Reviewerly/exHarmony huggingface dataset:
Acquiring a Low-Dimensional, Environment-Independent Representation of Brain MR Images for Content-Based Image Retrieval

To make content-based image retrieval (CBIR) technology for magnetic resonance (MR) images of the brain practical and useful for diagnosis and research, it is important to obtain low-dimensional representations that embody pathological attributes. However, recent evidence suggests that variations in domains resulting from differences in imaging equipment and protocols at each imaging facility can overshadow pathological attributes. In this study, we propose a novel approach known as multidecoder adversarial domain adaptation (MD-ADA) to obtain low-dimensional representations of brain MR images that preserve pathological features while mitigating domain differences. This method combines adversarial domain adaptation techniques with convolutional autoencoders that have distinct decoders for each domain and employs adversarial learning to prevent domain discrimination from the produced low-dimensional representations. Experimental evaluations on two datasets, ADNI and PPMI, comprising 4,168 brain images demonstrate that the proposed MD-ADA significantly reduces domain differences between datasets without compromising the recoverability of brain images or the accuracy of disease classification.
Trustworthy Data Sharing Scheme Based on Decentralized Oracle Networks

Data sharing is the core of the development of the digital economy, but the traditional scheme faces problems such as centralization, security and spatial efficiency. In traditional data sharing schemes, data providers are required to preemptively upload and store the data before sharing it with data users. This scheme faces threats such as monopolistic attacks and data privacy breaches due to its centralized nature. Ensuring the confidentiality and integrity of the data is becoming challenging. Moreover, real-time upload and storage of all changing data would consume significant storage space, leading to a diminished timeliness in data sharing. This paper proposes a trusted data sharing model based on blockchain and decentralized oracle network, and uses threshold signature and homomorphic encryption to improve data reliability. The scheme does not need to upload data to the central database, and achieves high quality assurance of data tamper-proof, anonymity and traceability. At the same time, the system meets the transmission requirements of existing common data types, and can provide solutions in the scenario of real-time data sharing. With the maturity of blockchain technology and the increase of application scenarios, the scheme can be promoted and applied in cross-chain data sharing, financial data sharing, IoT data sharing and other fields.
Blockchain-Based Distributed Resource Sharing Model Design

Computer resource sharing provides an effective solution to the problems of imbalance between the distribution of computer resources and computing power demand in the network and the waste of computer resources. In the power system, using computers for resource sharing can dispatch power resources and effectively solve various problems caused by power consumption peaks, thus achieving the purpose of saving resources and improving users' experience. However, the traditional computerized resource sharing system generally adopts the task allocation method, which makes it difficult for users to directly access the shared resources and participate in the whole scheduling process when they use electricity. In addition, the assignment and scheduling of tasks are managed by the central node, and once the central node is damaged, the normal operation of the resource sharing system will be unsustainable. To address the above problems, this paper proposes a distributed resource sharing model by introducing a block chain private chain to build the underlying architecture of the resource sharing model and provide shared resources directly to users in a P2P manner. This paper analyzes the key implementation mechanism of the resource sharing model and builds a distributed computing network to truly realize computer resource sharing. After verifying and analyzing the CPU and memory usage during the work of the resource sharing model, and combining the electricity consumption data of users in various regions and time nodes, it is verified that the model can effectively improve the efficiency of computer resources usage, enhance the overall computing capacity, and finally achieve the effective utilization of electricity resources.
Homeostatic System Design Based on Understanding the Living Environmental Determinants of Falls

