Reviewerly/exHarmony huggingface dataset:
AI based Automated Essay Grading System using NLP

The goal of this proposed work is to develop an AI-powered system for automated essay grading. The system will utilize natural language processing and Graph based techniques to analyze, and grade written essays. It not only checks the syntax, semantics and grammar but also grades according to the similarity of sentences using a Graph based approach. The system will be trained on a dataset of labelled essays and will be able to accurately grade new essays based on their content and writing quality. The system can be able to integrate with existing learning management systems. The goal is to provide a more efficient and accurate essay grading process, so the teachers can provide valuable feedback to students. The proposed work aims to develop an automated essay grading system using AI technology. The system will be able to analyze, and grade written essays by using natural language processing and machine learning techniques. The system will be trained on a dataset of labelled essays, which will be used to teach the system to recognize patterns and characteristics of high-quality writing. This will enable the system to accurately grade new essays based on their content and writing quality.
Automatic item generation for online measurement and evaluation: Turkish literature items

Developments in the field of education have significantly affected test development processes, and computer-based test applications have been started in many institutions. In our country, research on the application of measurement and evaluation tools in the computer environment for use with distance education is gaining momentum. A large pool of items is required for computer-based testing applications that provide significant advantages to practitioners and test takers. Preparing a large pool of items also requires more effort in terms of time, effort, and cost. To overcome this problem, automatic item generation has been widely used by bringing together item development subject matter experts and computer technology. In the present research, the steps for implementing automatic item generation are explained through an example. In the research, which was based on the fundamental research method, first a total of 2560 items were generated using computer technology and SMEs in field of Turkish literature. In the second stage, 60 randomly selected items were examined. As a result of the research, it was determined that a large item pool could be created to be used in online measurement and evaluation applications using automatic item generation.
Cricket Shot Detection using 2D CNN

Cricket is a sport that is widely popular worldwide and played at both amateur and professional levels. Due to technological advances, it is now possible to use computer vision and deep learning techniques to automatically detect and classify cricket shots from videos and images. This research study intends to develop a system for the automatic detection and classification of cricket shots from images using create a 2D CNN with multiple layers namely convolution, pooling, flattening, and full connection. This model has achieved an overall accuracy of 91.5% in detecting different types of cricket shots. The proposed model is tested on a separate test set consisting of unseen shots and obtained similar results.
Cloud Computing using OWASP: Open Web Application Security Project

Sharing data while ensuring its security is a difficult task nowadays. Users submit files in a data sharing system that have been encrypted using an encryption key. Any system for large-scale data exchange has to have this quality in light of a user leaks crucial information, it won’t be feasible for the system to maintain the information to be protected. For data owners, distributing their information via servers presents many difficulties. There are several approaches to fix these problems. These methods are crucial for managing keys shared by the data owner. In this study dependable source is used for user authentication of cloud data access. The key is first generated by the trusted authority using the SHA algorithm, and it is then sent to both the owner and the user. Second, the trusted authority module creates a sequence number using the MD-5 algorithm method after receiving an AES -encrypted file from the data owner. It stores a key in its database that is necessary for changing operations and for system identification. (CSP or Owner). a file was sent to the CSP module by this trusted authority for cloud storage. It is demonstrated that the produced key sets have a variety of useful characteristics that defend the privacy of the protection of communication channels from fraud attacks by the other nodes in the network.
Mood Detection and Emoji Classification using Tokenization and Convolutional Neural Network

Tokenization and convolutional neural networks (CNNs) is being used for the task of mood detection, emoji generation, and classification. Tokenization is helpful to break down text into individual sentences, words as well as phrases which is the important step in natural language processing (NLP). It allows the model to focus on individual words and phrases. The CNNs are then trained on labeled text datasets for mood detection, text-emoji pair datasets for emoji generation and emoji-label pair datasets for emoji classification. The results of the study indicate that tokenization and CNNs can be effectively used to understand the sentiment or emotion expressed in text data, to acquire high accuracy in classifying text as having a negative and positive or neutral sentiment, generating emojis that match the sentiment or emotion expressed in the text and classifying emojis as expressing a certain sentiment or emotion. For sentiment analysis, a CNN can be trained on a dataset of labeled text where the labels indicate the sentiment or emotion expressed. Once trained, the CNN can be used to classify new text as expressing a negative, neutral, or positive sentiment. As far as emoji generation is concerned, a CNN can be trained on a dataset that pairs text with corresponding emojis and labels. Once trained, the model can be used to generate emojis that align with the sentiment or emotion conveyed in new text inputs. As for emoji classification. This research study discusses about various techniques involved in tokenization emoji as well as text generation and classification.
Impact of Mixed Precision Techniques on Training and Inference Efficiency of Deep Neural Networks

