Miracle-dz/UniFAR-FTUs huggingface dataset:
Application Of Artificial Neural Networks For Exploratory Analysis Of Small Dataset

This study aims to explore the use of Artificial Neural Networks (ANNs) for estimating the relationship between accidents and other variables with a small dataset. ANNs have not been used to explore relationships between variables, especially for road accidents which have small datasets. Analysis of road traffic accidents is often hampered due to insufficient datasets. Especially, for the cases when specific highway facilities are considered. This issue is also gaining importance for analyzing traveler behavior with the advent of new technologies and implementation of concepts of smart cities. The accident sites selected for this study comprise of unsignalized intersections in Penang State of Malaysia. Accidents in Malaysia have become a major concern for the authorities. However, the data collection is a major issue hindering its analysis because of limited datasets. The safe operation of traffic on unsignalized intersections mainly depends on drivers’ judgement and decision making. Hence, the safety considerations on such locations are peculiar in comparison to other facilities. These facts led to carrying out the study for these sites. Two types of ANNs were used i.e. Multilayer Feedforward (MLFF) and linear. In addition, regression model and Mann-Whitney test were also used to compare the results from ANNs. It was found that regression model as well as Mann-Whitney test gave inconclusive results for the available dataset. On the other hand, ANNs were able to approximate the relationship in conformity to the previous studies. It was found that major road width and near-to-far-volume ratio increases the accidents while near-to-far-gap ratio reduces the accidents. The accuracy of ANNs was also better than regression model with an average error of less 40% for ANNs compared to 46% by regression model. However, larger datasets are expected to give better accuracies for regression as well as ANNs.
Efficient Concurrent Error Detection for SEC-DAEC Encoders

In the last decade, a number of Single Error Correction Double Adjacent Error Correction (SEC-DAEC) codes have been proposed to protect memories against Multiple Cell Upsets (MCUs). These codes are able to correct errors that affect two adjacent bits that is one of the most common MCU patterns. However, soft errors can also affect the encoder and decoder circuitry creating data corruption. An alternative to protect the encoders is to use parity prediction Concurrent Error Detection (CED) to detect errors and avoid writing erroneous words in the memory. This approach has been previously studied for Orthogonal Latin Square (OLS) codes and for matrix codes. In this paper, the implementation of parity prediction Concurrent Error Detection (CED) for SEC-DAEC codes is considered. To that end, first it is shown that CED has a significant cost for the existing SEC-DAEC codes. This is because they are odd weight codes and parity prediction is much simpler for even weight codes. Based on that observation, even weight SEC-DAEC codes are designed and evaluated. The results show that CED can be efficiently implemented in the proposed codes that achieve a significant reduction in encoder circuit complexity compared to previously proposed SEC-DAEC codes.
The role of User Entity Behavior Analytics to detect network attacks in real time

Organizations are using advanced security solutions to protect their information resources. However, even such high investments, traditional security approaches failed to protect the network structure against state-of-the-art attacks. New proactive approaches to security are on the rise such as User Entity Behavior Analytics (UEBA). UEBA is a type of cybersecurity process that uses machine learning, algorithms, and statistical analyses to detect real-time network attacks. This paper aims to assess the value and success of using behavior analytics in securing the network from not-before-seen attacks such as zero-day attacks. This paper uses a systematic literature review and self-administrated survey and interviews with convenience sampling of high profile network users and top security vendors. Survey and interviews with various security experts are utilized to verify the matter-of-fact effectiveness of the solutions based on behavior analytics. During collecting the primary data via a survey, researchers will go for a structured interview with vendors who are selling solutions to understand the performance of behavior analytics-based solutions and the distinct features of their solutions. The results of literature review, survey, interviews and focus groups will be used to assess the value and success of using behavior analytics in securing the network from not-before-seen attacks such as zero-day attacks. The endeavor of this paper is to highlight the weaknesses and strengths of different UEBA solutions and their effectiveness for detecting network attacks in real-time interaction. This research contrasts top fifteen UEBA technologies based on use cases and capabilities and highlights common usage scenarios. Based on the evidence, recommendations will be given.
Loose Game Theory Based Anomaly Detection Scheme for SDN-Based mMTC Services

