Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiomics to Predict Prostate CancerAggressiveness: A Preliminary Study

Radiomics is encouraging a paradigm shift in oncological diagnostics towards the symbiosis of radiology and Artiﬁcial Intelligence (AI) techniques. The aim is to exploit very accurate, robust image processing algorithms and provide quantitative information about the phenotypic differences of cancer traits. By exploring the association between this quantitative information and patients’ prognosis, AI algorithms are boosting the power of radiomics in the perspective of precision oncology. However,thechoiceofthemostsuitableAImethodcandetermine the success of a radiomic application. The current state-of-the art methods in radiomics aim at extracting statistical features from biomedical images and, then, process them with Machine Learning (ML) techniques. Many works have been reported in the literature presenting various combinations of radiomic features and ML methods. In this preliminary study, we aim to analyse the performance of a radiomic approach to predict prostate cancer (PCa) aggressivenessfrommulti-parametricMagneticResonanceImaging (mp-MRI). Clinical mp-MRI data were collected from patients with histology-conﬁrmed PCa and labelled by a team of expert radiologists. Such data were used to extract and select two sets of radiomic features; hence, the classiﬁcation performances of ﬁve classiﬁers were assessed. This analysis is meant as a preliminary step towards the overall goal of investigating the potential of radiomic-based analyses.
Real-time Face Recognition based on Deep neural network methods to solve occlusion problems

with massive amounts of crime around the world, the government must guarantee high security by using different methods to track and identify people in public places. Facial recognition is considered as the most effective tech for analyzing and identifying individuals’ videos scenes or during authentication systems, but it has many difficulties. To address this issue, this paper presents all my comprehensions and my critical synthesis about methods and algorithms applied in face recognition and detection to solve occlusion problems.
Robust Distributed Fault Diagnosis for Large-Scale Interconnected Multi-Motor Web-Winding Systems

Web-winding systems are generally large-scale interconnected systems with many motor-driven subsystems. If the centralized fault diagnosis methods are adopted, the information exchanges among the motor driven subsystems will be required. However, the information exchanges are not always available due to the communication restriction, high system cost and so on. Moreover, it will lead to the high computational cost. To solve this problem well, the web-winding system is considered as a synthetic system with several dynamic subsystems subjected to multiple disturbances and actuator faults. Then, the methods of disturbance attenuation based distributed fault diagnosis method and disturbance compensation based distributed fault diagnosis method are developed to estimate the actuator faults. The objective of fault detection, fault isolation and fault estimation can also be realized via these methods. Meanwhile, sufficient conditions of asymptotic stability of the estimation error system are derived based on the Lyapunov theory. Observer gain matrices are obtained by solving the linear matrix inequalities (LMIs). Finally, simulations and analysis are performed on the three-motor web-winding system to verify the effectiveness of the proposed two fault diagnosis methods.
A Hybrid 3D Registration Method of Augmented Reality for Intelligent Manufacturing

Presently, core 3D registration technologies for augmented reality have problems like low accuracy and poor tracking stability in natural environments pertaining to mass customization and intelligent manufacturing, resulting in error display or poor visual performance. (1) A non-linear scale space was used to alleviate the problem associated with scale invariance, the relevant calculations and construction methods were studied. The adaptive non-maximal suppression method was examined, which reduced redundancy of ORB (Oriented FAST and Rotated BRIEF) feature points. (2) The method of combining nonlinear local descriptors with LK method is studied to improve the low stability problem against Light change by LK method only, and a forward-backward error detection method was studied to evaluate feature point tracking results. (3) The improved ORB and LK methods are used to track the target and achieve data fusion. Then, the fused data is voted and clustered by means of consistent voting, only the maximum number of clusters within the threshold is left as the final tracking result to realize 3D registration. Finally, the paper validates the proposed method through the natural environment dataset of open source. The dimensions of verification include challenging scales, rotation changes, perspective changes, motion blur, occlusion, and out-of-view object.
Measurement-and-Simulation-Based Determination of Electrical Properties of Au-Ni Composite Pins in Elastomeric Sockets

Challenges with measurement and simulation of test sockets have been presented, with a focus on material characterization of a specific elastomeric rubber socket. A Parallel-Resonance-Frequency (PRF) methodology has been adopted for test vehicle design, and the measurements have been carried out of the test vehicle with socket pins mounted on it. The geometry simplification from a powder of spherical balls to a solid cylinder has enabled ease of simulation at a cost of unknown material properties, which has been addressed by a frequency dependent optimization through correlating to measurements. The resultant conductor properties have proven desirable accuracy to represent correct contact-resistance and inductance associated with the socket pin, enabling simple and accurate electrical representation.
A Novel Bearing Health Prognostic Method Based on Time-frequency Analysis and LSTM