In the deep learning community, increasingly large models are being developed, leading to rapidly growing computational costs and energy costs. Recently, a new trend has been arising, advocating that researchers should also report the energy efficiency besides their model’s performance in their papers. Previous research has shown that reduced precision can be helpful to improve energy efficiency. Based on this finding, we propose a simple practice to effectively improve the energy efficiency of training and inference, i.e., training the model with mixed precision and deploying it on Edge TPUs. We evaluated its effectiveness by comparing the speed-up of a state-of-the-art semantic segmentation architecture with respect to different typical usage scenarios, including using different devices, deep learning frameworks, model sizes, and batch sizes. Our results show that enabled mixed precision can gain up to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.9\times $ </tex-math></inline-formula> speedup compared to the most common and default float32 data type on GPUs. Deploying the models on Edge TPU further boosted the inference by a factor of 6. Our approach allows researchers to accelerate their training and inference procedures without jeopardizing the model’s accuracy, meanwhile reducing energy consumption and electricity cost easily without changing their model architecture or retraining. Furthermore, our approach is helpful in reducing the carbon footprint used to train and deploy the neural network and thus has a positive effect on environmental resources.
Detection of Prone Areas of Crime Against Children using DBSCAN

Crime against children is a heinous act that transgresses the moral fabric of the community and refers to an action which causes physical & emotional harm to children as well as their psychological well-being. This type of crime manifests in several forms including sexual abuse, rape & exploitation, child trafficking, abusing children or neglecting children, cruelty, children put for begging and child pornography etc. Children are innocent & delicate and require care and protection from the parents as well as the government which is responsible for the safety of society and public wellbeing. The Crime against children data published by the National Crime Records Bureau (NCRB) of India is used to analyze and detect prone areas using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method. The analysis & results of this study may help government to take additional precautionary steps and come up with effective strategies to fight against crimes on children
Blockchain-based Access Control Model for Student Academic Record with Authentication

Schools and Institutions maintain student’s records and certificates for future needs like verification and credit transfer. These procedures are carried out manually to ensure that documents and the data are valid and non-tampered, which usually needs more energy and time. Converting these procedures into digital form minimizes the time and energy consumption but with the cost of high-security risks. Storing student’s data on a centralized system has problems like data breaches due to human error, injection attacks, and buffer overflow attacks, and even if the server gets damaged physically, it’ll take more time to repair and recover the damaged data and start running. But in blockchain technology, the data stored in blocks with a cryptographical code of the previous block, gives enhanced security compared to the traditional database. If anyone tries to tamper or hack the data, the decentralized technology can easily recognize the modification and prevent it from happening. Three layers of blockchain are being employed to give the Academic Record System the highest level of security possible. Data Storage Blockchain is the third layer of the model which stores the student’s records. The Access Control Model Blockchain is the second layer, which includes additional layer security to the Data Storage Blockchain as it records all the activities around it. And the final layer is the Authentication Blockchain, which is based on Ethereum Blockchain. This model validates transaction using Smart Contracts given to them, creating a new user and validating the login process securely for the Authority, Institution, and the Student for accessing the Data Storage.
Using Function Call Sequence for Browser Fingerprinting Detection

