Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Data Mining and Machine Learning Methods for Student Scholarship Prediction

This review paper consists of literature survey to prediction of scholarship by using Machine Learning and Data Mining technique. Along with this it contains a small description of ML/DM which are used by the researchers. It also describes data sets as very important in ML/DM methods. Machine Learning becomes most popular in the field of IT industry. Nowadays Machine Learning and Data Mining turn as a powerful technique which applicable for various fields such as IT, Education sector and also in business sector too. The different types of ML/DM algorithms are addressed by using all this technique. The algorithms which give more accuracy results in detection of continuity of every student’s scholarship such as Naïve Bayes, Decision Tree and k-NN. Finally, the proposed model will provide a list of candidates, who deserve to have a scholarship and also discussion has been made on accuracy of each techniques which was used to get a result.
A hybrid approach to structural modeling of individualized HRTFs

We present a hybrid approach to individualized head-related transfer function (HRTF) modeling which requires only 3 anthropometric measurements and an image of the pinna. A prediction algorithm based on variational autoencoders synthesizes a pinna-related response from the image, which is used to filter a measured head-andtorso response. The interaural time difference is then manipulated to match that of the HUTUBS dataset subject minimizing the predicted localization error. The results are evaluated using spectral distortion and an auditory localization model. While the latter is inconclusive regarding the efficacy of the structural model, the former metric shows promising results with encoding HRTFs. Index Terms: Hardware—Digital signal processing; Computing methodologies—Neural networks; Applied computing—Sound and music computing
Classification of Mango Leaves Infected by Fungal Disease Anthracnose Using Deep Learning

Mango is the fruit of high economic and ecological importance in India, as it exports mangoes in large quantities. More than 1500 mango species are cultivated in India and more than 1000 of them are commercial varieties. Mangoes are highly affected by number of diseases, which hamper its appearance, taste and has huge impact on the economy. Amongst number of diseases, Anthracnose is the most commonly occurring fungal disease that is infecting mango trees in India. It is necessary to have an easy and appropriate method to diagnose this highly infectious fungal disease Anthracnose. It would be easier of mango cultivators to identify this disease beforehand and apply proper medication. This will help in preserving its quality and improving the production. Deep learning technologies, computer vision have dragged lot of research attention over past few years due to its high computation and accuracy to classify variety of fungal and bacterial diseases affecting Mango trees. This paper introduces a novel deep learning convolutional neural network (CNN) architecture to identify Anthracnose disease of mango. A real-time dataset captured in farms of Karnataka, Maharashtra and New Delhi is used for validation. It comprises of the images of mango tree leaves by including both healthy and diseased category. In comparison with other state-of-the-art approaches, the proposed algorithm gives higher classification accuracy of about 96.16%.
Impossible Open Spaces: Exploring the Effects of Occlusion on the Noticeability of Self-Overlapping Virtual Environments

Impossible spaces uses self-overlapping virtual architecture to in-crease the area of virtual environments that can be explored by walking. This paper details a preliminary study using psychophysical methods to explore if users’ ability to detect overlapping virtual spaces changes when the walls of overlapping rooms and the paths connecting them are removed. The results indicate that impossible spaces can be deployed, even when no walls are presented between adjacent rooms. However, the absence of walls appears to make it easier for users to detect impossible spaces.
Fault Detection of Wind Turbines by Subspace Reconstruction-Based Robust Kernel Principal Component Analysis

Data collected from the supervisory control and data acquisition (SCADA) system are widely used in wind farms to detect wind turbines’ (WTs) faults. However, it is challenging to extract wind turbines’ performance trends and provide accurate alarms based on SCADA data with large operating conditions fluctuations. This article presents a fault detection frame of subspace reconstruction-based robust kernel principal component analysis (SR-RKPCA) model for wind turbines SCADA data to extract nonlinear features under discontinuous interference. First, to improve the stability of the fault detection model of wind turbines, an RKPCA method is developed to decompose the original signal, decomposed into principal component subspace and residual subspace. Second, the permutation entropy is adopted to measure the residual matrix components’ disturbance level and remove trendless noise components. Then, a combined index is developed for fault detection with the aid of the residual space matrix with trend information. Ultimately, the proposed SR-RKPCA method for wind turbine detection is verified by investigating wind turbine faults cases. The proposed method has outstanding robustness and a more vital ability to extract nonlinear features for fault detection than traditional principal component analysis (PCA)- or KPCA-based methods.
The Modeling of Elastic Traffic Transmisson by the Mobile Network with NB-IoT Functionality

