Reviewerly/exHarmony huggingface dataset:
Diagnosis-based domain-adaptive design using designable data augmentation and Bayesian transfer learning: Target design estimation and validation

Research on prognosis and health management (PHM) technology is actively being conducted. Existing diagnostic technologies focus on qualitative results, i.e., anomaly detection and classification for maintenance. Therefore, quantified diagnostic solutions are required to allow users to take clear actions in advance. In this paper, we propose a diagnostic design solution methodology from a system design perspective, considering the degradation and uncertainty of the system via combined data-driven and model-based approaches using domain adaptation and designable data augmentation. When designing a new target system similar to an existing developed source system, the uncertainty of the diagnostic knowledge of the existing source system is quantified to adapt the domain knowledge to the new target system through Bayesian transfer learning. Additionally, with small amount of target data, a deep-learning-based design algorithm is used to estimate the system design solutions. To validate the proposed method, a case study was conducted using mathematical and Modelica-based physical system models. We verified that the system response obtained from the estimated design solution is more likely to belong to the normal class than to the initial design.
RSAGAN: Rapid self-attention generative adversarial nets for single-shot phase-shifting interferometry

The phase-shift operation in phase-shifting interferometry (PSI) often introduces deviation and limits its application in the field of dynamic phase measurement. Based on this, a rapid self-attention generative adversarial nets (RSAGAN) is constructed and applied to realize single-shot PSI. By constructing the neural network of attention mechanism, we can quickly obtain the region dependency of the whole interferogram, so as to obtain a more realistic result after effective iterations. Accordingly, we can take one interferogram as the input of the network, get another interferogram with arbitrary phase shifts, and then combine the two-step phase-shifting algorithms to get the measured phase information. This method only needs one interferogram combined with the trained RSAGAN to achieve the acquisition of phase information with a simple experimental device, and has better generalization ability than the direct end-to-end phase acquisition method. Compared with the network without attention mechanism, the designed network has higher learning accuracy and has the potential to be applied in the related fields of dynamic phase measurement.
Evolving population method for real-time reinforcement learning

Reinforcement learning has recently been recognized as a promising means of machine learning, but its applicability remains limited in real-time environment due to its short response time, high computational complexity, and instability in learning. Although researchers devised several measures in attempts to press beyond the horizon, the problems consisting of large branching factors with real-time properties still stays unconquered, demanding a new method for reinforcement learning as a whole. In this paper, we propose Evolving Population. This method improves the performance of reinforcement learning by optimizing hyperparameters and available actions. This method uses an iterative structure based on an evolutionary strategy to optimize these elements. We validate the performance of our method in an environment with real-time properties and large branching factors.
Weakly supervised real-time instance segmentation for ultrasound images of median nerves

Medical ultrasound technology has garnered significant attention in recent years, with Ultrasound-guided regional anesthesia (UGRA) and carpal tunnel diagnosis (CTS) being two notable examples. Instance segmentation, based on deep learning approaches, is a promising choice to support the analysis of ultrasound data. However, many instance segmentation models cannot achieve the requirement of ultrasound technology e.g. real-time. Moreover, fully supervised instance segmentation models require large numbers of images and corresponding mask annotations for training, which can be time-consuming and labor-intensive in the case of medical ultrasound data. This paper proposes a novel weakly supervised framework, CoarseInst, to achieve real-time instance segmentation of ultrasound images with only box annotations. CoarseInst not only improves the network structure, but also proposes a two-stage "coarse-to-fine" training strategy. Specifically, median nerves are used as the target application for UGRA and CTS. CoarseInst consists of two stages, with pseudo mask labels generated in the coarse mask generation stage for self-training. An object enhancement block is incorporated to mitigate the performance loss caused by parameter reduction in this stage. Additionally, we introduce a pair of loss functions, the amplification loss, and the deflation loss, that work together to generate the masks. A center area mask searching algorithm is also proposed to generate labels for the deflation loss. In the self-training stage, a novel self-feature similarity loss is designed to generate more precise masks. Experimental results on a practical ultrasound dataset demonstrate that CoarseInst could achieve better performance than some state-of-the-art fully supervised works.
Lexical complexity changes in 100 years’ academic writing: Evidence from Nature Biology Letters

