Miracle-dz/UniFAR-FTUs huggingface dataset:
A Semi-supervised Approach for Early Identifying the Abnormal Carotid Arteries Using a Modified Variational Autoencoder

Carotid artery lesions could be the pathology of subclinical atherosclerosis and hence lead to the onset of stroke. Early detection of abnormal carotid artery might help to better identify individuals susceptible to stroke. Considering the carotid artery ultrasonography is time-consuming and costly, the object of this paper is to establish a model to detect the status of carotid artery for preliminary screening of stroke, according to the simple physiological examination and survey information. However, most of the previous studies were based on the linear regression or the traditional machine learning methods, those suffer from two limitations. One is the limited labeled samples, and the other one is the missing data. To address these issues, we firstly propose a semi-supervised approach based on a modified variational autoencoder (VAE) to identify the abnormal carotid arteries. In this paper, a mixture of mean and K th nearest neighbours (MKNN) and a modified VAE were used for missing data imputation. The experimental results demonstrate that the proposed method can not only handle the missing values, but also outperform four widely used supervised approaches. Therefore, we can conclude that this semi-supervised model is a promising way to identify the abnormal carotid arteries.
Single Tuned Filter Planning to Mitigate Harmonic Polluted in Radial Distribution Network Using Particle Swarm Optimization

The increasing of nonlinear load has a currently become recurring problem which can worsen the condition of the system. Effect of placement and sizing single tuned filter on power quality of radial distribution system has been investigated to minimum power losses on passive and active system (after DG Placement). Particle Swarm Optimization (PSO) has been used to establish of the objective function. The effectiveness of method on power quality issues have been studied on IEEE 33-bus Standard System using MATLAB. The simulation result verified that using nonlinear load on each case the proposed strategy can be a robust approach to improve performance system on power quality for mitigate harmonic polluted.
The Head Posture System Based on 3 Inertial Sensors and Machine Learning Models: Offline Analyze

The current paper proposes and presents a new wearable system for head posture recognition, based on three inertial sensors used to prevent inadequate head posture during different office daily activities. During this experiment, 9 daily office activities were evaluated. The proposed model distinguished between bad or good posture with a high accuracy using the inertial time series's raw data. The performance of the proposed wearable system was evaluated offline with the help of machine learning algorithms. The advantage of the proposed approach is the possibility of transmitting data through the Wi-Fi connection, portability, low cost, and high performance. During this experiment, the best classification performances it was obtained with Decision Extra Trees Classifier, that was achieved an accuracy equal to 96.78%.
Meteorological Drought Hazard Assessment under Future Climate Change Projection for Agriculture Area in Songkhram River Basin, Thailand

The Songkhram River Basin (SRB) has recently experiencing related drought disaster and water scarcity because of reduction in rainfall and increasing high temperature. This paper presents an assessment of meteorological drought hazard of agriculture area in the river basin under future climate change projection. The study analyzed meteorological drought hazard using the Standardized Precipitation Index or SPI considering the observed daily rainfall data (1970–2010) from 6 meteorological stations and the average trend of the projected future climate change scenarios (2011–2100) from three Regional Climate Models (RCMs) under two Representative Concentration Pathways RCP4.5 and RCP8.5. The results showed that the future annual rainfall in the 21st century at Sakon Nakon weather station in SRB was reduced by -1.5% and -3.7% per decade for RCP4.5 and RCP8.5, respectively. The drought hazard assessment was quantitatively evaluated for rice and field crops based on the values of drought hazard parameters namely: standardized precipitation index SPI, distance from surface water resources and ground water yields. The weights of each drought parameter were determined by using Analytical Hierarchy Process (AHP). The result showed a change in potential drought hazard in the future in the study area of 12,880 km2, for SPI1 under RCP4.5; the low hazard area was 33%, medium of 22%, high of 45% in 2020s. They did not change much in 2050s but significantly changed from low to medium, medium to high and high to very high hazard respectively in 2080s. While under RCP8.5; the low, medium, high and very high hazard areas in 2020s significantly changed to medium, high and very high hazard respectively in 2050 and 2080s. A bit more significant droughts was expected to occur for SPI3 which is for field crop for both RCPs. Overall, under both RCP4.5 and RCP8.5, we can say that the droughts become more significant in the study area as the time goes on from 2020s to 2050s and to 2080s for both rice and filed crops. However, the predicted drought conditions were only based on standardized precipitation index SPI1 and SPI3 under predicted climate change conditions. In actual situations, other factors such as evapotranspiration, soil moisture conditions, change in runoff and irrigation as well as adaptation measures may change the results of this study. The temporal pattern of future drought hazard levels can be used by researchers and policy makers as a guideline for drought management and mitigation strategies in the study area.
Prediction of lncRNA-disease associations based on matrix factorization and neural network