Falls among older adults are a serious global challenge. The World Health Organization strongly recommends gait, balance, and functional training, Tai Chi, or home assessment and modification to prevent falls among older people, but these strategies have not changed over the past few decades. The purpose of the present study is to propose an innovative approach to fall prevention naturally embedded in the environment. We first suggest new methods for simplifying and understanding the relationship between falls and everyday life, and describe this relationship as a knowledge graph using emergency transport data on falls among the aged. As a result of a cluster analysis using knowledge graphs, we identified whole-body balance as a key factor and living environmental determinant of falls. We then propose the concept of a homeostatic spatial system and discuss our development of seven homeostatic products as a proof of concept. Finally, we develop an evaluation system to visualize specific locations in a defined area where people maintain whole-body balance using their hands or other body parts. Our findings confirm that the system is useful for evaluating how people change their behaviors to maintain whole-body balance based on homeostatic products and the living environment as a whole.
Reentrancy Vulnerability Detection Based on Semantic-Aware Graph Convolutional Neural Network

Aiming at the problems of low accuracy of conventional smart contract vulnerability detection methods, as well as the single form of feature extraction of deep learning-based methods, which are unable to adequately represent the rich semantic and structural information present in the source code, we propose a smart contract reentrancy vulnerability detection method based on semantic-aware graph convolutional neural network. The method first constructs the smart contract graph, extracts the graph structure features in the smart contract source code by the degree-free graph convolutional neural network, then slices the contract source code, extracts the sequence features by using BiGRU and attention mechanism, and finally splices the extracted graph structure features and the sequence features to find reentrancy vulnerability. The experimental findings demonstrate that, when compared to deep learning and conventional detection approaches, the suggested method in this work exhibits improved accuracy, recall, precision, and Fl score, with a detection accuracy of 89.77%.
Automated Construction and Application of Operations and Maintenance Knowledge Graph

This paper presents a novel method for automatically constructing an operations and maintenance knowledge graph for systems. By combining the application of integrated APM Agent and predefined Prometheus labels, entities and their relationships are extracted from multiple data sources and the knowledge is integrated and iteratively updated. The method was applied to a real IT system, and its effectiveness was evaluated. The results show that this method can rapidly and accurately build an IT operations and maintenance knowledge graph, thereby improving the efficiency of IT operations management.
A Practical Framework of Blockchain in IoT Information Management

In order to improve the security of IoT information systems, this paper proposes the Blockchain-based Framework for Securing IoT Information (BFSII), which is built on consortium blockchain and the edge IoT architectures. This paper addresses data security in smart hotels as a research scenario. The majority of data generated by IoT devices in smart hotels contains users' private information, which is susceptible to alteration and leakage during transmission and storage. The BFSII solution leverages the decentralized nature of blockchain to enhance data traceability and tamper-proof capabilities. And it uses edge IoT architecture and consortium blockchain to improve system operational efficiency. Sensitive data generated by IoT devices are protected in BFSII. The experiment's findings show that BFSII can boost smart hotel system security while maintaining operational effectiveness. The information management system of smart hotels is provided with an inventive and secure solution by the BFSII framework.
A Post-Selection Algorithm for Improving Dynamic Ensemble Selection Methods

Dynamic Ensemble Selection (DES) is a Multiple Classifier Systems (MCS) approach that aims to select an ensemble for each query sample during the selection phase. Even with the proposal of several DES approaches, no particular DES technique is the best choice for different problems. Thus, we hypothesize that selecting the best DES approach per query instance can lead to better accuracy. To evaluate this idea, we introduce the Post-Selection Dynamic Ensemble Selection (PS-DES) approach, a post-selection scheme that evaluates ensembles selected by several DES techniques using different metrics. Experimental results show that using accuracy as a metric to select the ensembles, PS-DES performs better than individual DES techniques. PS-DES source code is available in a GitHub repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> https://github.com/prgc/ps-des.
Autoencoder and Teaching-learning-based Optimizer for Mobile Edge Computing System Optimization Problems

