Reviewerly/exHarmony huggingface dataset:
How Big Can It Get? A comparative analysis of LLMs in architecture and scaling

Large Language models (LLMs) are increasingly becoming an integral part of our society. They play important roles in education, marketing and healthcare. These models exhibit emergent behavior when scaled, however such scaling might have its disadvantages as well. As a way to combat such disadvantages, new architectures and types of models have been designed. In this paper, we show the different architectural decisions when it comes to building LLMs as well as discuss how big a model needs to be in order to be specialized in a certain area or sector. We show that most specialized models are small in size yet outperform larger models in specific domain tasks.
Automatic Highlight Generation of Soccer Videos

Due to soccer’s immense popularity, numerous soccer matches are played each year and people watch or record these matches. However, considering that the duration of a game of soccer is at least 90 minutes and that important scenes such as goals and passes take up a small portion of the total time, we inevitably look for highlights rather than the entire game. Therefore, we propose a model that automatically generates a highlight video of a full soccer match in this paper. For this purpose, we searched diverse action-spotting models to choose a backbone model for our highlight classifier. Through various experiments, NetVLAD++ is selected as a backbone model and we created a fairly natural highlight generator by combining NetVLAD++ and our highlight classifier. Our results are meaningful because they can be applied in various fields and have huge room for improvement.
Data and Model Centric Approaches for Card Fraud Detection

Card fraud has become a widespread issue as technology advances and credit cards become more popular. Over the last decade, criminals have found diverse ways to steal card information or create duplicates without the cardholder’s knowledge by deploying adaptive approaches against the system. Therefore, detecting suspicious behaviour of a card is critical for preventing fraudulent transactions. However, detecting fraud behaviour is challenging task, given that the transactions’ data are imbalanced, and the adaptive fraudsters’ approaches against the system. In this paper, a combination of data and model centric approaches are proposed for card fraud detection. Starting by data analysis and preprocessing, this was followed by feature engineering stage, then two main family of models were used. The first is an Auto Encoder for anomaly detection, and the second is time series prediction, utilizing Long Short-Term Memory (LSTM) and Convolution Neural Network. The variations of LSTM used include CNN-LSTM, LSTM-Attention mode, and CNN-LSTM with an attention layer. The latter achieved the State-of-the-Art F-score of 93% on the IBM data set. Best F-scores of 94% is also achieved by stacked LSTM on Sparkov dataset.
Automatic Question Generation Using Natural Language Processing and Transformers

Online education’s rapid growth and the rise of E-learning tools have raised the demand for creating assessments and challenging questions for learners which require significant effort to create suitable content for testing. Consequently, automatic question generation aims to automate the creation of different types of questions in the shortest period of time possible by applying minimal human effort all using natural language processing and transformers. This paper proposes different methodologies to generate questions like true or false, fill in the blanks, matching, multiple-choice, and “Wh-” questions specified from a given context. Transformer models including GPT, T5, and BERT are used to achieve the methodologies needed to generate such questions. The system, tested through surveys, generated an 80% satisfaction rate among teacher participants and showed potential to generate questions similar to ChatGPT’s.
Applied Research on Attribute-Based Encryption Scheme with Two-level Encryption

This study addresses the issue of sensitive data protection in cloud computing environments and proposes a solution that combines Attribute-Based Encryption with the Rivest-Shamir-Adleman encryption algorithm to safeguard the data. The proposed solution enables efficient two-way encrypted communication using this two-level encryption approach. The advantages of this solution include enhanced access control, ensuring that only users with appropriate attributes can access the data, and strong security achieved through the combination of Attribute-Based Encryption (ABE) and Rivest-Shamir-Adleman (RSA) algorithms, effectively defending against man-in-the-middle (MITM) attacks and data leakage risks.
Byte-Sized Reviewers: Exploring Review Generation through LNRG-S

The development of network technology has led to an explosive growth of user-generated comments. These contents are based on users' experiences after using services and products, reflecting users' evaluations. Automatically generating high-quality and controllable sentiment comments can help creators improve work quality, help service providers enhance service levels, and improve consumers' consumption experience. However, current methods have some limitations. Seq2seq models have difficulty maintaining semantic consistency and generating long texts, LSTM can maintain a memory state and generate coherent text, but is prone to overfitting, generating repetitive text, and having difficulty establishing local coherence. N-gram models text locally but cannot understand the global context. This paper proposes a hybrid architecture, called LNRG-S (LSTM N-gram Reviews Generator with Sentiments), which combines LSTM and N-gram to model both global and local dependencies, and trains separate LSTM generators for positive and negative samples to control sentiment polarity of generated comments. We computed the perplexity of comments generated by different models. LNRG-S has 43.93% and 18.89% lower perplexity than LSTM and n-gram respectively, demonstrating that LNRG-S generates more fluent and coherent text.
Construction of Animal Protection Science Education System (APSES) Based on Unity3D*

