Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy Event-Triggered Fault-Tolerant Control for a Class of Uncertain Nonlinear Systems

This paper devotes to investigating the issue of the fuzzy adaptive event-triggered fault-tolerant control for multi-input and single-output (MISO) nonlinear systems with actuator failures and external disturbances. Based on the backstepping technique and fuzzy logic system (FLS), the fault-tolerant controller and the corresponding adaptive update laws are designed to eliminate the effect of actuator fault. At the same time, the event-triggered mechanism is introduced to reduce the computational load of the control input. The stability analysis shows that the control scheme has great tracking performance ensures the stability of the system. The simulation results further verify the validity of the above theoretical.
A Low-rate DDoS Strategy for Unknown Bottleneck Link Characteristics

Low-rate Distributed Denial of Service (LDDoS) attacks degrade the quality of TCP links with low average traffic by exploiting vulnerabilities in the TCP/IP protocol and routing queue management mechanisms. The ideal transmission rate of the attack pulse in the most classical RTO-based LDDoS attacks (aka: Shrew attacks) is greater than or equal to the bandwidth of the target bottleneck link, and it is assumed that the attacker is able to set a suitably large transmission rate. However, the attacker does not always know the bandwidth of the target bottleneck link in real-world attack scenarios. In this paper, we propose a strategy for executing a low-rate shrew DDoS attack against a bottleneck link of unknown bandwidth and buffer size under a scenario targeting a residential network. The proposed attack strategy degrades downstream traffic from a specific service to the target network to the desired quality while keeping the attack traffic stealthy by exploratively increasing the pulse rate while estimating the attack effect by building bot nodes in the residential network.
Automatic quality inspection in the automotive industry: a hierarchical approach using simulated data

Industry 4.0 is changing the manufacturing paradigms across industries. However, many repetitive processes still rely heavily on human workers, as in the case of the automotive industry, where the final quality inspection of assembled vehicles is still performed using a paper-based conformity list. We instead propose a hybrid solution where a deep learning-based hierarchical autonomous detection system identifies the non-conforming parts and informs the operator via a wearable device, trained exclusively with simulated data. This scalable and cost-effective system achieved a 65.7% accuracy score, which, considering the experimental nature of this work, further confirms the potential of this approach.
Deformable Mri To Transrectal Ultrasound Registration For Prostate Interventions With Shape-Based Deep Variational Auto-Encoders

Prostate cancer is one of the most prevalent cancers in men, where diagnosis is confirmed through biopsies analyzed with histopathology. A diagnostic T2-w MRI is often registered to intra-operative transrectal ultrasound (TRUS) for effective targeting of suspicious lesions during image-guided biopsy procedures or needle-based therapeutic interventions such as brachytherapy. However, this process remains challenging and time-consuming in an interventional environment. The present work proposes an automated 3D deformable MRI to TRUS registration pipeline that leverages both deep variational auto-encoders with a non-rigid iterative closest point registration approach. A convolutional FC-ResNet segmentation model is first trained from 3D TRUS images to extract prostate boundaries during the procedure. Matched MRI-TRUS 3D segmentations are then used to generate a vector representation of the gland’s surface mesh between modalities, used as input to a 10layer dense variational autoencoder model to constrain the predicted deformations based on a latent representation of the deformation modes. At each iteration of the registration process, the warped image is regularized using the autoencoder’s reconstruction loss, ensuring plausible anatomical deformations. Based on a 5-fold cross-validation strategy with 45 patients undergoing HDR brachytherapy, the method yields a Dice score of 85.0 ± 2.6 with a target registration error of 3.9 ± 1.4 mm, with the proposed method yielding results outperforming the state-of-the-art, with minimal intra-procedural disruptions.
Assessment of RF compact modelling of FD SOI transistors

In this work, the compact model for FD SOI transistors and its limitations are assessed by comparing Spectre simulations to experimental measurements in a wide range of frequencies. The impact of two phenomena, namely self-heating (SH) and substrate effect (SE), on the frequency response of output conductance and capacitance and their respective modelling are studied. This work shows that the present version of compact model is not sufficient to accurately model the experimentally observed transitions in the output conductance and capacitance frequency response related to these two phenomena.
A 3-D Capacitive-Detection Electrode for a Single Gold Proof-Mass Three-Axis MEMS Accelerometer