More and more evidences have shown that lncRNAs are involved with various complex human diseases. The small minority of experimentally validated lncRNA-disease associations have created an urgent demand for computational prediction models. Although many related approaches have been proposed, there still have much room for improvement. To address the cold-start problem and accurately represent associations, this paper considers the prediction of lncRNA-disease associations as a recommendation problem and proposes a matrix factorization and neural network based method. Firstly, to better represent lncRNAs and diseases, their embeddings are learned based on matrix factorization. And then, features of associations are represented by integrating embeddings of lncRNAs and diseases. Finally, neural network is applied for predicting potential associations. Experimental results show that our method can achieve better performance than the state-of-the-art approaches from several perspectives.
The Importance of Including Qualitative Data in Technology Evaluation - Investigating the Technology Implementation Evaluation Score (TIES)

The recent increase in welfare technology implementation calls for the development of evaluation methods. Previous research has identified the need for a coherent evaluation model to create indicators that combine individual, economic and organizational dimensions. The Technology Implementation Evaluation Score (TIES) works in this way. It provides a score by combining statistical and monetary data (quantitative data) with patient and caregiver opinions (qualitative data). This paper aims to investigate the effects of including qualitative data on the outcome of the TIES model, compared to only using quantitative data. Various combinations of inputs are investigated by performing a sensitivity analysis using extreme value scenarios. The result shows that the TIES model is more affected by the number of data types created and the weight attached to each input than the data used as inputs. The assessment shows that the model is appropriately constructed for the current home care case.
Analysis of Machine Learning Classifier in Android Malware Detection Through Opcode

As the development of technology in Android devices continue to rise, more mobile applications were introduced to make daily task much easier. The mobile applications has also used as platform for users to do social networking, online banking, online shopping, web browsing, and many more activities. Most of these applications requires user to provide private credentials, which could potentially be sniffed by cybercriminal and exploited. Various detection techniques have been introduced in mitigating mobile malware, yet the malware author has its own method to overcome the detection method. Therefore, an enhanced mobile malware detection technique is needed to defend smartphone users against malicious threats. Based on this reason, this paper explores the mobile malware detection through opcode analysis and proposed the optimum machine learning classifier method. Several machine learning classifier methods are selected based on the previous research, and their performance are analyzed. The machine learning classifier methods are evaluated based on the contribution it made to improve the True Positive Rate (TPR), False Positive Rate (FPR) in classifying benign and malicious mobile malware application.
Analysis and Detection of Lung Sounds Anomalies Based on NMA-RNN

Lung diseases are among the most widely recognized reasons for serious disease and passing around the world. The timely analysis is crucial to lessen the risk of any disease and if any disease is diagnosed, then precautions or medicines are immediately given. Therefore, for the diagnosis of lung sound auscultation, progressive computational tools are established and play a very vital role in the detection of disease-related anomalies. The aim of this study is the joint learning of the model that only extracts important breathing samples without generating redundant noise, and then uses this information to train lung sounds into four categories: normal, wheezes, crackles, and wheezes and crackles. This paper signified an unsupervised approach that depends on a Denoising Auto-Encoder (DAE). It utilizes the rebuild errors between the input and the output of the Auto-encoder as an activation audio signal to identify noisiness. A novel design of Recurrent Neural Network (RNN) called noise-masking anomalies recurrent neural network (NMA-RNN) for lung sound order is projected. ICBHI database used in this paper and some results of previous models were compared and achieved 95% accuracy.
ML-based Analytics Framework for beyond 5G Mobile Communication Systems

Predictive analysis of cellular network behaviour has many advantages, and will be one of the major breakthroughs on 5G and beyond 5G networks. Knowing the parameters that have impact on reliable network operation and their use in network performance analysis and evaluation are very important. Thus, in case of degradation, intervention is possible before the fault actually occurs and exactly on the faulty equipment. This brings an increase in efficiency with respect to the scenario in which the enhancement solutions are brought subsequent to network events. This paper proposes tracing a predictive behaviour for certain performance indicators of an LTE RAN network and in presenting optimisation solutions of the undesired behaviour. Also, we propose an analytics framework for the access network that can be applied to 5G and beyond 5G RANs. A set of APIs is used in order to extract all the parameters from the LTE RAN Network and store them into a database. After that, the data is exported for further processing and analytics. Results show that the Gradient Boosting algorithm is the most suitable to be used in such a framework.
Prediction of patients with heart failure after myocardial infarction

