Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing of Online Forums Effectiveness as Knowledge Sharing Media in Increasing Knowledge and Cooking Performance

Online forum as media for exchange information that can be accessed online by the public (citizen), especially cooking information. Despite the citizen can obtain various information through online forums, very little is known about factors affecting the effectiveness of online forums in cooking information. This study, therefore, seeks to analyze what factors can influence online forum's effectiveness as knowledge sharing media in increasing people's knowledge and cooking performance. There are four indicators used in this study, knowledge sharing, knowledge application, knowledge creation, and transactive memory system (which refers to three-point: specialization, credibility, and coordination). The survey was taken place and obtain 174 respondents as samples. Structural Equation Modelling (SEM) was conducted with SmartPLS tools to test this research. The result of this study concludes that knowledge application, knowledge sharing, and knowledge creation significantly influence knowledge and cooking performance by using transactive memory systems (TMS) theory.
Spiking Neural Network Architecture Comparison by Solving the Non-linear XOR Problem

This work presents an analog model for a cellular membrane of a neuron as well as its mathematical analysis. The simple model of a spiking neuron from Izhikevich is used to design two neural architectures. From the electrical analysis the synaptic current is obtained and used as an input parameter to the Izhikevich’s model. The first architecture has two neurons on the input layer and one on the output layer, using this architecture it is shown that the neuron models based on the behavior of biologic neurons can be used in a similar manner to the well-known second generation neural networks to solve classification problems. Moreover, in the present work it is shown the superiority of the spiking neuron models, which leads to the second architecture to be conformed by a single neuron. Both architectures are compared performance wise, through the classic problem of separating a non-linear dataset, XOR. The results show that these architectures can be used to the classification or clustering of patterns of features.
A Remote Immobilization System with GSM and GPS Technologies for Cargo Trailers

This article presents the design, development and implementation of a remote immobilization system for cargo trailers, using Global Positioning System (GPS) technology and a Global Mobile Communications System (GSM) to prevent and track their theft. The immobilization system works minutes after the possible infraction has been committed, this in order to protect the integrity of the driver. The design of the power supply card is shown both for the activation of a solenoid valve, and for feeding the telemetry from a trailer battery. It also shows the design of the communication and control card whose components are embedded in the same printed circuit board. Experimental tests are shown to demonstrate the effectiveness of the developed system.
Real-Time Tracking of Aircrafts in Crowdsourced Air Traffic Networks with Simple Localization Estimates

Crowd-sourced air traffic communication networks have gained importance over the past decade. They use distributed networks that are randomly deployed. Contrary to traditional and carefully planned receiver networks, crowd-sourced use of cheap sensors poses a number of new challenges to existing localization algorithms. The purpose of this paper is twofold: First, to survey the literature on Aircrafts’ real-time tracking in Crowdsourced Air Traffic Networks, and Second, sketch a big data architecture allowing real-time tracking of aircrafts and predicting missing localization data.
Where Shall We Log? Studying and Suggesting Logging Locations in Code Blocks

Developers write logging statements to generate logs and record system execution behaviors to assist in debugging and software maintenance. However, deciding where to insert logging statements is a crucial yet challenging task. On one hand, logging too little may increase the maintenance difficulty due to missing important system execution information. On the other hand, logging too much may introduce excessive logs that mask the real problems and cause significant performance overhead. Prior studies provide recommendations on logging locations, but such recommendations are only for limited situations (e.g., exception logging) or at a coarse-grained level (e.g., method level). Thus, properly helping developers decide finer-grained logging locations for different situations remains an unsolved challenge. In this paper, we tackle the challenge by first conducting a comprehensive manual study on the characteristics of logging locations in seven open-source systems. We uncover six categories of logging locations and find that developers usually insert logging statements to record execution information in various types of code blocks. Based on the observed patterns, we then propose a deep learning framework to automatically suggest logging locations at the block level. We model the source code at the code block level using the syntactic and semantic information. We find that: 1) our models achieve an average of 80.1% balanced accuracy when suggesting logging locations in blocks; 2) our cross-system logging suggestion results reveal that there might be an implicit logging guideline across systems. Our results show that we may accurately provide finer-grained suggestions on logging locations, and such suggestions may be shared across systems.
A Two-Stream Graph Convolutional Neural Network for Dynamic Traffic Flow Forecasting

