Reviewerly/exHarmony huggingface dataset:
Towards Bi-Level Out-of-Distribution Logical Reasoning on Knowledge Graphs

Logical reasoning is crucial in knowledge graphs, enabling the discovery of latent knowledge and facilitating various downstream tasks such as question-answering and knowledge discovery. Despite the promising results of state-of-the-art methods, their limited generalization capabilities in handling out-of-distribution (OOD) queries and KG data pose a significant challenge. This challenge arises from the complex variations in logical queries and the dynamic nature of graphs, resulting in two OOD scenarios at the query and KG data levels. To address this challenge, we propose a novel, generic framework that handles bi-level OOD logical reasoning on KGs by jointly modeling OOD queries and OOD KG data. First, we start with the uncertainty representation module to handle the KG-level OOD. Then, we further design an adversarial learning module to efficiently improve reasoning over query-level OOD. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework in handling these two levels of OOD scenarios.
Face to Face Augmented Reality - Broadening the Horizons of AR Communication

When users wear Augmented Reality (AR) Head Mounted Displays (HMDs), they occlude most parts of the face. While body language is crucial in effective team communication and negotiation tactics, the effect of eye contact and facial expressions cannot be neglected. However, the degree to which users in AR environments can expressively interact with each other is hindered by HMDs which occlude a large portion of the face and do not realistically blend with the terrain surroundings. Recent developments generate highly photorealistic avatars in Virtual Reality (VR) but they do not solve a multitude of questions which include blending of the virtual model in an AR setting, real-time performance along with high fidelity of temporal coherence as well estimation of high-resolution outputs. We propose to develop a technique for enabling natural and expressive face-to-face communication between subjects in AR environments by removing the HMD barrier where the virtual environments blend seamlessly with a photorealistic face model.
Dynamic Analysis for the Detection of Locked Ether Smart Contracts

Ethereum Smart Contract (SC) is a sophisticated technology that enhances the scope of BlockChain automation. However, SCs’ vulnerable programs have marred BlockChain’s nascent technology’s brighter aspects. Two critical hacks, the DAO attack, caused by reentrancy vulnerability, and the Parity attack, caused by unprotected selfdestruct and frozen Ether vulnerabilities, are famous for their historical cryptocurrency frauds. DAO attack robbed a sixty million dollar amount of cryptocurrency from the victim’s account. But the Parity attack created a new trend in software vulnerabilities by freezing a thirty million dollar amount of Ether. In fact, the Parity attack wiped out the library SC. Subsequently, all the SCs, depending upon the library functions (e.g., the Parity SC’s Ether transfer), became paralyzed, which locked the investors’ funds. This paper enhances our dynamic analysis tool, TechyTech, to detect the Locked Ether (i.e., Frozen Ether) vulnerability. Furthermore, TechyTech adopts a transfer-based approach and uses case studies to compare TechyTech’s performance with Remix.
Enhancing Object Detection in YouTube Thumbnails Forensics with YOLOv4

YouTube thumbnails play a vital role as visual indicators, succinctly capturing the essence of a video alongside its title and description. Beyond mere previews, these thumbnails have evolved into significant digital artifacts with implications for disk image encryption. This research delves into potentially integrating the advanced YOLOv4 (You Only Look Once) algorithm into creating YouTube thumbnails. YOLOv4 enhances the process by automatically identifying and emphasizing objects of interest in these visual previews. This paper diversifies the dataset to improve the model’s effectiveness, expanding its capacity to recognize and highlight objects more effectively. We address data security challenges by broadening the training data, incorporating authentication, and decrypting the dataset to align it with real-world thumbnail images. The primary objective is to assess the efficacy of YOLOv4 object detection models in authenticated YouTube thumbnail videos. The network underwent training to recognize 80 object classes, achieving a 90% prediction rate and a 92% confidence rate.
Fog Computing based Privacy Preservation via Distributed Access Control System in Blockchain Technology

The proliferation of loT -enabled gadgets means that ever more information is being created and transmitted over the web. The release of sensitive loT data, however, is a privacy breach. Sensitive information generated by loT devices poses a serious security risk since it is transmitted across an unsecured network. The storage issue may be managed with the help of an external cloud. Cloud storage has the potential to compromise users' privacy. To prevent unauthorised access and maintain confidentiality during data transmission, current systems include symmetric encryption and access restrictions. Blockchain is an exciting distributed ledger technology with the potential to safeguard stored information against manipulation by unscrupulous actors. To protect information from Internet of Things devices, this article suggests using a blockchain-based decentralised access control system. Fog computing and the idea of an alliance chain provide the basis for the suggested approach. In order to encrypt and divide data at the network's edge, fog nodes are employed. Homomorphic encryption is used on the third-party cloud to combine data that has been partitioned and encrypted. The suggested system provides dynamic and granular access control for loT data, mitigating the risk of a single point of failure in access control. Results from experiments showed that this approach effectively protects the confidentiality of data collected from Internet of Things devices.
Research on Anomaly Data Recognition Method Based on Deep Confidence Network

