Miracle-dz/UniFAR-FTUs huggingface dataset:
Certificate Revocation Schemes in Vehicular Networks: A Survey

The problem of security in vehicular networks is a vital issue and attracts researchers’ increasing attention for its unique characteristics, such as the vulnerability of wireless media, untrusted communication environment, and closely relationship with human lives and privacy. Authentication is an effective means to ensure the security of communication and provide trust. Authorized vehicle should be revoked if the vehicle become compromised. Designing an efficient revocation mechanism for vehicular networks is a challenging task because of the diverse security requirements and the unique network characteristics. Thus, a large body of work emerged in recent years, proposing revocation schemes tailored to vehicular networks. In this paper, we provide a comprehensive survey on revocation schemes in vehicular networks. Firstly, we investigate existing revocation schemes systematically and classify these schemes based on the location where the revocation information has been placed. Secondly, we divide the whole revocation process into three stages: 1) the resolution of revocation information, 2) the distribution of revocation information and 3) the use of revocation information. Typical works in each stage of the revocation process have been reviewed and we also point out the challenging problems and key enabled techniques in each stage.
Immersive Activities in Educational Role-Playing Game Based on Appreciative Learning and Artificial Intelligence

Experience design is a vital factor in improving course material understanding in educational game design. In Role-Playing Game (RPG) educational games, the activity is one of the main elements in shaping the experience. However, the absence of a model for activity design in educational RPG causes the resulting activities to be poorly conceived and lack of synchronization between gaming and educational principles. This paper discusses the design of an activity-based scenario, where the scenario is a collection of activities. Intelligent scenario directs in-game activity using dynamic rewards whose behavior is regulated using artificial intelligence with the Finite State Machine method. Then the resulting behavior is grouped into activity based on Appreciative Learning concept. Appreciative Learning is a concept in learning that focuses on positivity and an optimistic future. This concept consists of four stages, namely Discovery, Dream, Design, and Destiny. The intelligent scenario in this study is part of the Adaptive RPG For Education project, which contributes to the design of adaptive scenarios. Intelligent scenario produces an immersive experience in the form of activities with rewards that are conceptualized and synchronized between gaming and educational principles.
Simulation of Steering a Self-Driving Car Using 1) PID Controller 2) Neural Network

Over the last few years, self-driving vehicles have expanded dramatically. They look promising in decreasing traffic accidents and congestion on roads. In order for this approach to work, simulations tend to be a cheaper, more efficient, and a safer way than live testing. In this paper, we present a simulator that tests the self-driving car's CNN model in a virtual environment. To avoid the hassle of collecting huge amount of training and testing data for preliminary testing, we also propose a method to collect data (images, steering value and throttle value) using PID controllers in the virtual environment itself.
Design of an experimental platform for the automation of the goalkeeper of a foosball table

This paper describes the process of designing, de- ployment, and testing of an experimental platform whose goal is to automate the movement of the goalkeeper of a foosball table. The platform imposes several challenges that span different disciplines of Electronics Engineering, including computer vision techniques for detecting and tracking the position of the ball and players in the table, estimation algorithms for predicting the movement of the ball, and control algorithms for moving the rod that holds the player. Besides, the characteristics of the application impose stringent requirements in terms of precision and processing speed, which requires an adequate choice of the software and hardware for the communication, storing, and processing of data in real-time. The developed platform will serve in teaching and research activities in the context of modern cyber-physical systems, inclu- ding the experimental characterization and validation of machine learning, control, estimation and computer vision algorithms, and the integration and evaluation of specialized computing platforms for accelerating computationally intensive processes. Moreover, because of the popularity of foosball tables, this experimental setup serves as an ideal platform for outreach activities related to STEM in non-academic audiences.
Presumptive Detection of Cyberbullying on Twitter through Natural Language Processing and Machine Learning in the Spanish Language

Nowadays, the constant development of information and communication technologies (ICTs) has changed the inter-personal interaction, allowing to transfer real experiences to a virtualized medium such as Internet. In this sense, although the space-time barriers of traditional communication are broken and social relationships are strengthened, problems related to adverse behaviors may arise. Bullying, defined as an act that threatens a person’s holistic well-being, becomes cyberbullying when it is done over Internet, causing anxiety problems, depression and even suicide attempts. For this reason, it is essential to detect this type of behaviour in time. This research deploys a Spanish cyberbullying prevention system (SPC), which relies on Natural Language Processing (NLP) methods and different machine learning techniques (Naive Bayes, Support Vector Machine and Logistic Regression), using Twitter as the basis for the extraction of knowledge bases or corpus. Several precision metrics and variable corpus sizes are used for the training. The learning results reach a maximum accuracy of 93%, verified through the application of three study cases.
Atrial Fibrillation Detection in Spectrogram Based on Convolution Neural Networks

