Miracle-dz/UniFAR-FTUs huggingface dataset:
IKARion: Enhancing a Learning Platform with Intelligent Feedback to Improve Team Collaboration and Interaction in Small Groups

Due to a high number of participants and limited human tutoring capacities, close supervision and human guidance is hardly possible in large online courses. Thus, the aim of the IKARion project is to develop methods for intelligent and automated diagnosis and intervention in online learning environments. In this paper, we present our distributed and autonomous feedback system aiming to improve team collaboration and interaction by means of interventions. The feedback system detects and diagnoses existing and emerging problems within the groups based on their interaction patterns. Based on principles of pedagogy and educational psychology, analysis metrics and appropriate measures are provided for different types of feedback including mirroring for exploratory and informative prompts providing appropriate information about progress and actual state for the teamwork, and guiding prompts aiming to modify the interaction and collaboration of a team or an individual student. The distributed feedback system consists of three essential components: Moodle-based learning management system along with various prompts implemented as Moodle plugins, Learning Analytics Backend, and Rule-based Intervention System based on an expert system. We describe, on an architectural level, the distributed feedback system and its components. As a result, we show measurements of two long term experiments carried out in two online courses at university level with a duration of one semester each. We conclude the paper with an outlook on future work.
New Gamma Correction Method for real time image text extraction

Text extraction and character recognition in manuscripts are a very important part of image processing and pattern recognition. The recently proposed Gamma Correction Method (GCM) is a popular method used in the processing of a color image and in particular in the extraction of text from a complex image. However, The GCM consumes a lot of time to extract the text and this is because of the complex calculations of different operations in the different blocks of GCM which is not suitable to real-time applications. In this paper, we propose an efficient Gamma Correction Method acceleration. Our proposal allows to minimize the GCM execution time while ensuring the same reliability of the initial version. The experimental results show an important optimization compared to the literature.
Co-Primary Licensed Spectrum Sharing Techniques for In-building Small Cells in 5G Era

In this paper, we propose two co-primary spectrum sharing (CoPSS) techniques, namely dynamic spectrum pooling (DySP) and static spectrum renting (StSR), for small cells deployed in 3-dimensional (3D) buildings for the fifth-generation (5G) mobile networks. We consider a scenario of two mobile network operators (MNOs) where the whole spectrum of one MNO is considered sharing only with small cells per building of the other MNO by exploiting the external wall penetration loss of each building. More specifically, in DySP, unlike spectrum pooling, only the licensed spectrum of one MNO can be shared deterministically with small cells within each building of the other MNO and vice versa such that the network capacity demand of an MNO can be scaled easily. Likewise, in StSR, unlike mutual renting, a certain portion of the spectrum of one MNO is rented exclusively for a certain time period so that the rented spectrum can solely be used to small cells within each building of the buyer MNO without any preemptive priority of the owner MNO. Hence, the proposed CoPSS techniques can overcome the limitations of the generic CoPSS techniques, i.e. the spectrum pooling and mutual renting techniques. We derive average capacity, spectral efficiency, and energy efficiency metrics for both techniques and extensive performance evaluation is carried out to show the relative outperformance of the DySP in terms of spectral and energy efficiencies over the StSR. Finally, we show that the proposed CoPSS techniques can meet the requirements of spectral and energy efficiencies for 5G mobile networks.
EduRSS: A Blockchain-Based Educational Records Secure Storage and Sharing Scheme

Accurate and complete educational records are a valuable asset for people. In recent years, educational records have been digitized. However, there are still two key challenges that have not been resolved. One is to achieve secure and privacy-preserving storage of educational records, while another concern how to realize the sharing of educational records and ensure the security of the sharing process. In this paper, we propose EduRSS, a blockchain-based storage and sharing scheme for educational records is proposed, which combines blockchain, storage servers, and cryptography techniques to create a reliable and safe environment. In our proposal, the blockchain technology is used to ensure the security and reliability of data storage, while the smart contracts on the blockchain are used to regulate the process of storage and sharing. More precisely, the off-chain storage servers store the original educational records in encrypted form, while the hash information of the records is stored on the blockchain. The off-chain records are anchored periodically with the hash information on the blockchain to ensure the security of data storage. Cryptography techniques are utilized to handle records encryption and messages digital signature. To assess the effectiveness of EduRSS, we designed and tested a proof of concept of this scheme. The relative security analysis shows that EduRSS is safe and has a lower computational cost than that of the CP-ABE and the MA-CPABE schemes.
Single-Loop Opto-electronic Oscillator at 10.4 GHz with a cascaded Microstrip Bandpass Filter Configuration