In this paper, we exploit the game theory to integrate the strengths of statistics-based detection and the machine learning-based detection for wireless software-defined-networking (SDN) based massive machine-type-communications (mMTC) services. Different from existing game theory approaches that require perfect rationality of the players and the synchronized information shared between the players, the proposed scheme is designed to bear with fewer assumptions. A novel feedback mechanism is proposed to fed machine learning-based detection results into strategy selection for advanced strategy decision making when the maliciousness estimation is not accurate. Besides, a multi-hop architecture is proposed to enable distributed detection instead of common cluster style for enhanced scalability. Various scenarios are set to testify the performance of the proposed scheme. Simulation results show that the proposed scheme relives more pressure of controller, consumes less energy without showing evident influence on the regular packets delivery, under the condition of asymmetric knowledge and multi attack-pattern.
Estimating Zenith Tropospheric Delay Based on GPT2w Model

To precisely and conveniently estimate zenith tropospheric delay (ZTD), troposphere refractivity is deduced through global pressure and temperature 2w (GPT2w) model and surface meteorology devices, ZTD is estimated based on integration of refractivity in the zenith direction. When surface meteorological measurements are absent, all parameters including surface meteorology and lapse rate are estimated by GPT2w. Validation is carried out using observed data in the year 2012 at 8 international GNSS service (IGS) stations. Results indicate that when surface meteorological parameters are available, the annual accuracy is improved by 27% than Saastamoinen model. Meanwhile, annual bias of the other scheme is decreased by 21% than the GPT2w+Saastamoinen model. For most stations, the bias shows seasonal characteristics, high elevation, high longitude and low latitude can bring less bias.
The Analysis of Mobility Performance in MmWave-μWave Heterogeneous Networks

The forthcoming 5G cellular networks is expected to overlay mmWave transmission with the incumbent microwave (μWave) architecture. The mmWave links are sensitive to the blockage caused by obstacles, which affects both signal-to-interference-plus-noise-ratio (SINR) and handover performance for mobile users. In this paper, we analyze the mobility performance in the mmWave-μWave heterogeneous network mm-μ-HetNet). We begin by obtaining the handover rate for a mobile user in the mm-μ-HetNet with the locations of mmWave base stations (BSs) and μWave BSs modeled as two independent Poisson point processes (PPP). The received SINR distribution along with a chosen SINR threshold is then used to obtain the coverage probability. To capture the performance loss caused by user mobility, we derive the handover cost with the transmission delay induced by handover. Finally, we formulate an optimization problem to achieve the minimum handover cost subject to a certain coverage requirement. Numerical results show that it is better to preferentially deploy mmWave BSs when mmWave link blockage is severe and it is necessary to jointly deploy mmWave and μWave BSs when the mmWave link blockage is slight.
Mode Transition Coordination Control for PHEV Based on Cascade Predictive Method

In terms of parallel hybrid electric vehicles (PHEVs), adverse effects resulting from jerk and clutch frictional loss during mode transition operating could damage the ride comfort of passengers and clutch lifespan. To tackle these issues, a coordination control strategy based on cascade predictive control method is presented in this paper. Firstly, dynamic models of PHEV during different mode transition periods are discussed based on characteristics of power sources and clutch states. Secondly, based on the proposed transition models at different stages, model predictive control is considered based on multi-constraints including jerk restriction and physical constraints of dynamic components aiming to reduce the vehicle jerk and clutch frictional loss. Thirdly, in order to realize an accurate and fast response to engine torque, a cascade PID-based controller with high sampling frequency is employed to compensate the engine torque, and further to achieve a smooth mode transition process. Finally, the proposed control strategy is simulated and verified by co-simulation with Matlab/Simulink and Cruise, whose results show that the proposed methods can reduce the vehicle jerk and clutch frictional loss effectively.
Features-Level Software Effort Estimation Using Machine Learning Algorithms

Software effort estimation is a paramount mission in the software development process, which covered by project managers and software engineers. In the early stages, software system features are the only available measures. Therefore, cost estimation is a mission that comes under the planning stage of software venture management. In this paper, various machine learning algorithms are used to build software effort estimation models from software features. Artificial Neural Network (ANN), Support Vector Machines (SVM), K-star, and Linear Regression machine learning algorithms are evaluated on a public dataset with actual software efforts. Results showed that machine learning approach can be dependable on predicting the future effort of a software system.
Behavior Cloning for Autonomous Driving using Convolutional Neural Networks

In this paper, we propose using a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering maneuvering as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. This data is then used to train the proposed CNN to facilitate what we call it behavioral cloning. The proposed Behavior Cloning CNN is named as “BCNet” and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Scholastic Gradient Descent (SGD) technique. The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.
Homeland Security Video Surveillance System for Smart Cities

