Miracle-dz/UniFAR-FTUs huggingface dataset:
T-SP: A Timer-based Self-Pruning Broadcast Protocol using One-hop Neighbor Information

The broadcast protocol plays a crucial role in wire- less ad-hoc networks for fundamental network operations, route discovery, service discovery and even actual data transmission. The primary objectives of a scalable broadcast protocol involve minimizing the number of forwarding stations and reducing network overhead. To achieve these objectives, we are motivated by the use of one-hop neighbor information in self-pruning and timer mechanism of SBA. The use of two-hop information and random selection process to assign priority among the stations are the major deficiencies of the probabilistic timer mechanism of SBA. To solve these issues, we develop a broadcast protocol called timer-based self-pruning (T-SP) that uses deterministic timer mechanism and one-hop neighbor information. The simulation results show that the proposed T-SP ensures network scalability, reduces number of forwarding stations, network overhead and energy consumption in expense of broadcast delay.
XR Jam: Design Considerations for Music Networking with AI in XR

The XR Jam project presents an integrated system designed to enhance musical interaction and spatial awareness in Extended Reality (XR) environments. Leveraging AI-driven Virtual Musical Agents (AIVMs), the platform elevates traditional musical experiences. Built on an XR framework, the system pushes conventional boundaries by enabling novel forms of interaction, such as object manipulation and social drumming with AIVMs and remote players. Furthermore, avatar-based interfaces help overcome social inhibitions, offering players a unique, uninhibited interactive experience. Pilot studies revealed the possibility for effective use of haptics in spatial localization. While haptic feedback enhanced spatial orientation in specific scenarios, front and back directions remained challenging to discern. The system promotes musical interaction, social engagement, and spatial orientation, although further refinements are needed for synchronization and player experience. Overall, this research opens new avenues for immersive musical experiences through a fusion of AI, XR, and haptic technologies.
Fast In-Network Functionality Embedding in Software-Defined Service-Centric Networking

Joint resource allocation in integrated networking, computing, and caching frameworks has attracted plenty of attention. In software-defined service-centric networking (SDSCN), we investigate an energy cost economical functionality embedding (FE) problem. The FE problem is a non-convex quadratically constrained quadratic programming (QCQP) problem which is difficult to obtain its global optimal solutions. In this paper, we propose a low-complexity high-performance algorithm for energy-economical FE design in large-scale SDSCN systems by leveraging the alternating direction method of multipliers (ADMM) together with successive convex approximation (SCA). In specific, the FE problem is first approximated as a sequence of convex subproblems via SCA. Each convex subproblem is then reformulated as a novel ADMM form to enable parallel computations and closed-form solutions. Numerical results show that our fast algorithm reduces the complexity by orders of magnitude and obtains favorable performances compared with state-of-the-art algorithms.
Integrating Transitivity in Collaborative Filtering-Based Recommender System

The issue of data sparsity is receiving a lot of attention in the field of collaborative filtering-based recommender systems. Collaborative Filtering is one of the most efficacious recommendation techniques in the domain of recommender systems. It generates recommendations based on the preferences of the users that share similar tastes with the active user. For determining similarity among users or items, various metrics have been proposed in the literature. However, most similarity measures only consider the set of items that are commonly rated. Due to insufficient data, it becomes difficult to find similarities among every pair of users which in turn affects the quality of recommendation techniques. To address the problem of sparsity, the proposed work utilizes the concept of transitive similarity and thus generates recommendations where state-of-the-art techniques fail. Comparative analysis of the proposed technique is carried out with the traditional recommendation models based on the measures of MAE, RMSE, and F1-measure. Experimental results reveal that the proposed model consistently improves the quality of prediction.
Evaluating Network Boolean Tomography Under Byzantine Attacks

It is vital to closely track the operation statuses of network-internal links. Accurate knowledge of the operation statuses of network-internal links is vital for the management of many networks like the Internet, the satellite communication network, etc. Network boolean tomography can identify congested links just using end-to-end path status observations, and is able to work efficiently even without any available cooperation of internal nodes. Nevertheless, it heavily assumes that all the path status observations collected are true while some Byzantine attacks, e.g., the label flip attacks, could violate this assumption. In this paper, we present a performance evaluation of network boolean tomography under Byzantine attacks. Our results against various attacking rates, locations, and scales all show that Byzantine attacks could cause a significant performance degradation of network boolean tomography, suggesting a pressing need of developing the detection and countermeasure techniques.
A Visual Analytics Framework for Explainable Malware Detection in Edge Computing Networks

