Reviewerly/exHarmony huggingface dataset:
Salient Object Detection Based on Unified Graph Neural Network Joint Learning

In complex visual scene, the performance of existing deep convolutional neural network based methods of salient object detection still suffer from the loss of high-frequency visual information and global structure information of the object, which can be attributed to the weakness of convolutional neural network in capability of learning from the data in non-Euclidean space. To solve these problems, an end-to-end unified graph neural network joint learning framework is proposed, which realizes the joint learning process of salient edge features and salient region features. In this learning framework, we construct a multi-relations dynamic attention graph convolution operator, which captures non-Euclidean space global context structure information by enhancing message transfer between different graph nodes. Further, by introducing a graph attention fusion module, the full use of salient edge cues and salient region cues is achieved. Finally, by explicitly encoding the salient edge information to guide the feature learning of salient regions, salient regions in complex scenes can be located more accurately. The experiments on three public benchmark datasets show that our method has competitive detection results compared with the current mainstream deep convolutional neural network based salient object detection methods. More importantly, it uses fewer parameters and less computation, so it is a lightweight salient object detection model.
Instance-Wise Causal Feature Selection Explainer for Rotating Machinery Fault Diagnosis

Artificial neural networks in prognostics and health management (PHM), especially in intelligent fault diagnosis (IFD) have made great progress but possess black-box nature, leading to lack of interpretability and weak robustness when facing complex environment variations. When environment changes, the model tends to make wrong decisions leading to a cost, especially for major equipment if easily trusted by the users. Researchers have made studies on eXplainable Artificial Intelligence (XAI) based IFD to better understand the models. Most of them express their interpretability in the way of drawing gradient-based saliency maps to show where the model focuses on, which is of little consideration for causal effect and not sparse enough without quantitative metrics. To address these issues, we design an XAI method that utilizes a neural network as an instance-wise feature selector to select frequency bands that have stronger causal strength with the diagnosis result than others and further explain the diagnosis model. We quantify causal strength with the relative entropy distance (RED) and treat the simplified RED as the objective function for the optimization of the selector model. Finally, our experiments demonstrate the superiority of our method over another algorithm L2X measured by post-hoc accuracy (PHA), variant average causal effect (ACE), and vision plots.
Adversarially-trained Graph Convolutional Recurrent Autoencoder for Spacecraft Anomaly Detection with Missing Data

Telemetry data anomaly detection has a pivotal role in spacecraft health management. It can provide great support for operation engineers and guarantee the safe operation of satellites. And the complex inter-coupling among sensors and the missing data condition are the two most significant features of satellite telemetry data. We need a powerful method that not only can efficiently and comprehensively capture the required information, but also needs to be able to adapt to missing data for anomaly detection. We propose an Adversarially-trained Graph Convolutional Recurrent Autoencoder (AGCR-AE), which can be well applied to spacecraft anomaly detection with missing data. A graph-based recurrent module is equipped to our AGCR-AE to capture both sensor correlations and time correlations. Meanwhile, an adversarially-trained autoencoder mechanism is used to reconstruct data, thus we can detect anomalies with missing data. Experiments reveal that AGCR-AE outperforms baselines and has outstanding performance.
Research on Ship Detection Method of Optical Remote Sensing Image Based on Deep Learning

At present, the ship detection of optical remote sensing images based on deep learning has made great progress. However, due to the different use scenarios and specific tasks, how to select an appropriate algorithm according to the characteristics of the target and the target priority, so that achieve the detection goal while consider the detection accuracy and speed, still requires relevant research. In this paper, ship detection methods for optical remote sensing images are studied based on deep learning. First, to meet the needs of ship detection research, according to the characteristics of target size and type, datasets of medium and large ships and small target ships are made, and model training and testing are conducted based on Faster R-CNN, YOLOv4, and SSD algorithms respectively. The actual detection performance of the three algorithms under different ship sizes is obtained. The results show that for medium and large targets, Faster R-CNN has the highest precision, the next is YOLOv4, and SSD is the lowest. The detection speed is that SSD is the fastest, the next is YOLOv4, Faster R-CNN is the slowest. For small target ship detection, YOLOv4 has the best detection accuracy and SSD has the fastest detection speed. Faster R-CNN is not as accurate and fast as the other two algorithms. In addition, for different type ships, the detection results of different algorithms also have some differences. In practical applications, different methods should be used to achieve detection by comprehensively considering such factors as target size, target priority, detection accuracy and speed requirements.
Cross-Modality Global Correlational-based Visual Transformer for RGB-D Salient Object Detection

