Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto Generation of Gold Standard, Class Labeled Data Set and Ontology Extension Tool [QuadW]

Automatic Knowledge Extraction (AKE) from domain independent, unstructured text sources is a challenging task in Natural Language Processing and Text analytics. Though, supervised learning mechanisms are very much result promising, application is painful due to the mandatory requirement of a class labeled training data set, as it involves expensive manual effort which is more time consuming. As a solution for this problem, this paper introduces a novel mechanism to build a self-learned classifier model that can automatically generate class labeled training data set for Knowledge/Information Extraction from domain independent unstructured text. Sri Lankan English newspapers (which comprise unstructured text in unconstrained domains) are the main data source for this study and a prototype was built to Professional Information Extraction with the semantic pattern Who holds/held What position, Where and When (Four words start with ‘W’, hence named ‘QuadW’). Methodology uses advanced machine learning techniques such as, a Random Forest with Adaboost ensemble algorithm to build a composite classification model. This classifier is called as self-learned since, it generates its own training data set automatically. This composite model has improved accuracy and avoided over fitting to data as well. The rule-based feature extraction algorithm and the hand-craft ontology developed, can also be considered as novel components of this study. Self-learned classifier has been extensively improved and tested to show higher accuracy with precision and recall close to one. Therefore, the classified output from the self-learned classifier can be used as a gold-standard data set for future research in Professional Information Extraction. The constructed ontology with approximately 400 facts, also can be effectively used in future researches. Further, introduced classifier can be used as a tool to extend the existing ontology as well. A novel usage of machine learning algorithms to text classification demonstrates that, this study goes with the state-of-the-art technologies.
Method to Comprehensively Evaluate Road Network Traffic Conditions during Freeway Reconstruction and Extension

A method to comprehensively evaluate the traffic operational status during the reconstruction and extension of an freeway network is proposed. This approach guarantees the traffic capacity during freeway reconstruction and extension and aims to optimize the traffic flow of a regional road network. The method is based on the status quo of the road network traffic flow, the feasibility of the road network diversion, and the rationality of the traffic organization of the key nodes. The various indexes of the road network that are affected by the reconstruction and extension are analyzed by considering the Shenshan Expressway as an example. The capacity model for an expressway reconstruction and extension project is established by considering the constraints of the regional road network, such as the road service level. The influence of the reconstruction and extension on the traffic capacity for the control nodes of the expressway is analyzed using the VISSIM software. The results show that the reconstruction and extension project negatively impacts the traffic operation status of the entire road network where the construction section is located. Using the proposed comprehensive evaluation method to determine the traffic operation status of the road network allows the feasibility of the bottleneck sections and the associated diversion schemes to be analyzed. This effectively alleviates the traffic pressure from the reconstruction and expansion sections through optimal use of the existing road resources.
Time Series Prediction With Incomplete Dataset Based on Deep Bidirectional Echo State Network

In the complex industrial environment, data missing situation is often occurred in the process of data acquisition and transition. The major contribution of the paper is the proposal of a deep bidirectional echo state network (DBESN) framework for time series prediction with such incomplete dataset. Instead of data imputation methodology, a bidirectional fusion reservoir is here designed to extract the deep bidirectional feature along with forward and backward time scales, based on which a deep autoencoder echo state network (DAESN) and a deep bidirectional state echo state network (DBSESN) are constructed for the incomplete output and input samples, respectively. As for such two networks, a bidirectional echo state network (BESN) is proposed for connecting them to constitute the DBESN framework for prediction. To verify the effectiveness of the proposed method, one synthetic time series as well as two real-world industrial datasets are employed to conduct the comparative experiments. The experimental results demonstrate that the proposed method outperforms other comparative ones at various missing rates.
Short Term Prediction on Bitcoin Price Using ARIMA Method

Bitcoin is currently the most widely used digital currency. The popularity of bitcoin continues to increase and become an asset of investment. To handle with the erratic bitcoin price changes, a prediction method is needed to help its users in predicting the price in the future. By utilizing a method that is able to recognize the pattern of change in the data time series in a certain period of time can be known bitcoin price for several days ahead with a high degree of accuracy. This research uses experimental methods. Data obtained from www.coingecko. com on May 1, 2013 to June 7, 2019. Preprocessing stage includes attribute removal, stationary test, and differencing. Determination of the model candidate using correlogram method. The predictions are done using the Autoregressive Integrated Moving Average (ARIMA) method, which is capable of generating high accuracy in short-term predictions. Evaluating the prediction results using Mean Absolute Percentage Error (MAPE). The results showed that ARIMA (4,1,4) models resulted in predictions with the smallest MAPE, 0.87 for the next one-day prediction and 5.98 for the next seven days. Thus the ARIMA (4,1,4) model is feasible to be used as a predictive method of Bitcoin for one to seven days ahead.
Factor Graph Model Based User Profile Matching Across Social Networks