The emergence of new technologies for the fifth/sixth generation (5G/6G) wireless networks has led to the development of new services, resulting in an increase in malicious activities and cyber-attacks targeting various networklayers. Edge computing, a crucial technology enabler for 6G, is expected to facilitate traffic optimisation and support new ultra-low latency services. By integrating computing power from supercomputing servers into devices at the network edge in a distributed manner, edge computing can provide consistent quality-of-service, even in remote areas, which will drive the growth of associated applications. However, the complex environment created by edge computing also poses challenges for detecting malware. Therefore, this paper proposes a novel approach to malware detection using explain ability via visualization and a multi-labelling technique. An object detection algorithm is used to identify malware families within the dataset which is created by emphasizing key regions. Using features from different malware categories in an image, this model displays a thorough malware recipe. Our experiments using real malware data demonstrate that identifying malware by its visible characteristics can significantly improve the interpretability of the detection process, enhancing transparency and trustworthiness.
Impact of Geometrical and Process Design Parameters on the Performance of Schottky Barrier Reconfigurable Field Effect Transistor

In this study, the electrical properties of a Schottly Barrier Reconfigurable Field Effect Transistor (SBRFET) are thoroughly evaluated with the variation of important design parameters. The device proposed in this study operates in both n-type and p-type modes by exploiting electrical doping rather than additional physical doping. One of the critical design parameters is the gate work function, which significantly affects the charge density in the channel and subsequently alters the tunneling rate. The findings reveal that gate length variation has a negligible impact on the device's performance. The device achieved an on/off current ratio of $2 \times 10^{7}$ and a subthreshold swing of 87.5 mV/dec for n type operation, while for p-type operation, it achieved an on/off current ratio of $1.5 \times 10^{6}$ with a subthreshold swing of 95.7 mV/dec. The results of this study pave the way for developing high-speed, low-power digital circuits with reprogrammable logic.
Federated Approach to Online Pairwise Comparison Ranking Problem

In this work, we propose a federated learning (FL) framework for the online HodgeRank problem to obtain a global ranking based on the pairwise comparison data provided by users while respecting users’ data privacy. HodgeRank is a statistical ranking method that views pairwise comparison data as an edge-weighted directed graph, and, vertex weight function can be viewed as a ranking that recovers edge weight using difference. A critical assumption of the HodgeRank is the connectivity of the comparison graph. This assumption relies on the contribution of users’ data to form a weakly connected directed graph between items to be ranked. In this paper, we aim to find a framework to compute HodgeRank with the minimum usage of users’ data.
Multi -Type Water Contaminant Identification Using Electrochemical Impedance Spectroscopy and Machine Learning at Network Edge

Water contamination is a global crisis that threatens our health and the environment. Traditional methods for detecting and filtering contaminants are expensive and ineffective on a large scale. To address this problem, we propose a novel contaminant identification system that combines Electrochemical Impedance Spectroscopy (EIS) with the Internet of Things (IoT) and Machine Learning (ML) techniques. Our system is low-cost and scalable while offering unprecedented sensitivity and selectivity. Moreover, it efficiently extracts a rich set of (i.e., 1024) features and collects sufficient data samples for model training. Aided by edge computing, we develop a Random Forest-based classifier that can identify multi-type contaminants near the water source with 98 % accuracy. The experiment results demonstrate the potential of our proposed techniques that protect human health, preserve ecosystems, and mitigate the global water contamination crisis.
Secure and Efficient Decentralized Analytics on Digital Twins Using Federated Learning

Digital Twin, the Internet of Things, and Machine Learning have the potential to redefine our imagination of innovation and impact every sector in the future. A digital twin is a virtual replica of a physical object that facilitates real-time data collection from IoT sensors and uses machine learning/artificial intelligence to analyze and predict data. In this paper, we investigate the use case of federated machine learning for analyzing data on digital twins. Since digital twins capture data about their physical counterparts, they may contain private and sensitive information. This poses challenges in performing machine learning tasks on digital twins due to potential data privacy leakage threats. The motivation behind this research is to explore data ownership and privacy issues in the context of the vast amounts of data available in digital twins and to apply federated learning as a potential solution to address these challenges. We investigated how federated learning can enhance the predictive capability of digital twins by promoting knowledge sharing while maintaining privacy. Our findings indicate that federated learning is an effective approach for facilitating learning between twins and can significantly reduce communication overhead in comparison to training on physical edge devices. Additionally, we show that the convergence rate of federated learning on digital twins improves due to the greater amount of training data available on digital twins compared to the baseline federated learning approach.
Performance Modeling and Analysis of Distributed Deep Neural Network Training with Parameter Server