Previous diachronic studies of lexical changes in academic writing have focused on the evolution of specific sets or classes of words with specific grammatical or discourse functions, with no scholarly attention paid to diachronic changes in the overall lexical complexity of academic writing, a construct that has been shown to be closely related to text readability and writing quality. This study explores diachronic lexical complexity changes in academic writing based on a self-built corpus, the Corpus of Nature Biology Letters, comprised of 421 scientific letters sampled with ten-year intervals for 100 years (1929–2019). The corpus was analyzed for nine measures of lexical density, sophistication, and diversity to ascertain the trend and strength of lexical complexity changes. Our findings show that the patterns of diachronic changes in the lexical complexity of academic writing accord with the overall trends in academic writing towards greater compression and conventionalization. These findings add to our understanding of the evolution of the academic writing practices of the scientific community and may also have implications for academic writing pedagogy.
Small-Object Detection for UAV-Based Images

Unmanned aerial systems (UAS) are increasingly being deployed in civilian and commercial areas. The application of machine learning in UAS image analysis greatly promotes the progress of target detection and tracking algorithms. However, current object detection and tracking system algorithm can hardly be applied to detect aerial targets. Because the view of UAS changes and rotates quickly during the flight. In this paper, we propose a fast and accurate real-time small object detection system based on a two-stage architecture. The proposed addresses the small object detection challenges by combining the traditional target detection with deep learning. More precisely, it uses conventional background subtraction and deep learning algorithm to get the initial detection box, and then use target tracking to get the final result. We evaluated our approach on the small object data sets. Experimental results show that the proposed method has improved the aerial object detection performance compared with other conventional approaches.
Deep Transfer Learning With Multi-Level Features Extraction Approach For Breast Cancer Classification

Breast cancer is a deadliest disease worldwide among the women. By some means early detection is very important to overcome it by prognosing on time. Deep networks having promising performance for different signal and image processing applications, breast cancer classification is one of the applications to diagnose efficiently. Deep transfer learning approaches are one of its kind and are using nowadays to overcome the computational time increase the performance as well. Sparse deep network such as VGG19 and ReasNet50 etc. are giving low performance but these are light weight and can be trained fastly. So, to increase the performance of the systems designed using these networks is an important issue, intermediate features are collected to overcome the vanishing gradient problem hence the performance can be improved. Here in this work histopathology images are used from which breast cancer is diagnosed. VGG19 deep network is considered as backbone network which is light weight, easy to implement and can be trained fast. Transfer learning is adapted to solve the problem of training the network from scratch. All the intermediate stage features are concatenated and finally these feature maps are feeded to fully connected deep network. Finally, classification is done with the help of SoftMax classifier. The proposed model is giving 96.43% accuracy which is better as compare to the existing state of the methods.
Service Level Agreement Violation Detection in Multi-cloud Environment using Ethereum Blockchain

With the advancement of cloud computing technologies, it is necessary to maintain quality of service, for which service level agreements (SLAs) are one of the ways to improve this. A significant part of revenue will be contributed by the use of on demand resources (compute, network or storage) provided by this cloud service providers (CSP). Service level agreement (SLA) plays a key role in maintaining quality of services (QoS) of these on demand resources. The CSP enforces the SLA, by utilizing machine-generated logs to monitor machine performance. Additionally, some consumers may monitor resource performance themselves to ensure it meets their standards. This approach not only leads to traction between both CSP and cloud service consumers (CSC), but also leads to a duplication of the effort. The aim of this work is to create a system that is distributed in nature and could be trusted by both CSP and the consumer. The paper provides solution using a public blockchain and log based algorithm that evaluates resource performance based on several SLA criteria accepted by the CSP and consumers. If the system’s performance falls below a certain level, the CSC will be intimated through logs. This motivates the consumer to spend more money on the resources and compels the CSP to keep the number of SLA violations to a minimal. A web application with a blockchain as the back-end is used to monitor SLA violations and the compensation procedure. Finally, a multi-node ethereum blockchain network is used to conduct a performance analysis of the suggested solution.
IoT-Edge Computing for Efficient and Effective Information Process on Industrial Automation