This paper describes a novel capacitive-detection electrode for a single gold proof-mass three-axis MEMS (microelectromechanical systems) accelerometer. We propose a 3-D capacitive-detection electrode (3-DCE) for a high and uniform sensitivity to sense micro-G (G=9.Sm/s2) level. Based on the analysis in terms of the sensitivity and the Brownian noise BN, we design and fabricate the device with 3-DCE by the multi-metal layer technology for a post-CMOS process. The experimental results show that the obtained BNis 0.16 µG/√Hz and the sensitivities of X-, Y-, and Z-axis are 162, 210, and 341 fF /G, respectively.
STILE: a Tool for Parallel Execution of E2E Web Test Scripts

Automated end-to-end (E2E) Web testing relying on frameworks such as Selenium Web Driver is commonly used to assess the quality of web applications. However, the resulting test scripts may require long execution times, due to their interaction with the browser GUI and backend services. To avoid repeated and costly setup of the Web application state, testers tend to build test suites whose test scripts depend on each other (i.e., one test case sets up the application state expected by another test case). In this paper we present Stile, a tool for the parallel execution of Web test scripts that ensures the compliance of all execution schedules with the dependencies among the involved test scripts, while at the same time minimizing the execution time and the computation time required for such parallel execution. Experimental results show that execution times can be approximately halved thanks to Stile.
Estimation of the Radiated Emission from a Protective Turn of a Meander Line in the Air

This paper investigates an algorithm for estimating the radiated emission from transmission lines using quasi-static analysis. The algorithm was tested on a meander line turn of two coupled wires above the ground plane in the air dielectric filling. The dependences of the cross-section parameters and the characteristics of the turn on the temperature at the frequency of 10 GHz were obtained. Examples of the radiation patterns at different frequencies, as well as the current distributions are given. Frequency dependences of the maximum values of the electric field strength obtained by two approaches: quasi-static and electrodynamic are compared. The results were obtained and compared for the meander line with optimal parameters and for the unmatched meander line and compared with the results of a single matched transmission line of one wire over the ground plane and an unmatched one. It is noted that the results of quasi-static and electrodynamic analyses are close at low frequencies but the difference increases at high ones. It was revealed that the meander line structures are able to minimize the radiated emission in the most frequency range except at the resonance frequencies. The tested algorithm gave acceptable results comparing to electrodynamic analysis.
Patient-Specific 3d Cellular Automata Nodule Growth Synthesis In Lung Cancer Without The Need Of External Data

We propose a novel patient-specific generative approach to simulate the emergence and growth of lung nodules using 3D cellular automata (CA) in computer tomography (CT). Our proposed method can be applied to individual images thus eliminating the need of external images that can contaminate and influence the generative process, a valuable characteristic in the medical domain. Firstly, we employ inpainting to generate pseudo-healthy representations of lung CT scans prior the visible appearance of each lung nodule. Then, for each nodule, we train a 3D CA to simulate nodule growth and progression using the image of that same nodule as a target. After each CA is trained, we generate early versions of each nodule from a single voxel until the growing nodule closely matches the appearance of the original nodule. These synthesized nodules are inserted where the original nodule was located in the pseudo-healthy inpainted versions of the CTs, which provide realistic context to the generated nodule. We utilize the simulated images for data augmentation yielding false positive reduction in a nodule detector. We found statistically significant improvements $(p \lt 0.001)$ in the detection of lung nodules.
RCC: A Distributed Data Streaming Software Library for Robot-Cloud Platforms

Driven by new technologies such as cloud computing and AI, the cloud robot has entered a golden period of rapid development. The problem that restricts robot developers in current cloud robot research is the lack of a safe way to transmit messages between edge and cloud. Therefore, in this paper, we propose the RCC (Robot Cloud Communication), a Python-based software library, which can help swarm intelligence robots data to be efficiently and securely transferred to different types of business clouds or private cloud platforms. In addition, we integrate the RCC function library and can also provide high-quality data security guarantees for development users when sending and receiving data. Through our approach, we successfully implemented the development of an indoor 3D map application which is difficult to achieve on a single robot platform. Our experimental results show that our RCC is not only very stable in performance of reading and writing, but also shows great performance in data encryption and decryption.
Boosting Exploratory Testing of Industrial Automation Systems with AI

Testing of large and complex industrial control systems is challenging as the space of possible input and environmental parameters is large. Searching the entire space for potential failures is practically infeasible. This paper introduces an industrial control system robustness testing problem and evaluates artificial intelligence (AI) based strategies to efficiently explore the space and to identify parameter sets that can cause the system to fail. The proposed solution approach uses regression techniques to speed up the search and clustering methods to identify parameter sets that represent distinct system failures.
Human Knowledge Based Efficient Interactive Data Annotation via Active Weakly Supervised Learning

