Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Prediction and Forecasting Model for Dengue Disease using Machine Learning Algorithms

Globally, Dengue is one of the most quickly spreading vector-borne viral sicknesses with an expanding number of territories in danger. Many researchers have worked on different measures to control and prevent the spread of disease. The main objective of the research is to develop a forecast model to control the outbreak of dengue disease that will give an opportunity for medical professionals in designing, planning and handling the disease at an early stage. Moreover, the improvement of the assortment of strategies for determining and predictive modeling utilizing measurable, numerical examination of machine learning was studied. There are mainly six issues need to be solved in determination of dengue disease, those are exploring data sources, analyzing data sources, techniques for data preparation, data representation, dengue forecasting models and evaluation approaches. A major limitation of the traditional methods is that these methods need large volumes of data for data processing, to improve the dynamic characteristics. From the review of existing methods, it can be clearly stated that the K-means clustering method with fuzzy based system has high accuracy and it significantly improves the analysis/prediction of dengue disease. The k-means clustering algorithm separates the dengue diseased patient records into k divisions. As the dengue dataset were fully clustered, k-means clustering method improves the analysis or prediction of dengue disease. Similarly, the fuzzy based system The input factors and changing over these informational factors into fuzzy membership functions will make a better decision making in predicting dengue forecasting model. Thus, the issues stated from comprehensive research provide a useful platform for public health research and epidemiology.
Machinery Fault Diagnosis Using Recurrent Neural Network: A Review

Fault diagnosis is a kind of process to discover whether the continuous running mechanical system and equipment have faults using various inspection and test methods. It is widely applied in industrial engineering and has drew much attention recently since people need higher reliability and safety of machines to complete complex tasks, and the cost of repairment and maintenance is also high. While traditional fault diagnosis methods consume a lot of time and energy, those methods used deep learning which has excellent performance in feature extraction and pattern recognition become extremely important, thus a quantity of research work has been concluded in different networks of deep learning in this paper, a comprehensive and systemic review of machinery fault diagnosis using various forms of Recurrent Neural Network (RNN) and their combinations with other networks is introduced. First, some theoretical backgrounds of RNN are presented following its development, from RNN to Long Short-Term Memory (LSTM), bidirectional forms and Gate Recurrent Unit (GRU). Then, detailed literature survey of these networks and some combinations of RNN with Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in practical applications are given. Finally, some limitations and discussions of these networks are discussed.
Reference Service Process: A Normalized Cross-Over Service Collaboration Paradigm

The deep integration and innovation of cross-over services across the boundaries of different industries, organizations and individuals will provide developers with multidimensional, high-quality and valuable cross-over services, which has become an important innovation approach for the development of modern service industry. With the further development of this trend, services in the form of processes play an increasingly important role in the field of service computing research. However, with the rise of digitization and the escalation of cross-industry, cross-over participants face two tough questions: (l) What kind of service process can solve the complex business we face. (2) How can we access these service processes quickly and conveniently. In this paper, we propose the concept of reference service process to solve the problems above. The reference service process is a normalized cross-over service collaboration paradigm. On the one hand, reference service process of different functional topics can solve most complex businesses, eliminating the need for developers to design service processes. On the other hand, each reference service process establishes a mapping relationship with multiple general service processes, which solves the problem of selection by automatically selecting the optimal service process for developers.
The Output Feedback Control of LPV Stochastic Systems

This paper discusses a static output control issue for time-varying stochastic systems. Based on Linear Parameter Varying (LPV) modelling approach, time-varying parameters are represented by some constants with weighting function. And, the considered stochastic behavior is described by composing of continuous Brownian motion and states which is also called as multiplicative noise terms. For stability analysis, some sufficient conditions are derived via Lyapunov stability theory. Moreover, a transformation lemma is applied such that the sufficient conditions belong to Linear Matrix Inequality (LMI) form for applying the convex optimization algorithm. Furthermore, the output feedback controller is designed by the gain-scheduled control scheme to achieve the stability of LPV stochastic system. Finally, a numerical example is used to show the efficiency of this paper.
Towards Underwater Sustainability using ROV Equipped with Deep Learning System

Underwater pollution is a long-term environmental problem. Remotely Operated Vehicle (ROV) could promote the solution of kinds of the problem by detecting environmental problems such as rubbish. The paper suggests a system based on deep-learning algorithms that can detect rubbish underwater using ROV. We built our own dataset of three different types of underwater trash for a training model based on YOLO Neural Network architectures for object detection. In this paper we used YOLOv4. The training images from our dataset apply several filters for noise reduction and image enhancement processing to improve the accuracy of the results. In our experiment, the ROV could capture the image and stream it to the computer for analysis, enhancement, and recognition.
Pothole Detection and Avoidance via Deep Learning on Edge Devices