Forecasting the traffic flow is a critical issue for researchers and practitioners in the field of transportation. Using the graph convolutional network (GCN) is widespread in traffic flow forecasting. Existing GCN-based methods mostly rely on undirected spatial correlations to represent the features of spatial-temporal graph. What's more, the traffic flow renders two types of spatial correlations, including the stable correlation constrained by the fixed road structure and the dynamic correlation influenced by the traffic fluctuation. In this paper, we propose a two-stream graph convolutional network by considering stable and dynamic correlations in parallel, which is an end-to-end deep learning framework for dynamic traffic forecasting. We present an auto-decomposing layer to decompose real-time traffic flow data into a stable component and a dynamic component with different spatial correlations. Specifically, the stable component is constrained by the physical road network, and the dynamic component represents fluctuations caused by changes in traffic conditions such as congestion and bad weather. Moreover, we extract stable and dynamic spatial correlations through our two-stream graph convolutional layer. Finally, we use parameterized skip connection to fuse spatial-temporal correlations as the input of output layer for forecasting. Extensive experiments are conducted on two real-world traffic datasets, and experimental results show that our proposed model is better than several popular baselines.
Gated-Anode GaN HEMT Based Diode for Microwave Wireless Power Transfer

We have introduced a gated-anode diode using a normally-off AlGaN/GaN HEMT. For precise control of turn-on voltage without spoiling an advantage of mono-layer controllability in epitaxial growth, we have selectively dry-etched a GaN cap layer to expose AlGaN Schottky layer to a surface. Also, we inserted 5-doped region in the GaN cap layer to compensate negative polarization charges. Using these techniques, we have successful fabricated the normally-off AlGaN/GaN HEMT and the gated-anode diode using the HEMT. The gated-anode diode exhibited an anode current of 0.3 mA/mm at an anode voltage of 2 V and a breakdown voltage of approximately 40 V with a tum-on voltage of 0.3 V.
Dynamic Difficulty Adjustment in Digital Games Using Genetic Algorithms

The difficulty of a game is intrinsically connected with the experience of immersion in it and with its success. One of the main reasons for a player to drop a game is that the game is either too easy or too hard for him/her. In practice, players become either bored or frustrated if playing a game that is not balanced for them. An approach to prevent this kind of behavior is to dynamically adjust the difficulty of a game so that the game adapts to the player's experience by evaluating the difficulty of a game and changing its environment to become easier or harder for the player. In this paper, we propose a real-time solution using a Genetic Algorithm which helps to provide the exact amount of challenge that a player needs to not be bored or frustrated thus balancing the difficulty of a game. We review several other papers that approached this problem, which characteristics an algorithm has to have to approach the problem, and how to balance this in a generic way. The main idea of this paper is to create an approach that can be modified and coupled to any kind of game by using a Genetic Algorithm.
Deep Learning Based Sensing Resource Allocation for Mobile Target Tracking

Edge computing has emerged as a prospective paradigm to meet ever-increasing service demands in wide ranges of Internet of Things (IoT) applications. However, massive amount of application data and real-time response are not orchestrated smoothly in this pattern, especially in battery-powered and mission-critical sensor networks. In order to cope with the challenges, this paper proposes a new cooperative resource allocation algorithm which couples reinforcement learning networks and prediction neural networks for accurate mobile targets tracking. Specifically, a hierarchical structure that performs collaborative computing is designed for alleviating computing pressure of front-end devices which are supported by edge servers. The optimization objective leading to the minimal system cost is formulated which is constrained by the system response latency and the tracking accuracy. Simulation results demonstrate that the proposed algorithm outperforms the greedy scheme 24.5% in terms of the energy cost. It also obtains significant 23.1% enhancement in tracking accuracy compared with the non-cooperative scheme.
The Utilization of Cloud Computing for Facial Expression Recognition using Amazon Web Services

Face-to-face communication is a form of interaction that are done by every people, but it is hard to be implemented for blind person, especially for them to recognize their interlocutors’ facial expression. Hence, a device is needed for recognizing facial expression, so blind people could recognize their interlocutors’ facial expression clearly and those expressions converted into non-verbal information in type of sound.This research will use embedded device designed for machine learning called AWS DeepLens, which purposed for looking the performance of the device when using Amazon Web Services (AWS) cloud computing services for AI-trained model and compare it to on-premises deep learning. The device’s outputs will inform facial expression information to the user via sound. Deep learning algorithm success will be measured in confusion matrix and its average rate is 76,16%. This proposed device utilizes cloud computing system from AWS, with objectives of system robustness and real-time usage.
Adversarial Regularization for Explainable-by-Design Time Series Classification