Bearing plays an enormous role in modern factories. It is of great value to monitor the bearing health conditions. With the rapid development of data acquisition and transmission technology, it has been available to collect, transmit and store enormous operating data of the bearings, which will reflect the health conditions thoroughly. Deep learning is a promising method to completely extract hidden information from the big data and Recurrent Neural Network (RNN) is designed to process time relations of the input signals. However, there are still some shortcomings on the vanilla RNN, and Long Short-Term Memory (LSTM) neural network is proposed to optimize the model. In this paper, a new method based on time-frequency analysis and LSTM is proposed for bearing prognosis. Firstly, vibration raw signals are collected. Then, a time-frequency analysis will be applied on these raw signals. LSTM neural network is established and the time-frequency information is fed into the target network to train the target one or diagnose the bearing health state. Selection of some vital parameters in our model is discussed in detail, and then the proposed method is compared with other methods, such as vanilla RNN, MLP and SVM. Result shows that the proposed method achieves the highest accuracy rate over other methods.
Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey

With the increasing demands on quality healthcare and the raising cost of care, pervasive healthcare is considered as a technological solutions to address the global health issues. In particular, the recent advances in Internet of Things have led to the development of Internet of Medical Things (IoMT). Although such low cost and pervasive sensing devices could potentially transform the current reactive care to preventative care, the security and privacy issues of such sensing system are often overlooked. As the medical devices capture and process very sensitive personal health data, the devices and their associated communications have to be very secured to protect the user’s privacy. However, the miniaturized IoMT devices have very limited computation power and fairly limited security schemes can be implemented in such devices. In addition, with the widespread use of IoMT devices, managing and ensuring the security of IoMT systems are very challenging and which are the major issues hindering the adoption of IoMT for clinical applications. In this paper, the security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.
Snow Accumulation Effects in Screen Space for Real-Time Terrain Rendering

We propose a method to create a realistic snow accumulation effect in screen space for realistic natural environment rendering. Existing algorithms are not suitable for low-polygon terrain because they need high-resolution features on the surface. We develop a technique to enhance the resolution of the surface’s normal as geometrical features by extracting details from a decal texture of a terrain model. Our algorithm is implemented on GPU totally, and since it works as a postprocessing effect, it can be performed in the real-time calculation. Moreover, it is not dependent on the size and shape of the terrain. Our algorithm can be easily integrated into a common real-time rendering engine and can be applied a various type of terrain data which has decal textures.
Finite frequency H∞ control for T-S fuzzy systems: Application to wind turbine

For the H∞ control problem of continuous-time T-S fuzzy systems, an H∞ controller design method in low frequency domain is proposed. First, an state feedback control controller structure is given. To achieve better control performance of the system in low frequency, the H∞ index for attenuating the unknown low frequency disturbance is guaranteed by generalized Kalman-Yakubovich-Popov lemma. Then the stability and robustness conditions are converted into linear matrix inequality (LMIs) forms which can be solved directly by convex optimization technique. In simulation section, a vessel dynamic positioning task is carried out to show the effectiveness of the proposed method.
Dense Convolutional Networks With Focal Loss and Image Generation for Electrocardiogram Classification

In this paper, we propose a novel end-to-end learnable architecture based on Dense Convolutional Networks (DCN) for the classification of electrocardiogram (ECG) signals. This architecture is based on two main modules: the first is a generative module and the second is a discriminative one. The task of the generative module is to convert the one dimensional ECG signal into an image by means of fully connected, up-sampling, and convolution layers. The discriminative module takes as input the generated image and carries out feature learning and classification. To handle the data imbalance problem characterizing the ECG data, we propose to use the focal loss (FL) that is based on the idea of reshaping the standard cross-entropy loss such that it reduces the loss assigned to well-classified ECG beats. In the experiments, we validate the method using the well-known MIT-BIH arrhythmia database in four different scenarios, using four classes in the first scenario, five in the second and 12 in the third. Finally, supraventricular versus the other three and ventricular versus the other three from the scenario with four classes are used as the fourth scenario. The results obtained show that the method proposed here achieves a significant accuracy improvement over all previous state-of-the-art methods.
Experimental Study on Thermal Fatigue Life Prediction Model of PBGA Solder Joints