A novel Motion Estimation architecture, used for homeland security video surveillance systems, proposed in this paper. The proposed architecture is suitable to be adopted in H.265 High Efficient Video Coding (H.265/HEVC) encoder of video surveillance systems. The proposed architecture uses level A and level B data reuse to minimize memory I/O while fully utilizing the hardware resources. An efficient local memory is used for performing data reuse while loading both the current block and the search area inside the Processing Element (PE) array. The proposed architecture has a smooth and a regular data flow. The video resolution accuracy of the proposed architecture is very high. Subjective and quantitative measures are used to measure the performance of the proposed architecture. Our architecture can be adopted in the encoder of video surveillance systems and any High Quality (HQ) video related applications such as High Definition (HD) broadcasting. The time verification of the proposed architecture is tested using Modelism-version10.4a.
Crowd Counting with respect to Age and Gender by using Faster R-CNN based Detection

Masses gather on a daily basis in events like hajjs, shows, marathons, anniversaries, memorials, festivities, political events, protests and shopping malls. Visual understanding of crowd places is an emerging field in computer vision. Counting the people, based on gender, and estimation of their age in crowd is a tricky job but it has extensive applicability in surveillance, management and planning. Crowd counting is also vital to measure political worth of protests and other unwanted gatherings. In this work, we propose an innovative technique to solve the challenges of crowd counting, gender recolonization, age estimation and localization of people in visual frames. Faster R-CNN is trained to solve these four problems simultaneously. As the task of localization involves the annotations of high-quality frames, we introduce CVML-OCC dataset11Available for research community and non-commercial usage that incapacitates the limitations of already available datasets. Alongside, we provide evaluation procedures and comprehensive comparison with existing approaches. Our method expressively performs better than state-of-the-art modules with the training set of CVML-OCC dataset, which is a comprehensive dataset having precise annotations in diverse scenarios.
Low Overhead Scheduling of LoRa Transmissions for Improved Scalability

Recently, LoRaWAN has attracted much attention for the realization of many Internet of Things applications because it offers low-power, long-distance, and low-cost wireless communication. Recent works have shown that the LoRaWAN specification for class A devices comes with scalability limitations due to the ALOHA-like nature of the MAC layer. In this paper, we propose a synchronization and scheduling mechanism for LoRaWAN networks consisting of class A devices. The mechanism runs on top of the LoRaWAN MAC layer. A central network synchronization and scheduling entity will schedule uplink and downlink transmissions. In order to reduce the synchronization packet length, all time slots that are being assigned to an end node are encoded in a probabilistic space-efficient data structure. An end node will check if a time slot is part of the received data structure in order to determine when to transmit. Time slots are assigned based on the traffic needs of the end nodes. We show that in case of a nonsaturated multichannel LoRaWAN network with synchronization being done in a separate channel, the packet delivery ratio (PDR) is easily 7% (for SF7) to 30% (for SF12) higher than in an unsynchronized LoRaWAN network. For saturated networks, the differences in PDR become more profound as nodes are only scheduled as long as they can be accommodated given the remaining capacity of the network. The synchronization process will use less than 3-mAh extra battery capacity per end node during a one year period, for synchronization periods longer than three days. This is less than the battery capacity used to transmit packets that are going to be lost in an unsynchronized network due to collisions.
Efficient Large-Scale 1D FFT Vectorization on Multi-Core Vector Accelerator

The Matrix2 Accelerator is a high-performance multi-core vector processor for high-density computing that supports fused multiply-add instructions. We propose an efficient large-scale 1D FFT vectorization method according to the architecture characteristics of Matrix2. (1) An FFT vectorization method based on fused multiply-add instruction is proposed to accelerate FFT computation. By transforming the operation flow of FFT butterfly computation, the independent multiplication and addition operations in the traditional FFT computation method are combined into a smaller number of fused multiply-add operations. It reduces the number of the real floating-point operations in radix-2 FFT butterfly computation from original 10 multiplication/addition operations to 6 fused multiply-add operations, and reduces the number of the real floating-point operations in radix-4 FFT butterfly computation from original 34 multiplication/addition operations to 24 fused multiply-add operations. (2) An FFT vectorization method based on matrix Fourier algorithm is designed, which converts 1D FFT computation into 2D FFT computation. It contains three steps: column FFT computation, multiplication of the column FFT computation result and a factor matrix, row FFT computation. These three steps are all vectorized. (3) A factor matrix data layout and updating method is proposed, which can greatly reduce the memory capacity for factor matrix. It can avoid multiple data transmissions between the vector array memory and the global cache by combining the column FFT computation with the factor matrix multiplication, thus significantly improving the computational efficiency of FFT. (4) A double buffering DMA mechanism is adopted to optimize and smooth the data transmission between the multi-level storage structures, and the data transmission time is overlapped with the computation time so as to reduce the total computation time. The experimental results on Matrix2 show that the proposed vectorization method improves the computational efficiency of large-scale 1D FFT by an average of 5.56 times.
Machine Learning-Based Drone Detection and Classification: State-of-the-Art in Research

