Reviewerly/exHarmony huggingface dataset:
A Cloud-Based 3D Digital Twin for Arabic Sign Language Alphabet Using Machine Learning Object Detection Model

People with hearing loss or hard hearing struggle with daily life activities as sign language is not widely known by the public. There are many attempts to use technology to help assist hearing loss individuals. However, most proposed solutions are standalone applications or require special hardware like a wearable glove. Our goal is to leverage cloud computing and artificial intelligence (AI) to provide a solution that is portable and does not require any special hardware. We created a lightweight 3D model and rendered it on the browser along with another lightweight object detection model for Arabic Sign Language (ArSL) for real-time detection. Our contribution is primarily based on integrating our novel functional lightweight 3D avatar model and a lightweight ArSL alphabet detection model, which is trained on public ArSL21L dataset, that are suitable to be given as a cloud service. Prototypes of the 3D digital twin avatar model and AI model are publicly offered for the research community on GitHub. We will be working on a full-scale real-time cloud-based communication system in ArSL.
Git Merge Conflict Resolution Leveraging Strategy Classification and LLM

In the realm of collaborative software development, version control systems (VCS) like Git play an indispensable role, enabling concurrent development and facilitating seamless integration of disparate code contributions. Despite these benefits, merge conflicts resulting from simultaneous changes to identical code lines often pose significant challenges to the integration process. Addressing this challenge, our paper introduces a novel two-stage approach, termed as CHATMERGE, for resolving Git merge conflicts. CHATMERGE pioneers a unique strategy that employs machine learning to initially predict resolution strategies, and subsequently leverages a large language model, ChatGPT, to create resolutions for conflicts that necessitate complex resolution strategies. A series of comprehensive experiments validate CHATMERGE’s efficacy, demonstrating its impressive alignment with historical manual resolutions and its superior performance relative to existing, publicly accessible tools. The paper further explores the influence of various classification algorithms and the prompt construction process for ChatGPT, providing further insights into the merge conflict resolution process. Moreover, to foster continued advancements in this area, CHATMERGE, along with its associated training and testing datasets, is made publicly available, offering a valuable resource for both developers and researchers. This work, therefore, provides both an innovative solution to merge conflict resolution and a strong foundation for future explorations in this domain.
SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture

The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures. Yet, the integration of serverless features within peer-to-peer (P2P) distributed networks remains largely uncharted. In this paper, we introduce SPIRT, a fault-tolerant, reliable, scalable and secure serverless P2P ML training architecture. designed to bridge this existing gap. Capitalizing on the inherent robustness and reliability innate to P2P systems, we emphasized Intra-peer scalability for concurrent gradient to mitigate communication overhead from increased peer interactions. SPIRT, employs RedisAI for in-database operations, achieves an 82% reduction in model update times. This architecture showcases resilience against peer failures and adeptly manages the integration of new peers. Furthermore, SPIRT ensures secure communication between peers, enhancing the reliability of distributed machine learning tasks. Even in the face of Byzantine attacks, the system’s robust aggregation algorithms maintain high levels of accuracy. These findings illuminate the promising potential of serverless architectures in P2P distributed machine learning, offering a significant stride towards the development of more efficient, scalable, and resilient applications.
Design of Key Performance Indicator Dashboard for Indonesian Higher Education based on One Data

The Key Performance Indicators (KPIs), also known as Indikator Kinerja Utama (IKU) in Indonesian, were recently announced by the Minister of Education and Culture in Indonesia. These KPIs aim to introduce new performance indicators for universities in the country, placing a stronger focus on measurable outputs. The goal is to establish universities that are adaptable and responsive to changing needs and demands. The Indonesian Government has recently issued Presidential Regulation No. 39 of 2019, which focuses on One Data Indonesia. This regulation aims to establish a standardized framework for integrating data from various applications and organizations. The objective of this research is to develop a KPI monitoring dashboard for higher education institutions in Indonesia, utilizing a single data source. The research was conducted using the Dashboard Design Process. The outcome demonstrates the successful implementation of a dashboard designed to monitor key performance indicators for public higher education in Indonesia. The data source utilized for displaying information on the dashboard integrates data from various information systems into an integrated database. It is straightforward to monitor the achievement of various metrics at the level of study programs, faculties, and universities.
A Comparative Study of Transformer Based Pretrained AI Models for Content Summarization

