Reviewerly/exHarmony huggingface dataset:
Multi-Source Education Knowledge Graph Construction and Fusion for College Curricula

The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI). Among the various AI technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP) have emerged as powerful visualization tools for integrating multi-faceted information. In the context of university education, the availability of numerous specialized courses and complicated learning resources often leads to inferior learning outcomes for students. In this paper, we propose an automated framework for knowledge extraction, visual KG construction, and graph fusion, tailored for the major of Electronic Information. Furthermore, we perform data analysis to investigate the correlation degree and relationship between courses, rank hot knowledge concepts, and explore the intersection of courses. Our objective is to enhance the learning efficiency of students and to explore new educational paradigms enabled by AI. The proposed framework is expected to enable students to better understand and appreciate the intricacies of their field of study by providing them with a comprehensive understanding of the relationships between the various concepts and courses.
Investigating Trust in Context-Aware Recommender System in Education

Recommender systems (RS) are an efficient tool to reduce information overload when one has an overwhelming choice of resources. Embedding context-awareness into RS is found to increase accuracy and user satisfaction by allowing systems to consider users' current situation (context). Context-aware recommender system (CARS) has applications in various areas, including education, where it can help learners by suggesting learning resources, peers to collaborate with, and more. When CARS is used in a learning context, it adds to the issue of lack of trust in the information, source, and intention as one builds knowledge through it. Further, embedding context-awareness adds to the trust issue due to the additional layer of automated context detection and context interpretation without users' involvement. I investigate how to build trust in CARS in an educational setting. My investigation will be threefold (a) Understanding users' perceptions of CARS; (b) Investigating design interventions to build trust in CARS; (c) Designing and evaluating a multidimensional approach to build trust in CARS.
Learning Path Recommendation Based on Knowledge Tracing and Reinforcement Learning

Adaptive and intelligent web-based educational systems are made to automate the adaptation of the system to the learners' behaviors and needs. Personalized e-learning platforms should make adaptive adjustments according to the individual students' interactions and their knowledge states (KS). This study proposes a more effective personalized learning path recommendation algorithm to promote the individualized development of students. First, the Dynamic Key-Value Memory Network (DKVMN) is enhanced by integrating a learning behavior module, which is used to trace student knowledge states. Then, the proposed knowledge tracing model is used to simulate virtual students and train recommendation policy based on reinforcement learning (RL). The experimental results show that our personalized learning path recommendation algorithm increases the average knowledge state of students by 12.11% and 5.38% on two different data sets, respectively.
Multilingual Context-Aware Chatbots for Multi-domain Test Data Generation

Various novel communication channels have been developed in recent years. Examples are messaging apps, social media platforms, and packet-oriented telephony services. This development is accompanied by an increasing need for methods and tools that extract and explore the large amount of natural language data for forensic analyses. However, these tools have to be configured, trained, and evaluated before they can be deployed. For forensic test data, the provision of high-quality domain-specific data in various formats is of utmost importance. In this chapter, we present a chatbot framework for generating high-quality domain-specific forensic data sets for chat and audio communication. While actual chatbots are mainly implemented as human-to-bot systems, we have developed a bot-to-bot framework for interactive data generation. Our framework is open-source and based on state-of-the-art natural language processing methods. It allows a transfer of the synthetic generated data in real-world data types and different languages. We evaluate the quality of the generated data and discuss their usability.
A Novel Embryonic Cellular Architecture with BIST for Deep Space Systems

In deep space systems, it is necessary to design reconfigurable system with fault tolerance. The delay in communication and the unpredictable failures can be detrimental to the space systems. The emerging field of embryonics, inspired from biological processes, provides in-built self-repair features. The embryonic circuit growth and its operation are decided by the stored genome configuration data. The embryonic cellular structure has in-built fault tolerance due to its structure. Each cell contains the total genome data of fabric, so that in case of any one cell failure, the data can be copied to spare cell. The fault detection technique at cell level has been explored in this chapter. The novel embryonic architecture with BIST that is proposed in this chapter is evolved from the concept of cloning used by biological cell to grow into a bigger system. The embryonic architecture is integrated with self-test and self-repair functions. In case the fault detection is possible at cell level, the faulty cell isolation is easy to implement. A novel embryonic fabric architecture with built-in self-test is presented. The design is coded using verilog and simulated for 1-bit adder and 2-bit comparator cell design. The genome data are generated through evolutionary algorithm and decoded as Cartesian Genetic Programming (CGP).
A New Proposed Data Security Model in Cloud Computing Environment