With the growth of dataset size and the development of hardware accelerators, the application of deep neural networks (DNN) in various fields has made great breakthroughs. In order to improve the training speed of DNN, distributed training has been widely used. However, the imbalance between computation and communication makes distributed training difficult to achieve maximum efficiency. Therefore there is a need to detect the bottleneck state and verify the effect of some optimization schemes. Testing on a physical cluster incurs additional time and cost overhead. This paper builds a DNN-specific performance model that is used for bottleneck detection and tuning at a low cost. We build this model through detailed analysis and reasonable assumptions. We also focus on fine-grained modeling of scalability and network components, which are key factors affecting performance. Then we verify the performance model with an average error of 5% on testbed and emulator. Finally, we provide use cases of the performance model.
Computational Analysis of a Serpentine Pulley-Belt System

This paper analyzes the serpentine pulley-belt system, which consists of a flexible belt coupled with rigid pulleys. This system has been adequately described by a nonlinear model and linear approximations of it, obtained from systems implemented in physical form. This paper presents computational tools designed for the analysis of: (i) the feasibility of physical realization from a set of proposed parameters and (ii) the parametric dependence of the modes presents in the steady-state response. The computational tools together facilitate mathematical analysis, reduce design time and allow to obtain mathematically and graphically the static response of the system, since they allow to determine a priori the frequency, phase lag and amplitude of the oscillation of the band sections, which significantly helps the analysis and design of the same.
Privacy-Preserving and Robust Multimodal Monitoring System Using LWIR and mmWave for Abnormal Behavior Recognition in Hospital Wards

In this paper, we propose a multimodal monitoring sensing system of LWIR camera and mmWave radar for detecting abnormal health behaviors in hospital wards. LWIR cameras can recognize posture with high accuracy while preserving privacy with low resolution. However, it cannot be used in an invisible environment, such as closing curtains in a ward environment. To solve this problem, we intend to solve it using mmWave radar that can be sensed through a thin cloth. We design and implement a multimodal system that uses two sensors for privacy and environmental robustness. For proper use of both sensors, we propose a multimodal black-out network called COMBONet, which switches inputs based on confidence, considering background features and circumstances. Our implementation and experiments used people not used for training as the evaluation. It shows that it can be used comprehensively in various environments.
An End-to-End Chromosome Classification Method Based on Contrastive and Sparse Representation Learning

Chromosome classification is essential in karyotype analysis within the field of cytogenetics. Karyotype analysis is an essential method used by clinical cytogeneticists to identify numerical and structural chromosomal abnormalities. Despite the development of various methods, accurately and robustly classifying chromosomes and their polarity for clinical applications remains a challenging task. These challenges primarily arise from two aspects: 1) the diverse deformations in chromosome shape, 2) the similarity between chromosomes. To tackle these obstacles, we suggest an innovative end-to-end approach in this manuscript. Unlike existing methods, our approach focuses on learning sparse and contrastive features rather than simply identifying the classification features within the given samples. The methodology follows a dual-stage structure, comprising a feature extraction module and two modules dedicated to enhancing features. In the first stage, the basic visual features of chromosome are acquired. In the second stage, the contrastive features are obtained by triplet loss and the sparse features by ista-block. The assessment outcomes on a private dataset indicate that our approach attains 97.38% and 97.76% accuracy for the type task and polarity task, respectively, surpassing state-of-the-art methodologies.
Privacy Preserving Federated Learning Approach for Speech Emotion Recognition