In this study, we examine different transformer based pretrained Artificial Intelligence (AI) models on their ability to summarize text content from different sources. AI has emerged as a powerful tool in this context, offering the potential to automate and improve the process of content summarization. We mainly focus on the pretrained transformer models, such as Pegasus, T5, Bart, and ProphetNet for key point summarization from textual contents. We aim to assess the effectiveness of these models in summarizing different contents like articles, instructions, conversational dialogues, and compare and analyze their performance across different datasets. We use ROUGE metric to evaluate the quality of the generated summaries. The Facebook’s BART model had better performance across different textual datasets. We believe that our findings will offer valuable insights into the capabilities and limitations of Transformer-based AI models in the context of extracting essential points from large articles, making them useful as assistive tools for summarizing course content in educational environments.
Hierarchical Latent Dynamics Model with Multiple Timescales for Learning Long-Horizon Tasks

Long-horizon tasks require intelligent agents, such as robots, to handle both temporal uncertainty and temporal dependency. Although world models have shown promise for solving tasks across many domains, they often struggle with managing long context in tasks due to the limited representation ability of their latent dynamics models. To overcome this issue, we propose a novel hierarchical latent dynamics model that takes into account multiple-timescale dynamics. Specifically, our proposed model, called the “multiple timescale recurrent state-space model” (MTRSSM), comprises a higher level with slow dynamics and a lower level with fast dynamics, each incorporating both deterministic and stochastic latent states. We demonstrate, both quantitatively and qualitatively, that a world model with our proposed MTRSSM can generate superior video predictions for long-horizon robotic object-manipulation tasks through latent imagination compared with other baselines. Importantly, we emphasize the critical role of the higher level in effectively handling temporal uncertainty and temporal dependency in long-horizon tasks. These findings indicate that the proposed MTRSSM enables intelligent agents to acquire a better understanding of the environment and generate more accurate predictions, thereby facilitating their learning and planning of long-horizon tasks.
Enhancing FPGA-Based YOLO Object Detection: Multi-Bank Storage Optimization and Model Refinement for Real-Time Applications

Object detection algorithms are widely used in image classification and detection tasks recently. Among the other algorithms, You Only Look Once (YOLO) performs better in real-time detection. Convolutional neural networks (CNNs) in the algorithm require a large amount of computation and memory access. However, researchers tend to access parameters through the external memory in accelerator design, which leads to slow processing speed and high power consumption. Therefore, we propose a Multi-Bank storage structure that flexibly combines URAM and BRAM in storing feature maps, which significantly improves the throughput. We also improve the YOLOv3-Tiny model by distillation, quantization and pruning and make it more suitable for deployment in FPGAs. These methods can significantly reduce the number of parameters in the model while ensuring almost no decrease in detection accuracy. Finally, tested on VOC 2007, our model achieves a frame rate of 67.6 FPS with a reduction in mAP of 0.38%.
Analysis of Attack Scenarios Relating to Changes in Voter Votes on E-Voting Systems

One of the causes of the lack of public trust in this e-voting system is the untrustworthy technology used. There are several examples of attacks on the e-voting system that have occurred that have made people doubt the technology used in this e-voting. One of the closest issues to attacks on e-voting is Russia’s interference in the United States presidential election activities. The NSA stated that hackers from Russia had tried to inject malware into the DRE machine used. On the other hand, another source states that the attacks carried out are just the spread of hoaxes for campaigns. However, this issue has an impact on people’s confidence in the e-voting system. Based on the example of the attack, it can be concluded that in an e-voting design, it cannot be assumed that the system is safe against attacks. Therefore, this e-voting system’s vulnerability needs to be analyzed to design risk mitigations against possible attacks. In this study, an attack analysis was carried out to identify possible attacks on the e-voting system for vote changes from voters.
Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection

Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware. Advances in photonic computing can have profound impacts on applications such as autonomous driving and defect detection that depend on fast, accurate and energy efficient execution of image segmentation models. In this paper, we investigate image segmentation on photonic accelerators to explore: 3) the types of image segmentation DNN architectures that are best suited for photonic accelerators, and b) the throughput and energy efficiency of executing the different image segmentation models on photonic accelerators, along with the trade-offs involved therein. Specifically, we demonstrate that certain segmentation models exhibit negligible loss in accuracy (compared to digital <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{FLOAT}32$</tex> models) when executed on photonic accelerators, and explore the empirical reasoning for their robustness. We also discuss techniques for recovering accuracy in the case of models that do not perform well. Further, we compare throughput (inferences-per-second) and energy consumption estimates for different image segmentation workloads on photonic accelerators. We discuss the challenges and potential optimizations that can help improve the application of photonic accelerators to such computer vision tasks.
MMFusion: A Generalized Multi-Modal Fusion Detection Framework