This paper presents a comprehensive review of current literature on drone detection and classification using machine learning with different modalities. This research area has emerged in the last few years due to the rapid development of commercial and recreational drones and the associated risk to airspace safety. Addressed technologies encompass radar, visual, acoustic, and radio-frequency sensing systems. The general finding of this study demonstrates that machine learning-based classification of drones seems to be promising with many successful individual contributions. However, most of the performed research is experimental and the outcomes from different papers can hardly be compared. A general requirement-driven specification for the problem of drone detection and classification is still missing as well as reference datasets which would help in evaluating different solutions.
Indoor Positioning and Tracking using Sensors of a Smart Device

Global positioning system (GPS) tracks a desired location and display it on map. However, GPS may not work properly for indoor positioning, when we are concerned with an accurate location of a person inside a building. Therefore, to determine an accurate indoor positioning based on smart devices a scheme is proposed and called iLocation. iLocation measures the location by obtaining and computing values from an accelerometer and a barometer of a smart device. iLocation uses MySQL to maintain records of the computed indoor positions. Such positions are displayed on Google Maps with its images for the verification. iLocation is tested on various real time scenarios and experimental results shows the accuracy, which is better than other existing approaches.
A Wearable Internet of Things Based System with Edge Computing for Real-Time Human Activity Tracking

Wearable devices for human physical activity tracking are becoming a commonly used gadget in daily life. Commercial devices such as smartwatches and smart bands are currently available from many different vendors. Most of them are capable of providing health statistics through sensors embedded in them. However, they only provide the basic information in the form of a quantity, which does not have a direct clinical relevance. A major reason for this is the use of cloud computing by most of these devices. Cloud systems managed by commercial companies charge for their premium services, which allows users to get access to their data. Besides, healthcare regulatory bodies do not yet approve the analytics performed by cloud systems. This makes these systems unsuitable for home based physical therapy or rehabilitation monitoring. Leveraging on the Wearable Internet of Things (WIoT) and edge computing technologies, we developed a low cost, portable, and wearable device which can collect human activity data in real-time and transfer it to a local server for computing and analysis. We tested our device for two different activities and successfully transferred the data from tracking device to an edge computer at frequency of the order of 100 Hz.
VSC with Direct PI Power Control for Frequency Compensation in a Microgrid: a PBC Approach

The power systems with low inertia such as microgrids require advanced control strategies to improve their performance. These type of grids use voltage source converters to interconnect renewable energy sources and storage devices. The converters can present complementary ancillary services such as grid frequency response support. This paper uses a passive control strategy for power electronic converters based on direct power control model and presents the strategy to mitigate the fast changes of the dynamic grid frequency response. The real-time simulations validate the application of the method.
Working with the Dynamic Perpendicular Quadrilateral in a Whole Class Setting: Supporting Students in Developing a Hierarchical Classification of the Kites

Working with the Dynamic Perpendicular Quadrilateral in a Whole Class Setting: Supporting Students in Developing a Hierarchical Classification of the Kites Journal Item How to cite: Forsythe, Susan K. (2018). Working with the Dynamic Perpendicular Quadrilateral in a Whole Class Setting: Supporting Students in Developing a Hierarchical Classification of the Kites. International Journal for Mathematics Teaching and Learning, 19(2) pp. 269–286.
the remaining challenges for future energy storage

Rechargeable batteries can effectively store electrical energy as chemical energy, and release it when needed, providing a good choice for applications in electric vehicles (EVs). Naturally, safety concerns are the key issue for the application of battery technology in EVs. Olivine LiFePO4 is considered to be the most promising cathode material for lithium-ion batteries due to its environmental friendliness, high cycling performance and safety characteristics. Some important breakthroughs in recent years have allowed its successful commercialization. However, in spite of its success, the commercial application of LiFePO4 batteries in EVs is still hindered by some technological obstacles. Herein, we provide an update on our previous review, and overview the most significant advances in the remaining challenges for this promising battery material. New research directions and future trends have also been discussed.