Reviewerly/exHarmony huggingface dataset:
A Novel Deep Ensemble Learning to Enhance User Authentication in Autonomous Vehicles

For autonomous vehicles, face-based authentication is one of the best ways to identify or verify a user. Deep learning methods, such as Deep Convolutional Neural Networks (DCNN), have brought excellent accuracy in recent years. However, building an optimal DCNN and ameliorating face-based authentication performance are still the main challenges for autonomous vehicles. From these challenges, we first investigate and analyze the effect of several novel effective loss functions on DCNN, such as CosFace, ArcFace, and MagFace, for face-based authentication. We then propose a parallel ensemble learning, namely PelFace, by taking advantage of recent models based on CosFace, ArcFace, and MagFace. PelFace elaborates on the voting mechanism that utilizes non-optimal pre-trained models to obtain better discriminative ability for face-based authentication. In addition, we propose to perform the matching phase for each PelFace model in parallel. To prove the performance of both the speed and accuracy of PelFace, we carry out rigorous experiments using several popular benchmarks, including LFW, CFP-FP, and AgeDB-30, as well as two renovations of LFW: CALFW and CPLFW. The results of our experiments achieve state-of-the-art figures, which show the proposed method’s massive potential in improving face-based authentication performance. Ablation studies and overall benchmarks significantly confirm the effectiveness of our PelFace. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper motivates to construct and improve the discriminative power of face-based authentication based on Deep Convolutional Neural Networks (DCNN) for autonomous vehicles. Facial authentication is widely applied to identify or verify a user, but its accuracy and speeds still need improvement, especially for autonomous vehicles. Thus, in this work, we propose a parallel ensemble method called PelFace, by combing several novel DCNN models, such as CosFace, ArcFace, and MagFace. Our method is built by using the original Torch library for Python language. We also describe how such trained models can be combined to obtain SOTA performance, allowing us to reduce the time for loss function hyper-parameters tuning to get optimal models. In addition, we also indicate the drawback of the combination method as well as an approach to solving. Our experiments indicate that using this technique to create high-accuracy facial authentication is possible without expensive hardware. Current results of PelFace allow proving its ability to devote user authentication in autonomous vehicles, such as passenger identification and driver verification.
COMPARISON OF STEMMING AND SIMILARITY ALGORITHMS IN INDONESIAN TRANSLATED AL-QUR'AN TEXT SEARCH

The long history of information retrieval did not begin with Internet. Prior to widespread public daily use of search engines, in the 1960s information retrieval systems were discovered in commercial and intelligence applications. There are two stages in Information Retrieval in doing its main job which is to preprocessing text and to calculate similarity between term (word) and query (keyword) user searched for in a document. Stemming is final stage of pre-processing in an information retrieval system. The way stemming works is to remove affixes from a word, in form of prefixes, suffixes and insertions into form of basic word. Thus, in this paper we did compare search on information retrieval system without using stemming algorithm, using stemming Porter, Nazief &amp; Adriani and Enhanced Confix Stripping with similarity method used is cosine similarity and dice similarity. Based on test results, text search ability on dice similarity is faster in stemming process with Porter Stemmer and ECS algorithms. While in Nazief &amp; Adriani algorithm and without stemming, cosine similarity is faster than dice similarity.
Catch and release of propagating bosonic field with non-Markovian giant atom

Abstract The non-Markovianity of physical systems is considered to be a valuable resource that has potential applications to quantum information processing. The control of traveling quantum fields encoded with information (flying qubit) is crucial for quantum networks. In this work, we propose to catch and release the propagating photon/phonon with a non-Markovian giant atom, which is coupled to the environment via multiple coupling points. Based on the Heisenberg equation of motion for the giant atom and field operators, we calculate the time-dependent scattering coefficients from the linear response theory and define the criteria for the non-Markovian giant atom. We analyze and numerically verify that the field bound states due to non-Markovianity can be harnessed to catch and release the propagating bosonic field on demand by tuning the parameters of giant atom.
Small Target-YOLOv5: Enhancing the Algorithm for Small Object Detection in Drone Aerial Imagery Based on YOLOv5

