Reviewerly/exHarmony huggingface dataset:
Improving Image Classification with Quantum Neural Network

Image classification is a fundamental task in computer vision that involves assigning labels to images based on their content. In recent years, deep learning techniques based on classical neural networks (CNN) have achieved modern performance on many image classification tasks. However, there is growing interest in using quantum neural networks (QNNs) for image classification, as quantum computing has the potential to improve the accuracy and speed of computation. We demonstrate the potential of our QNN model and QCNN model by applying it to the fashion MNIST dataset. The results indicate that the QNN model exhibits faster training speed and higher testing set accuracy compared to a QCNN with a similar architecture.
Continuous Sign Language Recognition Based on I3D-TCP Network Structure

Continuous sign language recognition aims to recognize sign language in complex scenes. Previous work on sign language recognition using Inflated 3D ConvNets, operates on spatial and optical flow and demonstrates impressive performance. However, first, continuous sign language recognition still has some challenges: whether the I3D model extracts sign language information effectively in complex scenes and whether sign language videos' frame content and interframe relationship are relevant. Finally, a large amount of redundant information in sign language videos interferes with the effective feature extraction of relevant sign language information. This paper studies the I3D-TCP network structure. Previously, the I3D model was studied to extract sign language information features from sign language RGB videos, and the temporal attention covariance pool was used to obtain features that focus on the content correlation of sign language video frames and the inter-frame relationship to better realize the recognition of sign language. Experiments show that we use the innovative method of the I3D-TCP structure to evaluate the large-scale continuous Chinese sign language dataset and the Chinese sign language dataset of Tianjin University of Technology. Compared with the experimental results, this method is significantly better than most of the advanced RGB-based methods, which proves the method's effectiveness and can recognize sign language in complex scenes.
Convolutional Neural Network for Handwritten Recognition

Handwritten recognition is to make the machine to recognize the letter written by human, which is a general vision task in computer science. Numerous frameworks are proposed, such as supported vector machine, random forest, convolution neural network. This paper aims to exploit the effectiveness of CNN in this task. In specific, LeNet is introduced as our main structure. For the experiment, we utilize 143,000 pictures as training set and the execution is conducted in line with the program written in Python, in which the super parameters have been debugged. Finally, 23,400 pictures of the test set are used as the test set, and the peak of the accuracy rate finally reaches 93%. Finally, the prediction on 26 pictures written by myself is conducted, and 20 letters can be predicted accurately with an accuracy rate of 77%, in which ten tests are conducted in total, and the average accuracy rate reaches 65%.
Efficient Energy Trading and Charging System: Electric Vehicles with Blockchain

Vehicles that emit carbon dioxide are quickly giving way to greener transportation options. The growth of electric vehicles (EV) is an important step in developing sustainable modes of transportation. We need a broad and effective charging infrastructure as the number of electric vehicles increases steadily. It facilitates smooth invoicing and charging. Some users charge their electric automobiles by employing solar panels to produce electricity. On the other hand, some people use charging stations and pay to have their cars charged. The issue of transparency and trust is brought up by this. Numerous regulatory authorities do not strictly upheld the payment system fraud and hence customer may be overcharged by the service supplier. In this context, block chain is a cutting-edge technology that always concerns about privacy and trust. A block chain-based peer to peer energy trading and payment system for charging electric vehicles has been proposed. By using smart contracts, customers can use electronic wallets to cover their charging costs. Proposed work resource utilization is also presented.
Enhancing Machine Learning Based Solar Generation Forecasting with Time Data Utilization

Solar energy is expected to have a key role in the transition to a cleaner and environment-friendlier energy system, but given its highly weather dependent and intermittent nature, it creates operational challenges for the electric power grid because of the uncertainty involved in the output. Thus renewable energy generation forecasting becomes an increasingly important task in regard to not just the reliable operation of the energy system, but also from a financial standpoint. Here it is analyzed how the information carried by the timestamp can be utilized in order to improve solar generation forecasting accuracy. We devised and implemented our own time feature input, which we tested on several techniques based on machine learning for solar energy generation forecasting. In this paper, we demonstrate that, though often overlooked, the information carried by the timestamp is significant and can greatly improve the forecasting accuracy.
Medical Data Sharing Scheme Based on Blockchain-Based Attribute Proxy Re-Encryption