Times series classification can be successfully tackled by jointly learning a shapelet-based representation of the series in the dataset and classifying the series according to this representation. This shapelet-based classification is both accurate and explainable since the shapelets are time series themselves and thus can be visualized and be provided as a classification explanation. In this paper, we claim that not all shapelets are good visual explanations and we propose a simple, yet also accurate, adversarily regularized EXplainable Convolutional Neural Network, XCNN, that can learn shapelets that are, by design, suited for explanations. We validate our method on the usual univariate time series benchmarks of the UCR repository.
Interpretable Driver Models Discovery in Data

It is fair to assume that when humans drive, they rely on their prior knowledge to predict the behaviors of other vehicles. In order for a self-driving car to be driven safely with other surrounding cars, it must behave in a predictable way, similar to the behaviors of human-driven cars. It is common to adopt machine learning methods which have been considerably improved to predict behaviors of vehicles with big data. The more scenarios the model covers, the larger the model becomes. As a result, it is inevitable that the model loses interpretability. On the other hand, although a simple model represented by linear combination of features can be interpretable, it can cover only a limited scenario. In order to solve these limitations, we propose a method to build multiple linear models and a model to select appropriate one of them on the situation. This set of models is extracted from a trained machine learning model. The proposed approach was validated using real-world driving data, namely Next Generation Simulation (NGSIM).
Vehicle Longitudinal Control with Velocity Profile for Stop and Go Operation

In this paper, we propose a vehicle longitudinal control with a velocity profile for stop and go (SG) operation. A three-phase velocity profile is proposed to achieve smooth velocity control for stop and go operation. The proposed velocity profile makes the vehicle stop at the desired stationary point without overshooting. Given the maximum deceleration limit, we design a velocity profile for the first and second phases until the vehicle passes a setpoint near before the stop point. Once the vehicle passes the setpoint, the car is controlled by a position controller to make a complete stop at the stop point. The three-phase velocity profile considers the maximum jerk, the maximum acceleration, time to collision, and distance to go. With the proposed method, we can achieve the SG operation, providing a comfortable ride to passengers. The proposed SG operation method is compared with conventional adaptive cruise control. From the computational study, we confirm that the proposed method shows satisfactory performances for various initial speed and distance to the stationary point with CarSim and MATLAB/Simulink.
Leveraging Emergency Response System Using the Internet of Things. A Preliminary Approach

The Internet of Things (IoT) is a new paradigm that is arising in all areas of technology, finding novel applications every day that can improve many aspects of people’s lives. Several applications using IoT have been subject to research and development; one of these applications is traffic monitoring and management. In the case of an accident or a medical emergency, we know that a fast response is essential to people’s survival. However, in a city where the population increases, but the road space does not, traffic jams are an everyday thing. Traffic jams affect ambulance arrival to the hospital or a fire truck to the fire site. Thus, it is crucial to develop a system that can reduce the response time in case of an emergency. We propose to build an open IoT system over the current city architecture to communicate emergency vehicles to the surveillance cameras network and the traffic-light system to dynamically change their schedule and provide in this way a green wave reducing the response time. In this paper, we study the theory behind our proposed system as a whole. The results presented demonstrate that the subsystem used to identify and locate the emergency vehicles works properly. Also, we discuss the next steps to follow in order to implement all subsystems that need to be integrated.
Multi-task Learning based Pre-trained Language Model for Code Completion

Code completion is one of the most useful features in the Integrated Development Environments (IDEs), which can accelerate software development by suggesting the next probable token based on the contextual code in real-time. Recent studies have shown that statistical language modeling techniques can improve the performance of code completion tools through learning from large-scale software repositories. However, these models suffer from two major drawbacks: a) Existing research uses static embeddings, which map a word to the same vector regardless of its context. The differences in the meaning of a token in varying contexts are lost when each token is associated with a single representation; b) Existing language model based code completion models perform poor on completing identifiers, and the type information of the identifiers is ignored in most of these models. To address these challenges, in this paper, we develop a multi-task learning based pre-trained language model for code understanding and code generation with a Transformer-based neural architecture. We pre-train it with hybrid objective functions that incorporate both code understanding and code generation tasks. Then we fine-tune the pre-trained model on code completion. During the completion, our model does not directly predict the next token. Instead, we adopt multi-task learning to predict the token and its type jointly and utilize the predicted type to assist the token prediction. Experiments results on two real-world datasets demonstrate the effectiveness of our model when compared with state-of-the-art methods.
Performance Analysis for Incremental DF Relaying Networks with Non-linear Energy Harvesting