This study aims to address the difficulty of identifying abnormal data samples in cloud platforms by constructing a new anomaly data recognition method based on deep confidence network ensemble learning algorithm. The new method utilizes ensemble learning algorithm to solve the problem of imbalanced samples during data operation, and improves the traditional ensemble algorithm using temporal key indicators, Finally, a new adaptive threshold voting algorithm is proposed to improve the classifier of ensemble learning algorithms and improve the generalization of algorithm performance. The experimental results show that the improved algorithm has the highest sample accuracy of 99.52% and a recall rate of 99.29%. The accuracy of the improved algorithm is 0.85% higher than that of the support vector machine algorithm and 1.79 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> higher than that of the clustering algorithm. The improved algorithm has a higher accuracy than traditional algorithms and performs better in handling abnormal data.
CoMABO: Covariance Matrix Adaptation for Bayesian Optimization

Bayesian optimization, an effective method for searching the optimal solution of black-box functions, usually performs poorly for high-dimensional problems. Although many methods, such as TuRBO (Trust regions Bayesian optimization), have been proposed to solve the over-emphasis of exploration in high-dimensional global acquisition, they may converge slowly or stagnate in some cases. In this paper, we proposed a new method, called CoMABO (Covariance Matrix Adaptation for Bayesian Optimization) to enhance the convergence of TuRBO algorithm. Covariance matrix adaptation is a technique that builds Gaussian models based on the covariance matrix constructed from sampled points. CoMABO utilizes it to explore better candidate points and to optimize the complex surrogate model. Experimental results show CoMABO improves the convergence of TuRBO on various benchmark problems and real world applications.
Fine-tuned generative LLM oversampling can improve performance over traditional techniques on multiclass imbalanced text classification

A common challenge in classification and data mining is the class imbalance problem, where one class in a dataset has significantly more samples than another. The presence of this problem typically worsens the performance of classifiers trained on these datasets by giving them a strong preference for better-represented classes above others. Resampling is the process of adding (oversampling) or taking away (undersampling) samples from a dataset, typically with the purpose of introducing balance to the dataset before a classifier is trained. With recent advances in generative large language models (LLMs), certain authors have proposed the use of these models to generate new samples as a form of oversampling for imbalanced text data. This method has shown initial success, but has not been systematically compared to more traditional methods of resampling across many domains. We find that this use of generative LLMs generally outperforms other methods on imbalanced multiclass classification, but not on binary classification.
Knowledge Graphs for Competency-Based Education

The project Knowledge Graphs for competency-based Education (KG4CBE) conducts educational data science research to establish competency-based instruction in higher-education programs. In this paper, we propose the design of a knowledge graph to examine the impact of instructional design on student-teacher interaction to facilitate complex learning. For this purpose, the knowledge graph will incorporate the components of an online introductory data science course, including educational materials and learning tasks created with the Four Component Instructional Design (4C/ID) model. Furthermore, the knowledge graph will incorporate data recording the behaviors, interactions and assessments of participating students. To study the competency-based instruction process, the proposed knowledge graph must be scalable to the big data quantities common in educational settings. Therefore, the knowledge graph will be deployed as a tool with accompanying routines to acquire, simulate and load educational data. Furthermore, this tool will provide methods to interact with and visualize the stored information. As future research, we aim to evaluate the proposed knowledge graph in a large-scale educational design research study, to examine the impact of monitoring, forecasting and recommendations in complex learning settings.
Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks

The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is proposed and developed as an essential technique for the size reduction of scientific data with constrained data distortion. Among the diverse datasets generated by various scientific simulations, certain datasets cannot be effectively compressed by existing error-bounded lossy compressors with traditional techniques. The recent success of Artificial Intelligence has inspired several researchers to integrate neural networks into error-bounded lossy compressors. However, those works still suffer from limited compression ratios and/or extremely low efficiencies. To address those issues and improve the compression on the hard-to-compress datasets, in this paper, we propose SRNNSZ, which is a deep learning-based scientific error-bounded lossy compressor leveraging the hierarchical data grid expansion paradigm implemented by super-resolution neural networks. SRNN-SZ applies the most advanced super-resolution network HAT for its compression, which is free of time-costing per-data training. In experiments compared with various state-of-the-art compressors, SRNN-SZ achieves up to 75% compression ratio improvements under the same error bound and up to 80% compression ratio improvements under the same PSNR than the second-best compressor.
Dark Assistant: A Novel ML-Based Real-Time Object Recognition System for Blinds

This project focuses on developing an object identification system using Raspberry Pi, aiming to assist individuals with visual impairments in recognizing and identifying objects in their surroundings. The system leverages the capabilities of Raspberry Pi, combining it with computer vision techniques to provide real-time object recognition. The hardware setup includes a Raspberry Pi board, a camera module, and a speaker or headphones for audio feedback. The software implementation involves installing and configuring the operating system on Raspberry Pi and integrating computer vision libraries, such as OpenCV, for image processing and analysis. The object identification algorithm follows a multi-step process. Two controlled experiments are conducted where two state of the art object detection models SSD and YOLO are tested in how they perform in accuracy. Results show that the SSD model slightly outperforms YOLO in accuracy, but with the low processing power that the current generation of Raspberry Pi has to offer, none of the two performs well enough to be viable in applications where high speed is necessary. Object recognition is done by the Pre-trained model MobileNet for recognizing the object with significant accuracy. The proposed model time utilization for multiple object identification in same distance is from minimum of 16.7% to maximum of 20% in cumulative basis.
Patient Electronic Health Records Maintenance Using Blockchain

Even though blockchain has been a buzzword for a long time now, blockchain in healthcare is yet to take a big step in the real world. The benefits of having a distribution system to store patient records are immense. The hospitals now have a centralized architecture to store data which makes the system inflexible and inconvenient for the patients to organize their health records that can branch to n number of hospitals with numerous updates and changes. Medical faults are also a major drawback leading to unsuccessful medical attempts. Adding to this, patient data is very sensitive which calls for a more secure system like blockchain. There has been recent research based on these benefits of blockchain and in our proposed system, the blockchain system is entirely private reducing the risk of privacy as well as data breaches. Doctors can consult the patients after viewing their records on the blockchain and the patients can register for the same. Hence, it is a patient electronic health record system developed using the concept of blockchain. Traditional EHR-based systems are prone to data loss, security and immutability consensus over health records, gaped communication among constituted hospitals, among others. Blockchain has been developed as a decentralized technology that holds the promise to address the aforesaid facilities in EHR-based systems. We are working on first attempting to implement EHR using blockchain and then defining fine-grained access rules for network users to provide a high security vault.
Does a Dense Point Cloud for Training Data Generation Improve Segmentation Accuracy?

Semantic segmentation can provide significant information for a robot to actualize autonomous moving. To increase the classification accuracy of segmentation, appropriate datasets should be prepared, which requires huge human labors. The authors attempt to realize a semi-automatic method that generates two dimensional images having pixel-wise class labels from 3D point clouds obtained by a 3D scanner. In this study, dense point clouds are generated to improve the quality of training samples. Experimental results using data obtained around the course of the Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that the dense point clouds effective but appropriate variety of textures should be included in the training samples.
Viewer Perception Analysis of Video Collaboration of the Nihongo Mantappu YouTube Channel Using Aspect-Based Sentiment Analysis

YouTube is an online video-sharing platform, one of the YouTube channels with collaborative content with the highest number of subscribers in Indonesia is the Nihongo Mantappu channel. This study uses the aspect-based sentiment analysis method for content development to analyze audience perceptions of collaborative video comments from the Nihongo Mantappu channel. The aspect used in this study is the type of content consisting of Cooking, Challenge, Education, Mukbang, QnA, and Vlog content. The Decision Tree model, with a sentiment analysis accuracy rate of 96.08% and aspect-based settlement analysis of 98.36%, shows that the overall proportion of sentiment is dominated by neutral sentiment with a percentage of 60.5%, positive sentiment with a percentage of 23.8%, and negative sentiment with a percentage of 15.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> • The results of this study can be used as a reference in developing collaborative video content on the Nihongo Mantappu channel.
Earth Quack Prediction of Southern California Using Unsupervised Clustering Based Ensemble Deep Learning Model