Emotions are a critical factor in intrapersonal communication and significantly influence how we convey our intentions and feelings. Recognizing emotion through speech not only enhances our understanding of interpersonal dynamics but also holds immense potential across diverse sectors such as healthcare, human-machine interaction, automated customer service and more. However, the majority of the existing speech recognition systems are highly centralized, raising concerns over potential data leakage. To address this issue, we have introduced a privacy preserving system to recognize emotion from audio data using federated learning. Our approach leveraged the distributed model training and aggregation strategy, ensuring data privacy while eliminating the need for data sharing to a centralized system. Moreover, we explored the potential of CNN and LSTM models, both as a distributed and centralized formats, with MFCC as features in the federated learning setting. Experimental evaluation of our approach on the IEMOCAP and CREMA-D datasets achieved a maximum accuracy of 68.65% and 68.82%, surpassing the existing federated learning techniques and rivaling centralized benchmarks.
Guided Transformer for Machine Translation: English to Hindi

In the area of machine translation, the objective is to develop a model which can translate a piece of text from one language to another language. Large sized neural networks requires a large amount of training data to achieve a good performance level. The objective of this study is to build an explicit knowledge from the dependency parser, which can be integrated into transformer architecture. Adding an explicit knowledge to a neural machine translation network can enhance its learning process. The baseline transformer model and the proposed guided transformer model were tested on the same test data set of PMIndia Man Ki Baat session. With an addition of explicit knowledge, the proposed guided transformer model surpass the performance of baseline transformer model by +1.0 BLEU (approx.), +1.0 SacreBLEU (approx.), +1.48 Chrf, +1.44 Chrf+ and +1.39 Chrf++.
Highly Accurate Approximate Ternary Multipliers for Error Resilient Applications

Approximate computing is a technique to obtain notable improvement in power and power-delay product parameters where accurate computation is not necessary. This paper presents a simple and energy-efficient approximate 1-blt ternary multiplier and approximate ternary half adder, which are used to implement $6\times 6$ approximate ternary multipliers. The $6\times 6$ ternary multipliers are implemented by introducing errors in generation or reduction stages. The proposed approximate ternary $6\times 6$ multipliers show good accuracy-power trade-off compared to existing designs; it exhibits an improvement up to 99.6% in terms of NMED*Power parameter compared to existing approximate multiplier designs. Further proposed ternary multipliers are evaluated using image processing applications.
A Butterfly Malar Rash Detection Model for Early Systemic Lupus Erythematosus Diagnosis

As estimated by the Lupus Foundation of America, at least 5 million people worldwide including 1.5 million Americans (approx.) have some form of Lupus. Among the different forms of Lupus, Systemic Lupus Erythematosus (SLE) is most prevalent. Diagnostic procedures for SLE present challenges due to its’ elusive etiology and diverse symptomatology. However, among the symptoms of SLE, the Butterfly Malar Rash (BMR), manifesting as a butterfly-shaped rash across the cheekbone and nasal bridge, is a prominent indicator. The challenge in detecting BMR is due to their similarity with other rashes from rosacea, and fifth disease. In this paper, we develop a CNN-based model that can differentiate between BMR and other rashes in facial images. We also conducted a comparative study with other BMR detection models and observed that our proposed model outperforms the existing models with an accuracy of 93.2% on the dataset used in this work.
Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images

The human skeletal framework relies heavily on bones, and one such crucial component is the “Humerus. ” Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available “MURA” dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)" and "Ensemble-2 (DenseNet121 and DenseNet169)," which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model’s predictions, providing valuable insights for medical practitioners.
Task-Oriented Explainable Semantic Communications Based on Structured Scene Graphs

Semantic communications have been regarded as a promising solution for the next generation communication systems to alleviate the spectral resource shortage and the network congestion. Existing image semantic communication systems extract and transmit global semantics, which can cope with the downstream data reconstruction or intelligent tasks. However, the semantics involved in these methods are still severely redundant and uninterpretable. In this work, we propose a novel task-oriented semantic communication framework based on scene graph, named DeepSC-SG. Specifically, we first devise a scene graph based semantic encoder, which extracts the explainable scene graph semantics from the input images and encodes the semantics into informative graph embeddings. Then we design a joint source-channel (JSC) codec to combat physical channel impairment. After receiving the semantics, a semantic decoder is devised to achieve the downstream image retrieval task by computing scene graph similarities. Simulation results demonstrate that the proposed DeepSC-SG is fairly robust to the channel variations compared to the traditional communication systems, which has great potential in realizing downstream intelligent tasks like image retrieval with significantly reduced size of transmitted data.