Advancement in the environment of cloud computing has rapidly increases over the past years. In cloud computing protection of data utilizing centralized resources has arisen because of a growing number of businesses storing their data int the cloud helps them easily to access their data easily. The major issues faced by users nowadays, the safety and fetching of user cloud data is arisen rapidly. In this proposed algorithm, the user's data is encrypted using the advanced encryption standards algorithm, and the encrypted data is then performed with some xor operations in cypher block chaining mode using a secret key known only to the data owner to increase confidentiality and security level. If the user does not provide a secret key, the system can generate one on its own.
Accelerating fluid flow in Quantum Computing using GPU

In the last few years, quantum computing has evolved as the next big technological breakthrough. Many big companies are actively researching this area and have built open-source frameworks which can be used to develop solutions for problems that connect be solved by classical computers. One such problem is the solution of Navier-Stokes equations which takes exponential time for simulation but using quantum computers, they can be solved in polynomial time. But the current state of quantum computation does not enable everyone to test their implementation on a real quantum computer. In this situation, a simulator enables rapid prototype and testing techniques and algorithms for when the real quantum computer is ready. This paper focuses on simulating fluid flow algorithms using a quantum framework, such as Navier- Stokes equations, with the lattice Boltzmann method (LBM) used to numerically solve the relevant system of equations on both CPU and GPU to speed up the process. The quantum framework used in this paper is Qiskit, an open-source software development kit created by IBM that allows developers to create and test quantum algorithms. NVIDIA cuQuantum is an SDK of optimized libraries and tools for expediting quantum computing operations used in this paper to solve the fundamental problem of modeling large-size quantum circuits using the power of GPU
Gyroscope Aided Blur Kernel Estimation for IR Image Deblurring

Stabilized Electro-Optical Systems are used in multiple domains, like target detection, tracking, and area surveillance. Cooled thermal imagers are used for long range surveillance. The requirement of a fast steering speed of the gimbal during surveillance induces blur in the captured IR video frames, which is required to be correct in real-time. Existing blind deblurring methods take a long time to execute. Deep learning methods are fast but cannot be generalised for all practical scenarios. In this work, we propose a noble blur kernel estimation method, using inertial sensor, and integrate it with existing non-blind deblurring methods and a sensor based deep learning method. Our method shows good results and is a suitable candidate for deblurring the image captured by gimbal-based imaging systems.
Image Spam detection in E-mails using Grasshoppers optimization technique

one of the most frequent types of digital communication is email. Spam is unsolicited mass mail, whereas image spam includes spam text incorporated in images. This type of spam threatens email communications because spammers use it to avoid text spam filters. This paper provides a support vector machine (SVM) based classification algorithm and a biology optimization strategy to categorize emails as spam or ham. In this study, we examine image spam detection strategies using different combinations of image processing and machine learning algorithms. The grasshopper optimization algorithm (GOA) is an approach that statistically models and simulates the behavior of natural locust swarms. We investigate and analyze the performance of Grasshopper optimizations using several evaluation measures, such as accuracy, precision, recall, f1-score, and convergence rate to the global optimum solution.
Securing IoT with Blockchain: Detecting Malicious Nodes with TLS Certificates

Since the emergence of the Internet of Things (IoTs), the potential for connecting devices with other devices, to collect and exchange data has been widely becoming an integrated part of human society. The ongoing development and innovation have also reached various other domains. However, there are still some significant security measures and trust issues that need to be resolved to make the Internet of Things more secure and reliable. Blockchain technology emerged as one potential solution for the security-related goals of IoTs. There has been a growing interest in exploring the potential for integrating blockchain technology with the Internet of Things(IoTs). Blockchain provides distributed immutable ledger that keeps track of transactions done by nodes connected to its network. However, security risks remain, such as the potential inclusion of malicious nodes in the blockchain network. To address this, we propose a method that uses TLS certificates to identify and prevent malicious nodes from joining the blockchain network, ensuring the reliability and security of the distributed ledger.
Hybrid Model for Image Forgery Detection In Smart Healthcare

Smart Healthcare Systems (SHS) are gradually replacing traditional healthcare systems that involve human intervention. This is due to the advancement of IOT devices and also the introduction of edge computing. SHS provides security, flexibility, and efficiency. A key feature of SHS is to transport images from one place to another in real-time. Due to technological advancements, these images can tamper which is image forgery, this threats security in SHS. To tackle this problem, Image Forgery Detection using Support Vector Machines (SVM) and Extreme Learning Machines (ELM) is proposed in this model. We combine the scores of both SVM and ELM using Bayesian Sum Rule (BSR). The model is utilized to verify if the forgery is done or not. This model can be inbuilt into the SHS. Forgery Detection enhances security and reduces the threat of systems being failed, as images play a key role in the treatment procedure.
Dual-encrypted privacy preservation in Blockchain-enabled IoT healthcare system

