Reviewerly/exHarmony huggingface dataset:
2DPChain: Orchestrating Transactions in Order-Execute Blockchain to Exploit Intra-batch and Inter-batch Parallelism

The order-execute blockchains have two obvious features, namely batch processing and replicated state machine, which lead to two issues that degrade throughput. (1) Conflicting transactions within each batch degrade parallelism utilization, because conflicting transactions can only be executed serially. (2) Heterogeneous processing capabilities between nodes degrade parallelism utilization, because each node needs to execute each batch once. Therefore, we propose a collaboration-oriented parallelism enhancement architecture that is capable of exploiting intra-batch and inter-batch parallelism. The architecture is oriented towards parallel sub-batches, and includes a transaction management mechanism, a transaction packing mechanism and a parameter tuning and assignment mechanism. Experimental results show that our blockchain (2DPChain) effectively improves parallelism utilization and thus improves throughput compared to three related blockchains.
BEAR: Revolutionizing Service Domain Knowledge Graph Construction with LLM

Knowledge graph (KG), as a novel knowledge storage approach, has been widely used in various domains. In the service computing community, researchers tried to harness the enormous potential of KG to tackle domain-specific tasks. However, the lack of an openly available service domain KG limits the in-depth exploration of KGs in domain-specific applications. Building a service domain KG primarily faces two challenges: first, the diversity and complexity of service domain knowledge, and second, the dispersion of domain knowledge and the lack of annotated data. These challenges discouraged costly investment in large, high-quality domain-specific KGs by researchers. In this paper, we present the construction of a service domain KG called BEAR. We design a comprehensive service domain knowledge ontology to automatically generate the prompts for the Large Language Model (LLM) and employ LLM to implement a zero-shot method to extract high-quality knowledge. A series of experiments are conducted to demonstrate the feasibility of graph construction process and showcase the richness of content available from BEAR. Currently, BEAR includes 133, 906 nodes, 169, 159 relations, and about 424, 000 factual knowledge as attributes, which is available through github.com/HTXone/BEAR.
Object Detection in Dense and Mixed Traffic for Autonomous Vehicles With Modified Yolo

Autonomous vehicles rely on the accurate detection and recognition of objects in their surroundings, a critical requirement for safe operation, especially in congested traffic with diverse vehicle types. This study presents a novel dataset collected in various road conditions in Indonesia, and it focuses on the detection and classification of visual objects around autonomous vehicles. Object recognition is achieved through the use of YOLOv7-based deep learning, adapted to identify small, faint, and partially concealed objects. Key enhancements include the integration of a deformable layer and the transition from Non-Maximum Suppression (NMS) to Soft Non-Maximum Suppression (softNMS). The dataset comprises eight predefined custom classes commonly encountered in Indonesian traffic. We collected video data recordings of heavy traffic scenarios featuring a wide range of vehicle types as training and testing data. The object detection model is fine-tuned through transfer learning, with multiple learning configurations explored for comparison. Experimental results demonstrate that deep learning models trained with transfer learning outperform those trained from scratch. Specifically, the modified YOLOv7, referred to as YOLOv7-MOD, incorporates a deformable convolution layer for up-sampling, leading to a remarkable performance of 94.68% in Recall, 96.87% in Precision, and 95.76% in F1-score. The modification resulted in an additional performance increase of 1.05% on average compared to the original model. The findings indicate that YOLOv7-MOD enhances the precision of object detection and recognition compared to the original YOLOv7, making it a promising solution for autonomous vehicle perception systems.
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-Oriented Systems

Deep Reinforcement Learning (Deep RL) is increasingly used to cope with the open-world assumption in service-oriented systems. Deep RL was successfully applied to problems such as dynamic service composition, job scheduling, and service adaptation. While Deep RL offers many benefits, understanding the decision-making of Deep RL is challenging because the action-selection policy that underlies its decision-making essentially appears as a black box. Yet, understanding the decision-making of Deep RL is key to help service developers perform debugging, support service providers to comply with relevant legal frameworks, and facilitate service users to build trust. We introduce Chat4XAI to provide natural-language explanations of the decision-making of Deep RL. Compared with visual explanations, the reported benefits of natural-language explanations include better understandability for non-technical users, increased user acceptance, and more efficient explanations. Chat4XAI leverages modern AI chatbot technology and dedicated prompt engineering. Compared to earlier work on natural-language explanations using classical software-based dialogue systems, using an AI chatbot eliminates the need for eliciting and defining potential questions and answers up-front. We prototypically realize Chat4XAI using OpenAI’s ChatGPT API and evaluate the fidelity and stability of its explanations using an adaptive service exemplar.
Feature Preserving and Enhancing Network for Image Super-Resolution

