently enable consumers to comprehensively trace food products from farm to table, reveal counterfeits and contamination, and more accurately identify socially responsible products and firms. However, no previous empirical research has examined blockchain technology's impact on consumer marketplaces, and implementing a blockchain-based solution for supply chain transparency can be costly and complex. In collaboration with a leading Chinese technology company that operates the top online consumer grocery marketplace in China, we analyze its recent efforts to introduce blockchain tracing among its sellers. Our findings indicate that credible supply chain transparency encourages consumers to more readily buy traced products, especially those that are handling-sensitive or offered in less-trusted markets. Consequently, adopting third-party sellers experienced an average monthly revenue increase of up to 23.4%. By utilizing structural estimation to understand how consumers assess product attributes and quality, we highlight that consumer responses (and welfare effects) vary in sophistication and size based on their prior experience with the product category. Additionally, we establish that consumers deem blockchain-based information to be more reliable than sellers' claims.
Preserving the Public Trust: A Quality Management Framework for Federal Appellate Court Operations

In a system that is by design not fully transparent to the public, the federal judiciary can look to available quality management systems to improve their operations and drive greater confidence in the courts as an institution. Although state courts have developed over the years several quality-based concepts and tools, they have focused mainly on performance management approaches and, until recently, ignored the broader quality management landscape that has been applied in other areas of government. Evaluating state-focused resources for application to federal courts is a good step toward introducing some aspects of quality management practices, but these resources neither fully address the needs of the federal courts nor ensure the quality of the day-to-day, frontlines of court operations.An analysis of federal appellate court case processing and quality management practices reveals that federal appellate courts have significant variation in their case processing times. This variation provides an opportunity for leaders within the federal judiciary to integrate established quality management standards to improve the effectiveness of the federal appellate court system. Based on a review of practical applications of quality management methods and standards at the Federal Circuit, this paper recommends that federal appellate courts begin their quality journeys with the introduction of Lean Six Sigma practices followed by the adoption of quality management standards, namely ASQ/ANSI G1. To support public accountability within the federal courts and long-term integration of quality practices to improve court performance, changes are also needed with respect to the types of performance data that is collected, analyzed, and then made available to the public. This paper proposes a new circuit operational model scorecard and the establishment of national baseline performance targets based on case processing intervals, which can be used by both the public and federal court leaders to evaluate performance between the circuit courts, to encourage overall improved appellate court operations, and to support the continued public trust and confidence in the federal judiciary.
Multifractal Characteristics of Uranium Grade Distribution and Spatial Regularities in a Sandstone-Type Uranium Deposit in Xinjiang, China

Based on an analysis of the spatial distribution of uranium grade in 338 boreholes of a uranium deposit in Xinjiang, the enrichment and spatial variation of uranium ore in two stopes of the deposit are discussed using multifractal theory. The distribution characteristics of the uranium ore of the two stopes are studied by multifractal parameters: the scaling exponent of mass τ(q), the scaling exponent α(q) of each sub-set and its corresponding fractal dimension f(α), the fractal dimension D0 and information dimension D1. The differences of uranium distribution in the two stopes can be quantified well by using multifractal spectrum and multifractal parameters such as Δα, Δf and R. After a comprehensive multifractal distribution analysis, 10 m × 10 m is defined as a fence unit, and the window sizes ε=3,6,9⋯,45 are set; the singularity exponents α of the two stopes are calculated by using this element concentration–area method. The results show that the multifractal theory and model can organically combine spatial structure information, scale change information and anisotropy information to obtain low-grade and weak mineral resources information and can effectively distinguish complex and superimposed anomalies. This will provide a basis for the local concentration and spatial variation rules of uranium distribution and the design of the parameters of the leaching uranium mining well site.
A Data Generation Method for Image Flare Removal Based on Similarity and Centrosymmetric Effect