Existing convolutional neural networks (CNN) can provide contexture features within certain receptive fields and achieve promising performance for RGB-D saliency prediction tasks. However, these CNN-based models still suffer from the challenge of learning global cues due to an inherent restriction of CNN. To address this issue, we propose a pure transformer network to explicitly model the correlation of the cross-modality token and explore global-local hierarchical features. Specifically, a cross-modality fusion module (CMFM) is designed to integrate the congruity and difference information. We also conduct a transformer decoder to decode the global context and each local token. Finally, we validate our model on five challenging datasets under five evaluation metrics against ten representative models. The experiment results demonstrate that our model performance significantly exceeds previous SOTA models. The ablation experiments also reveal the benefits of our model.
Design and implementation of verification software for safety storage function of lunar probe return flight

As the main recording equipment of the detector, the safety data recorder comprehensively stores various important data and key action links in the flight process. In order to effectively verify the function and performance of the safety data recorder, it is necessary to conduct targeted special tests on the comprehensiveness and correctness of the recorded data in the whole device development stage. Taking the opportunity of the lunar probe, this paper finally forms a method to verify the correctness of offline data storage based on the historical data of Engineering telemetry channel through clever system integration.
Airborne Flight Test Data Online Preprocessing System Design and Development

The whole delivery of flight test data typically takes 1–2 days to complete in traditional civil aircraft flight testing. On the basis of an airborne high-performance server, a comprehensive online preprocessing system for airborne flight data testing was created. To execute real-time analysis, monitoring, and storing on real-time airborne data flow, this system makes use of hyperthreading, edge computing, and dynamic memory allocation. Since all flight test data can be sent as soon as the aircraft lands, this method has considerably increased the efficiency of flight testing.
Online and Offline Mixed Teaching Reform in the Practical Course of Artificial Intelligence and Application

It is necessary to ensure the quality of students' courses, especially practical courses, which is an important part of higher education, and plays a positive role in promoting and popularizing the improvement of innovation and entrepreneurship in the face of the non suspension of classes and schools under the COVID-19. This paper explores the mode of online and offline combined with ideological and political education mixed teaching reform in the course, in order to explore the educational functions and ideological and political elements of the course from the practical contents and objectives from the practical course of artificial intelligence foundation, explore the implementation methods and teaching concepts of ideological and political education in the course, so that students can better master and understand knowledge comprehensively, improve the results of students' ideological and moral education, and explore the reform mode which satisfy the requirements of talent training.
MT-U2Net: Mixed Transformed Base U2Net for MRI Segmentation

In this paper, we propose a powerful while efficient net architecture for deep learning, MT-U2Net for Magnetic Resonance (MR) image segmentation or other fields of computer vision like semantic segmentation, Salient Object Detection (SOD). As U-Net has made a lot of contribution to computer vision tasks, it is obvious that the network architecture can still be improved. Thus, we mainly target two weaknesses: one is the weakness of explicitly modeling long-range-dependencies, the other is missing details and features on multi-scale. We took the strengthen of MT-UNet and U2-Net so that we can handle with both the weaknesses. Thus, it is named Mixed Transformed U2Net. We coordinated the net architecture and turned it to another configuration with fewer layers to Maintain the net structural stability. However, we used the novel Transformer module named Mixed Transformer Module (MTM) supported by Local-Global Gaussian-Weighted Self-Attention (LGG-SA) and External Attention (EA) to mine the inter-connections while calculate affinities to themselves efficiently, ReSidual U-blocks (RSU) to ensure the architecture can be deeper. We completed our network so that we can segmentation images accurately.
Comparision and analysis of siamese network tracker family

Visual tracking is a critical technology in the field of computer vision due to its wide applications. However, many influencing factors may exist in complex scenarios which make it difficult for the technology to spread. Fortunately, significant progress has been made with the development of machine learning in the past several years. At present, siamese network has become a mainstream technology for visual tracking and many high-performance trackers have been proposed based on this method. In this paper, we focus on siamese network tracker family. At first, the network structure is analyzed for several typical trackers. Then experiments are performed with VOT2018 database to compare the performance of different trackers. The results are helpful to understand the development of siamese network tracker family and can provide useful ideas for practical application.
Prosformer: Accurate Surface Reconstruction for Sparse Profilometer Measurement with Transformer