Single image super-resolution (SISR) with deep convolutional neural networks has recently attracted increasing attention due to its potentials to generate rich details. To obtain better fidelity and visual quality, most of existing methods are of heavy design with the depth of network. However, the lack of high-frequency information in the deep network and the limit of non-local context relations of pixels remain the core issue. In addition, there are many arbitrary-scale image super-resolution tasks, so it is a focus that provides an effective and efficient model for super resolution with arbitrary-scale. To this end, we propose a Feature Preserving and Enhancing Network (FPEN) based on implicit representation, which is aims to preserving high-frequency information in deep network and enhancing non-local contextual features of pixels to output more realistic images at arbitrary scales. In particular, our proposed High Frequency Preserving Block (HFPB) can divide the features into high-frequency components and low-frequency components, and allocate more operations to high-frequency components to ensure that high-frequency components can be preserved in the network. Since high-frequency information contains the details and texture of the image, it can restore the finer details of the image. Moreover, Pixel Continuity Attention (PiCA) module our proposed utilizes visual cues observed from pixels to adaptively recalibrate the pixel range that needs attention in the image to better achieve feature enhancement and generate smoother images. Extensive experiments conducted on benchmark SISR models and datasets show that Feature Preserving and Enhancing Network can be employed for various SISR tasks with arbitrary-scale to obtain the better visual quality than other state-of-the-art SR algorithms.
A Fusional Intrusion Detection Method Based on the Hierarchical Filtering and Progressive Detection Model

The imbalanced distribution of intrusion detection dataset will usually result in low detection rates for the minority categories and more unreported attacks. To reduce the impact of imbalanced dataset on classification performance, a fusional intrusion detection method based on the hierarchical filtering and progressive detection model (HFPD-IDS) is proposed. This method first uses the idea of binary classification to preliminarily filter and identify the normal and abnormal data, and then uses a convolutional neural network model for progressive dimensionality reduction detection for the initially filtered abnormal data. The learning process during the progressive detection, which is entirely based on abnormal data, avoids the interference of normal data and integrates detection method different from the initial filtering process to improve the detection rate for minority categories. Finally, the normal dataset identified in the initial filtering process will be corrected to further identify the abnormal data that has been misjudged as normal data. The experimental results show that this method can significantly improve the detection rate of minority categories data without affecting the detection performance of majority categories data.
A Novel Joint Extraction Model for Entity Relations Using Interactive Encoding and Visual Attention

Relationship extraction is a fundamental task in natural language processing, with applications ranging from knowledge graph construction to information retrieval. Existing entity-relationship joint extraction models have made significant strides in this field. However, they still face limitations in effectively utilizing interaction information between subjects and objects, as well as capturing the spatial location relationships between entities. In this paper, we propose a novel relationship extraction model that addresses these limitations. Our model introduces innovative techniques to harness interaction information between subjects and objects. We employ subject gates, object gates, entity gates, and relationship gates to partition and filter interaction information between relationship triples during the encoding phase. Additionally, we leverage an attention mechanism inspired by the visual domain to capture spatial location relationships between entities during the decoding phase, transforming the entity-relationship joint extraction task into a table-filling task. To evaluate the effectiveness of our model, we conducted extensive experiments on multiple datasets, including WebNLG, NYT, and ADE. Our model achieved impressive F1 values of 93.65%, 92.58%, and 86.16% on these datasets, respectively, outperforming state-of-the-art models.
Variational Methods in Optical Quantum Machine Learning

The computing world is rapidly evolving and advancing, with new ground-breaking technologies emerging. Quantum Computing and Quantum Machine Learning have opened up new possibilities, providing unprecedented computational power and problem-solving capabilities while offering a deeper understanding of complex systems. Our research proposes new variational methods based on a deep learning system based on an optical quantum neural network applied to Machine Learning models for point classification. As a case study, we considered the binary classification of points belonging to a certain geometric pattern (the Two-Moons Classification problem) on a plane. We think it is reasonable to expect benefits from using hybrid deep learning systems (classical + quantum), not just in terms of accelerating computation but also in understanding the underlying phenomena and mechanisms. This will result in the development of new machine-learning paradigms and a significant advancement in the field of quantum computation. The selected dataset is a set of 2D points creating two interleaved semicircles and is based on a 2D binary classification generator, which aids in evaluating the performance of particular methods. The two coordinates of each unique point, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , serve as the features since they present two disparate data sets in a two-dimensional representation space. The goal was to create a quantum deep neural network that could recognise and categorise points accurately with the fewest trainable parameters possible.
Human Fall Detection Based on Re-Parameterization and Feature Enhancement