Medical data is a kind of personal information with high value. In the traditional medical data management system, it is usually stored in a centralized database, which has problems such as single point of failure, invalid access policy, and difficulty in sharing. Aiming at the above problems, a blockchain-based medical data sharing scheme is proposed. First, use the consortium blockchain to ensure the integrity and transparency of medical data, and use the stealth address method to ensure the impossibility of doctor-patient contact, propose an attribute encryption and proxy re-encryption scheme with a hidden strategy to achieve fine-grained data Access Control Scheme. Combined with smart contracts, the revocation of access rights and the control of access time are realized to protect the privacy of patients. Secondly, in view of the problem of centralized single point of failure and the excessive load of blockchain storage in the process of data sharing, the solution is integrated with blockchain, smart contracts and interplanetary file systems to realize distributed storage of original data ciphertexts under the chain. Low overhead mode shared on-chain with key information ciphertext. Finally, through the security evaluation, it is proved that the scheme can guarantee the safe sharing of data. Through comparison with similar data sharing schemes and performance analysis experiments, the results show that this scheme has better efficiency under the premise of ensuring safe sharing.
Robust Image Captioning Using Knowledge Distillation

With attention mechanism extensively exploited, significant progresses have been made in image captioning. However, it is still challenging to train models robust to various image corruptions, which is a common issue in domain of computer vision. In this paper, we propose a method using knowledge distillation to facilitate the training of image captioning model with corrupted dataset. In this scheme, the knowledge of a teacher model with excellent performance on sound images is distilled out to train a student model which is expected to possess better robustness on corrupted images. Experiments have shown that the introduction of distillation enormously improves the robustness of existing image captioning models. The code is available at https://github.com/GjQAQ/RobustImageCaptioning.
Road Density Prediction Based on City Transport GPS Time Series Data using a Support Vector Machine

Along with the development of the current era, urban transportation in Indonesia is very interesting, especially in the city of Bandung. City transportation is used by people who do not have private vehicles to carry out daily activities such as going to work, going to school, and travelling to a desired place. Generally, city transportation is also used because the fares given are quite affordable. To find out the coordinates of the location of city transportation, we can use the Global Positioning System (GPS). This study uses the Support Vector Machine (SVM) method. This method is used because of previous research; SVM produces a fairly high level of accuracy compared to other methods. SVM can also help improve system performance and find a relationship between urban transportation and congestion. The amount of data used in this research is 35320 data. From the results of the tests that have been carried out, the SVM classification method produces an accuracy rate of 98%.
Intelligent-based SIEM security email alert

SIEM, or Security Information and Event Management, can be considered the latest cybersecurity technology in the security strategy that was taken and utilized mostly by professional cybersecurity teams. Whether it’s from a large enterprise to a medium-small size company, it is used as a tool to monitor their IT environment to protect the company’s digital assets, security incident prevention, and in addition, protect the company’s reputation. Due to its reliability, it is fair to say that SIEM plays a vital role in the current cybersecurity trends since it can provide all these features through just a platform or web console compared to an antivirus. Even though SIEM includes many advanced security features. However, some pre-installed features contain limitations that may not suit a security team's needs when it comes to their operation manuals. For instance, the SOC (Security Operations Center) team is often required to review the reports generated by SIEM and send the info to their clients with the company’s customized email templates. This feature is not provided by most of the SIEM software. Thus, this paper aims to develop a system that can overcome the lack of email customization and SOC team-to-customers email sending-related issues in the SIEM that the SOC teams currently face in their daily operation.
Air Temperature Data as a Learning Material for Time-Series Analysis

In this study the authors tried to assess the effectiveness of air temperature data as a learning material for learners and beginners to understand time-series analysis. Therefore, the authors analyzed the air temperature obtained at 15 observation points in Japan. The analyses were conducted with ARIMA model and the SARIMA model. The results show that air temperature data has good properties as a learning material for time-series data analysis. It is remarkable in learning the process of model identification especially the process of ensuring stationarity.
Automating Citation Intent Classification on SciCite Dataset

Scientific papers often contain citations that provide important information on the background, methods used, and results obtained. Identifying the intent of these citations is a critical machine learning problem, and it is also essential for the automated analysis of scientific literature. The SciCite dataset, consisting of 11,020 rows and 15 features, was used for this purpose. Preprocessed data was then fed to machine learning models such as KNN, Random Forest Classifier, Light Gradient Boost Classifier, Gradient Boost Classifier, and XGBoost. A comparison was drawn among these models to identify the one that yields the highest weighted F1 score, which was found to be 87% for the Light Gradient Boost Classifier. The findings of this work demonstrate the effectiveness of using Machine Learning models and word embedding techniques, such as Doc2Vec and GloVe, to accurately classify the intent of citations in scientific papers.
Speech-enabled machine learning-based automated attendance monitoring system through face recognition