In the Prognostics and Health Management (PHM) technology of airborne products, the accuracy of life prediction model is very important for the life prediction. In order to acquire the proper life prediction model, the thermal fatigue life prediction model of PBGA solder joints was validated and modified by combining thermal cycle reliability experiment with finite element analysis. Firstly, during the thermal cycle reliability experiment, the resistance values of solder joints on test samples were surveyed and collected in real time, and the characteristic failure time of PBGA solder joints were obtained by Weibull probability distribution. Subsequently, the finite element analysis model of the PCB test sample was established based on the ABAQUS software, and Anand model was used to simulate the visco-plastic constitutive relationship of the solder joints, thus we can obtain the stress and strain data of the key solder joints, and the thermal fatigue life was calculated based on the Engelmaier model. Finally, the model parameters were modified by comparing the experimental results with the simulation results.
Flooding Level Classification by Gait Analysis of Smartphone Sensor Data

Urban flooding is a common problem across the world. In India, it leads to casualties every year, and financial loss to the tune of tens of billions of rupees. The damage done due to flooding can be mitigated if the locations deserving attention are known. This will enable an effective emergency response, and provide enough information for the construction of appropriate storm water drains to mitigate the effect of floods. In this work, a new technique to detect flooding level is introduced, which requires no additional equipment, and consequent installation and maintenance costs. The gait characteristics in different flooding levels have been captured by smartphone sensors, which are then used to classify flooding levels. In order to accomplish this, smartphone sensor readings have been taken by 12 volunteers in pools of different depths, and have been used to train machine learning models in a supervised manner. Support vector machines, random forests and naïve bayes models have been attempted, of which, support vector machines perform best with a classification accuracy of 99.45%. Further analysis of the most relevant features for classification agrees with our intuition of gait characteristics in different depths.
Finding High-Order Homologous Microbe Community Modules via Network Embedding

Microbial network analysis help with discovering microbe groups that covariate with environmental factors. However, microbial communities are highly diversified and localized, which poses challenges to existing correlation-based network construction methods in terms of stability and functional significance. In this paper, we propose to explore the high-level relationships in the microbial network structure with the aid of network embedding methods. Microbial function modules are then extracted by spectrum clustering on the embedded networks, rather than the original ones. By investigating the correlation between the obtained modules and the environmental factors on several real-world microbial datasets, we demonstrate that the embedded modules provide feature information of the microbial community that are distinct to traditional correlation-based network modules. Furthermore, we show that the introduction of high-order modules helps with improving the performance of prediction models comparing with using OTU features or traditional correlation-based modules alone. Our study demonstrated that high-order network modules created by network embedding can be served as a potential new biomarker for feature extraction of microbial communities.
The Influence of Consumer’s Decision Making Process to Online Purchase Behavior Analysis

The local Indonesian women apparel category has been growing recently with the support of technological advancements and the increasing trend of online shopping. Thus, by knowing and following the current patterns of consumer behavior at every stage in making online purchase decisions, expected to help local fashion brands to expand their market share. The research design in this study is conclusive descriptive by analyzing online purchase behavior through online brand experience mediated by online brand familiarity; the influence of central route & peripheral route on the information adoption; information adoption mediated by perceived e-WOM credibility; and online purchase experience. The sampling method is purposive sampling with a sample size of 368 people who have bought the Indonesian local apparel brand online. The data is processed using Structural Equation Modeling (SEM) method by LISREL software. The results show that online brand experience has a positive influence on consumer online purchase behavior only if mediated by online brand familiarity; central route & peripheral route have a positive influence on information adoption; information adoption mediated by perceived e-WOM credibility is not significantly influence consumer online purchase behavior; and online purchase experience have a positive influence on consumer online purchase behavior.
Prediction of power consumption in the factory using long-short term memory

This paper deals with the method of forecasting power demand using Long Short-Term Memory, one of the recursive neural network architecture. Prediction of power usage in actual environment become an important issue due to diversification of power industry and market. However, problems occur due to the prediction error cannot be evitable in actual environment. To solve these problems, researches are being conducted to minimize errors through suggesting more accurate forecasting methods. Accordingly, in this paper, we propose Long Short-Term Memory based power prediction method which derive a low-error prediction result. In addition, we represent the excellence of proposed method based on the comparison of results which use Double Seasonal Holts Winter algorithm which is one of representative methods of time-series forecasting. As a result, the method we proposed proved its performance by deriving higher prediction accuracy from root mean square error comparing with the result of Double Seasonal Holts-Winter algorithm.
An Openflow-based Scheme for Service Chaining’s High Availability in Cloud Network