LiDAR point clouds have become the most common data source in autonomous driving. However, due to the sparsity of point clouds, accurate and reliable detection cannot be achieved in specific scenarios. Because of their complementarity with point clouds, images are getting increasing attention. Although with some success, existing fusion methods either perform hard fusion or do not fuse in a direct manner. In this paper, we propose a generic 3D detection framework called MMFusion, using multimodal features. The framework aims to achieve accurate fusion between LiDAR and images to improve 3D detection in complex scenes. Our framework consists of two separate streams: the LiDAR stream and the camera stream, which can be compatible with any single-modal feature extraction network. The Voxel Local Perception Module in the LiDAR stream enhances local feature representation, and then the Multi-modal Feature Fusion Module selectively combines feature output from different streams to achieve better fusion. Extensive experiments have shown that our framework not only outperforms existing benchmarks but also improves their detection, especially for detecting cyclists and pedestrians on KITTI benchmarks, with strong robustness and generalization capabilities. Hopefully, our work will stimulate more research into multimodal fusion for autonomous driving tasks.
Utilization of Artificial Intelligence for Social Media and Gaming Moderation

As the world continues to evolve, technology has proven to be a necessity in the lives of everyone. Evolving beyond professional use, cyberspace is now populated by online communities being used for communication, learning, and entertainment. However, the increased online presence exposes users to a variety of cultures, personalities, and levels of maturity. Some may also seek to cause harm to others through cyberbullying or may display toxic behaviors. This research aims to tackle the growing problem of toxicity and harassment in online environments. The proposed solution will utilize Artificial Intelligence (AI), and more specifically Natural Language Processing (NLP), to moderate communication and detect malicious language and behavior. The efforts shared in this paper specifically present a work-in-progress. For the time being, two models have been tested with a single dataset from Twitter: a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). The results of experimentation show a promising start for the use of NLP in moderation with an 83% accuracy using an RNN.
Some Classes of Cryptographic Power Functions and Permutation Polynomials with Low (-1)-Differential Uniformity in Cyber Security

Cryptographic functions with low differential uni-formity have wide applications in cryptography and cyber security. In this paper, we further investigate c-differential uniformity proposed by Ellingsen et al. (IEEE Transactions on Information Theory. 2020, 66(9): 5781–5789). Specifically, two classes of power functions with low (-1)-differential uniformity over finite fields with odd characteristic are obtained. In addition, a class of permutation polynomials and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{P}_{-1}\mathrm{N}$</tex> or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{AP}_{-1}\mathrm{N}$</tex> functions over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{3^{n}}$</tex> are proposed. These functions with low <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> -differential uniformity are utilized to resist against <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> -differential attacks in cyberspace from those with the usual low differential uniformity.
Enhancing Quality of Experience by Monitoring End-User Streaming Behavior

The objective of this study is to address the startup delay and exit before video start problem in live video streaming which affects the Quality of Experience (QoE). To this end, we propose an end-user video streaming behavior adaptation algorithm that works in cooperation Service Delivery Platform (SDP). The SDP describes the setup of a multimedia communication session and allows all endpoints to effectively participate in the session. In the proposed framework, an analytic tool continuously monitors the network and connected clients' information and keeps track of clients' history in a client attitude database. The SDP controls end-user behavior and applies control mechanisms for those who download a large segment but exit the program without displaying all the contents stored in the buffer. In this way, it prevents the waste of resources and allows other users to use the bandwidth fairly. Simulation results show that the proposed algorithm slightly improves QoE metrics.
An Autonomic Architecture for Multi-Agent Self-Maintaining Robotic Systems

Autonomic Computing (AC) systems have four maj or characteristics: self-healing, self-configuring, self-optimizing and self-protecting, combined refer to self-CHOP. This paper examines the self-healing properties of an autonomic system and how they can be applied to a multi-agent autonomous robotic system. This work defines an architecture that incorporates the maintenance of robots into the autonomic management of multi-agent systems. Related concepts from self-healing, prognostics and health management (PHM), and non-destructive evaluation (NDE) domains are discussed and integrated into the design of the architecture. A simple simulation applying the architecture is implemented using python and preliminary results are presented.
Adversarial Machine Learning Architecture in AI-Driven Power Systems