Surface micro-structure measurement is significant for precision manufacturing. However, existing stylus profilometer is inefficient, and sparse line-scan measurement can't support accurate surface description. To improve the reconstruction performance, we propose a high-accurate reconstruction method with sparse line-scan measurement based on attention mechanism. We first arrange the sparse-line measurement in the 2D matrix and crop as patch region. Then we utilize transformer to construct semantic relationships between patches and assign new weights to each patch to accurately model the structural relationships of target region and perform feature extraction, where the self-attention can enhance the description of local details while cross-attention will interact with global information. Finally, a fully connected network as a decoder is adopted to reconstruct accurate surface details with complete geometric representation. We refer to this model as Prosformer. Furthermore, we simulate a larger-scale surface micro-structure dataset to drive the training process and measure micro-structures to valid Prosformer. Experiments show proposed method can effectively restore complex surface details.
Distributed Ledger Technologies for Managing Heterogenous Computing Systems at the Edge

The increased use of Internet of Things (IoT) devices —from basic sensors to robust embedded computers— has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established as a standard architecture for developing IoT solutions, since it can optimize the workload and capacity of systems that depend on cloud services by deploying necessary computing power close to where the information is being produced and consumed. However, as the network scale in size, reaching consensus becomes an increasingly challenging task. Distributed ledger technologies (DLTs), which can be described as a network of distributed databases that incorporate cryptography, can be leveraged to achieve consensus among participants. In recent years DLTs have gained traction due to the popularity of blockchains, the most-well known type of implementation. The reliability and trust that can be achieved through transparent and traceable transactions are other key concepts that bring IoT and DLT together. We present the design, development and conducted experiments of a proof-of-concept system that uses DLT smart contracts for efficiently selecting edge nodes for offloading computational tasks. In particular, we integrate network performance indicators in smart contracts with a Hyperledger Blockchain to optimize the offloading on computation under dynamic connectivity solutions. The proposed method can be applied to networks with varied topologies and different means of connectivity. Our results show the applicability of blockchain smart contracts to a variety of industrial use cases.
APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning

Cameras are increasingly being deployed in cities, enterprises and roads world-wide to enable many applications in public safety, intelligent transportation, retail, healthcare and manufacturing. Often, after initial deployment of the cameras, the environmental conditions and the scenes around these cameras change, and our experiments show that these changes can adversely impact the accuracy of insights from video analytics. This is because the camera parameter settings, though optimal at deployment time, are not the best settings for good-quality video capture as the environmental conditions and scenes around a camera change during operation. Capturing poor-quality video adversely affects the accuracy of analytics. To mitigate the loss in accuracy of insights, we propose a novel, reinforcement-learning based system APT that dynamically, and remotely (over 5G networks), tunes the camera parameters, to ensure a high-quality video capture, which mitigates any loss in accuracy of video analytics. As a result, such tuning restores the accuracy of insights when environmental conditions or scene content change. APT uses reinforcement learning, with no-reference perceptual quality estimation as the reward function. We conducted extensive real-world experiments, where we simultaneously deployed two cameras side-by-side overlooking an enterprise parking lot (one camera only has manufacturer-suggested default setting, while the other camera is dynamically tuned by APT during operation). Our experiments demonstrated that due to dynamic tuning by APT, the analytics insights are consistently better at all times of the day: the accuracy of object detection video analytics application was improved on average by ∼ 42%. Since our reward function is independent of any analytics task, APT can be readily used for different video analytics tasks.
Secure Sparse Gradient Aggregation in Distributed Architectures

Federated Learning allows multiple parties to train a model collaboratively while keeping data locally. Two main concerns when using Federated Learning are communication costs and privacy. A technique proposed to significantly reduce communication costs and increase privacy is Partial Weight Sharing (PWS). However, this method is insecure due to the possibility to reconstruct the original data from the partial gradients, called inversion attacks. In this paper, we propose a novel method to successfully combine these PWS and Secure Multi-Party Computation, a method for increasing privacy. This is done by making clients share the same part of their gradient, and adding noise to those entries, which are canceled on aggregation. We show that this method does not decrease the accuracy compared to existing methods while preserving privacy.
A Smart Approach using Multi–agent System for Big Data Security