The opto-electronic oscillator is a well-known microwave photonic device that produces high-frequency signals in the microwave range. One of the main advantages of the opto-electronic oscillator is that it produces high-frequency signals with low phase noise thanks to the resonator's properties. In most cases the opto-electronic oscillator faces the problem of generating side modes besides the oscillation signal due to non-ideal filtering. In this paper we propose a solution for the additional suppression of these undesired harmonics using a combination of two slightly detuned bandpass microstrip filters. We report an improvement for the side-mode suppression ratio about 8.3 dB with a single-loop 90-m-long opto-electronic oscillator at 10.4 GHz.
Study of Partial Discharge Behavior at Flight-Altitude Pressures under 60 Hz and Impulse Voltages for Samples Related to Aircraft Motors

More electric airplane (MEA) calls for higher aircraft electric system voltage level and more implementations of inverter-controlled machines, which make partial discharge (PD) a more urgent issue. Although several PD standards exist for power frequency voltage (up to 400 Hz) under normal pressure conditions, there is a lack of a well-established test procedure and standard for PD induced by ac and impulse voltage under low pressure conditions. Thus, in this paper, experimental results and associated analysis for samples of aircraft motor-related components under various pressures with 60 Hz and impulse voltage excitations are presented. The samples involved are twisted pairs, shielded wires, printed circuit boards (PCB) and a motorette. Test pressures involved the range from 87 Torr (50,000 ft.) to 760 Torr (sea level). Observations, statements and hypotheses on low pressure PD behavior under both 60 Hz and impulse excitations are put forward, based on test results.
Sentiment Analysis and Emotion Extraction: A Review of Research Paradigm

Micro-blogging is one of the most popular social network services that enable users to share their thoughts, ideas and discuss current issues every day. Some popular micro-blogging sites are Tumblr, Twitter, Pinterest, Flattr, Meetme. This research will work to represent the study of sentiment analysis to extract emotions from textual contents by using several methods of tracking mode. Several classifiers are trained to extract basic moods such as Joy, Trust, Fear, Surprise, Sadness, Disgust, Anger, and Anticipate from text. The aspect of this research is to give a nearly concept about sentiment analysis techniques and the related areas.
Secure V2V Communication with Identity-based Cryptography from License Plate Recognition

The deployment of secure vehicle-to-vehicle communication is essential for recent technologies such as autonomous driving and traffic optimizations. In this paper we experiment with the use of license plates as a mean to identify vehicles and use this identification number to bootstrap security based on identity-based cryptographic schemes. Since the deployment of the public-key infrastructure may be difficult at a large scale for the automotive environment, the use of identity-based cryptography may offer benefits since it does not require public-key certificates. We present experiments based on Android smartphones in order to determine the feasibility of deploying this technology. Our experiments are concerned with both the impact of the underlying cryptographic primitives and the range at which license plates can be recognized by smartphone cameras.
MD Simulation of Elastic Field at an Inhomogeneity in Graphene

A comprehensive molecular dynamics study is conducted to investigate the elastic field at an atomic inhomogeneity in graphene in the form of a circular hole or a circular boron-nitride inclusion. In addition, the effect on the stress field due to the interaction between an inhomogeneity and a crack is investigated. The results confirm that consideration must be given to the mechanical properties of the resulting system when atomic defects and inclusions are introduced to graphene to tailor optical and electronic properties.
Performance Enhancement of Modular Multilevel Converter by using Modulation Technique

This paper presents the comparative analysis of modular multilevel converter by using a multicarrier modulation technique. The total harmonic distortion (THD) analysis has been done by using phase disposition-pulse width modulation (PD-PWM) and phase opposition disposition-pulse width modulation (POD-PWM) technique at a variable modulation index and fixed switching frequency. This analysis demonstrates that the POD-PWM technique provides improved harmonic performance and better response. The THD of MMC at a variable carrier frequency and unity modulation index has also been analyzed. It shows that THD is improved beyond the carrier frequency. All the results have been analyzed and verified with the help of Matlab/Simulink.
IoT based Consumer-Centric Packaged Drinking Water Quality Monitoring System

Internet of Things has been successful in achieving the goal to build a connected future. Everything around us can communicate mutually and exchange data to work smart, thanks to IoT. In a world where health problems decelerate human harmony, that rise due to the lack of detailed information and misleading advertisements about the food that they consume, the water that they drink, the air that they breathe; a concept like IoT could serve the purpose of enabling communication between all the ’things’ that matter a lot as far as human health is concerned and human beings themselves, in order to make health-friendly choices about food and water, with greater precision. Water-a ‘thing’ that can be a serious threat to human life, even if it is consumed slightly untreated requires constant monitoring. This paper focuses on drinking water quality monitoring and employs IoT for the same and stands apart from conventional ideas in being people-centric. The water in a packaged drinking water plant is monitored for its TDS-the total dissolved solids which at higher values can imply adverse long-term effects on human beings. The TDS values measured using a TDS sensor interfaced with a ESP8266 ESP12-E board, is sent to an open IoT platform like Thingspeak, where data is efficiently visualized. The path specific URL that houses these data is embedded onto a QR code that is attached to the bottle containing the water from which the data has been extracted. People are provided with a smartphone app that can be used to scan this QR code, which would take the users to the URL where the data regarding the water that they have purchased/yet to purchase is stored. The app also has text bodies that explain what a TDS value would mean to a particular user. This project uses the ESP8266 ESP12-E (NodeMCU) microcontroller to achieve the purpose. All data transactions between the microcontroller and the cloud platform is achieved using the MQTT protocol which is extremely light weight and is thus popular among IoT applications.
Information Retrieval System in Bangla Document Ranking using Latent Semantic Indexing