Heart failure is one of the most common causes of death for all different racial groups all over the world. It is noticed that a high proportion of patients diagnosed with heart failure (HF) within a short period of time after suffering from myocardial infarction (MI). This study is designed to translate existing real world data to real world evidence by exploring associations between historical comorbidities and heart failure diseases. Machine learning technologies were applied to predict whether patients with myocardial infarction would develop heart failure within a specific time period, and to remind patients how to strengthen personal self-care to avoid the transition towards heart failure or postpone the occurrence of the preceding events. In this study, patients with heart failure after myocardial infarction were divided into two groups according to a median age of 71 years old, and corresponding prediction models were constructed for two different age groups respectively. Three different machine learning technologies, namely logistic regression, random forest, and XGBoost were used to construct prediction models and a 5-fold cross-validation was applied to evaluate prediction accuracy and stability of prediction models. The results of our proposed method reveal that if a prediction model was constructed without age stratification, the constructed prediction model provided inferior performance compared to stratified groups by employing identical features. The analytical results from three different machine learning techniques consistently supported that the prediction models of myocardial infarction resulted in accelerated transition towards heart failure within a specific interval should be constructed by stratifying age groups first, and then training the corresponding data for better system performance.
Robust Dual Control of Batch Processes with Parametric Uncertainty using Proximal Policy Optimization

This study presents a robust dual control method for batch processes under parametric uncertainty. Proximal policy optimization (PPO), a policy gradient reinforcement learning algorithm, is employed to construct an implicit dual controller in a computationally amenable way. The proposed control method can robustly and actively cope with uncertainties seen in a repeated sequence of batch operations by incorporating a penalty term for constraint violation into the reward function and by considering the effect of control inputs on future uncertainty. An application to a bioethanol fermentation process is discussed to demonstrate the effectiveness of the proposed control strategy. It is shown that the proposed robust dual controller has an active learning feature such that the overall performance improves compared to a certainty-equivalence based approach.
EDUTECH - Good Practices in Cooperation between Schools and Academia Attracting Candidates for Technical Education

The paper is based on idea realized under Erasmus+ Strategic Partnership Key Action 2 and focused on educational activities for attracting technical education to young people. The project based on exchange of good practices involves seven partners from three countries (Poland, Estonia and France). Three of them represents sector of higher education and four are schools located in the neighbour of the technical university. Unfortunately, in recent years number of candidates entering to technical universities (particularly in Europe) decreases. Partners exchange ideas how to increase number of students who starts education in technical area. Another project goal is to examine possibilities how the university can contribute in increasing quality of technical education at schools. Main project ideas and results of project activities are reported in the paper.
Consumer Demand Modeling During COVID-19 Pandemic

The current pandemic has introduced substantial uncertainty to traditional methods for demand planning. These uncertainties stem from the disease progression, government interventions, economy and consumer behavior. While most of the emerging literature on the pandemic has focused on disease progression, a few have focused on consequent regulations and their impact on individual behavior. The contributions of this paper include a quantitative behavior model of fear of COVID-19, impact of government interventions on consumer behavior, and impact of consumer behavior on consumer choice and hence demand for goods. It brings together multiple models for disease progression, consumer behavior and demand estimation-thus bridging the gap between disease progression and consumer demand. We use panel regression to understand the drivers of demand during the pandemic and Bayesian inference to simplify the regulation landscape that can help build scenarios for resilient demand planning. We illustrate this resilient demand planning model using a specific example of gas retailing. We find that demand is sensitive to fear of COVID-19 – as the number of COVID-19 cases increase over the previous week, the demand for gas decreases-though this dissipates over time. Further, government regulations restrict access to different services, thereby reducing mobility, which in itself reduces demand.
Secure State Estimation and Control of Cyber-Physical Systems: A Survey

Cyber-physical systems (CPSs) empower the integration of physical processes and cyber infrastructure with the aid of ubiquitous computation resources and communication capabilities. CPSs have permeated modern society and found extensive applications in a wide variety of areas, including energy, transportation, advanced manufacturing, and medical health. The security of CPSs against cyberattacks has been regarded as a long-standing concern. However, CPSs suffer from extendable vulnerabilities that are beyond classical networked systems due to the tight integration of cyber and physical components. Sophisticated and malicious cyberattacks continue to emerge to adversely impact CPS operation, resulting in performance degradation, service interruption, and system failure. Secure state estimation and control technologies play a vital role in warranting reliable monitoring and operation of safety-critical CPSs. This article provides a review of the state-of-the-art results for secure state estimation and control of CPSs. Specifically, the latest development of secure state estimation is summarized in light of different performance indicators and defense strategies. Then, the recent results on secure control are discussed and classified into three categories: 1) centralized secure control; 2) distributed secure control; and 3) resource-aware secure control. Furthermore, two specific application examples of water supply distribution systems and wide-area power systems are presented to demonstrate the applicability of secure state estimation and control approaches. Finally, several challenging issues are discussed to direct future research.
A kinematic hybrid feedback controller on the unit circle suitable for orientation control of ships