For the benefit of many healthcare stakeholders, wearable technology and the Internet of Things (IoT) are now experiencing exponential growth in the healthcare sector. Yet, due to the IoT’s centralized aspect, the existing healthcare system encounters privacy and security difficulties while storing and exchanging individual healthcare data over the Internet. Fortunately, the underlying Blockchain technology offers decentralization qualities with great security and high trust. In this paper, We propose a framework with the integration of Blockchain & IoT in the Healthcare system. A novel Dual-encryption approach, which combines symmetric and asymmetric cryptographic techniques, is used here to maintain data privacy. Additionally, we use a digital signature technique to improve security and privacy as the system contains vital patient information. Moreover, a Comparative analysis has been done with existing models to ensure the better performance of the proposed model.
Three Dimensional Hand Movement Tracking with Machine Learning and Stereo Camera

A novel methodology is suggested to track the hand movement in 3-dimensions, using a computer vision based machine learning model to identify various points on the hand and doing it repeatedly in each frame . The proposed method is based on Stereo camera setup to calculate the change in depth, in which, the hand is recognized at different points using machine learning in both cameras and is used to calculate the change in position with respect to a initial position. The Computer vision machine learning model is implemented on python. The displacement is recorded with respect to the starting position of the hand initialized, co-ordinates are printed on the camera window as well as exported to a CSV file. The various positions of the hand are observed and the displacement from initial recorded.
Key-Frame based Video Summarization using Optimized K-means Clustering

Video content has been increasing at a very high pace, so summarizing videos is of urgent need. Video summa-rization emphasizes the quick go-through of video content. In the past decades, the field of video summarization has attracted a lot of research. Many of the techniques have been proposed by different researchers. However, most of them are not able to create summaries of general videos, i.e., they are generally good for a set of a category of videos. So, to solve this problem, we have proposed a deep feature-based clustering framework for video summarization. We have used the pre-trained resnet151v2 model for feature extraction, and based on the extracted features, we group the frames of the video using the K-means clustering algorithm. After that, we selected the central frame from each video cluster and finally combined them to form the video summary. The experiments were performed on different types of videos taken from the different datasets and the results show that the proposed methodology performs better compared to some existing video summarization techniques.
FPGA Implementation and Validation of Particle Swarm Optimization suited for varied applications

Particle Swarm Optimization (PSO) is a metaheuristic algorithm that iteratively optimizes a problem to get the optimal solution. In the field of electrical and electronic engineering, it has numerous applications. This optimization technique is largely used in software. However, hardware implementation of PSO results in high performance. In this paper, the three-variable sphere function is the problem that is being optimized. PSO is implemented on FPGA by developing Verilog HDL code. Digilent Basys3 Xilinx board is used to implement PSO in hardware and display the results. The binary output is shown on the basys3 board using LEDs, and it is then converted to decimal numbers using a binary-to-decimal number converter. The entire Verilog HDL code contains 3 modules and six 16 bit binary outputs. The basys3 board’s LEDs are used to display these 16 bit binary outputs. Then two 16 bit numbers are merged to form a 32 bit number and it is converted into a decimal number using a binary-to-decimal converter. The values obtained from the MATLAB implementation of PSO are used to verify the final outputs of the hardware implementation. Also, the results obtained from the hardware implementation are converging and are accurate with the results obtained from MATLAB. Finally, an application is implemented where PSO is used to estimate the equivalent circuit parameters for the induction motor.
Dual-Layered Defence Mechanism For Prevention of XSS Attack

Over the years the usage of web applications has been increasing, and with the increase in usage and popularity comes security risks. Cross-site scripting attack is one such threat to web applications. Cross-site scripting also known as XSS is an attack wherein a malicious code is injected into a web application which leads to compromise of user data. One of the biggest effects of XSS is that it can lead to exposing user’s credentials and data. So this paper discusses an approach to mitigate the attack by using a dual-layered defence mechanism. In the first defence layer, a KNN model is used to prevent any attacker from injecting XSS scripts into the application, if the model fails to prevent this attack in the first layer, then a second layer of defence is used which uses a session mapping method where the user’s session is mapped with the user’s system details, these details include IP, OS and browser, so if the attacker tries to log in to the application with the victim’s session and if the system details don't match, then the attacker will be restricted from accessing the application. With this dual-layered defence mechanism in the application, it is very hard for an attacker to access a victim’s account.
Multilingual Obnoxious Message Classification using Bidirectional Encoder Representation from Transformers (BERT)