Browser fingerprinting is a stateless tracking technique that combines browser attributes into a single identifier for tracking users. As browser fingerprinting is used by a growing number of websites, many works have focused on browser fingerprinting detection. Existing works mostly consider detection through the perspective of script, it will case false negative when script is split. In this paper, we propose BFDetector, an approach for better detecting browser fingerprinting, to solve the problem of script splitting. To this end, BFDetector uses function call sequence of web pages to detect browser fingerprinting through NLP-based one-class deep learning ensemble. We collected 7,800 function call sequences as dataset from Alexa top-10K websites’ homepage. After collecting these function call sequences, we trained a Javascript API embedding to convert each function to a vector for training deep leaning classifier. Through the test of our own dataset, the accuracy of classifier has reached 95.53%. Afterward, we analysed the characteristic of some known fingerprinting techniques’ function call sequence and found a newly fingerprinting technique that we call it media-based fingerprinting.
Secured Cloud Environment for Improved Web Services in Agricultural Sector

In recent years, there has been an increasing demand for organic foods and home products due to their health benefits and eco-friendliness. However, the lack of standardization and certification has made it difficult for consumers to trust the authenticity of these products. This article presented here is a cloud-based application that enables consumers to purchase and verify the authenticity of organic foods and home products. The proposed system uses a cloud solution for ensuring product authenticity, while the cloud infrastructure provides scalability and security for the system. The application includes a user-friendly interface that allows consumers to easily browse and purchase products, while also providing detailed information about the product’s origin and certification. The cloud-based solution ensures that consumer data is secured and protected from unauthorized access. Overall, the proposed system provides a secure and reliable platform for consumers to purchase and verify the authenticity of organic foods and home products.
Wireless Sensor Network based Anomaly Detection using SVM-RFE-MRMR

There has been a meteoric rise in the use of wireless sensor networks (WSNs) in recent years. WSN’s compact size and low cost are enticing many different industries to adopt them for use in a wide range of applications. There are several applications of IoT technology, including environmental monitoring, building security, and precision agriculture. Given that most WSNs are deployed in an unsupervised and potentially hostile environment, security risks are significant. To ensure the privacy of data during transmission from sensors to the base station, a number of methods have been proposed. The work in question is concerned with a crucial part of data and network security: detecting attacks. With the goal of preventing malicious attacks and maintaining the security of wireless sensor networks, anomaly detection has emerged as a significant obstacle. After the input is given, It consist of three phases such as preprocessing, Feature selection and training the model using SVM-RFE-MRMR. The SVMRFE-MRMR methodology is used for classification in the suggested method. While SVM-RFE takes feature-decision correlation into consideration, it does so by neglecting feature-feature relationships, MRMR is able to provide features with little redundancy and maximum relevance. Because of this SVM-RFE-MRMR is used to generate a set of features that is both minimal in redundancy and maximal in relevance. The proposed model produces an accuracy of about 98.6% when compared other models such as SVM and CNN.
Building Robust FinTech Applications and Reducing Strain on Strategic Data Centers using the LoTus Model

Agile is a well-known project management approach that has been used for many years. It places a strong emphasis on client satisfaction, adaptability, and teamwork. Agile was first developed as a software development approach, but it has now been modified for application in other sectors including marketing and finance. The Agile Manifesto, which was released in 2001 and explains the principles and ideals of Agile development, is the foundation of the Agile ideology. One or more of the guiding principles is to adapt to change instead of following a plan, prioritize functional software over thorough documentation, and collaborate with customers over negotiating contracts. Agile has gained popularity over time as businesses try to be adaptable and responsive to their customers’ constantly changing business demands. The lack of predictability in Agile is one of its key drawbacks. Agile stresses client cooperation and adaptation, therefore the finished product could differ somewhat from what was originally planned. For businesses that depend on meticulous planning and a rigid schedule, this lack of predictability can be problematic. It faced a serious problem during the process of building a finance application called JazzFinance. This has led to build another robust and systematic software development method called as LoTus model. The proposed LoTus is an acronym for two abbreviations. Those are lean optimization TypeFace for Unified Systems (LoTus) and Locate dependencies, optimize for reusability, Test-Driven environment, Unify Design and Scalability. This article goes through the development of LoTus and how it has helped us build a stable finance application within a small amount of stipulated time.
Learning knowledge representation with meta knowledge distillation for single image super-resolution