The Internet of Things (IoT) has transformed the way industrial systems operate by allowing physical devices, machines, and systems to be connected and monitored over a network. The Internet of Things has facilitated the development of smart factories and industrial automation systems, in which data from various sources is collected and analyzed in real-time to make informed decisions. IoT-enabled industrial automation systems can perform a variety of tasks, including predictive maintenance, process control, and energy management, among others. The main advantages of IoT in industrial automation are increased efficiency, increased safety, and cost savings. The generation of massive amounts of data from numerous sources or sensors is common in the IIoT world, but there are numerous challenges. The goal of this study is to demonstrate how the Internet of Things and AI enable Industrial Automation to develop in response to real-world issues.
Password Manager with Multi-Factor Authentication

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user’s data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication.
Applying 3D Object Detection from Self-Driving Cars to Mobile Robots: A Survey and Experiments

3D object detection is crucial for the safety and reliability of mobile robots. Mobile robots must understand dynamic environments to operate safely and successfully carry out their tasks. However, most of the open-source datasets and methods are built for autonomous driving. In this paper, we present a detailed review of available 3D object detection methods, focusing on the ones that could be easily adapted and used on mobile robots. We show that the methods do not perform well if used off-the-shelf on mobile robots or are too computationally expensive to run on mobile robotic platforms. Therefore, we propose a domain adaptation approach to use publicly available data to retrain the perception modules of mobile robots, resulting in higher performance. Finally, we run the tests on the real-world robot and provide data for testing our approach.
Gadgets of Gadgets in Industrial Control Systems: Return Oriented Programming Attacks on PLCs

In industrial control systems (ICS), programmable logic controllers (PLCs) directly control and monitor physical processes in real-time such as nuclear plants, and power grid stations. Adversaries typically transfer malicious control logic to PLCs over the network to sabotage a physical process. These control logic attacks are well-understood containing machine instructions in network packets and are likely to be detected by network intrusion detection systems (IDS). On the other hand, return-oriented programming (ROP) reuses blocks (or gadgets) of existing code in computer memory to create and execute malicious code. It limits or eliminates the need to transfer machine instructions over the network, making it stealthier. Currently, ROP attacks on control logic has never been discussed in the literature to explore it as a practical ICS attack. This paper is the first attempt in this direction to explore challenges for a successful ROP attack on real-world PLCs, including maintaining a continuous (control logic) scan cycle through ROP gadgets, no user input (to cause a buffer overflow) to overwrite the stack for gadget installation, and limited ROP gadgets in a PLC memory to find blocks of instructions equivalent to the high-level constructs of PLC programming languages (such as instruction list, and ladder logic). We identify and utilize typical PLC design features (that we find exploitable) to overcome these challenges, which makes ROP attacks applicable to most PLCs e.g., no stack protection, and remote access to certain PLC memory regions via ICS protocols. We demonstrate two successful ROP attacks on the control logic programs of three fully-functional physical processes, i.e., a belt conveyor system, a four-floor elevator, and a compact traffic light system. The first ROP attack manipulates a PLC’s current control logic and has two variants involving either a single or multiple gadgets; the second ROP attack constructs a control logic from scratch using gadgets in a PLC’s memory. Our evaluation results show that the attacks can be performed using a set of small-sized gadgets with no significant effect on a PLC’s scan time.
Eye Detection For Drivers Using Convolutional Neural Networks With Automatically Generated Ground Truth Data

Eye detection is an essential feature for driver monitoring systems acting as a base functionality for other algorithms like attention or drowsiness detection. Multiple methods for eye detection exist. The machine learning based methods involve a manual labeling process in order to generate training and testing datasets. This paper presents an eye detection algorithm based on convolutional neural networks trained using automatically generated ground truth data and proves that we can train very good machine learning models using automatically generated labels. Such approach reduces the effort needed for manual labeling and data preprocessing.
Privacy-Preserving Federal Learning Chain for Internet of Things

The expansion of Internet of Things (IoT) spawns large on-device machine learning demands, while the machine learning can be a hard task for resource constrained IoT terminals with fragmented data set. Federal learning (FL), which aims to build a joint model across multiple devices, IoT-FL has now become a promising path for learning on terminals. In broad FL fields, current server–client pattern cannot jump out of the third-party self-trustless problem, and recent researches suggest that even sharing training results may also reveal the raw data sets. Homomorphic encryption (HE) is a powerful method in privacy preserving, while so far it is hard to apply HE into multiparty computing (MPC) scenarios including FL. Combining with the existing IoT architecture, in this article, we customize a scheme (FL chain) dedicated for the privacy and trustiness issues in IoT-FL scenarios, which integrates blockchain smart contract and HE. Differ from traditional schemes, our FL chain is highly adaptive with current IoT architecture and it is the first scheme that applied HE into IoT-FL privacy preserving. Theoretical analysis and experimental results prove the feasibility of FL chain.
Towards Unified Objectives for Self-Reflective AI