The development of the concept of the Internet of Things (loT) and the use of existing mobile networks for organizing machine-to-machine communication networks require new approaches to planning a network resource. The most popular solution in this area is NB-IoT (Narrow Band Internet of Things), a mobile communication standard for telemetry devices with low communication volumes. This paper investigates a case of network slicing and processor sharing (PS) service policy using in mobile network with batched arrival of requests from smart objects. The mathematical model of the corresponding model is corresponding system is built. Markov process that describes model function is constructed. The numerical results are presented that illustrate the solution of three different problems of planning network resource using the proposed algorithms.
Credibility of Browser Extension Program Based on Behavioral Statement

As users have increasing demand for communication at anytime and anywhere, browser plays an increasingly greater role in the Internet, with browser extension program more widely used. However, problem regarding credibility of browser extension program has also arisen. This paper proposes a method to automatically acquire the behavior and actions of extension program during the browser running phase. In this method, a series of user behaviors will be simulated to maximize the exposure of behaviors and actions by the extension, thus collecting the API calling information of browser extension program as comprehensively as possible. According to the acquired browser extension, API information is called to plot the browser extension behavior diagram. Malicious extension and non-malicious extension behavior diagram are compared to infer the malicious behavior calling sequence, thus declaring the untrusted behavior model. Finally, through experiments, feasibility of this method is verified.
Position Invariance for Wearables: Interchangeability and Single-Unit Usage via Machine Learning

We propose a new methodology to attain invariance to the positioning of body-worn motion-sensor units for recognizing everyday and sports activities. We first consider random interchangeability of the sensor units so that the user does not need to distinguish between them before wearing. To this end, we propose to use the compact singular value decomposition (SVD) that significantly reduces the accuracy degradation caused by random interchanging of the units. Second, we employ three variants of a generalized classifier that requires wearing only a single sensor unit on any one of the body parts to classify the activities. We combine both approaches with our previously developed methods to achieve invariance to both position and orientation, which ultimately allows the user significant flexibility in sensor-unit placement (position and orientation). We assess the performance of our proposed approach on a publicly available activity data set recorded by body-worn motion-sensor units. The experimental results suggest that there is a tolerable reduction in accuracy, which is justified by the significant flexibility and convenience offered to users when placing the units.
Power Optimization for Low Interference and Throughput Enhancement for 5G and 6G systems

While the first 5G releases have been defined, it is expected that future Beyond 5G (B5G) and 6G networks will further reduce inter-site distances between access points. Accordingly, ensuring co-existence and controlling interference in such scenarios is pivotal for the success of future wireless systems. In this work all the unused wireless time-frequency resources in non-congested cells are leveraged to propose a novel Link Adaptation (LA) scheme, whose paradigm is to maximize resource utilization and reduce transmit power as much as possible. Note that this contrasts currently considered mechanisms that simply maximize spectral efficiency, demanding power optimization with long-term updates from other procedures. The proposed scheme does not require inter-cell coordination and is transparent to other MAC procedures, making its practical implementation trivial. We analytically establish that the mechanism achieves at least the same capacity in all cells and reduces transmit power when compared to state of the art LA mechanisms. In addition, extensive 3GPP-compliant system level simulations are conducted to assess the performance of the proposal, showing substantial gains in terms of achievable cell throughput in congested cells and overall transmit power reduction.
Stock prediction under COVID-19 based on LSTM

Stock prediction has been considered as a significant problem in both economics and computer fields. Since stock historical price is extraordinary crucial that it can affect investors' attitude towards whole market, and people's emotions will influence the stock price in the future, sequence-based predict models have received much attention these years. In this paper, we propose using LSTM to predict accurate single stock price, with whose history index as experience. To take public mood into account, numerous tweets were collected and quantified by deep learning method. To revise the LSTM model, we applied attention mechanism to extract vital factors and realign weights between history index and public sentiment. Experiments demonstrate that this model performs well, for the forecast price curve almost coincides with the real price curve. But in some extreme circumstance, models without sentiment as input performed better, which indicates that population sentiment can be a noise of prediction.
Advanced IOT based System for Cricketers Health Supervision

Cricket is a sport which is the most popular sport in India and also in many countries all over the world. In comparison with any sport, cricket sport contains a highest fan following all over the world. Cricketers face many health-related issues during the match or in training. A lot of effort has to put by cricketers during training and matches which leads to shortness in breath, hypoglycaemia, and many more. Many sponsors and Cricket committees spent lot of money on cricketers. Health issue of any player gives huge blow to their spending on individual. Internet of Things (IoT) is reaching new heights with its technical support to enhance human lives. IoT is a technology which can give support to the players during the match or during the training by detecting the health issues of cricketers in the early stage. In this paper, an advanced IoT support methodology is proposed which monitors the healths of a cricketer by using embed sensing devices, tele communication technologies and cloud computing.
Image classification of sugar crystal with deep learning