Hitting potholes can not only cause damage to vehicles but also put passengers into danger. However, due to the various factors, such as budget consideration and soil erosion, it is impossible to fix all the potholes on the roads. Instead of avoiding potholes directly, most researches tend to collect the position of potholes on the roads for developing more effective road maintenance strategies. In this paper, a pothole avoidance system deployed on an edge platform is proposed, which can avoid potholes automatically. We use Deep Q-Network (DQN) as an agent, and train it on CARLA driving simulator with cross-task unsupervised transfer learning. To accelerate the executing speed of the system on the edge platform, the model is quantized to an 8bit integer. Finally, we implement the pothole avoidance system on the Xilinx ZCU104 evaluation board, which is satisfied with the deployment limitations of vehicles, such as power consumption. To evaluate the proposed system, we have set up a virtual road environment with CARLA, and generated several potholes on the road randomly. Then, a vehicle controlled by the pothole avoidance system was tested in the environment. The evaluation results show that the proposed system can effectively reduce the opportunity of hitting potholes by 31% compared to the baseline. Besides, the system deployed on the Xilinx ZCU104 can execute at 30 FPS, and the energy efficiency is 0.91 FPS/W. In conclusion, the proposed system can effectively avoid potholes on the road, and its execution speed and power consumption meet the requirements of the actual driving situation.
A 1.65V Indirect-Compensated Class-AB OTA for Low-Power Low-Voltage Applications

This work presents the design and simulation of an indirect compensated class-AB operational transconductance amplifier (OTA) suitable for low-power low-voltage applications. The designed OTA is made up of a two-stage amplifier that is stabilized using an indirect Miller compensation capacitor while a Class-AB modification used to enhance the slew rate of the overall amplifier. Self-cascode composite transistor were used to improve the amplifier DC gain. The designed OTA presents a DC gain, bandwidth, gain-bandwidth product, and phase margin of 79.6 dB, 79 Hz, 655 kHz, and 42°, respectively. Also, this OTA exhibits a slew rate of 0.371 V/μs, a power consumption of 1.32 μW, and a silicon area of 170.7 μm x 191.25 μm. A 0. 50μm CMOS standard technology was used for simulation and layout purposes.
Distance-Based Embedding Learning for Remaining Useful Life Estimation

Remaining useful life (RUL) estimation is a core content of Prognostics and Health Management (PHM). Easy access to sensor data allows data-driven methods to achieve better results than physical or expert modeling methods in complex systems. However, the lack of run-to-failure data remains a problem. To maximize the use of failure samples, in this paper, long short term memory (LSTM) network is utilized to learn a robust embedding. Then RUL of a sample is obtained by calculating the distance between embedding of failure and embedding of the sample. An end-to-end model is trained to achieve the aforementioned functions. Experiments on C-MAPSS dataset show competitive results especially in subsets of multiple conditions.
Intelligent Decision Support for Power Grids Using Deep Learning on Small Datasets

The larger dataset possesses the greater appliance for further data analysis and using machine learning potential. However, the unique types of complex infrastructure objects exist which can be described by the features sets specific to each object only. These include electrical networks and large power electrical equipment of railway traction substations. Datasets that are acquiring from such equipment during monitoring and maintenance procedures have high dimensional by parameter variety but small samples by its length. Because of this fact, conventional machine learning approaches based on big datasets are unsuitable for intelligent decision support systems in railway power grids. This paper proposes a novel approach to deep learning problems on small datasets by the example of deep feature selection. Pretrained models and transfer learning techniques are modified in this paper to remain robust under noise and outliers. The paper presents a new architecture for intelligent decision support with deep features selection in high dimensional size-limited datasets.
Modeling the relationship between acoustic stimulus and EEG with a dilated convolutional neural network

Current tests to measure whether a person can understand speech require behavioral responses from the person, which is in practice not always possible (e.g. young children). Therefore there is a need for objective measures of speech intelligibility. Recently, it has been shown that speech intelligibility can be measured by letting a person listen to natural speech, recording the electroencephalogram (EEG) and decoding the speech envelope from the EEG signal. Linear decoders are used, which is sub-optimal, as the human brain is a complex non-linear system and cannot easily be modeled by a linear decoder. We therefore propose an approach based on deep learning which can model complex non-linear relationships. Our approach is based on dilated convolutions as used in WaveNet to maximize the receptive field with regard to the number of tunable parameters. Comparison with a model based on a state of the art linear decoder and a convolutional baseline model shows that our proposed model significantly improves on both models (from 62.3% to 90.6% (p<0.001) and from 78.8% to 90.6% (p<0.001) respectively). Best results are achieved with a receptive field size between 250-500ms, which is longer than the optimal integration window for a linear decoder.
Research on the Modeling and Simulation of the Equipment Installation System with Flexible Foundation