Nowadays, like the English and other languages, Bangla also plays a significant role to strengthen the web repository. The storing rate of Bangla information is augmented day-by-day. Because of the numerous documents in the World Wide Web, it is very difficult for a user to retrieve the desired information. Furthermore, finding the useful documents tends to be more time spending as well as an annoying job. These demands emerge to develop an Information Retrieval (IR) system to document ranking for Bangla language. In this paper, we have built such a retrieval system where users can find their needed documents which correspond to their own query strings throughout the ranking index. Although a lot of works have been done for English and other languages to rank the documents, unfortunately, we have found a very negligible amount of contributions in Bangla Language. Many methods such as – Boolean model, Maximal Marginal Relevance (MMR), Portfolio Theory (PR), Quantum Probability Ranking Principle (QPRP), Query Directed Clustering (QDC), Vector-based TFIDF and so on, have been proposed to implement the document ranking system. Here, we have applied a new approach, called Latent Semantic Indexing (LSI) to do the same task for Bangla documents. LSI uses the mathematical method called Singular Value Decomposition (SVD). After that, we have applied the cosine similarity to rank all the documents. We believe that the performance result of our proposed system has reached the trustworthy level.
Advancing IP/IMPLS with Software Defined Network in Wide Area Network

New technologies introduce more services which will call for significant advances and changes in Multiprotocol Label Switching (MPLS) networks. Networks will have to go in the direction of network programmability, virtualization and cloud-based services. In this paper, we examined Software Defined Wide Area Network solution, its architecture and benefits of use. In order to analyze benefits of using SD-WAN two scenarios are compared: (i) Policy-based routing on WAN links in classical IP/MPLS network, and (ii) Using SDN controller to manage traffic on WAN links. Aim in both scenarios is to use the most optimal path for specific network.
Design and Implementation of an IoT Based Home Automation

Advancement in latest technologies has been helping to accomplish our day to day activities in faster and smoother ways. The advanced technologies provide solutions on multi-tasking with added security features in different sectors. Moreover, automation processes have been making regular life style smarter with less time-consuming activities. At the same time, the number of internet users has been increasing day by day. Internet of things (IoT) is one of the latest and emerging technologies that includes wireless monitoring & controlling functions with the help of internet. An IoT based Home Automation System (HAS) uses computers or webpage or android app for monitoring various parameters to control e.g., different electronic home appliances. By using the HAS technology, the users can access multiple appliances over the internet. By monitoring the parameters like temperature, light intensity, and movement of a human being e.g., using temperature sensors, the electrical appliances such as fan & light can be switched ON/OFF via internet from anywhere of the world. This paper proposed an IoT based home automation system which is designed with an aim of cost reduction and user friendly operation of the system.
Deep CNN & LSTM network for appliances energy forecasting in residential houses using IoT sensors

In this paper, the author has proposed the combination of deep convolutional neural networks as feature learning and long short-term memory as time series forecasting algorithm to forecast appliances energy consumption in residential houses using 28 data features collected from IoT sensors database. It is shown in experiment that the forecasting performance can be improved with comparison methods such as GBM (Gradient Boosting Machine), Support vector Machine with Radial Kernel, and RF (Random Forest).
Simulating Massive Open On-line Courses Dynamics

Learning without constraints of space and time has nowadays become a reality thanks to the exponential growth of the Internet, and distance learning is one of the areas for which that growth has been the most beneficial. The Web offers several platforms for distance learning, calibrated for the public or private sector. Among these, the Massive Open Online Courses (MOOCs) are one of the most popular online learning assets. In MOOCs, a crucial feature is given by the amount of enrolled learners: thousands can attend such courses. Consequently, monitoring the learning process of individual students is a challenging task for teachers in MOOCs. Peer Assessment can be used, in these case, as a teaching strategy, to follow the dynamics of a MOOC while not checking directly on each one of the individual students. In previous work we have presented a method to support peer assessment, and grades inference, mediated by a limited grading work performed by the teacher. The method is based on a definition of student model based on a modified version of the machine learning technique called K-NN. In this paper we present a system able to simulate the dynamics of a MOOC class where peer assessment, mediated by the teacher, takes place. The system supports the creation of a (big) class, and the simulation of peer assessment sessions, where peer grading, and teacher grading take place. The system, then, allows for a statistical analysis of how the student models computed by the system, and the grades inferred for the peers' tasks, converge towards the real values for the student models and grades.
An Effective Economical Hierarchical Control Scheme for Low-Voltage AC Microgrids