Machine learning has been contributing significantly to various pervasive computing systems. Further diffusion of such systems will require reducing the obstacles of huge data annotation costs and the uninterpretability. Weakly supervised learning is gaining attention as a method to solve these problems, especially in natural language processing (NLP). Its advantage is due to human-defined labeling functions (LFs) based on human knowledge instead of attaching a label to each data point manually. However, this method alone cannot reduce the actual process cost. Creating LFs can be a very costly process if there is no insight or support. We propose an interactive data annotation method via weakly supervised learning with an uncertainty-based active learning strategy. The proposed method iteratively presents a few highly-prioritized data points to be annotated considering the outputs of LFs and the uncertainty of the prediction. The humans' task is only implementing their knowledge that can be applied to the presented data points as an LF. We also verified the effectiveness of the proposed method through two classification tasks (NLP and non-NLP tasks). The experimental results indicate its effectiveness and high potential without limitation of application fields.
Visualization of Big Data with Augmented Reality

In the current scenario, huge amount of data is produced every now and then. A large number of machines, devices and people are connected with each other through different communication frameworks. Since the data is huge in amount and takes a large storage space, it is named as big data. Such big amount data cannot be analyzed using the traditional methods of data analysis. Big data analysis needs new methods and approaches to handle their complexities. Numerous methods have been developed in the recent years to process this large amount of data. The data can be stored in cloud anytime and can also be retrieved from cloud anytime based on the requirements. As an alternative for data analysis and computing various attempts have been made using new tools. Augmented reality (AR) is an extended feature of virtual reality and it is helpful in visualizing huge amount of data obtained from heterogeneous sectors in a simpler manner. There are a few digitized tools available these days with the help of which process of visualization can be made faster. The scheme of convergence of both the tools (i.e., big data and AR) results in productive applications that are better than a few of existing technologies. This article provides a detailed study on big data, AR, and the utilities of big data using AR. Also, this research work has gone through the foreseeable future scopes and the main issues and challenges associated with AR based big data analysis.
Application of Topological Data Analysis in Co-infections and Its Effectiveness

In this article, the author studies the related researches of using Topological Data Analysis(TDA) for modeling immune responses during co-infections. Specifically, the author first explains how specific concepts in TDA are used in exploring the three stages of co-infection. Secondly, the advantages and limitations of using TDA in the biological context are also discussed. Finally, the author concludes that while applying TDA in studying co-infections is plausible and beneficial, some improvements still need to be made, such as the choice of clusters, percentage overlap, and lens function, so that the accuracy and generalizability of results can be enhanced.
Endogenous conduction mechanism of economic cycle fluctuation and economic vortex

Faced with various complex unfavorable factors at home and abroad, how to realize the stable and sustainable growth of the national economy has become an important problem to be solved urgently. Based on the assumption of "continuous medium" and "fluid property" of the macro-structure of an economy, a nonlinear model of economic fluctuation is established by analyzing the endogenous conduction mechanism. It is found that there are three transmission mechanisms in an economy: multiplier effect, inflation (contraction) effect and industry correlation effect. They have a nonlinear amplifying effect on external shocks, which will cause continuous deformation of the economic growth curve and unbalance the industrial structure. Then a certain kind of chaotic phenomenon is produced, resulting in an economic vortex. Economic vortex refers to the "rotation" phenomenon in the process of economic operation, which can not only cause the loss of social welfare, but also cause the overheating of the economy, leading to the outbreak of economic crisis. Therefore, the paper puts forward the methods of industrial structure optimization, such as mending and integrating industrial chain, extending industrial chain and constructing industrial network, which can not only expand the analysis perspective of economic cycle theory, it can also promote the smooth operation of the economy.
Analysing Smart Home Security Using Packet Tracer Simulation Software

The advancement in the Internet of Things for electronic devices and applications has attracted people, especially younger generations, to move into the interconnected world. A smart home is an example of an application that is highly in demand. Together with the rise is cybersecurity issues. By being connected, many users are exposed to security threats without their knowledge. This paper presents a security analysis on the smart home environment using Packet Tracer Simulation Software. A testbed of a basic smart home set consisting of electronic appliances, gadgets, and surveillance system is set up in the Cisco Packet Tracer software version 7.1. Possible vulnerabilities issues were simulated at the network and application layers. The vulnerabilities and possible attacks on the smart home network environment were successfully identified. Mitigation techniques on the attack were proposed to reduce the probability of the network attacks.
A Mobile-Based Multimodal Framework for Pervasive Health Monitoring