The contemporary societal concern for the preservation of animal species is inexorably intertwined with the imperative facets of human reproduction and the sustainable progression of our planet. In 1998, the seminal publication of the “China Red Data Book of Endangered Animals” by the Science Press marked a pivotal milestone, furnishing comprehensive and systematic data concerning the endangered animal species within China. In order to better promote the public’s visual recognition and understanding of endangered animals and promote the development and application of interactive information experimental systems, we independently developed the Animal Protection Science Education System (APSES) based on Unity 3D. APSES mainly includes e-book modules, model display modules, game interaction modules, all designed to improve user experience and information cognition.APSES seamlessly integrates computer-aided design, advanced 3D technology, and interactive methodologies. This system serves as a scientific medium through which users undertake a comprehensive exploration of the ecological status, physical characteristics, natural habitats, and behavioral patterns by six endangered species in China. Each module within APSES is meticulously encapsulated within an immersive 3D environment, thus establishing the foundational infrastructure requisite for subsequent endeavors in virtual reality design. Through APSES, we aim to raise public awareness of endangered animal issues, promote people to become more actively involved in animal protection, and also provide an experimental basis for research and development of science education.
An Improving Framework for Movie Recommendations with a Fusion of Deep Learning and K-Nearest Neighbor Algorithms

This paper investigates the application of two improved deep learning and K-nearest neighbor algorithms, respectively named as AutoDeepKNN and DeepKNR, in movie recommendation systems. We conduct experiments on two large-scale publicly available movie recommendation datasets, Movie-lens and Netflix. Detailed settings for experimental parameters, including data size, number of users, number of items, and the ratio of training and testing sets, were established. By utilizing various evaluation metrics such as precision, recall, F1-score, NDCG, and coverage, and comparing with baseline methods and traditional statistical models, the experimental results demonstrate significant performance improvements in all metrics from our proposed AutoDeepKNN and DeepKNR. Particularly, the AutoDeepKNN method, during the evaluation of precision and loss value, surpassed other methods significantly for 80% of the time.Our research not only enhances the accuracy of movie recommendations but also provides new methods and insights for other types of recommendation tasks.
Research on AI composition based on deep learning techniques

With the recent wave of deep learning networks, its application in generating creative content, including music, grows wider. The research of automatic music generation, as a way of understanding human’s artful expression, and a promising field of high commercial value, has been progressed rapidly. Multiple networks have been used for music generation. Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN) are most frequently used because of their advantages in processing time sequences and generating new creations. The results of diverse generative algorithms for intelligent composition vary greatly. This article analyzes some cases of RNN-based and GAN-based music generation methods, and discusses how the application of multiple networks, such as C-RNN-GAN, integrates the advantages of different networks, showing a trend in the future progress of music generation. It also discusses the necessity of building precise methods for evaluating generated music, and describes some cases where deep learning method has helped in this realm.
An Overview of Machine Learning Techniques for Onboard Anomaly Detection in Satellite Telemetry*

Satellite telemetry is often the only insight into how a satellite system is performing. The information contained in the telemetry channels can vary, but all of them can give information about the operation of the spacecraft. When down-linking telemetry, a limiting factor on all space missions is the link budget, i.e., how much data can be downlinked. In nominal spacecraft operation, not all data can be downlinked and is therefore either lost or monitored onboard. Normally, real-time monitoring of several hundred telemetry channels is required for the data that does make it to ground. Monitoring this volume of telemetry data can benefit from some machine assistance for the satellite operators. Typically, this is in the form of basic thresholding or more advanced statistical methods, such as ARIMA, applied to the telemetry signals. However, advances in modern computing hardware and machine learning techniques make onboard solutions feasible. This paper explores using Machine Learning techniques such as Autoencoders, Long Short Term Memory networks, Variational Autoencoders, and Generative Adversarial Networks for onboard anomaly detection from satellite telemetry data. The performance of these methods is compared to basic statistical methods, and the applicability of these models to small-scale Edge AI or FPGA-based hardware systems, allowing deployment onboard spacecraft, is assessed.
Decentralized Identifiers and NFT-Driven Blockchain Solution for Personal Data Trading