Everyday we witness incidents of social media bullying, harassment and various other toxic remarks. It has now become the part and parcel of social networking. Promotion of violence and hate speech, etc involve toxic comments in various forms on social media. The proposed model in this paper helps with the identification of all the obnoxious and questionable content on the internet by classifying the online comments as toxic or clean to tackle this problem. This is realised by using a pre-trained model - BERT. To enhance the performance, this model used RoBERTa (Robustly Optimized BERT Pre Training Approach), which has basically been formed by altering some hyperparameters which are tuned during the pre-training of BERT.
A Light Convolutional Neural Network Architecture for Cross-Spectral Face Recognition

The rise in the utilization of multispectral imaging in surveillance, biometrics, and forensics has led to Cross-Spectral Face Recognition becoming a pertinent problem to solve. For instance, surveillance cameras switch from utilizing the Visible (VIS) spectrum during the day to the Near Infrared (NIR) spectrum at night, in order to capture footage in the dark. The process of matching face images captured on different sensing modalities is called Cross-Spectral Face Recognition (CSFR) also known as Heterogeneous Face Recognition (HFR). The variation in spectral characteristics between the NIR and VIS face images makes face recognition across these two information domains a challenging problem to solve. This adds to the complexity of building an efficient and accurate model that performs cross-spectral face recognition across the NIR and VIS domains. This paper proposes a light CNN model that performs cross-spectral face recognition across the NIR and VIS spectra. The proposed model has obtained higher accuracy scores when compared to denser Convolutional Neural Networks on CSFR across both spectra on various benchmark datasets such as CASIA NIR-VIS 2.0 (97.12%), Oulu-CASIA (99.85%), and HIT Lab2 (96.88%). Furthermore, this model attained the aforementioned accuracy scores with significantly fewer trainable parameters (0.102 million parameters).
Fall detection system for monitoring elderly people using YOLOv7-pose detection model

The elderly population in the world is continuing to grow at an unparalleled rate and it is important to keep in mind about the safety of the aging population. Falls have become one of the major reasons leading to injury to them and if timely help is not provided, it could lead to serious complications. There are various traditional methods that have been used to detect human falls and wearable devices are one of these methods that contain sensors like accelerometer, gyroscope, barometers, etc. These devices are highly instrumental in computing various parameters based on which the fall will be detected. However, these devices have certain limitations, as they are complex for the elderly people to understand and use. The accuracy of such devices is very low and there are high chances for false alarms as well. Hence, applying a vision-based object detection algorithm for detecting these human falls is highly significant in order to overcome such challenges faced by wearable devices. In this research work, an Object detection based Automated Fall Detection System has been proposed wherein, the YOLOv7 (You Only Look Once) pose model is used to discriminate the fall and non-fall activity. Given a video, the YOLOv7 model will first distinctly separate all the video frames and then pre-process these frames. This pre-processed data is sent for estimating the pose of the person. This resulting output is further subjected to classification as fall or non-fall activity. The dataset used in our approach are self-generated videos that cover a set of daily human activities. The strength of the proposed method has been proved through various performance measures like precision and classification accuracy.
Metahuristics based task scheduling algorithm in cloud

Cloud computing is the most trending technologies over the internet that offers numerous computing services to the users like servers, databases, software and storage that are highly scalable and cost-effective. The services provided by the cloud work on the pay-as-you-go principle which means that you can either scale up or down the resources provided by the cloud as per your requirements. Cloud computing is a highly effective and reliable technique, however, scheduling numerous tasks over cloud can result in underutilization or overutilization of resources. Thus, to map the tasks on the resources efficiently, the need to use meta-heuristic algorithms arises. The use of these algorithms provides a near-optimal or optimal solution. MEALPY is a python library which offers optimized solutions for nature-based meta-heuristics algorithms. Biological systems, Swarm intelligence, Evolutionary-based and human based meta-heuristic algorithms are used to solve real world scheduling optimization problems. In this study, we have proposed a detailed and substantiated comparison of different nature-based meta-heuristic algorithms on the basis of various variables which affects the effectiveness of execution time in scheduling tasks over cloud for achieving better optimal solution for task scheduling.