The paper investigates the use of AI in power systems to improve monitoring, control, and optimization. For successful classification tasks on various datasets, ML models are critical, boosting power system dependability and efficiency. The research focuses on ML model designs in AI-driven power systems for binary, triple, and multiclass classification. Misclassifications can destabilize grid stability, underlining the importance of precise classification in decision-making processes. To combat this, this research proposes a DNN model that uses specific parameters that were fine-tuned for greater accuracy in classification tasks. Model correctness is an important performance metric that can be improved by architecture design and fine-tuning. For successful optimization, three essential loss functions (binary cross-entropy, categorical cross-entropy, and sparse categorical cross-entropy) are investigated. We have achieved great results with utilizing the sparse categorical cross-entropy loss function to minimize the error during training periods. The study underscores the critical relevance of correct categorization and the possibility for robust AI applications in power systems, which contribute to grid stability and sustainability.
Fast Spectral Graph Partitioning with a Randomized Eigensolver

A known problem in parallel computing is how to partition a matrix such that work can be distributed among several processors efficiently. One technique to do this is spectral graph partitioning, which uses the eigenvectors of the graph Laplacian to determine the optimal way for the matrix to be divided. This partitioning method is particularly suited for parallelization, specifically for GPUs, as it mainly relies on linear algebra operations. However, this increased parallelism may come at the cost of accuracy. In this work, we present a novel improvement to spectral graph partitioning by replacing the exact eigensolver (LOBPCG) with a randomized eigensolver roughly an order of magnitude faster. While the accuracy of the eigensolver is typically worse, we show that for graph partitioning this is sufficient. Our algorithm is implemented in the Sphynx spectral graph partitioner, contained in the Zoltan2 package of Trilinos. Results show this randomized method in general gives a substantial speedup with minimal loss in the quality of the edge cut. In some cases the randomized method even gives slightly better edge cuts than the LOBPCG eigensolver.
An All-Optical Neuron for Scaling Integrated Photonic Neural Networks

We propose an all-optical neural network where signal processing requires no optical-to-electrical conversion. Weight multiplication, addition, and nonlinear activation of artificial neurons are performed in the photonic domain. Successful implementation will advance photonic neuromorphic computing, enabling practical solutions in artificial intelligence-driven tasks.
BGFormer: Deep Bayersian Gabor Filtering Guided Transformer for Medical Image Denoising

In recent years, modern diagnostic medical technology has developed rapidly, and computer tomography (CT) has become an important tool due to its fast detection speed and low cost. It is frequently used to assist in diagnosing complex conditions such as fractures and tumors. However, low-dose CT can lead to a decrease in image quality. For de-noising low-dose CT images, the enhanced performance not only relies on local features but also depends on the overall global characteristics due to the specific nature of medical images, where the shape and location of lesions vary for each patient. Nowadays, deep learning methods have gradually replaced traditional methods as the main-stream direction in low-dose CT image denoising, owing to their powerful feature representation capabilities. However, most algorithms still suffer from issues such as unclear local details, blurred line contours, and high computational complexity. In this paper, we propose a medical image denoising method based on gabor filtering neural network, combining the advantages of traditional filters with the unique strengths of deep learning. First, since gabor filtering is particularly effective for medical image processing, we use gabor filter obtained through variational inference instead of traditional convolutions, which enhances performance while reducing computational complexity. Second, we incorporate the use of Transformers in feature extraction to address the limitations of upsampling and downsampling operations, which cannot effectively model long-term context interactions and maintain the overall texture features of the images. Finally, we validate our method on the 2016 AAPM-Mayo Clinic Low-Dose CT Grand Challenge dataset and a fluorescence microscopy denoising dataset. The results show that our approach outperforms existing denoising methods in terms of PSNR, SSIM, and RMSE metrics, demonstrating its superiority.
Fully Integrated Photonic Tensor Core for Neural Network Applications

Neural Networks applications have exploded in recent times. Here, we present the first fully-integrated Photonic Tensor Core, capable of accelerating the computation of Neural Networks by exploiting a multi-wavelength light approach. In a single chip, we integrated all the optical components, from laser to photodetectors.
A Theoretical Framework for Supporting Clustering Validation via Non-Negative-Matrix-Factorization Trace Sequences Over Probabilistic Spaces

This paper focuses the attention on the annoying problem of clustering validation, intended as a relevant task of major analytical processing. The proposed method argues to build and analyze a data matrix from probabilistic spaces and to exploit Non-negative Matrix Factorization (NMF) to identify existing clusters and select them according to a Bayesian inter-pretation.