Image pairs in under-illuminated scenes along with the presence of complex light sources often result in strong flare artifacts in images, affecting both image quality and the performance of downstream visual applications. Removing lens flare and ghosts is a challenging issue, particularly in low-light environments. Existing methods for flare removal are mainly restricted by inadequate simulation and real-world capture, resulting in singular categories of scattered flares and unavailable reflected ghosts. Therefore, a comprehensive deterioration procedure is crucial for generating a dataset for flare removal. We propose a methodology based on spatial position relationships for generating data pairs with flare deterioration, which is supported by theoretical analysis and real-world evaluation. Our procedure is comprehensive and realizes the similarity of scattered flares and the symmetric effect of reflected ghosts. We also construct a real-shot pipeline that respectively processes the effects of scattering and reflective flares, aiming to directly generate data for end-to-end methods. Experimental results demonstrate that our methodology adds diversity to existing flare datasets and constructs a comprehensive mapping procedure for flare data pairs. Our method facilitates the data-driven model to achieve better restoration in flare images and proposes a better evaluation system based on real shots, thus promoting progress in the area of real flare removal.
RAD-XP: Tabletop Exercises for Eliciting Resilience Requirements for Sociotechnical Systems

Despite noble intentions, new technologies may have adverse effects on the resilience of the sociotechnical systems into which they are integrated. Our objective was to develop a lightweight method to elicit requirements that, if implemented, would support sociotechnical system resilience. We developed and piloted the Resilience-Aware Development Exercise Protocol (RAD-XP), a method to generate tabletop exercises (TTXs) to elicit resilience requirements. In the pilot study, this approach generated 15 requirements from a one-hour TTX, where the majority of requirements were found to support resilience. Participants indicated via survey that RAD-XP was effective and efficient, and that they would want to use RAD-XP regularly throughout the agile development process. We discuss future research and development to refine this approach to eliciting resilience requirements.
An Online Method for Supporting and Monitoring Repetitive Physical Activities Based on Restricted Boltzmann Machines

Human activity recognition has been widely used to monitor users during physical activities. By embedding a pre-trained model into wearable devices with an inertial measurement unit, it is possible to identify the activity being executed, count steps and activity duration time, and even predict when the user should hydrate himself. Despite these interesting applications, these approaches are limited by a set of pre-trained activities, making them unable to learn new human activities. In this paper, we introduce a novel approach for generating runtime models to give the users feedback that helps them to correctly perform repetitive physical activities. To perform a distributed analysis, the methodology focuses on applying the proposed method to each specific body segment. The method adopts the Restricted Boltzmann Machine to learn the patterns of repetitive physical activities and, at the same time, provides suggestions for adjustments if the repetition is not consistent with the model. The learning and the suggestions are both based on inertial measurement data mainly considering movement acceleration and amplitude. The results show that by applying the model’s suggestions to the evaluation data, the adjusted output was up to 3.68x more similar to the expected movement than the original data.
SECP-Net: SE-Connection Pyramid Network for Segmentation of Organs at Risk with Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. The accurate and automatic segmentation of computed tomography (CT) images of organs at risk (OAR) is clinically significant. In recent years, deep learning models represented by U-Net have been widely applied in medical image segmentation tasks, which can help to reduce doctors' workload. In the OAR segmentation of NPC, the sizes of the OAR are variable, and some of their volumes are small. Traditional deep neural networks underperform in segmentation due to the insufficient use of global and multi-size information. Therefore, a new SE-Connection Pyramid Network (SECP-Net) is proposed. For extracting global and multi-size information, the SECP-Net designs an SE-connection module and a pyramid structure for improving the segmentation performance, especially that of small organs. SECP-Net also uses an auto-context cascaded structure to further refine the segmentation results. Comparative experiments are conducted between SECP-Net and other recent methods on a private dataset with CT images of the head and neck and a public liver dataset. Five-fold cross-validation is used to evaluate the performance based on two metrics; i.e., Dice and Jaccard similarity. The experimental results show that SECP-Net can achieve SOTA performance in these two challenging tasks.
New Hybrid Graph Convolution Neural Network with Applications in Game Strategy

Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent and learn strategies from expert players on different sides. Many game states can be expressed not only as a matrix data structure suitable for DCNN training but also as a graph data structure. Most of the available DCNN methods ignore the territory characteristics of both sides’ positions based on the game rules. Therefore, in this paper, we propose a hybrid approach to the graph neural network to extract the features of the model of game-playing strategies and fuse it into a DCNN. As a graph learning model, graph convolutional networks (GCNs) provide a scheme by which to extract the features in a graph structure, which can better extract the features in the relationship between the game-playing strategies. We validate the work and design a hybrid network to integrate GCNs and DCNNs in the game of Go and show that on the KGS Go dataset, the performance of the hybrid model outperforms the traditional DCNN model. The hybrid model demonstrates a good performance in extracting the game strategy of Go.
An Effective Method for Constructing a Robot Operating System Node Knowledge Graph Based on Open-Source Robotics Repositories