Object detection in drone aerial imagery has been a consistent focal point of research. Aerial images present more intricate backgrounds, greater variation in object scale, and a higher occurrence of small objects compared to standard images. Consequently, conventional object detection algorithms are often unsuitable for direct application in drone scenarios. To address these challenges, this study proposes a drone object detection algorithm model based on YOLOv5, named SMT-YOLOv5 (Small Target-YOLOv5). The enhancement strategy involves improving the feature fusion network by incorporating detection layers and implementing a weighted bidirectional feature pyramid network. Additionally, the introduction of the Combine Attention and Receptive Fields Block (CARFB) receptive field feature extraction module and DyHead dynamic target detection head aims to broaden the receptive field, mitigate information loss, and enhance perceptual capabilities in spatial, scale, and task domains. Experimental validation on the VisDrone2021 dataset confirms a significant improvement in the target detection accuracy of SMT-YOLOv5. Each improvement strategy yields effective results, raising the average precision by 12.4 percentage points compared to the original method. Detection improvements for large, medium, and small targets increase by 6.9%, 9.5%, and 7.7%, respectively, compared to the original method. Similarly, applying the same improvement strategies to the low-complexity YOLOv8n results in SMT-YOLOv8n, which is comparable in complexity to SMT-YOLOv5s. The results indicate that, relative to SMT-YOLOv8n, SMT-YOLOv5s achieves a 2.5 percentage point increase in average precision. Furthermore, comparative experiments with other enhancement methods demonstrate the effectiveness of the improvement strategies.
Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review

Artificial Neural Network (ANN) is one of the machine learning algorithms that is widely used for classification cases. Some examples of classification cases that can be handled with ANN include classifications in the health sector, banking, and classification in image processing. This study presents a systematic literature review (SLR) of the ANN algorithm to find a research gap that can be used in future research. There are 3 phases used in preparing the SLR. Those are planning, conducting, and reporting. Formulation of research questions and establishing a review protocol is carried out in the planning phase. The second phase is conducted. In this phase, searching for relevant articles is carried out, determining the quality of the literature found and selecting particles according to what has been formulated in the planning phase. The selected literature is then carried out by the process of extracting data and information and then synthesizing the data. Writing SLR articles based on existing findings is carried out in the last phase, namely reporting. The results of data and information extraction from the 13 reviewed articles show that the ANN algorithm is powerful enough with satisfactory results to handle classification cases that use tabular datasets or image datasets. The challenges faced are the need for extensive training data so that ANN performance can be better, the use of appropriate evaluation measures based on the cases studied does not only rely on accuracy scores, and the determination of the correct hyperparameters to get better performance in the case of image processing.
Video-Based Engagement Estimation of Game Streamers: An Interpretable Multimodal Neural Network Approach

In this paper, we propose a non-intrusive and nonrestrictive multimodal deep learning model for estimating the engagement levels of game streamers. We incorporate three modalities from the streamers' videos (facial, pixel, and audio information) to train the multimodal neural network. Additionally, we introduce a novel interpretation technique that directly calculates the contribution of each modality to the model's classification performance without the need to retrain single modality models. Experimental results demonstrate that our model achieves an accuracy of 77.2% on the test set, with the sound modality identified as a key modality for engagement estimation. By utilizing the proposed interpretation technique, we further analyze the modality contributions of the model in handling different categories and samples from various players. This enhances the model's interpretability and reveals its limitations, as well as future directions for improvement. The proposed approach and findings have potential applications in the fields of game streaming and audience analysis, as well as in domains related to multimodal learning and affective computing.
An Evolving Preference-Based Recommendation System

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in a surge of the on-line digital activity and e-commerce. Because of its practicability, the matrix factorization (MF) technique has been widely applied for recommendation systems. Prior MF-based studies on recommendations generally extract latent factors from users and items to make recommendations. However, user's preferences may change over time in real-world applications. In this paper, by integrating the transformer and matrix factorization techniques, a novel recommendation system, namely Evolution-Based Transformer Recommendation (Evo-TransRec), is developed to effectively describe the evolution of user preferences over time. Several optimization techniques are equipped to Evo-TransRec to capture the evolution relations and predict the user preference. The experimental results show that Evo-TransRec outperforms all the state-of-the-art baselines on real datasets to demonstrate the practicability.
Blockchain and the Law – Legality &amp; Legal Applications