Although the deep CNN-based super-resolution methods have achieved outstanding performance, their memory cost and computational complexity severely limit their practical employment. Knowledge distillation (KD), which can efficiently transfer knowledge from a cumbersome network (teacher) to a compact network (student), has demonstrated its advantages in some computer vision applications. The representation of knowledge is vital for knowledge transferring and student learning, which is generally defined in hand-crafted manners or uses the intermediate features directly. In this paper, we propose a model-agnostic meta knowledge distillation method under the teacher-student architecture for the single image super-resolution task. It provides a more flexible and accurate way to help teachers transmit knowledge in accordance with the abilities of students via knowledge representation networks (KRNets) with learnable parameters. Specifically, the texture-aware dynamic kernels are generated from local information to decompose the distillation problem into texture-wise supervision for further promoting the recovery quality of high-frequency details. In addition, the KRNets are optimized in a meta-learning manner to ensure the knowledge transferring and the student learning are beneficial to improving the reconstructed quality of the student. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation-related distillation methods and can help super-resolution algorithms achieve better reconstruction quality without introducing any extra inference complexity.
Cross-layer Network Bandwidth Estimation for Low-latency Live ABR Streaming

Low-latency live (LLL) adaptive bitrate (ABR) streaming relies critically on accurate bandwidth estimation to react to dynamic network conditions. While existing studies have proposed bandwidth estimation techniques for LLL streaming, these approaches are at the application level, and their accuracy is limited by the distorted timing information observed at the application level. In this paper, we propose a novel cross-layer approach that uses coarse-grained application-level semantics and fine-grained kernel-level packet capture to obtain accurate bandwidth estimation. We incorporate this technique in three popular open-source ABR players and show that it provides significantly more accurate bandwidth estimation than the state-of-the-art application-level approaches. In addition, the more accurate bandwidth estimation leads to better bandwidth prediction, which we show can lead to significantly better quality of experience (QoE) for end users.
Design and Evaluation of Virtual Reality Environments for Claustrophobia

Abstract Claustrophobia is a specific phobia characterized by fear of enclosed spaces (such as elevators, subway tunnels, etc.). While some individuals with claustrophobia might experience only mild anxiety and discomfort in enclosed spaces, others might suffer from panic attacks usually triggered by a fear of losing control or being unable to escape when needed. One of the most recent and successful treatment methods applied in specific phobias are virtual reality (VR)-based exposure therapy techniques. This feasibility study aimed to design and validate a set of immersive virtual environments (VEs) for exposure therapy in claustrophobia, with interactive scenarios enabling systematic, gradual, and controlled induction of claustrophobic fear. In total, eighteen individuals participated in the feasibility study. Participants were recruited from a general population and divided into two groups (phobic vs. control group). Each participant was asked to evaluate a set of various virtual environments. The study results indicate that the designed virtual environments have the potential to induce claustrophobic fear and are thus considered suitable for use in the exposure therapy of claustrophobia. These virtual environments also evoked a sense of presence, particularly the spatial presence component, while the reported cybersickness symptoms were none or minimal.
A Dataset of Food Intake Activities Using Sensors with Heterogeneous Privacy Sensitivity Levels

Human activity recognition, which involves recognizing human activities from sensor data, has drawn a lot of interest from researchers and practitioners as a result of the advent of smart homes, smart cities, and smart systems. Existing studies on activity recognition mostly concentrate on coarse-grained activities like walking and jumping, while fine-grained activities like eating and drinking are understudied because it is more difficult to recognize fine-grained activities than coarse-grained ones. As such, food intake activity recognition in particular is under investigation in the literature despite its importance for human health and well-being, including telehealth and diet management. In order to determine sensors' practical recognition accuracy, preferably with the least amount of privacy intrusion, a dataset of food intake activities utilizing sensors with varying degrees of privacy sensitivity is required. In this study, we collected such a dataset by collecting fine-grained food intake activities using sensors of heterogeneous privacy sensitivity levels, namely a mmWave radar, an RGB camera, and a depth camera. Solutions to recognize food intake activities can be developed using this dataset, which may provide a more comprehensive picture of the accuracy and privacy trade-offs involved with heterogeneous sensors.