Robot software development can be considered as a component-driven process, and existing ROS components, such as an ROS node, can be reused to construct robot applications. By reusing the ROS node, the development process of robot software can be significantly accelerated. However, the challenges in reusing ROS nodes primarily lie in the scattered organization of ROS node information. To address this challenge, this paper proposes a method to construct an ROS node knowledge graph (RNKG) based on high-quality open-source robot projects. In order to build a high-quality knowledge graph of ROS nodes, we first constructed a high-quality dataset of open-source robot projects. Since ROS node knowledge can exist in both text and code formats, we initially separated the data in the dataset into code data and text data, and then applied different knowledge extraction methods to extract corresponding entities. Finally, we integrated a series of ROS node knowledge and organized it into a knowledge graph. To validate the effectiveness of the constructed ROS node knowledge graph, we first verified the completeness of the entities and the accuracy of relationships in the knowledge graph. Next, we evaluated the performance of the ROS node knowledge graph in assisting developers with the downstream task of finding ROS nodes. These findings suggest that our proposed method for constructing an ROS node knowledge graph is feasible and demonstrate that the ROS node knowledge graph helps search ROS nodes.
A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security

The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues regarding their applicability. A system named Incentivizing Secure Federated Learning Systems (ISFL-Sys) is proposed, consisting of a blockchain module and a federated learning module. A data-security-oriented trustworthy federated learning mechanism called Efficient Trustworthy Federated Learning (ETFL) is introduced in the system. Utilizing a directed acyclic graph as the ledger for edge nodes, an incentive mechanism has been devised through the use of smart contracts to encourage the involvement of edge nodes in federated learning. Experimental simulations have demonstrated the efficient security of the proposed federated learning mechanism. Furthermore, compared to benchmark algorithms, the mechanism showcases improved convergence and accuracy.
Face Detection Based on DF-Net

Face data have found increasingly widespread applications in daily life. To efficiently and accurately extract face information from input images, this paper presents a DF-Net-based face detection approach. A lightweight facial feature extraction neural network based on the MobileNet-v2 architecture is designed and implemented. By incorporating multi-scale feature fusion and spatial pyramid modules, the system achieves face localization and extraction across multiple scales. The proposed network is trained on the open-source face detection dataset WiderFace. The hyperparameters such as bottleneck coefficients and quality factors are discussed. Comparative experiments with other commonly used networks are carried out in terms of network model size, processing speed, and network extraction accuracy. Experimental results affirm the efficacy and robustness of this method, especially in challenging facial poses.
A Chinese–Kazakh Translation Method That Combines Data Augmentation and R-Drop Regularization

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity, as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese–Kazakh (Arabic script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively. By combining the generation of pseudo-parallel corpora through phrase replacement with the application of R-Drop regularization, there is a significant advancement in machine translation performance for low-resource languages.
A Multi-Channel Parallel Keypoint Fusion Framework for Human Pose Estimation

Although modeling self-attention can significantly reduce computational complexity, human pose estimation performance is still affected by occlusion and background noise, and undifferentiated feature fusion leads to significant information loss. To address these issues, we propose a novel human pose estimation framework called DatPose (deformable convolution and attention for human pose estimation), which combines deformable convolution and self-attention to relieve these issues. Considering that the keypoints of the human body are mostly distributed at the edge of the human body, we adopt the deformable convolution strategy to obtain the low-level feature information of the image. Our proposed method leverages visual cues to capture detailed keypoint information, which we embed into the Transformer encoder to learn the keypoint constraints. More importantly, we designed a multi-channel two-way parallel module with self-attention and convolution fusion to enhance the weight of the keypoints in visual cues. In order to strengthen the implicit relationship of fusion, we attempt to generate keypoint tokens to the visual cues of the fusion module and transformers, respectively. Our experimental results on the COCO and MPII datasets show that performing the keypoint fusion module improves keypoint information. Extensive experiments and visual analysis demonstrate the robustness of our model in complex scenes and our framework outperforms popular lightweight networks in human pose estimation.