It is easy to fall when the stairs, subway stations, bus stations, and factories are crowded. Real-time detection of human falls is helpful for timely assistance. In this paper, we propose an efficient and real-time detection network ED-YOLO for human fall detection. Firstly, a re-parameterization backbone is proposed. The shallow convolution(conv) modules in the backbone are replaced by DBBConv and DBBC3 modules, which 1*3 and 3*1 convs are used to replace pooling in DBBConv, and DBBConv is used to replace normal conv in DBBC3. The deep conv module in the backbone are replaced by E-DBBConv and E-DBBC3 modules, which can improve the ability to extract detailed features. Then, a novel feature enhancement module (FEM) is proposed to enhance the features representation of the region of interest and the fusion of features. FEM is added to the feature pyramid network (FPN) to improve detection accuracy. Finally, the CIoU Loss is replaced by Gradient Smoothing-SIoU loss (GS-SIoU Loss), and gradient smoothing is introduced to improve the regression speed and accuracy of the prediction box. In order to further reduce the inference over-head of the model, the network proposed in this paper is pruned. The mAP of the proposed network achieves 96.25%, while the parameters of the model are only 6.34M, and the detection FPS reaches 31 in RTX2080ti. The proposed network and other mainstream lightweight networks are tested on the test set. The experimental results show that the performance of human fall detection in the proposed network is superior to other networks. Especially, the mAP is 2.42% higher than YOLOv5s, and the detection speed is 14.8% faster than it.
Anomalous Indoor Human Trajectory Detection based on the Transformer Encoder and Self-Organizing Map

Anomalous human trajectory detection is a critical task in security surveillance in working areas. To identify anomalous human trajectories, understanding features of their movement plays an important role. Therefore, in this work, a Transformer encoder and self-organizing map-based model called TENSO is proposed to learn trajectory characteristics for detecting anomalies. In particular, the proposed model learns the internal characteristics of normal trajectories and clusters of normal trajectory representations in a latent space. To learn the internal characteristics of normal trajectories, the encoder of Transformer with a self-attention mechanism first encodes trajectories into sequences of embedding vectors of trajectory points in the latent space. Then, a decoder reconstructs the trajectories from the latent space. In addition, to learn clusters of normal trajectory representations in the latent space, the self-organizing map (SOM) layer is used, which gets its input as the output of the Transformer encoder. In the training phase, the TENSO model is trained using a total loss of trajectory reconstruction and SOM losses. In the anomaly detection phase, a test trajectory is evaluated to determine whether it is an anomaly based on trajectory reconstruction errors and the quantization error on the SOM. In this phase, a new metric is proposed which, namely WS, is the weighted sum of recall and precision to choose the appropriate threshold for detecting anomalies. The TENSO model-based framework is evaluated using two real trajectory datasets: MIT Badge and sCREEN. Experimental results show that the proposed framework identifies anomalies effectively and outperforms the baselines.
Improved Open World Object Detection Using Class-Wise Feature Space Learning

Open-world object detection is a challenging set of tasks in the realm of computer vision. In these tasks, the object detection model processes the input image or video and undergoes inference to identify objects or features present in the image or video. The main objective of the model is to identify the seen and unseen classes rather than identifying only the classes that that were introduced to it during training. The challenging task is to create instance-level true bounding boxes tightly around the true objects and classify and localize them with their true class labels, without missing any of the true object or assigning false positive labels to them. Motivated by the pressing need to advance the capabilities of open-world object detection we present a novel clustering technique called margin-based latent space clustering and deployed it in the classification head of the Faster RCNN. Furthermore, we propose a novel loss function called margin-based loss coupled with regularization parameters aiming to optimize the outlier identification. The proposed method is also capable of incremental learning. Overall, all four incremental tasks are assessed by employing benchmark evaluation metrics. The proposed method outperforms the existing state-of-the-art method, with significant improvements in mean average precision (mAP). We improved the mAP by 4.49% on task 1, 3.71% on task 2, 6.19% on task 3 and 2.81% on task 4. We also reduced the ‘Wilderness Impact and Absolute Open-Set Error’ metrics (For assessing the false positive detection, both metrics are employed), on every task.
A Blockchain-Based Auditable Semi-Asynchronous Federated Learning for Heterogeneous Clients