In this paper, we investigate the outage performance of an incremental decode-and-forward (DF) relaying protocol based simultaneous wireless information and power transfer (SWIPT) network, whose non-linear property is described by a piecewise linear model. Considering a non-linear energy harvesting (EH) model, we propose an optimal power splitting radio to minimize the outage probability of the destination node. On this basis, an incremental relaying protocol is proposed to enhance the system performance, which allows the source to adaptively switch between the direct link (DL) transmission mode and the cooperative relaying (CR) transmission mode via 1-bit feedback from the destination node. The closed-form expressions for the outage probability and system throughput are both derived. Numerical results show that the proposed scheme based on the non-linear energy harvesting (EH) model is able to achieve a lower outage probability and a higher throughput than the existing schemes.
Studying Data Privacy Management in Small and Medium-Sized IT Companies

Today, poorly implemented information security and data privacy measures may cause significant threats to companies’ existence and business continuity. Additionally, European Union has established strong data protection regulation rules for companies operating within EU. In order to be compliant with these new rules and regulations, organizations have to put a lot of resources to create data privacy policies and plans as well as to adjust tools to manage data privacy requests and fullfill privacy by design and privacy by default principles. Especially for small and medium-sized (SME) Information Technology (IT) firms and software development organizations with limited resources, new GDPR legislation and stricter requirements for information security have caused several challenges and uncertainty on what is adequate level of data privacy. In this paper, we focus on exploring Finnish IT SMEs and their actions and feelings on data privacy and information security. The research problem of this study is: How small and medium sized companies have prepared for growing data privacy and information security requirements? The main contribution of this paper is to show how small and medium sized IT companies in Northern Savo region did prepare for EU data privacy regulation and what types of challenges did exist in the GDPR preparation phase.
Methods for Prediction Optimization of the Constrained State-Preserved Extreme Learning Machine

Finding the maximum testing accuracy in Machine Learning has been the goal since its conception. From this goal, neural networks have been the primary source of continual improvements in prediction performance. Traditionally, backpropagation has been the primary way of training neural networks and the Levenberg-Marquardt (LM) backpropagation has become the fastest method. Recently, the Extreme Learning Machine was introduced which randomizes weights and biases of hidden layers and uses the Moore-Penrose generalized inverse of a matrix to calculate the output weights and biases, providing competitive results at significantly faster training times. In this study, we continue our work on the Constrained State-Preserved Extreme Learning Machine (CSPELM) with a Forest optimization (CSPELMF) and $\varepsilon$ constraint Rangefinder (CSPELMR). Furthermore, we provide hyper-parameter settings for the CSPELM to optimize accuracy over training time. Our results show that our methods outperformed the LM backpropagation in a majority of the 13 tested datasets and that the CSPELMF and CSPELMR matched or outperformed the CSPELM in all classification datasets.
Multimodal Cooperative ITS Safety System at Level-Crossings*

Safety al Level crossing (LC) is a minor issue for the road sector since it represents less than 1% of the accident mortality, but it is highly important for the railway sector, which accounts for thousands of accidents and collisions every year. In total, more than 500 causalities occur every year in the surroundings of LCs in the United States and the European Union Member States combined. This paper presents a multimodal cooperative safety system to alert about the vicinity of a LC and, if any, an approaching train. The system processes spatial data of trains and private nearby LCs and generate alerts about the presence of a nearby LC and the estimated time of arrival for approaching trains. The system was tested in the LCs of Thessaloniki by professional taxi drivers during a 12month period. Qualitative analyses indicate positive acceptance by the drivers as well strong perception of reliability and safety impact of the system.
Behavior and Interaction-aware Motion Planning for Autonomous Driving Vehicles based on Hierarchical Intention and Motion Prediction

Safe motion planning in complex and interactive environments is one of the major challenges for developing autonomous vehicles. In this paper, we propose an interaction and behavior-aware motion planning framework based on joint predictions of intentions and motions of surrounding vehicles. A multimodal hierarchical Inverse Reinforcement Learning (IRL) framework is designed to learn joint driving pattern-intention-motion models from real-world interactive driving trajectory data. Using the learned models, the proposed approach can probabilistically predict the continuous motions partitioned by discrete driving styles and intentions, while taking into account of the interactions between participants’ actions. A chance-constrained POMDP strategy is utilized to generate risk-bounded motion plans based on those predictions. Simulation case studies based on challenging scenarios demonstrate that the proposed framework is effective in predicting surrounding vehicles’ behaviors and generating safe motion plans in complex traffic environments.