Nowadays, the computerized electrocardiogram (ECG) interpretation is the best available tool for the detection of heart diseases. The symptoms of atrial fibrillation, one of the heart disease, are the most challenging task of heart disease that relies on the cardiologist to read the ECG. However, this task involves a time-consuming process, leading to fatigue-induced medical errors. In this paper, a novel atrial fibrillation diagnostic algorithm based on deep learning architecture is proposed which incorporates the spectrogram to further improve the accuracy performance. As shown in the experimental results, the proposed method on PhysioNet /CinC Challenge 2017, which contains 8528 of single-lead ECG recordings, achieves the 10-fold cross-validation set performance of 78%.
A Systematic Literature Review on Adaptive Gamification: Components, Methods, and Frameworks

Nowadays, learning technologies have solved problems of distance, time, and cost in learning. However, another problem with e-learning, which is lack of student motivation, still remains. Some researchers must apply gamification in e-learning with the aim of giving participation and increasing student motivation. Characteristics of different users, the same learning content, and static gamification elements do not increase the expected motivation. To overcome this problem, gamification must be adapted to the characteristics of the learners. This study uses a Systematic Literature Review (SLR) to analyze research components, methods, and framework used in adaptive gamification. The first step is to determine the research question (RQ) and then to search on several kinds of literature published in popular journal databases, namely IEEE Xplore, Science Direct, Scopus, Springerlink and ACM from 2011 – 2019. As a result of a review of 25 selected articles, there are four components of adaptive gamification, namely, player/learner profiles, learning style, behavior, and skill/knowledge. Meanwhile, there are eleven types of methods used in adaptive gamification. The most popular method is scoring and Felder-Silverman Learning Style Model (FSLSM). Generally, the proposed framework consists of three elements namely adaptive gamification engine, adaptive component, and gamification display.
Radio-Frequency Based Energy Charging- An Experimental Study

Radio frequency based wireless charging is an emerging technology that has been recently explored by various companies. This paper demonstrates the use of Powercast P2110 evaluation board to testify the far-field wireless charging using radio frequency. The maximum harvested power and the first charging time as a function of the charging distance are measured and compared to the theoretical ones for dipole and patch antennas. The experimental results are found to be very close to the theoretical ones. Further, the obtained results show that this technology has a promising future for being integrated into wireless sensor networks and several Internet of Things applications and scenarios.
Feature Extraction for 3-D Palmprint Recognition: A Survey

The 3-D palmprint recognition has been investigated for over ten years, and various methods have been proposed for 3-D palmprint recognition. In this article, we present a comprehensive overview of feature extraction and matching for 3-D palmprint recognition over the past decade. First, we introduce the 3-D palmprint data acquisition and preprocessing processes. Second, we comparatively study the state-of-the-art feature extraction schemes for 3-D palmprint recognition. Third, we conduct selected experiments to comparatively analyze the representative 3-D palmprint feature descriptors. Finally, we offer selected potential directions for 3-D palmprint recognition in future work. We believe that this article provides a helpful guide to researchers working in the fields of palmprint-based and 3-D-based biometrics.
Reliability Engineering Enabling Continued Logic for Memory Device Scaling

Continued scaling of DRAM technologies has required a limitation of the power dissipation from the logic components on-chip, while downscaling both transistor oxide thickness and gate length. One route to enable further scaling, while circumventing excessive leakage currents, is the integration of high-κ metal-gate (HKMG) components into the logic and high-voltage (e. g. I/O) devices. The requirement of a gate-first flow for devices in the peripheral region introduces significant reliability challenges. Even though Negative Bias Temperature Instability (NBTI) performance of CMOS and memory thermal budget compatible transistors are aligned with conventional HKMG integration with thin oxide devices, it is not the case for thick oxide devices. In particular, it will be shown that strong NBTI lifetime degradation is observed as soon as high-κ layers are deposited on top of the thick interfacial layer. In this work, a review of the impact of these high-κ layers on the Negative Bias Temperature Instability (NBTI) of high voltage logic for memory devices is presented. The stress induced degradation is correlated to a diffusion of metal atoms from the HKMG gate stack towards the silicon surface. Directions for reliability improvements are then defined. The presence of Nitrogen throughout the HKMG stack can originate either from high-κ processing or metal-nitride gate electrode. It is shown that preventing nitrogen diffusion towards the Si/SiO2 interface region, together with AlOx and/or F incorporation at the HKMG interface, can tune device threshold voltage and modulate access to donor trap-defect bands. The result of these effects is a vast improvement in NBTI performance. A detailed study of NBTI-degradation, supported by physical analysis, assessing the impact of various tuning components within the stack (interface layer, high-κ fluorination and/or cap, metal gate) will be presented. Potential solutions for this reliability challenge will be reported.
A Support Vector Regression Framework for Indian Bond Price Prediction