With the onset of the AI era, the demand for personal data is on the rise. Numerous companies are gathering user data to enhance their services. As the utilization of this personal data surges, its intrinsic value also escalates. Despite individuals being the source of this valuable personal data, it's the centralized service providers that reap the profits. This research introduces a blockchain-based trading framework for personal data utilizing Decentralized Identifiers (DID) Non-Fungible Token (NFT). The proposed system empowers users to amass personal data within their designated storage space provided by the system. DID and NFT serve the roles of verifying user identity and establishing data ownership, respectively, all without relying on centralized systems. To guarantee data integrity and transaction history, Ethereum is employed, which constitutes a decentralized blockchain nodes. The operational demonstration of our system is showcased through a monitoring setup that offers real-time insights into user data and trading activities. The research validates that our proposed system enables seamless personal data transactions between two parties - a seller and a buyer - eliminating the need for centralized service providers.
Research on Technical and Economic Calculation Method Based on Digital Model of Ultra High Voltage Engineering

In view of the current situation that the components and attribute information of the three-dimensional design model of the transmission and transformation project cannot meet the requirements for the calculable quantity and accuracy of the automatic technical and economic calculation in the design stage of the project, this paper proposes a research method of automatic technical and economic calculation based on the improved three-dimensional model of UHV project. Firstly determine the work basis for the research, sort out the quantity items and characteristic information under the technical and economic dimension to build the budgetary estimate quota model of UHV project; Then sort out the component structure and attribute information of the 3D design model to discuss the mapping between the budgetary estimate quota information model and the 3D design model, and propose the improvement method of the 3D design model of the UHV project; Finally, design the mapping rules between the improved 3D model and the budgetary estimate quota information model and achieve the research that economic calculation of UHV project based on improved 3D model.
Research on the A-star Algorithm Based on Path Finding

The A star pathfinding algorithm is one of the most commonly used pathfinding algorithms. This paper will introduce A new algorithm, which is improved on the basis of the A-star algorithm, and this improved algorithm reduces the interval complexity and memory consumption. The improved the A-star algorithm can find A smoother path more quickly.
Enhanced Semantic-based Chaotic System for Cyber-grooming Classification and Harassment Detection

In recent years, cyber-grooming and cyber-harassment have become increasingly prevalent issues across social media, posing significant challenges for their detection. The UN reports that cyber-harassment can have severe consequences, including mental health issues like depression and anxiety, and in extreme cases, suicide. In this paper, we propose a novel approach to address the problem of cyber-grooming and cyber-harassment. Our approach involves classifying the 6 stages of grooming and detecting instancs of harassment using a semantic-based chaotic model that leverages the Luring Communication Theory (LCT). The proposed model is unique in its ability to incorporate the principles of chaos theory, which enables it to capture the underlying complexity and non-linearity of the text data. This feature provides a more effective approach to prevent and address online harassment by accurately identifying predatory behavior. The results show that the GRU-Semantic Chaotic model significantly outperformed the CNB-TFIDF-count based model by 23.7%. Specifically, the GRU-Semantic Chaotic model achieved high accuracy and F1-scores of 0.982 and 0.973 for harassment and non-harassment classes, respectively, using a stacked GRU. The overall accuracy of the GRU-Semantic Chaotic model was 97.9%, which is significantly higher than the overall accuracy of the CNB-TFIDF-count based model of only 74.2%. These findings highlight the effectiveness of our proposed model in enhancing the analysis of complex cyber-grooming and harassment conversations and providing a more effective approach to prevent and address online harassment.
A Proposed Model for Enhancing Product Recommendation Based on Word Embedding

Online commerce is a thriving sector, marked by millions of daily transactions across the globe. This paper introduces a model for product recommendation grounded in customer behavior within e-commerce platforms. Leveraging data from customer profiles, the model incorporates purchase history, product ratings, and browsing patterns to construct comprehensive user and item profiles. Utilizing word embedding techniques, latent factors and patterns in user-item interactions are identified. To address the complexity of user behavior, model facilitates both random forest and tree ensemble models are integrated. The proposed highly personalized and relevant product recommendations, enhancing user satisfaction and driving sales. Experimental validation demonstrates the model’s effectiveness compared to existing approaches, emphasizing the role of product annotation enhancements.
On-Board Anomaly Detection on a Flight-Ready System

Within the scope of an ESA funded activity, Airbus Defence and Space GmbH completed a research and development study in order to provide a novel dataset to ESA and develop a flight-ready system for on-board anomaly detection. This work includes the extraction of satellite telemetry data, the identification of anomalies, the development of machine learning models and the flight-ready system and finally the deployment of the machine learning algorithms via hardware acceleration. We present the benchmarking results of three accelerated ML algorithms from within the final flight-ready system.
Privacy-preserving Collaborative Computation: Methods, Challenges and Directions

