Miracle-dz/UniFAR-FTUs huggingface dataset:
Trans regulation of an odorant binding protein by a proto-Y chromosome affects male courtship in house fly

Y chromosomes have male-limited inheritance, which favors the fixation of alleles that affect spermatogenesis, courtship, and other male-specific traits. Y-linked male-beneficial alleles can also have female-deleterious (sexually antagonistic) effects because they never experience direct selection in females. However, determining the mechanisms underlying these male-beneficial effects is challenging because it can require studying Y-linked alleles while they still segregate as polymorphism. We used a Y chromosome polymorphism in the house fly, Musca domestica, to address this challenge. Two common male-determining Y chromosomes (YM and IIIM) segregate as stable polymorphisms in natural house fly populations, and they differentially affect multiple traits, including male courtship performance. We performed a meta-analysis of RNA-seq data and identified differentially expressed genes encoding odorant binding proteins (in the Obp56h family) as candidate causal agents in the courtship differences. The Obp56h genes are not found on either the YM or IIIM chromosomes, suggesting that they must be regulated in trans by one of the house fly sex chromosomes. Using a network analysis and allele-specific expression measurements, we identified multiple genes on the house fly IIIM chromosome that could serve as trans inhibitors of Obp56h gene expression. One of those genes is homologous to D. melanogaster CG2120, which encodes a transcription factor that binds both up- and down-stream of Obp56h. We found that up-regulation of CG2120 in D. melanogaster nervous tissues reduces copulation latency, consistent with this transcription factor acting as a negative regulator of Obp56h expression. We propose the name speed date (spdt) for CG2120, with the house fly homolog named Md-spdt. The expression of spdt across D. melanogaster development and tissues suggests that evolution of higher expression in neurons may be constrained by pleiotropic or sexual antagonistic effects. We hypothesize that a cis-regulatory allele that increases expression of Md-spdt on the IIIM chromosome exists because Y-linkage of this allele releases it from those constraints. This provides evidence for a molecular mechanism by which a Y-linked gene can evolve a male-beneficial function regardless of the negative effects on females.
Algebraic stability of non-homogeneous diffusion processes with and without regime-switching

Some sufficient conditions on the algebraic stability of non-homogeneous diffusion processes and regime-switching diffusion processes are established. In this work we focus on determining the decay rate of a stochastic system which switches randomly between different states, and owns different decay rates at various states. In particular, we show that if a two-state regime-switching diffusion process is p-th moment exponentially stable in one state and is p-th moment algebraically stable in another state, which are characterized by a common Lyapunov function, then this process is ultimately exponentially stable regardless of the jumping rate of the random switching between two states.
Dependence of Total and Carbonaceous Aerosol Concentrations on Transport Pathways in Seoul, Korea

Recently increased anthropogenic aerosols change the radiative energy balance and affect human life. The management of air quality requires monitoring both the local emissions and transported pollutants. In order to estimate the quantitative contribution of long-range transport from remote sources on aerosol concentrations in Seoul, the airmasses were classified into five types with respect to their pathways. When airmass came from west over strong emission regions in China, high concentrations of PM 10 , PM 2.5 , black carbon (BC), organic carbon (OC), and elemental carbon (EC) were found, even higher than those for the stagnated airmass. High OC concentrations were found when airmass came from north while BC, EC, and PM 2.5 concentrations were lower than those of the stagnated airmasses. During dust events, the PM 2.5 and PM 10 concentrations increased significantly while carbonaceous aerosol concentrations did not increased. The temporal variations of aerosol concentrations in Seoul were affected by the seasonal variations of airmass pathways. The high PM 2.5 concentrations over 100 μg m ? 3 appeared most frequently when the airmasses came from west.
Modeling Learner Profiles using Ontologies and Machine Learning

In recent years, E-learning technologies have altered the way we teach and learn, making it an intriguing research topic for enhancing education. A key component of these systems is the ability to tailor the learning experience to the needs of the individual student. According to researches, modeling student profiles with an ontology is quite relevant. However, the ontology must consider every aspect of learner representation. Therefore, there is an urgent need for new comprehensive information to improve the learner profile. In this paper, we propose a semantic approach to define an ontology of learner profiles. In addition, a learning style prediction system based on machine learning techniques is developed. Empirical results show a promising gain in performance for learning style prediction systems.
Research on Multi-Label Text Classification Based on Multi-Channel CNN and BiLSTM