Abstract: In our thorough exploration of the intricate relationship between blockchain technology and the legal landscape, we uncover three key intersections: legality, law-like characteristics, and legal applications. Delving into the realm of legality, we analyze the profound impact of jurisdiction-specific regulations governing cryptocurrencies, intellectual property, and taxation. This includes considerations such as identity verification, exchange regulation, securities laws, bankruptcy regulations, and anti-money laundering enforcement, shaping the multifaceted legal terrain. Shifting focus to the law-like characteristics inherent in permissionless blockchains, our examination reveals decentralized governance facilitated through consensus mechanisms. The blockchain protocol operates as a dynamic "constitution," actively shaping the behavior and incentives of network participants, with forking introducing a distinctive form of institutional innovation during disagreements over protocol changes. Expanding our exploration to the transformative legal applications of blockchain, we emphasize smart contracts and decentralized autonomous organizations. Despite remarkable advancements, challenges persist in automating intricate contractual arrangements and organizational functions, particularly within the context of multi-stage interactions embedded in broader legal and social frameworks. In conclusion, our comprehensive findings highlight that blockchain's unique capacity for direct economic value exchange requires innovative legal treatment. It not only influences user incentives through its rule-based structure but also catalyzes transformative changes across legal and contractual domains. However, as the blockchain landscape evolves, persistent limitations become apparent, particularly in addressing relational agreements and ensuring constitutional resilience. The evolving nature of this technology continues to shape and redefine the intersection between blockchain and the law
Effects of Optimal Genetic Material in the Initial Population of Evolutionary Algorithms

The quality of individuals in evolutionary algorithms (EAs) is usually measured in terms of their fitness. If an individual has a good fitness, a good genome is assumed. However, a good fitness value does not guarantee that the individual can produce good offspring and guide the algorithm towards the global optimum. Answering the question of what makes a genome good is not trivial, especially when considering different types of crossover operators, copying or combining genome values. This work aims towards answering this question by evaluating the influence of optimal gene values in the initial population of EAs. In computational experiments, a random population is seeded with generated individuals of different fitness qualities and containing different amounts of optimal genetic material. Tests are done for multiple dimensions and with crossover operators copying or combining the parents genes to the offspring. Data is evaluated both in terms of algorithmic performance and population dynamics, clearly showing the influence of optimal gene values.
Spark-based Big Data Sentiment Analysis of Social Media Comments

YouTube is widely regarded as the most crucial place for YouTubers to communicate their idea with the rest of the world through the internet. Big Data is a massive accumulation of data in terms of volume, diversity, and velocity. YouTube is the most important source of big data. The video on YouTube gets comments. Typically, a YouTuber attempts to provide followers with the most outstanding content possible by reviewing previous videos' comments. On average, the number of comments can reach ten thousand, making it nearly impossible to read through each and understand viewers comments. This work presents a spark-based model that extracts the comments from YouTube on a specific video from an education and fitness domain. A Machine Learning Classification Model is added to the extracted input to provide sentiment analysis of posted comments. Through the result, the YouTuber has a better grasp of the viewers' thoughts, which in turn helps them better understand their audience. The findings include a comparison of various machine-learning techniques for sentiment classification. The comparison of the proposed result helps content creators improve the channel's decision-making and revenue-generation performance.
Three Bricks to Consolidate Watermarks for Large Language Models

Discerning between generated and natural texts is increasingly challenging. In this context, watermarking emerges as a promising technique for ascribing text to a specific generative model. It alters the sampling generation process to leave an invisible trace in the output, facilitating later detection. This research consolidates watermarks for large language models based on three theoretical and empirical considerations. First, we introduce new statistical tests that offer robust theoretical guarantees which remain valid even at low false-positive rates (less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> ). Second, we compare the effectiveness of watermarks using classical benchmarks in the field of natural language processing, gaining insights into their real-world applicability. Third, we develop advanced detection schemes for scenarios where access to the LLM is available, as well as multi-bit watermarking.
Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature]

Reservoir computing (RC) is a neural computing paradigm especially well-suited for learning dynamical systems by leveraging an untrained reservoir layer, providing high-dimensional input encoding with fading memory property. Since only the readout weights are trained under RC, linear regression learning algorithms are sufficient, leading to significant improvements in computational complexity and energy efficiency as compared to other deep neural networks (DNNs). RC offers an alternative solution to sidestep the shortcomings of data scarcity and the vanishing gradient problem. More importantly, such a network structure is amenable to hardware implementation using a variety of devices, circuits, and systems, making RC a good candidate to replace sophisticated DNNs as a lightweight classifier at the edge for internet of things (IoT) applications. In this article, we provide an overview of recent advances in RC hardware and their applications for mobile edge intelligence. Specifically, we will demonstrate the design strategies of RC in opto-electronic configuration, fully digital system, and silicon with the mixed-signal integrated circuit approach. Moreover, we will expose a novel implementation approach using emerging materials, designing the way for RC to be used in the next-generation neuromorphic computing systems. Building upon these efficient RC models, their applicability and effectiveness against the state-of-the-art are then demonstrated through diverse machine learning benchmarks spanning the area of IoT, communication networks, and healthcare.
Semantic Compression with Large Language Models