Both bonds and stocks are two major forms of investment under financial instruments, but they represent fundamentally two different forms of securities. In computational finance, a significant amount of work has been done for prediction of future prices of stock market. However, bond price prediction has not been exploited to a great extent. Keeping this in mind, a Support Vector Regression (SVR) based approach has been proposed to predict the future bond price of Indian market. SVR takes the past bond price as input and predicts the future price of bond. To exploit the usefulness of the suggested scheme, it has been compared with the traditional neural network based approach in terms of Mean Squared Error (MSE) and Squared Correlation Coefficient. From the simulation result analysis, it has been observed that SVR shows improved overall performance as compared to the neural network based approach.
VaLLR: Threshold Voltage Distribution Aware LLR Optimization to Improve LDPC Decoding Performance for 3D TLC NAND Flash

Low-density parity-check (LDPC) codes have been widely adopted in NAND flash in recent years to improve data reliability. However, their error-correction capability degrades due to inaccurate log-likelihood ratio (LLR). To improve LLR accuracy of LDPC decoding, this paper proposes a threshold voltage distribution aware LLR optimization scheme, called VaLLR. Firstly, we build a threshold voltage distribution model for 3D triple-level cell (TLC) NAND flash. Then, by exploiting the model, we introduce the VaLLR scheme to quantize LLR during soft-decision decoding. And by amplifying a portion of small LLRs, which is essential in the layer minsum decoder, more precise LLR can be obtained. Finally, we study the influence of the reference voltage arrangement on LLR calculation and apply the VaLLR scheme during decoding. The simulation shows that the proposed approach can improve the FER performance for several orders of magnitude.
Soft Actuator Model for a Soft Robot With Variable Stiffness by Coupling Pneumatic Structure and Jamming Mechanism

Current research into soft robots not only needs to improve their compliance, but also requires consideration of the real-time controllability of rigidity and flexibility. By combining the advantages of Pneu-Net structure and the driven jamming mechanism, we developed a soft actuator model for a soft robot with real-time variable stiffness. Firstly, the model of the soft actuator coupled with pneumatic structure and jamming mechanism was built. Secondly, we analyzed the pneumatically-driven structure by using the finite element method and researched the influences of pressure in cavities as well as shape and size of cavities on the performance of bending motion. On this basis, the pneumatically-driven structure was optimized. Finally, a prototype of the soft robot arm with variable stiffness was designed to carry out the experiment for verifying the variable stiffness of the soft actuator. Theoretical analysis and experimental results demonstrate that the soft robot can withstand a variable stiffness of between 0.025–0.138N/mm. In addition, the maximum elongation of the designed coupling mechanism can reach 25mm.
Block Source Coding with Sequential Encoding

We introduce the concept of achievable cumulative rate distribution functions (CRDF) to characterize sequentially encoding processes that ensure a lossless or lossy reconstruction subject to an average distortion using a non-causal decoder. Utilizing tools from majorization theory, we derive necessary and sufficient conditions on the CRDF for a given IID source. It turns out that the optimal achievable distortion level can be adequately characterized by the concave-hull of the CRDF.
A New Transparent Planar Reflector Antennas for Satellite DTV Applications

This paper presents the new thick film coating technology for transparent conducting film (TCF). The coated thin film is having the microwave characteristic of high transparency and low resistivity. Design of traditional dipole antenna consisted of iron wire, which can be manufactured via the simple bending process as that for the reflector antennas. On the surface is designed, the planar reflecting layer of using our invented activation process is used to rearrange the direction of liquid nano-material the thin film from disorder to ordered arrangements. The optical transmittance of reflector layer can be achieved in 82%. The overall dimension size of the is 210×290 mm2, The transparent reflective layer is prototyped can apply on the Ku-band for the satellite DTV.
INSTRCR: Lightweight instrumentation optimization based on coverage-guided fuzz testing

In Fuzzing facing binary coverage, the main role of instrumentation is feedback code coverage (in the case of Fuzz for binary, instrumentation can provide coverage information, which plays an important role in guiding the operation of seeds in Fuzz) . The current instrumentation optimization technique mainly relies on the control flow graph (CFG) to select key basic blocks at the basic block level, but the accuracy of this method is not high enough. Considering that the actual path in the actual operation of the binary may be different from the CFG generated in advance, this paper is based on the indirect jump that cannot be accurately analyzed in the CFG, and some of the basic blocks that can be optimized for high-frequency interpolation. According to the algorithm proposed in this paper, The combination of static analysis and dynamic analysis is used to continuously adjust and select key basic block nodes for instrumentation. It is verified by experiments that this kind of instrumentation method can effectively improve the coverage rate and reduce the overhead, and provide effective guidance for Fuzzing, which can effectively reduce the Fuzzer’s false negatives.
A Line Of Sight approach for Multi-agent SLAM