This paper presents a hybrid feedback controller suitable for orientation control of ships. A hybrid kinematic controller on the unit circle is constructed from the gradient of a synergistic potential function, which globally asymptotically stabilizes a desired orientation, with yaw rate viewed as control input. While this idea is not new, the potential function is novel and possesses some desired properties. The kinematic controller generates smooth reference signals for the desired velocity and acceleration, except at instances when the controller switches. Continuity of velocity and acceleration is achieved by controlling the yaw rate through a double integrator. Moreover, the velocity and acceleration converge to their desired values exponentially. The resulting closed-loop system is stable, provided the controller gains satisfy mild constraints. This is shown using a hybrid Lyapunov function.
Time-Varying DOA Tracking Algorithm Based on Generalized Labeled Multi-Bernoulli

Direction of arrival (DOA) tracking for multi-sources is a hot issue in array signal processing. To deal with the problem that sources DOA and their number are time-varying, a DOA tracking algorithm based on Generalized Labeled Multi-Bernoulli (GLMB) filter is proposed. Since the measurement value has only one set of data, the measurement association mapping (MAM) does not match, which leads to deviations in the GLMB filter update step. In this regard, we used the estimated sources number of the previous time step as the measurement number of the current time step, and successfully achieved MAM matching. Subsequently, particle filtering is used to approximate the posterior distribution of DOA, where the particle likelihood function can be calculated by the multi-signal classification (MUSIC) spatial spectrum function. In addition, by exponentially weighting the likelihood function, the number of particles in the high likelihood region of the posterior distribution increases, which makes the GLMB filter pruning and merging operations more effective. Simulation results show that the method is better than the probability hypothesis density DOA (PHD-DOA) algorithm in tracking state sources and estimating the number of targets.
How Do Innovation Intermediaries Influence Outbound Open Innovation in China? A Moderated Mediation Mechanism

Although firms devote much attention to outbound open innovation for economic or strategic benefits, how outbound open innovation is conducted effectively is still under-studied in the literature. Our paper introduces innovation intermediaries as an antecedent, and based on capability-based framework for open innovation and institutional theory constructs a moderated mediation mechanism through which innovation intermediaries influence outbound open innovation in China. The results of empirical test show that innovation intermediaries influence outbound open innovation via desorptive capacity, particularly in the form of two parallel routes, namely via identification capacity and transfer capacity. The relationship between innovation intermediaries, desorptive capacity and outbound open innovation is stronger in underdeveloped regions than in developed regions.
Iterative Learning Control for Region to Region Tracking

Iterative learning control (ILC) is a high performance control design method for systems working in a repetitive manner by learning from previous experience. Most existing ILC design considers the problem where the desired reference trajectory is defined either fully on the trial duration or on a finite number of intermediate time instants. This paper further expands the applicability of ILC by studying a more general case (named region to region tracking) where there is no desired trajectory defined at all; instead only a region where the system output should reach is given. To solve this problem, a novel ILC algorithm with an norm optimal ILC step and a projection operation is developed. Convergence properties of the algorithm are analysed rigorously. It is also shown that traditional reference tracking problems can be solved as special cases of the proposed design, resulting in a well-known norm optimal ILC algorithm being recovered and a new point to point ILC algorithm. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach.
ERID: A Deep Learning-based Approach Towards Efficient Real-Time Intrusion Detection for IoT

In the 5G and Internet of Things (IoT) era, the threat of network intrusions has greatly affected people's work and life. The increasing complexity of intelligent devices in IoT brings huge challenges to the network intrusion detection. We address these issues and propose a novel intrusion detection system (IDS) called ERID, which is based on a real-time anomaly detection approach. A new type of unsupervised stacked auto-encoders (SAE), is trained by using normal network traffics, then is assessed on its ability to detect four types of attack in IoT. In this work, we mainly focus on the classification of normal and threat patterns. The extensive experimental results based on a famous real-world dataset have been conducted, which also demonstrated the effectiveness and superiority of our novel ERID scheme compared with the existing works in the literature. We hope our work can be used to further stimulate real-time intrusion detection approaches for IoT.