In modern society, it is common for people to be active in many different online social networks at once. As new social network services arise every year, it remains a great challenge to integrate social data. Discovering multiple profiles of a single person across different social networks is a precondition for integration, but it is still challenging due to the inconsistency and disruption of the accessible information among social media networks (SMNs). Many studies have made efforts on user’s profiles, users’ contents, and network structure to address this issue, but the issue of how to consider all these information in a unified model and tackle them simultaneously still remains challenging. Considering that identical users tend to have partial similar friend relationship structures in different SMNs, especially friendship SMNs, we deepen the analysis of “friend” relationships (mutual following connections) in different SMNs, and propose PIFGM (Pairwise Identical Factor Graph Model), a novel factor graph model-based model, to address this problem by considering both user attributes and friend relationships across networks. We also present a distributed learning algorithm to handle large-scale social networks. We evaluate the proposed model on two different data collections: SNS and SR. Our experimental results validate the effectiveness and efficiency of the proposed model. The proposed PIFGM significantly outperforms several alternative methods by up to approximately 10%~20% in terms of F1 and precision on SNS and SR respectively.
A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing

This paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18- $\mu \text{m}$ complementary metal-oxide-semiconductor (CMOS) process. Based on this ROIC, a wearable device module and its wireless system prototype are implemented to recognize five kinds of real-time hand-gesture motions, where the power consumption is further reduced by adopting low-power components. Real-time measurements of sEMG signals and APWP data through this wearable interface are wirelessly transferred to a laptop or a sensor hub, and then they are further processed to implement the pseudo-wavelet transform under the MATLAB environment. The resulting APWP-augmented pattern-recognition algorithm was experimentally verified to improve the accuracy by 7 % with a real-time frequency analysis.
Challenges Ahead in Cyber Physical Systems: A Coding Survey

This paper is directed towards discussing the challenges associated with cyber physical Systems (CPSs) as they encompass a broad collection of components integrating cyberspace and mechanical elements together. For a better understanding of the challenges associated with CPS, the paper provides a detailed foundation of CPSs including definitions of the various elements. Subsequently, we provide a qualitative content analysis of the current research in the field demonstrating issues related to the technical development, economic policies and the effect of those on the design phase of a CPS. Finally, we provide several recommendations to improve the design and implementation of CPS.
DriveMU: A Real-time Road-Traffic Monitoring Android Application for Mauritius

Traffic congestion has long been an issue in Mauritius, with steps taken so far to beat it resulting in very limited success. Hence, a real-time road-traffic monitoring mobile application has been developed to leverage the benefit of the fast proliferation of smartphones and the abundance of trajectory data they now provide. The application uses paradigms such as mobile crowdsensing and crowdsourcing, in an attempt to alleviate the strain on Mauritian roads, especially during rush hours. The system combines data from smartphone sensors, traffic-related posts from road users on a crowdsourcing platform, live road-related articles from online news outlets and predictions from historical data to provide live traffic updates. Users can then estimate their travel times, plan their routes beforehand, be aware of potential road hazards and thereby enjoy a better and stress-free journey.
Scenario-based Serious Game to Teach about Healthcare

In this paper, we explore the concept of a scenario-based serious game for healthcare solutions. The complexity of the interactions and the multitude of actors is captured in a scenario, which is then played with the help of an additional game platform, here called “scenario system”. The platform allows high school students to play through a healthcare scenario, possibly including eHealth technology, thereby teaching them about interactions and technology used in modern healthcare. The mobile serious game is based on context and role-triggered tasks for the players so that the game is guided, but includes a certain dynamic and flexibility. The project contributes a method and a tool to use scenarios for user testing of eHealth systems and services as well as an alternative method for teaching the complex domain of eHealth systems to unfamiliar users (e.g. highschool students).
Classification of Subcellular Protein Patterns in Human Cells with Transfer Learning