High availability (HA) is one of the important requirements of many end-to-end services built on network function virtualization (NFV) and NFV-enabled service function chaining (SFC) due to hard- and soft-ware failures. Once a virtual network function (VNF) fails, the entire SFC operations are broken down. Existing HA solutions for SFC in NFV environments require a significant delay for fault notification to the NFV-MANO (Management and Orchestration) and restoration notification of NFV-MANO to affected controllers. This leads to a significant load at the NFV-MANO, delays the recovery action of the SFC, and causes packet loss during the recovery time. In addition, in Software-Defined Networking (SDN) architecture, controller-based HA solutions are not efficient because studies have shown that SDN centralized architecture may not be able to achieve failure recovery within a 50 ms interval. To address above issue, this paper presents a proposal and implementation of an efficient controller-independent HA scheme based on OpenFlow. In particular, we exploit the OpenFlow group table to design protection plans and a fast failover mechanism, which enable a quick fault recovery for SFCs without requiring fault notification to the NFV-MANO or controllers. Experimental results using OpenFlow with OpenStack show that our proposed scheme achieves a significant improvement in terms of SFC link throughput and packet loss compared to existing schemes.
Employing Conversational Agents in Palliative Care: A Feasibility Study and Preliminary Assessment

Recording of patient-reported outcomes (PROs) enables direct measurement of the experiences of patients with chronic conditions, including cancer; thus, PROs are a critical element of high quality, person-centered care for cancer patients. A growing body of literature reports on the feasibility of using electronic tools for the collection of Patient reported Outcomes (ePROs), although the usability of available solutions does affect their acceptance and use. In parallel, recent advancement in artificial intelligence, machine learning and speech recognition have led to the growing interest in conversational agents, i.e. software applications that mimic written or spoken human speech. In the present manuscript we provide a review of current developments regarding the implementation of conversational agents and their application in the domain of palliative care for oncology patients and also present (i) a methodology for the implementation of a conversational agent able to collect ePRO health data and (ii) initial evaluation results from a relevant feasibility study. Our approach differs from other available systems since the conversational agent reported in the present work is not based on rules, but rather uses machine learning algorithms and more specifically recurrent neural networks (RNN) for identifying appropriate answers. Evaluation results of user experience provided promising results and highlight that users gave positive responds when interacting with the system. Based on the User Experience Questionnaire, pragmatic quality and overall quality were categorized as excellent and hedonic quality was categorized as good. The result of this research can be used as reference for the future development and improvement of the conversational agents in the healthcare domain.
Early Detection of Heart Failure by Reducing the Time Complexity of the Machine Learning based Predictive Model

Heart failure (HF) is a deadliest disease. Unfortunately, the diagnosis of HF is very expensive and need high level of human expertise. Hence, there is a dire need of development of automated methods for diagnosis of HF with high accuracy and lower computational cost. To reduce the time complexity of the machine learning model established for detection of HF, in this paper, a hybrid diagnostic system is developed. The proposed hybrid model hybridizes two models i.e., L1 regularized support vector machine (SVM) and linear discriminant analysis (LDA) model. The SVM model is used to remove redundant features or features which are not relevant while the LDA model is used as classifier to classify the input feature vector as that of healthy or diseased person. The elimination of irrelevant features from features space reduces the time complexity of the predictive model, thus, reduces its training time. Simulation results point out that the developed hybrid system achieves better accuracy than twelve previously reported methods that yielded HF risk prediction accuracies in the range 57.8% to 89.01%. Thus, the proposed hybrid model is effective from both aspects i.e. processing time and classification.
A global multipath load-balanced routing algorithm based on Reinforcement Learning in SDN

Routing and load balancing are two important factors that guarantee Quality of Service (QoS) in the Internet. Meanwhile Software Defined Networking (SDN) has introduced a new networking paradigm that make routing and load balancing policies implemented more quickly and flexibly in the system. In the SDN architecture, the “intelligence” of the network is centralized in the application layer and control layer instead of distributing in the network device in traditional network. Current studies using SDN-based routing and load balancing still stop at the scope of finding appropriate network resource for one single data flow but not considering network resource for all flows coming from different sources as the whole. In this paper, we propose a global load-balanced routing scheme, which can take advantages of global view of the SDN controller to make a global policy for routing and load balancing. The solution has been shown to outperform traditional algorithms and recent SDN-based artificial intelligence algorithms in terms of delay and network utilization.