Pervasive health monitoring tools and applications based on mobile devices can be used for digital phenotyping, thereby facilitating diagnosis and treatment of diseases. We present the development of an unobtrusive health monitoring framework, enabling multimodal data collection of activity information from wearable devices, smartphones, and third-party apps, keystroke typing data, location information, data from medical and environmental IoT devices, and self-reports. The architectural elements of the framework are presented, and proof-of-concept case studies based on the developed framework are demonstrated, to show its feasibility and value in building different pervasive health applications.
A New Measure to Characterize the Self-Similarity of Binary Time Series and its Application

In this study, the branch-length similarity entropy profile is estimated by mapping the time-series signal to the circumference of the time circle, and the self-similarity is defined based on the profile. To explore the self-similarity property, the effect of the distance between two signals, “0” and “1”, on the entropy value for signal “1” is investigated. Furthermore, two application problems are addressed: quantification of the mixing state of fragments and clusters, and characterization of the behavioral trajectory of an organism. The results indicate that use of the self-similarity property solves both the problems. Additionally, the problems that must be addressed to broaden the applicability of self-similarity are discussed.
Unknown Payload Anomaly Detection Based on Format and Field Semantics Inference in Cyber-Physical Infrastructure Systems

A cyber-physical infrastructure system (CPIS) is a system that controls and manages critical infrastructure such as smart manufacturing, water treatment facilities, power generation, and distribution facilities. Although these CPISs focus on the security of air-gapped network environments, strict isolation from the outside network is difficult to achieve, leading to various attacks. CPISs also comprise various devices and proprietary communication protocols that are used exclusively for each domain and site. Therefore, experts have to adopt a customized strategy to enhance security in CPIS networks after analyzing each domain, device, and protocol in advance. These methods require a significant amount of time, cost, and manpower; consequently, they are difficult to apply existing security methods in the real field. As a solution, a method is proposed herein that includes the following: 1) inferencing the CPIS protocol format and field semantics based on the characteristics of CPIS networks and protocols; 2) multilevel anomaly detection based on the meaning and values of each inferred field. The proposed method does not require knowledge of each site and protocol. In addition, the inference method can be used to analyze the payload field, including the state and measurement value, as well as the header field. Finally, we validate the proposed technique using an open-source CPIS network dataset including response injection, command injection, denial-of-service, and reconnaissance attacks. In addition, in the aspect of detection efficiency, the proposed technique exhibits comparable performance to that of existing knowledge-based anomaly detection methods.
A Study Framework of Industrial Electricity-Consumption Correlation Clustering: Taking Xiaoshan Textile Industry as Example

Based on daily industrial electricity-consumption data, a study framework of industry clustering is proposed. It consists of qualitative analysis, clustering and forecast. In qualitative analysis, seasonal decomposition is performed and distinguishable characters, i.e., macroscopical trend, periodic/accidental disturbance-response and noise, are separated. It is found that industries in the same category show strong similarity in electricity consumption pattern, on the basis of which a clustering analysis is made with Pearson-correlation-coefficient-based distance and Minimum Spanning Tree method. Typical industries are clustered in the tree topology. Without a-priori economic hypothesis, conclusions beyond traditional cognition are drawn from discussions on Xiaoshan textile industry clustering results. With the clustered industries, Vector-Auto-Regression-based multivariate regression model is applied in electricity consumption forecast in order to take the causality between related industries into consideration. The forecast results coincide well with the observation data and are able to envelop most of the fluctuation.The aim of the study is not only to make a comprehensive forecast for short-to-medium term electricity-consumption, but also to build a cognitive method using ontology of data and logistics of clustering to release more socio-economic value from electricity-consumption data.
Designing advance production planning and scheduling optimization model for reduce total cost of the cement production process under time-of-use electricity prices

This research study on planning and scheduling for the cement production process of one industrial plant that is related to the constraint of the electricity price that changes over the time in a time-of-use (TOU) price pattern. Including production constraints of multiple parallel machines and multiple products which the demand of products are varied over time. Researcher have designed the discrete-time mixed-integer linear programming models (MILP) for solving the best solution to a production scheduling problem where the manufacturing process and its variables are written in the resource task network (RTN) representation. Numerical results within planning time frames from 14 to 60 periods from various demand datasets are presented to demonstrate model performance and practical planning capabilities. When compared to a simple manual scheduling procedure currently used in production planning, it was found that the production scheduling model presented able to yield a scheduling solution that results in a lower average total cost of production 9.11% due to the solver tries to keep the production schedule in a low electricity price periods and in accordance with the pattern of the product demand for each period as much as possible.