In this paper, we present a deep learning method for classifying subcellular protein patterns in human cells. Our method is mainly based on transfer learning and utilizes a newly proposed loss function named focal loss to deal with the problem of severe class imbalance existing in the task. The performance of our method is evaluated by a MacroF1 score of total 28 classes, and the final MacroF1 score of our method is 0.706.
Design and Construction of a UAV VTOL in Ducted-Fan and Tilt-Rotor Configuration

This paper presents the development of an hybrid Unmanned Aerial Vehicle that combines the capabilities of Vertical Takeoff and Landing (VTOL) as well as the hover flight of an helicopter, with the autonomy, load capacity and speed offered by an airplane. We start from an aircraft concept that uses the Tilt-Rotor configuration to perform the transition phase from one flight mode to another and simultaneously we used Ducted-Fans for increasing the flight performance during the vertical flight phase. The aerodynamic design is developed using computational tools, such as XFLR5 for preliminary design and ANSYS FLuent®for the verification of the preliminary fixed wing design. While for the vertical flight, experimental tests were conducted in order to determine the lift system requirements by using Ducted-Fans. Afterwards, the manufacturing process derived from the aerodynamic design is presented. The avionics developed is described for handling the control surfaces during the airplane mode and the main lift system during the vertical flight as well as the integration of mechanism and sensors used. Finally, the obtained results from the conducted vertical flight tests at outdoors environments are presented in order to show the effectiveness of the aircraft concept.
MobiDroid: A Performance-Sensitive Malware Detection System on Mobile Platform

Currently, Android malware detection is mostly performed on the server side against the increasing number of Android malware. Powerful computing resource gives more exhaustive protection for Android markets than maintaining detection by a single user in many cases. However, apart from the Android apps provided by the official market (i.e., Google Play Store), apps from unofficial markets and third-party resources are always causing a serious security threat to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the threat of attackers. Consequently, a last line of defense on Android devices is necessary and much-needed. To address these problems, in this paper, we propose an effective Android malware detection system, MobiDroid, leveraging deep learning to provide a real-time secure and fast response environment on Android devices. Although a deep learning-based approach can be maintained on server side efficiently for detecting Android malware, deep learning models cannot be directly deployed and executed on Android devices due to various performance limitations such as computation power, memory size, and energy. Therefore, we evaluate and investigate the different performances with various feature categories, and further provide an effective solution to detect malware on Android devices. The proposed detection system on Android devices in this paper can serve as a starting point for further study of this important area.
Exercise Intensity in Exergaming: An approach by GameFiTT

Active video games are exergames that track players’ body movement in relation to the game requirement. While active video games provide the necessary modes of exercise types in relation to aerobic, resistance, yoga and stretching, they are limited by compartmental movements that needed to be selected by players to form one complete exercise training to include warm-up, endurance and aerobic as well as cooling-down. The GameFiTT proposed an integration of a complete exercise workout of 35 minutes through pre-recorded exercise video and playing. Real time exercise intensity will be exploited to motivate and encourage players’ motivation to exercise. This approach is hoped to improve the current exergame as to achieve a higher level of intensity during game play which is needed to create health impact.
Compressing by Learning in a Low-Rank and Sparse Decomposition Form

Low-rankness and sparsity are often used to guide the compression of convolutional neural networks (CNNs) separately. Since they capture global and local structure of a matrix respectively, we combine these two complementary properties together to pursue better network compression performance. Most existing low-rank or sparse compression methods compress the networks by approximating pre-trained models. However, the optimal solutions to pre-trained models may not be optimal to compressed networks with low-rank or sparse constraints. In this paper, we propose a low-rank and sparse learning framework that trains the compressed network from scratch. Our compressing process can be described as the following three stages. (a) In the structure designing stage, we decompose a weight matrix into sum of low-rank matrix and sparse matrix, and then the low-rank matrix is further factorized into product of two small matrices. (b) In training stage, we add $\ell _{1}$ regularization to the loss function to force the sparse matrix to be sparse. (c) In the post-processing stage, we remove the unimportant connection of sparse matrix according to its energy distribution. The pruning process in the post-processing stage reserves most of capacity of the network and keeps the performance of the network to a great extent. The performance can be further improved with fine-tuning, along with sparse masked convolution. Experiments on several common datasets demonstrate our model is superior to other network compression methods based on low-rankness or sparsity. On CIFAR-10, our method compresses VGGNet-19 to 3.14% and PreActResNet-56 to 29.78% without accuracy drop. 62.43% of parameters of ResNet-50 are reduced with 0.55% top-5 accuracy loss on ImageNet.
Relaxed Conditions for Stability and Stabilization of Takagi-Sugeno Fuzzy Systems with Time-Delay via Non-quadratic Functional