Predicting when and where an earthquake will occur is a persistent topic in seismology that has attracted the interest of researchers and the general public. There is currently no physical or statistical model that can reliably anticipate the occurrence of major earthquakes, despite continuous research into the fundamental factors that cause them. Random forests and long short-term memory (LSTM) networks are two examples of machine learning techniques that show promise for enhancing earthquake prediction accuracy because of their ability to quickly discover patterns in vast databases. An unsupervised ensemble learning model trained on a labeled dataset has been built for earthquake detection in this article. An unlabeled dataset can be transformed using an ensemble machine learning strategy, which is the result of combining clustering methods with weighted voting. The Adaptive Tunicate Swarm Optimization Algorithm (ATSOA) is used to select the LSTM's weight. The dataset gathered by the Southern California Seismic Network is used for the method's training and testing. The results show that the suggested technique has a high potential for accurate seismic signal detection and phase identification. The provided approach outperforms other suggested deep learning algorithms on the Southern California Earthquake Data Center's recorded dataset by around 4% in earthquake detection and by a small margin in seismic phase identification. Furthermore, the experimental result shows that the model is reliable in recognizing low-magnitude seismic phases. Finally, we conclude that the suggested learning provides a viable option for enhancing earthquake occurrence prediction, and that minor earthquakes include information useful to forecasting future maj or earthquakes.
Evaluation Model of English Teaching Effect Based on Neural Network Algorithm and Support Vector Machine

The optimize the analysis of support vector machines in response to the problem that traditional English teaching models cannot solve the accuracy and efficiency of student English teaching effectiveness evaluation, nor can they meet the requirements of English teaching effectiveness evaluation. Firstly, a multi-indicator English teaching effectiveness evaluation system with teachers and students as the main body was constructed using relevant theories, and the indicators were divided according to the data requirements of English teaching effectiveness evaluation indicators to reduce support vector machines in interference factors. Then, the neural network algorithm is used to solve the optimal solution of the kernel function parameters and regularization parameters of the support vector machine, forming a support vector machine scheme, and the support vector machine results are comprehensively analyzed. MATLAB simulation shows that neural network algorithms and support vector machines have the best evaluation accuracy and short evaluation time for English teaching effectiveness under certain evaluation criteria.
Stabilizing Adversarial Training for Generative Networks

Generative modeling is a powerful technique that involves creating machine learning models capable of creating new data similar to the data it was trained on. Generative Adversarial Networks (GANs) are a leading approach for generative modeling. However, GAN training is known to be a notoriously difficult task. GAN convergence issues are largely caused by the supports of the real and generated distributions being disjoint. To tackle this open problem, we propose a novel GAN pre-training process that effectively aligns the supports of the generated and real data prior to applying traditional adversarial GAN training. The key component of our method, called AlignGAN, is learning a mapping between the input data distribution and a latent representation defined over a hypersphere, regularized by a One Class Classifier. This successfully encourages the generator to produce samples throughout the support of the real data, while not generating samples outside the support. We maintain support alignment through low-bandwidth noise convolutions and additional One Class regularization, leading to continued stable GAN training. We validate our approach against leading stabilization methods on three benchmark datasets, showing AlignGAN routinely produces the best results.
Human Activity Recognition Using Hybrid Feature Extraction and Machine Learning

Significant progress in Human Activity Recognition has been accomplished in the field of image recognition using computer vision and machine learning. This study, which employs a CV-based technique, focuses on the accurate identification of behaviors such as walking, falling, and chitchatting. The dataset had 1305 photos that were evenly distributed throughout three activities, with 400–450 images for each activity. The feature extraction techniques VGG16, Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) were utilized as well as many classifiers such as Random Forest, Artificial Neural Network (ANN), and Logistic Regression. The SVM and LR classifiers performed the best, with 97% accuracy. This research has several uses, including surveillance, assisted living, healthcare, human-robot interactions, and entertainment.
Oral Chinese as a Foreign Language Training Method and System Based on Intelligent Question Answering

This article proposes an intelligent question and answer method for writing phrase analysis in response to issues such as incomplete oral training system and improper word usage. Firstly, use cognitive learning theory to evaluate outstanding students and divide indicators according to the requirements of oral Chinese language training for foreigners, in order to reduce interference factors in oral Chinese language training for foreigners. Then, cognitive learning theory evaluates the trainees' writing wording, forms an oral training system plan, and conducts a comprehensive analysis of the oral training results. MATLAB simulation shows that under certain evaluation criteria, the intelligent question answering method is superior to basic wording training, and can make students' writing more professional and practical.