With the evaluation of technology and the appearance of new tools that help store the information we create, especially in Banking, business intelligence, and even Education as Datawarehouse, Big data, and cloud computing. Those new tools create another obstacle: how we can secure and protect the information and the data stored in them. This paper treats the problem of Big Data security and privacy using mobile and stationary agents’ technologies. The main important security proprieties in big data are integrity, authentication, privacy, and access control. For integrity, the problem lies in checking the integrity of data and if this data is good and can be used since the Big Data is receiving data from different sours and different formats (structured, semi–structured, and non-structured). In access control, we need to consider the users’ secrecy, monitor the authorities, and properly apply the confidentiality requirement. The problem with authentication is to authenticate each user over the network, while big data collect sensitive data from trusted or untrusted users. While the privacy policy problem revolves around data collection and the use of transparency. To answer the previous needs for security in big data, we have proposed a smart approach using multi–agent systems (MAS) as a model for our solution.
Offline PSRO with Max-Min Entropy for Multi-Player Imperfect Information Games

Game theoretic learning for the blueprint strategy is the central topic of offline game solving. Offline learning is one typical data-driven paradigm that learns from logged dataset. This paper provides one offline PSRO algorithms with max-min exploration that can solve extensive-form games with imperfect information. After constructing the offline dataset with expert interaction experience, the offline PSRO is trained with best response generated from the entropy regularized reinforcement learning method. This work will provide a reference for offline game solving, the experimental results demonstrated the effectiveness of our framework.
DataX Allocator: Dynamic resource management for stream analytics at the Edge

Serverless edge computing aims to deploy and manage applications so that developers are unaware of challenges associated with dynamic management, sharing, and maintenance of the edge infrastructure. However, this is a non-trivial task because the resource usage by various edge applications varies based on the content in their input sensor data streams. We present a novel reinforcement-learning (RL) technique to maximize the processing rates of applications by dynamically allocating resources (like CPU cores or memory) to microservices in these applications. We model applications as analytics pipelines consisting of several microservices, and a pipeline’s processing rate directly impacts the accuracy of insights from the application. In our unique problem formulation, the state space or the number of actions of RL is independent of the type of workload in the microservices, the number of microservices in a pipeline, or the number of pipelines. This enables us to learn the RL model only once and use it many times to improve the accuracy of insights for a diverse set of AI/ML engines like action recognition or face recognition and applications with varying microservices.Our experiments with real-world applications, i.e., face recognition and action recognition, show that our approach outperforms other widely-used alternative approaches and achieves up to 2.5X improvement in the overall application processing rate. Furthermore, when we apply our RL model trained on a face recognition pipeline to a different and more complex action recognition pipeline, we obtain a 2X improvement in processing rate, thus showing the versatility and robustness of our RL model to pipeline changes.
Robust Semantic Communication Driven by Knowledge Graph

Semantic communications could improve the transmission efficiency significantly by extracting the semantic information. In contrast to traditional communication systems, semantic communication systems are additionally impacted by semantic noise because of the semantic, in addition to the physical noise present in the wireless communication environment. Semantic noise is a type of noise that leads to decoding errors and misunderstandings of semantic information, which leads to misdirection between the transmitted and received semantic symbols. In this paper, we present a knowledge graph enabled robust semantic communication system to avoid semantic noise from influencing semantic communication systems. In order to improve the robustness of semantic communication systems, we design a knowledge graph shared by both the transmitter and the receiver for encoded symbols representation. The transmitter only needs to transmit the indices of these symbols in the knowledge graph. A knowledge graph completion module is designed at the receiver to restore the semantic. Compared to baseline models for text transmission that merely take physical noise into account, the proposed scheme yields a remarkable improvement in avoiding semantic noise under various signal-to-noise ratios.
Internet of Things (IoT): A Way to Expedite Production and Service Performance Empirical, Evidence from Textile Industry of United Arab Emirates (UAE)

The textile sector in UAE is limited to product and service innovation and performance. This failure of the textile sector is due to a lack of internet and ICT integration in process of working. Therefore, this study aims to determine the role of the internet of things in expediting production and service performance based on empirical evidence from the textile industry of the UAE. This study is based on the quantitative data collected from the personnel of the textile industry in the UAE. 900 questionnaires were distributed and 600 questionnaires were finalized for the data analysis of the study. This study concluded that the internet of things and ICT are critical for production and service performance with the help of production innovation, service innovation, and process innovation. This study contributes to the knowledge with a research framework that emphasizes the role of ICT and the internet of things in the improvement of production and service performance in the textile sector of UAE. Also, this study has important theoretical and managerial implications to improve the performance of the production and service in the textile sector of the UAE.