Nowadays, artificial intelligent control is essential to replace experienced workers. The correct classification of sugar crystals during the production process is the basis for the control of the sugar crystallization process. Correct Classification of sugar crystals is the basis necessary for automatic control of process. This research uses the principles of deep learning using a neural network to identify the crystallization of sugar from the actual production process of sugar factories in Thailand. Performance was measured and compared with the Fine-tuning VGG16 model. It was accurate to identify sugar crystals between 82% and 92% of four classes sugar crystal images classified by the crystallization conditions. The results of this study also show that this model is more accurate than other models. It can be used as a benchmark for monitoring the crystallization of sugar production processes. It is also the basis of an artificial intelligent control system based on transcribing human expertise
Integration of Concept Maps into the Mixed Reality Learning Space: Quasi-Experimental Design and Preliminary Results

It is difficult to establish the relationship among complex concepts learned in virtual learning activities in the absence of appropriate instructional scaffolds, which may lead to students’ confusion and frustration. To address such an issue, a mixed reality (MR) friction experiment system integrated with interactive hierarchical concept maps with the name of MMRCM is proposed as a scaffolding tool to guide students in learning friction concepts. Results of a quasi-experiment assessing the effectiveness of MMRCM showed that students’ academic performance improved significantly. Interviews with students and teachers also showed that the MMRCM system could help learners navigate educational concepts and material efficiently.
A Novel Method for Detecting Fake news: Deep Learning Based on Propagation Path Concept

In the modern world, social media are extensively used for the purpose of communication, business and education. Although ease of use and simple accessibility to social media has expanded their applications, but unfortunately, they are associated with potential dangers which may negatively influence users. As main item, the publication of fake news can negatively affect various aspects of life (political, social, economic, etc.), therefore researchers have studied various methods to address the fake news detection. One way to check and detect fake news is to use the available features in news propagation path, news publisher and users. In this paper, an attempt has been made to investigate fake news detection based on these features and a proposed deep neural network model.
Modeling of Nonlinear Distortions in 5G NR Systems

Due to the peculiarities of the physical level of 5G NR (Fifth Generation New Radio) systems, nonlinear distortion of the amplifying path is one of the main negative affecting on the signal. Existing mathematical models of amplifier nonlinearities in the context of their application in the simulation of fifth-generation signals require adaptation. The work proposes the coefficients for nonlinear distortion of power amplifiers for Saleh and Ghorbani models. Based on the simulation model developed in MA TLAB, the spectral characteristics of the simulated processes of nonlinear distortion, amplitude-amplitude and amplitude-phase distortions, as well as the effect of nonlinearity on the noise immunity of the system (the dependence of the bit error rate (BER) from the signal-to-noise ratio (SNR)) for the 5G NR signal of the second numerology (200 MHz signal bandwidth) for all types of modulation supported by the standard on sub carrier frequencies (QPSK, 16QAM, 64QAM, 256QAM) are studied.
Higher Order Crossings Analysis of Signals Over Graphs

We propose the extension of the Higher Order Crossings (HOC) analysis concept for signals over graphs. The HOC over graphs sequence, (<inline-formula><tex-math notation="LaTeX">$HOC_g$</tex-math></inline-formula>), is defined and its efficacy on graph signal discrimination is investigated. Compared with state of the art graph signal discrimination algorithms, the proposed <inline-formula><tex-math notation="LaTeX">$HOC_g$</tex-math></inline-formula> combined with a Support Vector Machine classifier achieves fast and similar or better performance over five different benchmark data sets.
Methodology of Microscopic Traffic Prediction for Automated Driving

We present a methodology for short-time (several seconds) microscopic prediction of traffic situation round of an automated driving vehicle moving in mixed traffic flow. The methodology for short-time microscopic prediction of traffic situation can be used by the automated driving vehicle for the planning of vehicle trajectory with the objective of speed harmonization in mixed traffic flow, collision avoidance, increase traffic safety and comfort.
A Network-Aware Internet-Wide Scan for Security Maximization of IPv6-Enabled WLAN IoT Devices