Federated learning (FL) is a privacy-preserving approach in Artificial Intelligence (AI) that involves exchanging intermediate training parameters instead of raw data, thereby avoiding privacy breaches and promoting effective data collaboration. However, FL still faces several unresolved challenges, including trust issues among participants because traditional FL lacks a mutual consensus auditing mechanism. Another challenge is that when the number of participating nodes is large and resources are heterogeneous; this can lead to low efficiency. To overcome these challenges, we propose a Blockchain-based Auditable Semi-Asynchronous Federated Learning (BASA-FL) system. BASA-FL includes a smart contract that coordinates and records the FL exchange process, enabling the ability to trace and audit the behavior of participating workers. In addition, we proposed an efficient semi-asynchronous approach in blockchain-based distributed FL as the main contribution to addressing heterogeneous problems. We designed a method to quantify worker contributions and distribute rewards based on their contributions. We used a multi-index comprehensive evaluation to motivate workers to maintain high-quality and efficient participation in FL tasks. We conducted several simulations to evaluate the effectiveness of the semi-asynchronous mode, the reliability of the audit mechanism, and the contribution quantification strategy.
Delving Deeper Into Image Dehazing: A Survey

Images captured under foggy or hazy weather conditions are affected by the scattering of atmospheric particles, resulting in decreased contrast and color variation, thereby limiting their practical applications. In recent years, deep learning methods showcase significant advancements in image dehazing. However, the complexity and degradation factors in hazy images challenge the generalization capacity of dehazing methods. This paper comprehensively reviews the recent developments in single-image dehazing techniques based on deep learning. From the perspectives of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN), different models are introduced and classified into four categories: Encoder-Decoder, Multi-Module, Multi-Branch, and Dual-Generative Adversarial Networks. The robustness and effectiveness of deep learning models are analyzed by comparing their performance and model complexity on public datasets. Additionally, limitations of current benchmark datasets and evaluation metrics are identified, and unresolved issues and future research directions are discussed. Our efforts in this paper will serve as a comprehensive reference for future research and call for further development in deep learning-based image dehazing.
Automated Data Model Generation From Textual Specifications: A Case Study of ECHONET Lite Specification

ECHONET Lite stands as a leading protocol for smart home appliances in Japan, and the publication of data models plays a critical role in fostering collaboration with other ecosystems, not just for ECHONET Lite but for any protocol or standard. Typically, data models are meticulously crafted by experts through the arduous task of condensing and summarizing extensive specification documents. As an illustration, generating a data model solely for the ECHONET Lite protocol (without incorporating other protocols) can demand thousands of working hours. This paper presents an AI-driven solution aimed at alleviating the burden of laborious, repetitive tasks prone to errors during the creation of data models from ECHONET Lite specifications, automating these processes to save human effort. The proposed solution employs Natural Language Processing techniques to extract key vocabularies from natural language descriptions of the specification, mirroring the approach of experts. Consequently, this solution can generate several data models for the ECHONET Lite protocol in a matter of seconds, all using a standard laptop. The findings indicate that machines are capable of emulating experts in extracting vocabularies, ensuring both syntactic error-free outcomes and consistency in the generated data models. Furthermore, the machine offers rapid, dependable results and enhances the reusability of exported data models across various platforms. The generated data models meet the same requirements as those created by humans. This solution is integrated into an official workflow for generating data models for the ECHONET Lite web API and others.
Homography-Based Visual Servoing of Robot Pose Under an Uncalibrated Eye-to-Hand Camera

Visual servoing can enhance the flexibility of robot control in unstructured environments. 3-D robot visual servoing is nontrivial since the image space is 2-D. Homography-based visual servoing (HBVS) with adaptation has been investigated to achieve 3-D pose control under parametric uncertainties, but it relies on a stringent condition termed persistent excitation (PE) for parameter convergence, which restricts the performance of pose control. In this article, we propose a dynamics-based control method called composite learning HBVS for robotic 3-D pose regulation under eye-to-hand monocular cameras with uncalibrated extrinsic parameters, where a composite learning law is applied to achieve camera parameter convergence without PE. Compared with existing adaptive HBVS methods, the proposed method achieves the accurate estimation of the camera's extrinsic parameters under a weaker condition of interval excitation, which avoids the tedious offline calibration. Besides, it requires no measurement of reference plane rotations or the detection of valid vanishing points, which makes practical applications convenient. Simulations and experiments on an articulated collaborative robot with 7 degrees of freedom have verified that the proposed method performs well for both extrinsic parameter estimation and robot pose control.