This paper presents more relaxed delay-dependant conditions for stability analysis and stabilization of time-delay nonlinear systems expressed as a Takagi-Sugeno model. These conditions arise using a combination of a non-quadratic Lyapunov-Krasovskii functional with a recent integral inequality that reduces the conservatism of previous approaches. Sufficient conditions for stability and controller design are established in terms of linear matrix inequalities. Some numerical examples are given to show the effectiveness of the proposed approach in comparison with other recent results.
Investigating the Influence of Environmental and Design Parameters on the Thermal Regime of a LED Lamp

The increase of the LED’s temperature above a certain threshold decreases significantly their live expectancy. Therefore, it is important to investigate LEDs’ thermal regime during the design process in different conditions for exploitation. The influence of the construction and the environment parameters on the thermal regime of LED lamp was investigated in this paper. The study was done with a specially developed software, based on a model, describing the thermal regime of LED lamps. Next, a methodology for the research has been developed and a simulation study was carried out. The results of this investigation are represented graphically.
How to Improve the Deep Residual Network to Segment Multi-Modal Brain Tumor Images

Brain tumor segmentation plays an important role in diagnosing brain tumor. Nowadays, intense interest has been received in applying convolution neural networks in medical image analysis, but its performance is restricted by the limitation of the depth of the network. And how to accelerate the information propagation and make full use of all the hierarchical features in the network is also of vital importance. To address these problems, this paper proposed Deep Residual Dilate Network with Middle Supervision (RDM-Net), which combines the residual network with dilated convolution. It can solve the problem of vanishing gradient and increase the receptive field without reducing the resolution. During images processing, some information of regions of small tumor could be discarded, for its resolution is attenuated to a single pixel by continuously convolutional operations. Therefore, the spatial fusion block, consisting of a pixel discriminator and a region discriminator, has been designed to reserve the detailed information in the region of small tumor. It evaluates the relationship between this single pixel and its adjacent region to obtain the spatial structure information of brain tumors. Furthermore, the middle supervision block consisting of proposal pyramid and multi-hierarchical loss is proposed, which shortens the distance of information path and reduces cumulative errors during the network training. The proposal pyramid is inspired by the idea of boost learning, which fuses each proposal at multiple resolution level to ensure that the network produces better predictions. Multi-hierarchical loss combines the loss of intermediate proposal in the middle layers and the loss of prediction of the output layer to achieve the effect of middle supervision. The results of experiments illustrate that our framework can effectively propagate features of each layer and increase the diversity of information to enhance feature hierarchy for medical image recognition. Compared to other state-of-the-art methods, our framework has performed well in the BRATS2015 challenge. In summary, the main contribution in our paper is that this work is an early attempt to adopt the concept of “middle supervision” on multi-modal brain tumor segmentation.
Study on the Optimal Distance Between Two Ships Encountering in a Channel Based on Ship-Ship Interactions and Bank Effects

When a ship moves in the proximity of the bank and other ships, lateral force and yaw moment are induced due to the influence of asymmetrical flow around the ship, which are known as "ship-ship interaction" or "bank effects". For Two ships encountering in a channel, on the one hand, if separation distance is too small, serious "ship-ship interactions" may results in a collision; on the other hand, if separation distance is too large, serious "bank effects" may result in grounding. Therefore, a reasonable choice of the separation distance is important, especially for unmanned high-speed intelligent ships. This paper analyzed ship-ship interactions and the bank effects of two ships encountering in a channel, attempt to find a certain distance so as to minimize the absolute values of resultant force and moment of the two ships in the two-way channel, and built a optimal model to express the optimal meeting distance, which is extremely practical for navigation safety, and can be applied to autonomous decision of intelligent ships. Through a series of tests, the optimal distance between the two ships from the center line of the channel should be roughly equal, and the speed and draft of the ship draft, the depth and slope of the channel relevant to the optimal separation distance.