Although data mining is very relevant to the medical sector, it has also raised privacy concerns since it is applied to sensitive data, which undoubtedly affects citizens’ rights and freedoms, which are strictly regulated by the EU through the General Data Protection Regulation (GDPR). This concern creates a big gap between the data owner and the data analyst, and it is not easy to connect them. Thus, it is evidently important to ensure privacy. This need for privacy becomes a necessity when data from multiple entities aim to collaborate. To tackle this gap, several techniques worth mentioning can be employed during data analysis to ensure privacy, including secure multiparty computation, homomorphic encryption, and federated learning. In this paper, we present the state-of-the-art of existing approaches and discuss their drawbacks to finally identify outstanding challenges in this field.
A Hybrid LSTM and Probabilistic Model for Modeling Repeat Purchase Behavior

Personalized product recommendations are essential in the business world nowadays. Collaborative filtering is a common approach in recommendation systems. However, it faces constraints in scalability and the ability to capture sequential patterns in user behavior. These constraints can be addressed using techniques such as Recurrent Neural Networks (RNNs). RNNs are increasingly being used in recommendation systems to capture users’ sequential purchasing behavior. The problem with RNNs is that their predictive accuracy can be limited if they do not consider users’ repetitive purchase behavior along with the exact timing. In this paper, a hybrid LSTM and Repeat Purchase Probabilistic Model (LSTMRPPM) is proposed to capture the sequential order and timing of basket purchases. The LSTM-RPPM predicts the next basket of products and takes into account repeatable purchased products while estimating the expected time of purchase and incorporating a time signal denoting the customer’s last purchase of the product to avoid sub-optimal recommendations. The LSTM-RPPM has been tested on the Instacart dataset and has improved the results in comparison to the Multi-period LSTM, GRU-based and DREAM models.
Fuzzy FCA-based Elderly Activity Recognition

Nowadays, monitoring systems are used to ensure elderly people are safe at home. New approaches consist of developing automatic activity monitoring systems in smart homes equipped with wearable sensors, environmental sensors and visual sensors. In this framework, we proposed an approach that provides an activity recognition system for elderly people in a smart home. Our proposed system is built on three phases. The first phase consists of integrating the quality of context (QoC) of data from the IoT environment. The second phase classifies and labels the sensors based on the QoC using the Fuzzy FCA technique. Finally, the activity recognition phase is based on machine learning by applying Random Forest (RF) with hyperparameter optimization. Following the implementation of our proposed system, the application of RF with hyperparameter optimization demonstrated impressive results. Achieving a precision of 98% these outcomes surpass the performance of RF without optimization. This highlights the efficacy of our approach in enhancing the accuracy and reliability of activity recognition for elderly individuals in smart home environments.
Design and Implemetaion of a Cloud-Based Metaverse Virtual Performance Platform

This paper presents a comprehensive design and implementation of a cloud-based metaverse virtual performance platform, using AWS(Amazon Web Services). We proposed a method to effectively support interaction between audiences while supporting real-time virtual performance content data transmission based on a network structure with two tier servers, and utilized cloud services to support more than tens of thousands of audiences.
Towards Evaluating Image Recommendations in Digital News and Media Ecosystem

The News and Media landscape has undergone significant transformations in recent years, driven by the rise of new technologies and the widespread use of social media. This evolution introduces unique challenges for professionals working within this environment (e.g., journalists, content creators, and news authors), with a major one being the efficient sourcing of images that complement article content. In response to this challenge, we developed VIREO, a tool that recommends images based on textual content. In this paper, we make a step towards the practical effectiveness of VIREO’s core models in recommending images for real-world articles, with a specific focus on image recommendation efficiency. Our results indicate that VIREO offers a promising solution for professionals seeking to meet the evolving demands of the News and Media landscape while maintaining content quality and engagement.
Usefulness of using Nvidia IsaacSim and IsaacGym for AI robot manipulation training

AI technology is increasingly being used to learn robotic behavior. Various types of learning techniques are being applied to learn optimal robotics control or autonomous driving of driverless cars, and attempts are being made to use reinforcement learning techniques to learn robot behavior. When building data for robot behavior learning is not practical in terms of cost and time, imitation learning and reinforcement learning become realistic options for behavior learning using simulators. In this paper, we introduce a robot learning method using Nvidia’s IsaacSim and IsaacGym, and examine the performance result required for learning to test its feasibility.