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Privacy-Preserving Cross-Silo Federated Learning Atop Blockchain for IoT

Cross-silo federated learning (FL) is promising in facilitating data collaboration across various organizations, which greatly alleviates the information silo problem in industries and promotes the data intelligence of internet of things. With the advances of decentralized FL, the higher requirements of trust and privacy are put forward. Traditional FL heavily relies on a central coordinating server, which suffers from single points of failure and lacks trust in the correctness of aggregation results. What’s more, the intrinsic privacy issues of FL have aroused public attention, such as gradient inversion attack in local gradients. However, the privacy of quantized gradients remains serious and lacks attention, especially the most extremely 1-bit quantization in sign-based FL. In this paper, we demonstrate the potential privacy risk in sign-based FL by presenting a new gradient inversion attack, which successfully restores the original data from sign-based quantized gradients. And then we tackle the above two challenges via constructing a self-aggregation privacy-preserving federated learning atop blockchain, which takes advantage of a variant of ElGamal encryption to protect the privacy of local sign-based quantized gradients, and leverages the smart contract to achieve secure self-aggregation for participants without involving a centralized server. Moreover, we analyze that the proposed protocol achieves privacy and public verifiability. Finally, we evaluate the performance of the proposed protocol with a real deep learning model, and the results show that our protocol is resilient against gradient inversion attack in a decentralized environment without sacrificing learning accuracy.
Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering

In recent years, graph-based deep learning algorithms have attracted widespread attention in the field of consumer electronics. Still, most of the current graph neural networks are based on supervised learning or semi-supervised learning, which often relies on the true labels of the given samples as auxiliary information. To solve this problem, we propose a Deep Self-Supervised Attention Convolution Autoencoder Graph Clustering (DSAGC) model and use it for social networks clustering. We divide the proposed model into two parts: a pretext task and a downstream task. In the pretext task, we obtain pseudo-labels of samples by graph attention autoencoder and clustering, then adopt the proposed reliable sample selection mechanism to gain a high confidential sample. These samples and corresponding pseudo-labels are selected as input of downstream task for helping the downstream task to finish training. The model obtains the predictive labels of the sample from the downstream task. This model does not depend on the true label of the sample for training and uses the pseudo-labels produced by unsupervised learning to improve the clustering performance. We apply the proposed model to three commonly used public social network datasets to test its performance. Compared with the presented unsupervised clustering algorithms and other deep graph neural network algorithms, various metrics have shown that the proposed model consistently outperforms the state-of-the-art. The proposed method can be used for consumer behavior analysis. We release the source code at https://github.com/hulu88/DSAGC.
Joint Learning of Image Deblurring and Depth Estimation Through Adversarial Multi-Task Network

Self-supervised monocular depth estimation methods have achieved remarkable results on natural clear images. However, it is still a serious challenge to directly recover depth information from blurred images caused by long-time exposure while camera fast moving. To address this issue, we propose a unified framework for simultaneous deblurring and depth estimation (SDDE), which has higher coupling performance and flexibility compared with the simple concatenation strategy of deblurring model and depth estimation model. This framework mainly benefits from three features: (1) a novel Task-aware Fusion Module (TFM) to adaptively select the most relevant intermediate shared features for the dual decoder network by aggregating multi-scale features, (2) a unique Spatial Interaction Module (SIM) to learn higher-order representation in the encoder stage to better describe complex boundaries of different classes in high-dimensional space, and focuses on the task-related region by modeling the pairwise spatial correlation of the holistic tensor, (3) a Priors-Based Composite Regularization term to jointly optimize the shared encoder-dual decoder network. This work was evaluated on multiple datasets, including: Stereo blur [1], KITTI [2],NYUv2 [3], REDS [4] and our own large-scale stereo blur dataset, resulting in state-of-the-art results for depth estimation and image deblurring, respectively.