Despite unprecedented advancements, wireless local area network (WLAN) technologies for the Internet of Things (IoT), such as IEEE 802.11ah (i.e., WiFi-HaLow), are prone to serious security threats, owing to their constrained computational and memory resources, which limit the use of heavyweight intrusion protection and security protocols. To address this problem, security administrators (sec-admins) must perform regular and comprehensive vulnerability assessments of IoT devices. An Internet-wide port scan (IWPS) is the initial step. However, the medium access control mechanism of IEEE 802.11ah, designed specifically for heterogeneous IoT traffic and low-power operations, can degrade network performance in the case of traditional port-scan traffic. Moreover, Internet-security (IPSec) protocol support is mandatory for IPv6-enabled IoT devices to ensure data confidentiality, integrity, and availability. Although the objective of a port scan is to improve IoT security, the resultant network performance can adversely affect IPSec services. Therefore, in this study, we optimize the IWPS to maximize the IoT security over IEEE 802.11ah WLAN. To this end, we propose novel mathematical models to evaluate IoT security based on port-scan network performance and IPsec services, which derives an optimal scan rate for sec-admins. The effectiveness of the proposed framework is verified by comprehensive numerical analysis, which shows that our approach minimizes the risk to IoT devices while probing them at an optimal scan rate.
A Complete Open Source Design Flow for Gowin FPGAs

In this paper, we propose an open source design flow for the low cost FPGAs from the company Gowin. Open source design tools are opening the door for custom extensions and modification in the design flow. The proposed design flow which supports almost all Gowin FPGA resources, is based on well-known open source tools, like Yosys and nextpnr, as well as on our proposed open source bitstream generator. The necessary architectural details of the FPGA family are gathered by input fuzzing and comparisons with the vendor tool flow. Experimental results show an almost similar performance as the vendor tools.
Optimizing the Opening Velocity for a Vacuum Circuit Breaker With Cup-Type Axial Magnetic Field Contacts

This article extends the quantitative method of optimizing the opening velocities for a vacuum circuit breaker (VCB) with cup-type axial magnetic field (AMF) contacts. The material of the AMF contact is Cu<sub>50</sub>Cr<sub>50</sub>. We define <inline-formula> <tex-math notation="LaTeX">$v_{1}$ </tex-math></inline-formula> as the initial opening velocity over half the full contact gap and <inline-formula> <tex-math notation="LaTeX">$v_{2}$ </tex-math></inline-formula> as the average velocity over the full contact gap of 20 mm. We optimize <inline-formula> <tex-math notation="LaTeX">$v_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$v_{2}$ </tex-math></inline-formula> using the transition of anode-discharging modes, of which we combine the footpoint mode and anode spot mode into a large-gap high-current (LGHC) mode. The experiment shows that <inline-formula> <tex-math notation="LaTeX">$v_{1}$ </tex-math></inline-formula> should be higher than a threshold velocity <inline-formula> <tex-math notation="LaTeX">$v_{\mathrm {1\_{}th}}$ </tex-math></inline-formula>, which corresponds to a peak critical contact gap <inline-formula> <tex-math notation="LaTeX">$d_{\mathrm {1\_{}th}}$ </tex-math></inline-formula> for the transitions of an intense arc mode into a diffuse arc mode, in high-current interruption. The higher <inline-formula> <tex-math notation="LaTeX">$v_{1}$ </tex-math></inline-formula> is than <inline-formula> <tex-math notation="LaTeX">$v_{\mathrm {1\_{}th}}, $ </tex-math></inline-formula> the faster the intense arc mode evolves into the diffuse arc mode. This helps successfully interrupt the short-circuit current in a short arcing time. Another threshold velocity, <inline-formula> <tex-math notation="LaTeX">$v_{\mathrm {2\_{}th}}$ </tex-math></inline-formula>, corresponds to the maximum critical contact <inline-formula> <tex-math notation="LaTeX">$d_{\mathrm {II\_{}th}}$ </tex-math></inline-formula> for the formation of an LGHC mode in the second arc current half-wave. A high <inline-formula> <tex-math notation="LaTeX">$v_{1}$ </tex-math></inline-formula> design needs <inline-formula> <tex-math notation="LaTeX">$v_{2}$ </tex-math></inline-formula> to be lower than <inline-formula> <tex-math notation="LaTeX">$v_{\mathrm {2\_{}th}}$ </tex-math></inline-formula>. The lower <inline-formula> <tex-math notation="LaTeX">$v_{2}$ </tex-math></inline-formula> is than <inline-formula> <tex-math notation="LaTeX">$v_{\mathrm {2\_{}th}}$ </tex-math></inline-formula>, the better the VCB avoids the LGHC mode in high-current interruption. This helps interruption over a long arcing time. We propose an intelligent opening and breaking operation for VCBs with cup-type AMF contacts, whereby the VCB opens with different opening velocities according to the short-circuit current level.