An installation system can restrain the transmission of vibration exciting force caused by the large-scale equipment on the offshore platform and play a vital role in ensuring the equipment operation. It is significant to establish the model of the installation system precisely and rapidly for the prognostic and the health management of the equipment. In this paper, a new approach for the design of equipment installation systems is developed to suppress the vibration excitation, and the model of equipment installation system consisting of “support subsystem-base subsystem” is established. Moreover, which considers the flexibility of the installation deck and aims to control the vibration of the key equipment on the offshore platform. The response of the excitation load transferred to the flexible deck during the running time of typical installation equipment is calculated by a sequential solution. The modeling approach for the installation system and the base system is validated by comparing the theoretical calculation results with the simulation results. This study provides a method for modeling the prognostic system of the large-scale equipment installation part on the offshore platform.
Developing a Fuzzy Multi-Criteria Evaluation Model for Prefabrication Development Maturity of Construction Firms

Prefabricated construction (PC) is considered as a sustainable construction mode that can improve project performance such as quality and efficiency. Even though, Chinese governments at different levels have formulated many guidelines and incentives to help construction firms better implement PC, their implementing PC level is still low. To some extent, this is likely attributed to ineffective measures of improving PC projects’ performance for construction firms lacking adequate understanding of their PC development level and corresponding influencing factors. Furthermore, there are relatively scant research works exploring the issue. Thus, this study aims to formulate a PC maturity model that can be used to evaluate the PC development level of construction firms. It is also expected that this model can identify the key factors that influence the development maturity. The model is firstly established using a four-step methodology, namely, maturity dimension determination, maturity-associated criteria identification, criteria weights calculation based on Entropy and Fuzzy Analytical Hierarchy Process (FAHP), and maturity level evaluation based on fuzzy integral. Next, three construction firms are selected as study cases to test the model’s effect on mapping out PC maturity and key influencing factors. Key findings are eventually analysed to derive effective strategies for improving the maturity of construction firms, further expediting the sustainable transition of the construction sector into PC.
Group Nonnegative Matrix Factorization with Sparse Regularization in Multi-set Data

Constrained joint analysis of data from multiple sources has received widespread attention for that it allows us to explore potential connections and extract meaningful hidden components. In this paper, we formulate a flexible joint source separation model termed as group nonnegative matrix factorization with sparse regularization (GNMF-SR), which aims to jointly analyze the partially coupled multi-set data. In the GNMF-SR model, common and individual patterns of particular underlying factors can be extracted simultaneously with imposing nonnegative constraint and sparse penalty. Alternating optimization and alternating direction method of multipliers (ADMM) are combined to solve the GNMF-SR model. Using the experiment of simulated fMRI-like data, we demonstrate the ADMM-based GNMF-SR algorithm can achieve the better performance.
Learning from Context: A Multi-View Deep Learning Architecture for Malware Detection

Machine learning (ML) classifiers used for malware detection typically employ numerical representations of the content of each file when making malicious/benign determinations. However, there is also relevant information that can be gleaned from the context in which the file was seen which is often ignored. One source of contextual information is the file's location on disk. For example, a malicious file masquerading as a known benign file (e.g., a Windows system DLL) is more likely to appear suspicious if the detector can intelligibly utilize information about the path at which it resides. Knowledge of the file path information could also make it easier to detect files which try to evade disk scans by placing themselves in specific locations. File paths are also available with little overhead and can seamlessly be integrated into a multi-view static ML detector, potentially yielding higher detection rates at very high throughput and minimal infrastructural changes. In this work, we propose a multi-view deep neural network architecture, which takes feature vectors from the PE file content as well as corresponding file paths as inputs and outputs a detection score. We perform an evaluation on a commercial-scale dataset of approximately 10 million samples - files and file paths from user endpoints serviced by an actual security vendor. We then conduct an interpretability analysis via LIME modeling to ensure that our classifier has learned a sensible representation and examine how the file path contributes to change in the classifier's score in different cases. We find that our model learns useful aspects of the file path for classification, resulting in a 26.6% improvement in the true positive rate at a 0.001 false positive rate (FPR) and a 64.6% improvement at 0.0001 FPR, compared to a model that operates on PE file content only.
Burmese Text Images Classification of Based on Neural Network