Natural language processing research is moving in a significant improvement toward multi-label text classification. Multi-label text classification is presently used intensively in practical disciplines. In the text classification task, due to the professional and complex diversity of the text, it is difficult to fully represent the semantic of the text only by relying on the existing word vector representation method, which leads to the low accuracy of the classification task. In order to even get dynamic semantic information of text, this dissertation proposes a hybrid neural network model that incorporates use of Bert's pre-trained language model. Bert model has achieved remarkable results in text classification task. In order to analyze the text content, multi-channel CNN and BiLSTM are proposed for feature fusion. Simple CNN (Convolutional Neural Network) and RNN (Recurrent Neural Networks) cause problems such as loss of key feature information and poor classification performance when processing classification tasks. Multi-channel CNN uses different convolution kernels to obtain local semantic features of text, and BiLSTM model uses gating mechanism to obtain global information of text context. The ultimate vector representation of the text proposed in this study is the result of superimposing the two components mentioned above, and Sigmoid is exploited for classification. The suggested multi-label text classification method provides a stronger classification effect than existing models, thus according experimental results. It can be found that our model has achieved the maximum value in three indexes, with Precisionmicro, Recallmicro and F1micro reaching 0.9455, 0.9181 and 0.9315.
Automation in Fog Cloud assisted Internet of Things Ecosystem: Challenges, Components and Protocols

The amount of physical objects and sensors used in internet of things technology is increasing constantly but due to the high latency of loT data, it is unable to host this data by Cloud Computing. There has not been sufficient research on automating components to cope with real-time tasks and big data in Fog - Cloud assisted Internet of Things ecosystem, despite all challenges in processing big data of loT sensors present on the cloud. For example, devising automated data transfer to the cloud layer from the fog layer that includes massively distributed appliances is difficult. Dealing with decentralized appliances in fog and loT layer, and considering fog as a supporting processing layer led to assume other automated means to handle the current heterogeneity. The heterogeneity and big data challenges helped in making other automated components for fog resilience that have addressed the third challenge of the ecosystem. The processing of tasks in loT layer becomes autonomous of the cloud layer due to fog resiliency. The main innovation of this study is to explore and investigate automatic functions to form a taxonomy that assists researchers to develop algorithms for various applications of loT. Through this scientific research, the authors showed automatic functions with respect to each challenge.
Analysis of Learners’ Sentiments on MOOC Forums using Natural Language Processing Techniques

MOOCs have gained a lot of popularity for past few years. Especially after the outbreak of Coronavirus, everyone is trying to gain some knowledge and skill while being at the comfort of home and making themselves safe. Due to sudden increase in the number of participants on MOOCs there is a need for an automated system to be able to assess the reviews and feedbacks given by the learners and find the sentiments behind their statements. This analysis will help trainers identify their shortcoming and make their courses even better. For sentiments analysis, several approaches may be used. This research aims to provide a system which will perform sentiments analysis on the novel dataset and show the comparison of lexicon-based vs transformer-based sentiment analysis models. For lexicon based, VADER was chosen and for transformer-based, state-of-the-art BERT was chosen. BERT was found to be exceptionally good with an accuracy of 84% and F1-score of 0.64.
A Review of Recent Advances in Deep Learning for Object Detection

Computer Vision has given a way for computer systems to see by way of deciphering the surrounding objects, which has been considered a crucial problem over the years. There are quite a number of techniques devised for object detection and deep learning, but most of the research has been focused on deep learning in recent years. Visual object detection covers numerous recognition pattern tasks like image classification. This article aims to review a visual analytics approach for better understanding, identifying, and cleansing object detection frameworks and the effects of some factors such as sampling strategies, feature learning, detector architectures, proposal generation, etc., on visual object detection with special reference to detection components, learning strategies, and their applications along with benchmarks.
Monoscale and Polyscale Analyses of Physical Signals for Compressive Detection of Relevant Complexity Features

Dealing with the large volumes of data generated in the digital world requires efficient data interpretation techniques. Some compressive feature detection techniques are proposed to efficiently minimize the data size. Most of these algorithms in the signal processing area are based on monoscale or multi-scale methods. These methods are not sensitive to the reshuffling of the data, and critical features like correlation and covariance are lost. An alternative approach is to use polyscale algorithms. This paper provides a polyscale approach using the variance fractal dimension to be developed for compressive detection. Some critical aspects of using these measures are discussed, including robustness to noise and simplicity concerning other existing methods. These measures are applied to some speech utterances and experimental data measured from an electro hydrostatic actuator test rig to show the efficiency of this method. This work can provide insight into practical compressive detections to explore this promising area for future developments.
Predicting Students' Behavior Towards their Degree using Machine Learning Techniques