The rise of large language models (LLMs) is revolutionizing information retrieval, question answering, summarization, and code generation tasks. However, in addition to confidently presenting factually inaccurate information at times (known as “hallucinations”), LLMs are also inherently limited by the number of input and output tokens that can be processed at once, making them potentially less effective on tasks that require processing a large set or continuous stream of information. A common approach to reducing the size of data is through lossless or lossy compression. Yet, in some cases it may not be strictly necessary to perfectly recover every detail from the original data, as long as a requisite level of semantic precision or intent is conveyed. This paper presents three contributions to research on LLMs. First, we present the results from experiments exploring the viability of “approximate compression” using LLMs, focusing specifically on GPT-3.5 and GPT-4 via ChatGPT interfaces. Second, we investigate and quantify the capability of LLMs to compress text. Third, we present two novel metrics-Exact Reconstructive Effectiveness (ERE) and Semantic Reconstruction Effectiveness (SRE)-that quantify the level of preserved intent between text compressed and decompressed by the LLMs we studied. Our initial results indicate that GPT-4 can effectively compress and reconstruct text while preserving the semantic essence of the original text, providing a path to leverage more tokens than current limits allow.
AI to Judge AI-Generated Images: Both Semantics and Perception Matter

Recent advances in AI-generated contents (AIGC) could revolutionize the digital content creation in many applications, such as e-learning and communication. The goal of this study is to predict the perceptual quality of AI-generated images. While the existing studies just focus on perceptual features only, our hypothesis is that both perceptual features and semantic features are important. Through experiments, we show that combining these two kind of features can improve prediction performance. To our best knowledge, this is the first work that applies multiple AI tools to predict this kind of quality.
Clinical Knowledge and Reasoning Abilities of Large Language Models in Pharmacy: A Comparative Study on the NAPLEX Exam

This study aims to evaluate the capabilities and limitations of three large language models (LLMs)-GPT-3, GPT-4, and Bard, in the field of pharmacy by assessing their reasoning abilities on a sample of the North American Pharmacist Licensure Examination (NAPLEX). Additionally, we explore the potential impacts of LLMs on pharmacy education and practice. To evaluate the LLMs, we utilized the sample of the NAPLEX exam comprising 137 multiple-choice questions. These questions were presented to GPT-3, GPT-4, and Bard through their respective user interfaces, and the answers generated by the LLMs were subsequently compared with the answer key. The results reveal a notable disparity in the performance of the LLMs. GPT-4 emerged as the top performer, accurately answering 78.8% of the questions. This marked a substantial 11% and 27.7% improvement over Bard and GPT-3, respectively. However, when considering questions that required multiple selections, the performance of each LLM decreased significantly. GPT-4, GPT-3, and Bard could only correctly respond to 53.6%, 13.9%, and 21.4% of such questions, respectively. Among the three LLMs evaluated, GPT-4 was the only model capable of passing the NAPLEX exam. Nevertheless, given the continuous evolution of LLMs, it is reasonable to anticipate that future models will effortlessly excel in this context. This highlights the significant potential of LLMs to influence the field of pharmacy. Hence, we must evaluate both the positive and negative implications associated with the integration of LLMs in pharmacy education and practice.
Machine Learning in Disaster Management: Flood Control Management

In the world one of the most significant impacts is flood disaster. Flood is mostly caused by the natural disaster or human activities. Effect of flood can be prevent or reduce by the various methods of flood control. For flood management we use advance techniques in order to assist the flood, like machine learning and the image processing. This research is based on the flood control management with machine learning in disaster management. The data can be analyzed and collected by the satellite image processing; it helps in analysis the data to take measure against the flood management. The technique that used in this ML process is CD (Change Detection). This system helps in risk the flooding, provide hope for flood nuisances detection, and also by the satellite imaging, flood risk is reduced with planning and designing for transportation and infrastructure.
Exploring the Potential of Large Language Models to Generate Formative Programming Feedback

Ever since the emergence of large language models (LLMs) and related applications, such as ChatGPT, its performance and error analysis for programming tasks have been subject to research. In this work-in-progress paper, we explore the potential of such LLMs for computing educators and learners, as we analyze the feedback it generates to a given input containing program code. In particular, we aim at (1) exploring how an LLM like ChatGPT responds to students seeking help with their introductory programming tasks, and (2) identifying feedback types in its responses. To achieve these goals, we used students' programming sequences from a dataset gathered within a CS1 course as input for ChatGPT along with questions required to elicit feedback and correct solutions. The results show that ChatGPT performs reasonably well for some of the introductory programming tasks and student errors, which means that students can potentially benefit. However, educators should provide guidance on how to use the provided feedback, as it can contain misleading information for novices.