Abstract bilingual scene text detection and recognition is the detection detection results are mainly divided into three categories: successfully detected regions of Burmese, Russian text, and non-words regions with failed predictions. If the detected text image results are accurately classified, then the non- text images should be filtered in the recognition phase, meanwhile, the Burmese and Russia text images can be identified by using different classifiers, such a procedure can reduce the complexity of classification and recognition of two different characters by one recognition model. Accurate classification of Burmese and Russian text images is mattered. This paper researches the classification of Burmese and Russian text image and non-text images by using convolutional neural networks. We perform a series of exploration about the classification accuracy of Burmese and Russian text images and non-text images and compare the accuracy with the classification results based on the transfer learning then analyze it. The results show that using a 7-layer convolutional neural network has reached saturation, and increasing the network depth does not improve the results, which provides reference values for Burmese and Russia text image classification.
Galactomannan Gelation: A Thermal and Rheological Investigation Analyzed Using the Cascade Model

High-temperature water soluble fractions of locust bean gum with average galactose contents of 15.2% and 16% w/w displayed conventional gelling behavior. Gel properties were dependent on galactose content, gelling temperature and concentration. The gels appeared elastic, failed at up to 150% strain, and exhibited true gellike character with long-lived cross-links. Rheological meltdown occurred over a broad temperature range, and the gels melted at temperatures between 100 and 110 °C. Cascade analysis of the storage modulus vs concentration data gave critical gelling concentrations, Co = 0.14−0.24% w/w after 3 months gelation, but this analysis was unable to uniquely determine the number of potential cross-linking forming sites per molecule, i.e., the functionality, f. A value of f = 5, as well as average values of the standard molar enthalpy, ΔH° = −99.6 kJ/mol and standard molar entropy, ΔS° = −0.201 kJ/(mol K), for cross-link formation, was subsequently determined through an extension of the cascade for.
Teager Energy Cepstral Coefficients for Classification of Normal vs. Whisper Speech

The whispered speech is quite different from natural speech in the context of nature, acoustic characteristics, and generation mechanism. In order to improve the robustness of Automatic Speech Recognition (ASR) system, it is very important to analyze the mismatched training and testing situations and propose a robust acoustic features to enhance the whisper recognition. In this paper we propose to use Teager Energy Cepstral Coefficients (TECC) which uses Teager Energy Operator (TEO) for estimating "true" total energy of the signal, i.e., the sum of kinetic and potential energies which is contradictory to the traditional signal energy approximation, which only takes kinetic energy into account, i.e., L2 norm of the signal. In this study, experiments are performed on wTIMIT and CHAINS corpus. For wTIMIT corpus, frame-level accuracy of 92.22 % is obtained and for CHAINS corpus, it is 95.61 %. We have also estimated the performance measure of the classifier by using Matthew Correlation Coefficient (MCC), F-measure, and J-statistics. Furthermore, experiments are performed by considering latency period from a practical deployment viewpoint, and the trade-off between latency period vs. accuracy is discussed for both the corpora.
On the computation of marginal likelihood via MCMC for model selection and hypothesis testing

In the Bayesian setting, the marginal likelihood is the key quantity for model selection purposes. Several computational methods have been proposed in the literature for the computation of the marginal likelihood. In this paper, we briefly review different estimators based on MCMC simulations. We also suggest the use of a kernel density estimation procedure, based on a clustering scheme, within some of them. Numerical comparisons are also provided.
GMAS: A Geo-Aware MAS-Based Workflow Allocation Approach on Hybrid-Edge-Cloud Environment

Cloud computing is expanding to distributed edge computing(or known as fog computing). Connecting edge and cloud open great potential for real-time and mobility support workflow applications. However, scheduling workflow on a hybrid edge-cloud environment is an NP-hard problem. This paper proposes the Geo-Aware Multi-Agents-System-Based Workflow Allocation Approach(GMAS). Leveraging a novel geo-aware negotiation mechanism, GMAS addresses resource location caused transmission delays, which are the primary sources of workflow bottlenecks. In Multi-Agents-System(MAS), this paper proposes a geo-aware cost model and a dynamic workflow re-structuring strategy that decrease the impact of resource locations on workflow cost. Finally, this paper evaluates GMAS on Cloudsim, and the result shows that GMAS decreases the workflow makespan and traffic overheads.