Verification, and validation are important issues in computerized systems for security in almost every domain. Amongst the many authentication methods, identification via Face ID is an important task. Face recognition plays a huge role in authenticity and is majorly used in the applications of theft prevention, door control systems (DCS), video surveillance systems, and aerial drones. Most universities still rely on traditional methods such as manual name-calling or paper-based record-keeping, which are both inefficient and prone to errors while taking attendance. To make this attendance monitoring system more hassle-free, in this paper, we have proposed and implemented an automated speech-recognized attendance monitoring system primarily based on image processing, speech recognition, and the machine learning algorithm LBPH. The method proposed in this paper aims to take attendance by the teacher in a full-fledged Speech automated system. The system automatically accumulates the required information in the classroom environment and records their attendance with the teacher’s commands by taking live images through a camera. Our proposed system has been tested as robust and efficient in automating the monitoring task.
Detecting Deepfake Human Face Images Using Transfer Learning: A Comparative Study

Deepfake technology has made it easy to create realistic, manipulated videos and images, raising concerns about the potential for them to be used to spread misinformation or harm individuals. Deepfake technology has advanced significantly in recent years, making it increasingly difficult to distinguish between real and fake images. This has raised concerns about the potential for deepfake images to be used maliciously, for example, in political propaganda or cyber fraud. As a result, there is a growing need for effective methods of detecting deepfake images. So, it became important to propose an effective classification on deepfake. In this paper the authors have proposed an effective classification on deepfake images using Transfer Learning. This approach will help in classification of Real and fake through Human face images. The authors have utilized Real vs Fake Face classification dataset to apply the proposed approach. The Loss, Accuracy, Precision, Recall, AUC, and F1 Score parameters have been used to analyze the performance of proposed approaches. The outcome of this work shows the EfficientNetV2L Model with the accuracy of 99% as compared among EfficientNetV2B0-B3 and S, M, L models.
Volume Control feature for gesture recognition in Augmented and Virtual reality applications

Gesture recognition is the revolutionary change in the world of Virtual and Augmented reality development allowing seamless non-physical control of computerized devices to create a highly interactive and flexible hybrid reality. Volume has been controlled using buttons and switches in many devices. For enabling real-time experience as the mirrored interaction, gestures introduce a new arena in controlling features such as volume control using palm detection and hand tracking model. Gesture volume control can be utilized in various human-machine interactions such as gaming and media player control. The main objective of the study is to provide an overview of the concept of mounting digitalization utilizing for enhancing augment reality and virtual reality applications. In this proposed work, OpenCV library along with Pycaw and MediaPipe is used to implement gestures in Human-computer-interaction. The results obtained from the study yield results that were promising.
Unveiling the Innovative Empowerment of Non-Fungible Token for Creative Students: A Conceptual Framework

The emergence of Non-Fungible Tokens (NFTs) has given digital artists more control and freedom in their creative pursuits, empowering them to create and sell passionate and innovative art without restrictions. With the given advantage of this technology, creative students can reap significant advantages as it grows increasingly vital in the digital era. Although NFTs are gaining popularity, many people in Malaysia still have doubts about the technology and its potential impact on their daily lives. Moreover, there is limited research exploring the potential of NFTs to empower aspiring artists and creatives, especially among creative students. This study intends to employ qualitative research based on state-of-the-art in leveraging technology for education, the potentials of NFTs for artists, and the technology acceptance model to measure the acceptance of NFT adoption by creative students (TAM). In addition, secondary sources such as books, journal articles, and other published academic materials were reviewed. By uncovering the potential benefits of NFTs for creative students, the research will serve as a foundation for future studies and initiatives to integrate NFTs into high art education and support the creative industries growth and development.
Resolving Single-Sentence Answers for the development of an Automated Assessment Process

The field of information retrieval systems includes question-answering systems. In the Question Answering system, the online examination was used to conduct tests and do the assessment online throughout the COVID-19 period. The majority of the work in question-answering systems has been done on objective-type questions, while researchers are currently working on descriptive-type questions. In this paper, we was creating a word-answer evaluation for an online objective exam. In objective-type questions, we created questions along with a model answer. The system database contains all the responses that students answered to the question, and it compared the student’s responses to the model response using similarity finding. However, the result was wrong when a response includes synonyms, misplaced words, or case sensitivity; this issue is not resolved by objective-type questions. Thus, we focused on the issues that are present in the online examination system. As result, the manual calculation was done on the responses submitted by students based on the Misplaced word, synonyms, and case sensitivity, and observed that this answer is correct but the system generate the wrong result.
Hoax Identification On Social Media Using Recurrent Neural Network (RNN) And Long Short-term Memory (LSTM) Methods