In this paper, a hierarchical control scheme for low-voltage AC microgrids is presented. The proposed control scheme provides coordinated and reliable operation of AC microgrids while enabling their most economical operation. The proposed control scheme has four coordinated levels and the desired operation of each level is directly dependent on proper operation of the lower level control systems. Moreover, the top control level can modify the performance of a lower control level to minimize the cost of supplying loads. An approach for implementing each control level is discussed and then applied to a sample distribution system. Finally, the effective operation of the proposed hierarchical control scheme is verified by real-time simulations for a representative low-voltage microgrid.
Many-to-One Gesture-to-Command Flexible Mapping Approach for Smart Teaching Interface Interaction

Mapping between gestures and semantics is inherently challenging, especially in the aspect of defining meaningful gesture semantics that are easy to understand and remember. To address this challenge, we put forward an intent-driven approach in this paper, which is based on users’ interaction intent. We implemented a many-to-one flexible mapping between multiple gestures and one semantic object. We also designed and evaluated a domain-specific gestural input technique for classroom use by considering the intent of the user. The main contributions of this work consist of: (1) a mapping between multiple gestures and one semantics and (2) an intelligent and natural interacting interface model for a three-dimensional (3D) user interfaces (UIs). The proposed algorithm is evaluated with five frequently used gestures and applied to the UIs system. The experimental results demonstrate that the gesture-based algorithm performs well and can substantially reduce the memory loads of the user.
A Zero-Mode Induced mmWave Patch Antenna With Low-Profile, Wide-Bandwidth and Large-Angle Scanning for 5G Mobile Terminals

Millimeter-Wave (mmWave) antennas for 5G mobile terminals require a wide bandwidth, large-angle beam scanning, low-profile design, and multi-substrate compatibility for module-level integration. In this paper, we propose one candidate design employing a patch structure with the shorting pin to particularly generate extra zero modes. By taking advantages of the 2nd zero-mode with the TM01 mode, we can obtain a wide bandwidth covering 23.5~28 GHz, a large-angle beam scanning with ±60°, as well as keep the substrate as low profile as 0.508 mm. Thanks to the zero-mode induced patch-type design, it is compatible to multi-layer configuration possessing the extensibility and flexibility for further module design. We experimentally valid the design of a $4\times2$ array with multi-layer configuration in a cell phone environment. Good RF performances with ±60° scanning in the wide bandwidth indicate this proposed design can be an appropriate candidate for 5G mobile terminals.
Comparative Analysis of Classification and Location of Faults in a Long Transmission Line Using Random Forest Technique

This paper represents random forest based (decision tree) location estimation and classification of fault in 400 KV, 300 km long transmission line (TL). The technique extracts single cycle post fault current and voltage signal and fed to signal processing method wavelet packet transform for useful features. Optimal features are extracted by feature selection method and then normalized. Taking a dissimilar simulation state like (10 types of fault, fault resistance, inception angle of the fault and distance of the fault) train and test matrix is generated. Then decision tree based (random forest) technique is used for location and classification of fault under study model. Simulation results are presented the proposed technique gives (99.5%) accuracy of location of faults and (less than 0.05%) error for classification of fault. The projected method is compared with another researcher approach.
Segmentation-based Deep Learning Fundus Image Analysis

Diabetic retinopathy is the most common cause of new cases of blindness in people of working age. Early diagnosis is the key to slowing the progression of the disease, thus preventing blindness. Retinal fundus images form an important basis for judging these retinal diseases. To the best of our knowledge, no prior studies have scrutinized the predictive power of the different compositions of retinal images using deep learning. This paper is to investigate whether there exists specific region that could assist in better prediction of the retinopathy disease, meaning to find the best region in fundus images that can boost the prediction power of models for retinopathy classification. To this end, with image segmentation techniques, the fundus image is divided into three different segments, namely, the optic disc, the blood vessels, and the other regions (regions other than blood vessels and optic disk). These regions are then contrasted against the performance of original fundus images. The convolutional neural network as well as transfer deep learning with the state-of-the-art pre-trained models (i.e., AlexNet, GoogleNet, Resnet50, VGG19) are deployed. We report the average of ten runs for each model. Different machine learning evaluation metrics are used. The other regions’ segment reveals more predictive power than the original fundus image especially when using AlexNet/Resnet50.