This paper presents an algorithm for Simultaneous Localization and Mapping (SLAM) by a multi agent system, for the spatial and planar cases. At each time step the agents’ positions are calculated relative to a single or a set of landmarks. Alternatively, positions can be calculated relative to agents with known positions. Each pair of agents which are in a line-of-sight-position helps the mapping task by "coloring" a segment in the obstacle-free-workspace in addition to the agents’ trajectories. Here, unlike traditional SLAM, the localization procedure is independent to the mapping. We present simulation results for the SLAM and show the advantage of using multi agent system for such tasks. In addition, we exemplify our approach using low-cost-opticsensors, placed on an omni wheeled platform and show the positioning accuracy.
A Blockchain-Based Framework for Secure Log Storage

Logs are critical data, which can help us to troubleshoot and identify the person in charge of an unexpected accident. Log systems have been widely used for log storage. However, the traditional log system can’t prevent the log from being tampered. Centralized servers are more vulnerable to be attacked. Those who have permission to operate records can easily tamper with logs. Blockchain is a disruptive technology in recent years, which has the advantages of decentralization, tamper-proof and traceability. Given its decentralization and tamper-proof properties, the paper proposed a blockchain-based framework for secure log storage. However, the cost of storing big files in the blockchain is very high. Thus, the paper utilized the InterPlanetary File System(IPFS) to store log files instead of a blockchain. Besides, the paper adopted Ethereum blockchain to store the hash of log files and a smart contract to create an index for log files. The solution is not only applicable to log but also other scenarios requiring secure data storage and efficient retrieval.
Automatic Feature Selection for Modified K- Nearest Neighbor to Predict Student's Academic Performance

Educational Data Mining (EDM) has been widely used in the world of education, one of them is to predict student's academic performance. There are several methods that are often used in EDM, including classification and clustering. One of the most used classification methods is K- Nearest Neighbor (K-NN), which has several modifications to improve its accuracy. To improve accuracy, we can also use the best features that can represent the whole object, therefore feature selection methods is needed to get the best results. In this paper, genetic algorithm is proposed, which have a good heuristic properties method for automatic feature selection while modified K-NN will be used as the classification method. This benefit can be used as feature selection in both K-NN and modified K-NN. The result shown that feature selection using genetic algorithm in modified K-NN is better with 82.6% accuracy than the ordinary one.
Detection of Network Intrusions using Hybrid Intelligent Systems

With the advancement in technology, our society has become so dependent on the internet and the number of internet users keeps rising each day. However, with the increased users comes the risk of hacking and malicious activities. One of the major concerns facing the technology sector today is the risk of intrusion. Thus, in the domain of security and computer networks, research in intrusion detection is essential. To combat the threats and malicious activities of the internet, the computer industry has gone a mile by creating new software and hardware products such as the Firewalls, Intrusion Prevention Systems and Detection Systems. Recently researchers created a Network-IDS to prevent such intrusions. However, these systems were prone to manipulations and had defects that were based on classification techniques. These systems failed to provide the necessary protections as they used a single classifier system or the individual classification technique. A single classifier classifies all of the data as normal or not, however due to the evolution of new attack patterns these systems failed to provide optimal attack detection rates with poor false alarm rates. The rise of different attack patterns meant that these systems cannot offer complete protection hence researchers came up with more sophisticated classification techniques that uses blends of several classification algorithms known as a hybrid intelligent system, leading to more detection accuracy. The aim of this study is to contrast various classifiers for network intrusions while combining these classifiers to direct the study towards hybrid intelligent systems. The study is carried out by performing an empirical and literature review while simultaneously providing a base for future studies.
An Integrated Approach of Clustering Based Trust Management with Energy Consumption for Service Oriented IoT

Internet of things is connecting our real physical world to the virtual world through sensors. In IoT environment, service provider nodes and consumer nodes has continuous interaction with each other in order to avail different services. An efficient trust management scheme helps in increasing efficiency of the system by reducing risk involved in wrong selection of service provider. Generally, conventional models are based on direct and indirect trust only is not much efficient as they tend to compute trust for unnecessary nodes also. Thus in this paper, trust is calculated by performing filtering of service Provider nodes based on their services through clustering and energy consumption algorithms handled by cluster head. This also helps in uniform energy distribution among service Provider. Also, a CPA document is signed between consumer and SP holding terms and conditions, guaranty, maintenance time, any negotiations etc. The CPA helps in increased consumer Satisfaction.