Technological developments support the rapid spread of hoaxes on social media. Information dissemination needs to be identified as hoaxes to prove that not all information can be received immediately. The speed at which news spreads through social media can be used as an obstacle to the identification of hoaxes. This can be overcome by taking direct data and then identifying it using the Recurrent Neural Network (RNN) method and Long Short-Term Memory (LSTM). The identification results show that the RNN classification method is a classification method with a greater increase in accuracy than the LSTM method, which is equal to 98.32% and for the LSTM the final accuracy value obtained is 95.63%.
A Compatible Hexadecimal Encryption-Booster Algorithm for Augmenting Security in the Advanced Encryption Standard

Among the most prominent encryption algorithms, Advanced Encryption Standard ranks first. Even so, many familiar characters can be seen when an AES encrypted file is opened. As of today, there have been very few contributions to research on suppressing known characters in AES encrypted files. It is possible to identify encrypted files not only by their name and content, but also by their size. As a result, hackers can identify files at source and target locations by comparing their sizes. In this paper, a methodology is presented to address these two research gaps. As a result of the proposed algorithm, almost all characters are transformed into an unintelligible format not only for humans, but also for computer interpreters. As an additional benefit, the proposed method makes the encrypted file appear smaller and conceals its actual size. The proposed Encryption Booster algorithm is also easily integrated with Advanced Encryption Standard.
IoT-Based Smart Green Campus Leveraging Machine Learning

For the well-being of all inhabitants of the planet and future generations, it is important to foster a green mindset. In order to increase the likelihood of a successful transition, the utilization of recent technological tools is fundamental. Green campus is about adopting practices that prioritize the protection and preservation of the ecosystems in universities. Smart green campus makes use of the recent technologies in an intelligent way in order to enhance effectiveness of the universities’ sustainability initiatives. Smart involves the interconnection of technological devices, which is a feature of the Internet of Things (IoT) that allows real-time data acquisition via various sensors. This paper proposes a system that gathers data from the sensors via an IoT network in the university premises. These data can be used to assist the management of the university in the decision-making process. Prediction using Machine Learning (ML) is used for analyzing data and to gain further insights of the data. Thus, preventive actions can be formulated to, in turn, protect the environment based on the predictions. With the help of Streamlit, the real-time and prediction data are presented on a web-application. This system aims to improve the usage of resources in an efficient and sustainable way for the day-to-day activities carried out in the campus.
Style Augmented Transformer Architecture for Automatic Essay Assessment

In this paper, we present a grammar and style aware transformer-based neural network for computing the quality of a text in an automatic essay-scoring task. The proposed model takes into consideration different grammatical error categories and discourse writing styles like, concreteness, uncertainty, conviction and commitment in text along with the pre-trained language models of a text document. We have evaluated the proposed model with the automated student assessment dataset. Our preliminary investigation shows that incorporating such stylistic vectors and grammatical error categories with the BERT based language model can give us a better understanding of improving the overall evaluation of the input essays.
Innovating Assessment with Conversational Agents: A Technology-Enhanced Approach to Formative Assessments

Conversational agents (e.g., ChatGPT) have become popular tools for simulating formal and casual human-like dialogues. Researchers have designed conversational agents to improve instruction quality and support student learning and investigated their effects on learning outcomes. In addition to their instructional use, conversational agents can also be incorporated into assessments to measure student learning. Students can answer items within a digital conversational environment by typing their answers while receiving feedback for correct responses and follow-up questions for incorrect responses. This study introduced a conversation-based assessment (CBA) with three selected-response and two constructed-response tests and evaluated its performance based on intent classification and confidence score. CBA was designed using Rasa and deployed to Google Chat to share with students in an undergraduate-level course. The results showed that CBA with both selected-response and constructed-response tests produced high performance and confidence scores for student responses. In particular, CBA with the selected-response format showed perfect accuracy between system design and implementation. In comparison, CBA with constructed-response items consistently matched student responses to the appropriate conversation paths for the most part. Overall, this study shows the potential of CBA as a technology-enhanced assessment tool.