The phase of professional education is the most crucial part of a student’s life. One’s career might solely depend on how well the performance is. For better performance, a perfect attitude or behavior of the student is needed towards the degree. If a student is having a good mindset for the degree pursued, it will help them gain better results. In this paper, we are investigating the behavior of students toward their degrees. We conducted a survey and collected data from bachelors, masters, and doctorate students. To predict students’ behavior toward their degrees, we applied Machine Learning algorithms. We used a support vector machine, linear regression, k-nearest neighbor, naive bayes, and decision tree techniques to classify and predict the behaviors of students. Out of these techniques, the support vector machine performed well giving an accuracy of 59%. We applied the k-fold method to find the results. According to the results, 52.6% of students are optimistic about their degree, 40% consider it trustworthy, 3.5% think it is untrustworthy and 3.9% are pessimistic about their degree. Knowing the behavior or interest of students in their degree can help in boosting their productivity and increase their performance.
Work in Progress: Development of a web-based educational robotics learning monitoring system using free software

Educational robotics encompasses the integration of robotics into educational processes for educational purposes. An example of educational robotics is Lego Education WeDo 2.0, whose workshops include Exploration, Creation, and Sharing phases. Students build and design robots based on instructions to achieve goals. However, all the gathered information of the students during the workshops is not stored and published in a web system. Thus, workshop information is not delivered to teachers in a web manner to monitor students' learning. Since this, a web-based system has been developed to track such experiences. It allows the monitoring and control of student performance at each stage of the learning process based on a non-intrusive method to capture information, such as detecting emotions through a webcam. A Scrum methodology is used, which allows user stories definition. Usability assessment with 17 experts shows an overall score of 88.30 percent.
Fusion of Iris, Face, Fingerprint using Score Level Mechanism for Biometric Application

When it comes to authentication, biometric systems evaluate unique characteristics about a person, such as their face, iris, or fingerprint. However, although this may seem like a greatsolution, multi-model biometrics may also lead to assaults like spoofing and limited accuracy. The capacity of multi-model biometrics technology to address challenges and fuel their development has recently drawn attention (Using VGG 16 and grassmann). With 92% accuracy, 1000 photos were classified into different categories using the proposed system's visual geometry group 16 for object identification and classification. In order to better organise and coordinate the images with the accounts, we may use the Grassmann manifoldlearning technique to finish the still to video planning strategy. The results of the experiment are quite convincing.
Anomaly Detection and Enterprise Security using User and Entity Behavior Analytics (UEBA)

Digital frauds are made possible by a lack of transparency and other security flaws in a system. Consequently, it has grown to be the most pervasive problem in the world. When these frauds emerge from within businesses, they are referred to as insider threats and may lead to severe consequences. There have been various frameworks proposed to lessen this problem, however, transparency still remains a challenge. Conventionally, storing data in chronological order to prevent data manipulation is one technique to ensure traceability and security. In this paper, we present a framework based on User Entity and Behavioral Analysis (UEBA) approach to study user profiles over time and classify them as normal or aberrant. The proposed framework utilises additional information including IP addresses, location data, and the users’ organizations etc. We focus on applying data science and analytical methods to create data visualizations for analysis and anomaly identification.
Universal Extra Dimensional Particles Dynamics in Annihilation Channels: Proof of Dark Matter Evidence

The question as to how this universe came into being and as to how it has evolved to its present stage, is an old question. The answer to this question unfolds many secrets regarding fundamental particles and forces between them. Theodor Kaluza proposed the concept that the universe is composed of more than four space-time dimensions. In his work electromagnetism is united with gravity. Various extra dimensions formulations have been proposed to solve a variety of problems. Recently, the idea of more than four space time dimensions is applied to the search for particle identity of dark matter (DM). Signature of dark matter can be revealed by analysis of very high energy electrons which are coming from outer space. We investigate dynamical conditions of annihilation channels of Lightest Kaluza Klien particle LKP which is suggested to be concrete evidence of dark matter in galactic subhalos.
Vehicle Detection using Artificial Intelligence based Algorithm in Intelligent Transportation Systems

Intelligent transportation systems (ITS) are the future of inter- and intra-city mobility. ITS has several features, and vehicle detection is a crucial system component. This research paper implements the You Only Look Once (YOLO) algorithm, which is a neural network-based technique for detecting vehicles on roads and streets. The algorithm detects vehicles rapidly and accurately. The YOLO implementation of this study confirms the efficacy of this algorithm, detecting vehicles successfully in various scenarios. Vehicles are detected and displayed separately within boundary boxes. Our implementation of this method is limited to detecting up to five vehicles on a single road or street. The vehicles could be parked or waiting at a traffic signal for it to turn green. Vehicle tracking has increased the effectiveness of the system because it follows the vehicle's path until it disappears from the CCTV image. MATLAB simulations are used to validate the results of this study.
Development of a Massive Open Online Course on Electrical Microgrids