By using an autoencoder as a dimension reduction tool, an Autoencoder-embedded Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving high-dimensional computationally expensive problems through several widely used function problems. However, the following two crucial issues have not been resolved, 1) ATLBO should be verified by solving real-life optimization problems; and 2) how autoencoder parameters and structures impact AEO's performance. In this work, ATLBO is verified by an energy consumption minimization problem (ECM) in mobile edge computing systems. To design an effective autoencoder for ATLBO, this work proposes a parameter tuning optimization strategy for autoencoders. By using the proposed Autoencoder Parameter Tuning (APT) strategy, ATLBO can enjoy higher robustness than those without it. The experimental results show that it is three to six times better than state-of-the-art methods in solving ECM. We consider the strategy-induced overhead and take the execution time as the primary criterion to evaluate them. In addition, the experimental results show that, against the conventional wisdom that higher-accuracy auto encoders bring higher system performance, lower-accuracy ones can actually assist ATLBO in locating the best solutions. This work promotes a novel application of autoencoders in optimization theory and practice.
Research on Federated Learning Traffic Prediction Algorithm Based on Deep Autoregressive Networks

Wireless traffic prediction is critical for cellular networks to achieve intelligent network operation, and it is well known that the adoption of traffic prediction can facilitate resource allocation, improve efficiency, and ultimately enable intelligent cellular networks. Existing prediction methods usually use centralized training architectures and require the transmission of large amounts of Cellular traffic data, which may cause latency and privacy issues in some scenarios. In this paper, we propose a new efficient prediction framework for wireless network traffic, called Federated Learning Traffic Prediction Algorithm based on Deep Autoregressive Networks (FL-DeepAR), by which multiple edge clients can collaborate to train high-quality prediction models independently. The framework uses a homogenized global shared augmented dataset strategy to overcome the challenge of statistical heterogeneity of data; a deep autoregressive network is used to capture the multidimensional data features that will have an impact on the future values of the base station network traffic; a gradient-based similarity aggregation scheme is proposed to aggregate the global model by comparing the gradient similarity of the models across clients. The experimental results show that FL-DeepAR outperforms the mainstream FedAvg and FedAtt methods, and the proposed method also improves the prediction performance on both datasets compared with the traditional training method and the non-federal learning training method.
Using SMOTE-based Data Augmentation for Social Media Time Series Prediction

In the context of predicting activity on a social network, data for any individual will be limited. Also, low levels of activity for a topic of interest may make it difficult to build a strong predictive model. This work examines how augmentation by oversampling all activity data used to build a predictive model of user activity on Twitter can be used to improve the fidelity of predictions. Our features are counts of activity by deidentified users at the granularity of hours. It is shown that for some topics oversampling the data by creating new synthetic examples provides an effective way to increase the accuracy of predictions of future activity.
Automaton-Based Data Consistency Detection

Data plays a very important role in documents. Although much effort has been made in data processing, little research has been done on data consistency. Data inconsistency may lead to data analysis errors or inaccurate analysis results. In text, mismatches of data referred to by the same label and incompatibility in the implied semantic relationship between data can lead to data errors or data relationship errors. In order to check data consistency of the goal, we rewrite the adaptive automaton algorithm and extend tree automaton to allow the construction of deterministic tree automaton to express data relationships. In the financial report of the Postal Savings Bank of China in the past three years, we use the data in the report to model adaptive automaton models and bottom-up tree automaton, which include financial data, quarterly financial data, and data from the income statement and balance sheet, and used consistency detection algorithm evaluation. Experimental results show that the automated method simplifies the verification process, provides accurate and efficient results, and ensures data consistency and reliability in the text.
Illusory Control with Instant Virtual World Environment

We proposed a teleoperation method, illusory control (IC), that provides a comfortable operation experience using a seamless transition between real and pre-prepared virtual environments. Therefore, the mobile robot with IC could function solely in familiar environments. To make IC applicable in unfamiliar environments, this study proposes a novel method, instant IC, that eliminates the requirement for a pre-prepared virtual environment. The proposed robot system can instantly generate a virtual environment using actual 360° images of the robot in motion, utilizing instant neural graphics primitives and neural radiance fields. The 360° images allow the entire surrounding environment to be virtualized without requiring specific camera orientations. In addition, by optimizing the density of neural radiance fields using depth estimation results beforehand, the reconstruction accuracy at unknown poses can be guaranteed. Furthermore, we propose a depth scaling method based on the actual measurements obtained by LiDAR to increase the consistency of virtual and real environments. With this instant virtual environment, the proposed system enables teleoperation in unknown environments via the seamless transition between real and virtual environments. The experimental results exhibit consistent and smooth back-and-forth transitions between virtual and real space in mobile robot teleoperation.
An Adaptive Detection and Recognition Method for Traffic Sign Based on Multi-Scale Attention