In research area associated with Smart Grids the U.S. National Science Foundation (NSF) has offered support for projects for workforce development and research in cybersecurity, state estimation and optimization in electrical microgrids. These aspects represent key areas with the necessity of further research, especially in simulations with real dataset. This paper focuses on the proposed future design of tools for a Massive Open Online Course (MOOC) on Microgrid State Estimation, Optimization and Cybersecurity. Thus, students and instructors may be able to count on a resource to learn and expand their knowledge on microgrids. This paper presents the main ideas associated with the future design of the MOOC, as well as practical laboratories simulations in MATLAB and Python and the interpretation of example results that will be included as part of the MOOC.
Design of TeleHaptic Simulators for Various Control Architectures

In this paper, we discuss the design of different models for teleoperation control by presenting the significant benchmark results for various important force reflecting control architectures used in 1-DOF bilateral teleoperation. They are used to analyze the results of force and distance tracking graphs in free motion and contact mode obtained from various control models by varying dynamics of master, slave, environment, and communication channel. In this regard, different force reflecting teleoperation control architectures are simulated via proposed model design, such as two-channel (2C) architectures, three-channel (3C) and finally four-channel (4C) architecture. These benchmark results are presented for each architecture via MATLAB/Simulink-based simulator.
Machine Learning Models for Student Performance Prediction

Education is the key to success which provides multiple opportunities in the life. Education not only contributes to individual success but also contributes to the success of the society. The Indian education system still follows the traditional way of the teaching learning process which lacks in interactive sessions. So, it becomes very difficult to continuously monitor student performance. Indian population is huge which makes the monitoring and analyzing student performance challenging. The Indian education system lacks in the standards for accessing the student performance and achievements. The proper methodology is required to monitor student's academic progress and development. Multiple parameters influence a student performance so it is a very challenging task to find out the significant parameters which are affecting a student performance. Another challenge in the education system is to identify the slow learners at an early stage. In this research work, various factors affecting student performance are analyzed and visualized like student's age, parent's education & occupation, health etc. The visualization technique is used to identify the weak students at an early stage to work on their improvement. To forecast the student performance, a variety of Machine Learning (ML) methods, including K Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machine (SVM), are used. The SVM model with linear kernel gave the best accuracy 84.37%for the selected dataset.
Intrusion Detection using Deep Learning Techniques

All firms use intrusion detection systems as a fundamental element of their cyber security procedures. As more and more information become accessible in digital form on the internet, the demand for robust cyber security measures to protect against data breaches and malware assaults has grown. Automated intrusion detection systems are needed to keep pace with the ever-increasing number of assaults and new malware varieties. Computer vision, natural language processing, and voice recognition are all examples of applications of Deep Learning methods that are currently being used in the realm of cyber security. An in-depth analysis of twenty-three papers using Deep Learning in intrusion detection systems is performed in this study.
Performance Evaluation of Deep Learning Models for Leaf Disease Detection: A Comparative Study

Early detection of plant diseases is crucial before plant growth is affected. Plant diseases have been detected and classified using a variety of machine learning (ML) models in the past. Deep Learning (DL) appears to have great potential in terms of increased accuracy; however, in agricultural applications of Convolutional Neural Networks (CNN) has widely been utilised by researchers. CNNs are so effective at identifying plant species, managing yields, detecting weeds, managing soil, and water, counting fruits, detecting diseases and pests, and evaluating plant nutrient status. A farmer can diagnose plant diseases quickly and accurately with an automated disease detection system. To speed up crop diagnosis, plant leaf disease detection systems must be automated. In this paper, we evaluated twelve different models on a new plant diseases dataset and demonstrated that the most accurate model was Densenet169. In training and validation, the accuracy was 97.2% and 97.8%, respectively.
Work in Progress: Exploration of Research-study Dual Cycle Mode for Simulation Practice Teaching

Traditional information course simulation teaching is often based on specific simulation software to simulate and verify some theoretical models in the course knowledge points. Under the constraints of fixed topics and methods, the limited teaching content leads to the limitation of students' innovation ability and practical ability. In view of the above problems, the present paper undertakes the progress of the dual center teaching model, with the help of an extensible online simulation platform, to design the simulation problems of related artificial intelligence algorithms for the actual engineering problems in the communication network, forming a simulation teaching mode of research and learning double cycle through which students simulate cases and apply these algorithms to innovation and entrepreneurship practice.