Traffic sign detection aims to locate and classify traffic signs in real time and accurately. But because of their small size and complex backgrounds, some smaller traffic signs are harder to detect than larger ones. On the other hand, some false information is always detected due to the influence of light changes and bad weather. Therefore, in order to solve the problems of missing detection and false detection, this paper proposes an adaptive multi-scale spatial-channel attention fusion center network (MSCA-Center Net). Firstly, a residual space-channel attention module combined with multi-channel information is proposed. The module divides the channels in the feature map into several groups, generate separate spatial and channel attention for each group, and combine the spatial and channel information of the multi-channel. Then, a multi-scale attention fusion module is proposed to integrate the extracted high-level and low-level features, which can improve the detection and classification accuracy. In addition, a coordinate attention module is introduced to obtain the final feature code, so that the model can locate and identify the target area more accurately. Finally, the optimal detection box is generated adaptively according to the feature coding. The proposed model was tested and evaluated on the CCTSDB dataset. Compared with the existing methods, the proposed method can detect traffic signs adaptively in real time under complex background, which verifies the effectiveness of the proposed method.
Improved P&amp;ID Symbol Detection Algorithm Based on YOLOv5 Network

The existing object detection algorithms have the problems of poor detection performance on the piping and instrumentation diagram(P&ID) symbol dataset. Therefore, this paper proposes a symbol detection algorithm in P &ID images using improved network YOLOv5. Firstly, due to the limited number of samples in the symbol dataset and the class imbalance problem, this paper proposes a Random Splicing algorithm suitable for P &ID images. It can selectively augment one or more classes of symbols in P&ID images. Then, to address the problem of subtle differences and difficulty in dis-tinguishing between symbols, this paper introduces the CBAM and improves its “series” structure to a “parallel” structure, enhancing the information fusion between channel feature maps and spatial feature maps through concatenation and channel shuffle, after using a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1$</tex> convolution to achieve channel alignment, resulting in the PCSC_CBAM (Parallel Concat Shuffle Convolution_CBAM). Finally, the YOLOv5+PCSC_CBAM model is obtained by introducing PCSC_CBAM into the Neck of YOLOv5, which can effectively detect symbols in P&ID images and reduce the possibility of false negatives and false positives. The experimental results show that under only 1.97M parameters added, the YOLOv5+PCSC_CBAM model achieves high precision, recall, and mean Average Precision of symbol detection, reaching 95.6%, 95.0%, and 97.2%, respectively.
Object-Aware Attention Branch Network for Interior Style Scene Recognition

Recently, there has been a growing interest in developing recommendation systems that capture users' preferences and interests, while focusing on visual styles has gained increasing attention. However, understanding interior-style scenes is challenging because of the complex interplay of various objects that must be correctly interpreted. To properly understand interior-style scenes, extracting appropriate image features and providing visual explanations for the objects that determine the style is necessary. This study proposed a model that can simultaneously extract image features at multiple scales and provide visual explanations for the objects that characterize each class. Specifically, we adopted a hierarchical attention branch network (ABN) for visual explanations and applied atrous convolution to the feature extractor. We hypothesized that atrous convolution could extract various specific objects of each class that differ in size because its algorithm extracts features at an arbitrary resolution. Our evaluation results show that a conventional hierarchical ABN could not extract interior objects accurately, and our proposed model can detect specific objects by incorporating atrous convolution.
OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the CODA dataset show that the proposed method can obtain better performance than that of the existing methods.
Concept-Based Anomaly Detection in Retail Stores for Automatic Correction Using Mobile Robots

Tracking of inventory and rearrangement of mis-placed items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.