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Optimal Aggregation via Overlay Trees: Delay-MSE Tradeoffs under Failures 25

Optimal Aggregation via Overlay Trees: Delay-MSE Tradeoffs u...

引用

Abstracts of the 2025 ACM SIGMETRICS international conference on measurement and modeling of computer systems

作者： Parikshit Hegde Gustavo de Veciana Chandra Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas USA

ISBN: (纸本)9798400715938

Many applications, e.g., federated learning, require the aggregation of information across a large number of distributed nodes. In this paper, we explore efficient schemes to do this at scale leveraging aggregation at intermediate nodes across overlay trees. Files/updates are split into chunks which are in turn simultaneously aggregated over different trees. For a synchronous setting with homogeneous communications capabilities and deterministic link delays, we develop a delay optimal aggregation schedule. In the asynchronous setting, where delays are stochastic but i.i.d., across links, we show that for an asynchronous implementation of the above schedule, the expected aggregation delay is near-optimal. We then consider the impact that failures in the network have on the resulting Mean Square Error (MSE) for the estimated aggregates and how it can be controlled through the addition of redundancy, reattempts, and optimal failure-aware estimates for the desired aggregate. Based on the analysis of a natural model of failures, we show how to choose parameters to optimize the trade-off between aggregation delay and MSE. We present simulation results exhibiting the above mentioned tradeoffs. We also consider a more general class of correlated failures and demonstrate via simulation the applicability of our techniques in those settings as well.

关键词： optimal estimation

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Musimple: A Simplified Music Generation System with Diffusion Transformer

Musimple: A Simplified Music Generation System with Diffusio...

引用

Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE international conference on

作者： Zheqi Dai Haolin He Qiuqiang Kong Electronic Engineering The Chinese University of Hong Kong

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

Music generation has become an important area of research, with applications in automated composition, personalized soundtracks, and virtual entertainment. Recently, diffusion models such as Diffusion Transformers (DiT) have shown promising results in generating high-quality audio. However, many previous music generation systems rely heavily on large datasets and require significant computational resources. This makes them challenging to reproduce and less accessible for broader research. To address these limitations, we propose a simplified text-to-music generation system. Our model uses the small-scale GTZAN dataset and 2D mel spectrograms as the primary audio representation. By avoiding complex components like VAEs, we streamline the architecture and reduce computational overhead. We evaluate our model using metrics such as FAD, KL and CLAP, achieving scores of 5.0, 2.31, and 0.18, respectively. These results demonstrate that our system generates musically acceptable audio even with reduced data. Our code, data, and pre-trained checkpoints are fully open-sourced and available at https://***/ZheqiDAI/Musimple.

关键词： Training measurement Computational modeling Entertainment industry computer architecture Transformers Diffusion models Multiple signal classification Speech processing Spectrogram

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Precise Segmentation of Eye Muscle Area in Sheep Based on Multi-Scale Feature Fusion and Attention Mechanism

Precise Segmentation of Eye Muscle Area in Sheep Based on Mu...

引用

international conference on computer Supported Cooperative Work in Design

作者： QingQing Ling Haizheng Yu Hong Bian College of Mathematics and Systems Science Xinjiang University Urumqi P.R. China School of Mathematical Sciences Xinjiang Normal University Urumqi P.R. China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In modern animal husbandry, the eye muscle area is a key meat quality indicator, influencing consumer choices. Traditionally, measuring this area involves post-slaughter manual tracing, which is time-consuming and labor-intensive. In this work, due to a lack of public animal ultrasound datasets, this study established the EM-Sheep dataset for automated eye muscle in sheep using ultrasound images. We propose an advanced ultrasound image segmentation algorithm, MOC-YOLO, based on multi-scale feature fusion and attention mechanisms. This algorithm builds upon YOLOv8 as the base network and integrates the MOCA module to enhance multi-scale feature extraction and address complex boundary recognition in the segmentation of the eye muscle area. Additionally, the C2f-CGA module addresses noise, low contrast, and artifacts in B-mode ultrasound images. Experimental results show that MOC-YOLO significantly improves segmentation performance on the EM-Sheep dataset, with a 2.4% increase in IoU, 7.2% in accuracy, 3.9% in Dice and F1 scores, and boosts in Precision and Recall by 2.4% and 5.3%, respectively.

关键词： YOLO Image segmentation Ultrasonic imaging Attention mechanisms Animals Ultrasonic variables measurement Computational modeling Muscles Feature extraction Robustness

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joint Learning of Object Graph and Relation Graph for Visual Question Answering

Joint Learning of Object Graph and Relation Graph for Visual...

引用

2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Li, Hao Li, Xu Karimi, Belhal Chen, Jie Sun, Mingming School of Electronic and Computer Engineering Peking University China Baidu Research Cognitive Computing Lab China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

modeling visual question answering (VQA) through scene graphs can significantly improve the reasoning accuracy and interpretability. However, existing models answer poorly for complex reasoning questions with attributes or relations, which causes false attribute selection or missing relation in Figure 1(a). It is because these models cannot balance all kinds of information in scene graphs, neglecting relation and attribute information. In this paper, we introduce a novel Dual Message-passing enhanced Graph Neural Net-work (DM-GNN), which can obtain a balanced represen-tation by properly encoding multi-scale scene graph infor-mation. Specifically, we (i) transform the scene graph into two graphs with diversified focuses on objects and relations;Then we design a dual structure to encode them, which in-creases the weights from relations (ii) fuse the encoder out-put with attribute features, which increases the weights from attributes;(iii) propose a message-passing mechanism to en-hance the information transfer between objects, relations and attributes. We conduct extensive experiments on datasets in-cluding GQA, VG, motif-VG and achieve new state of the art. © 2022 IEEE.

关键词： Message passing

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CyHy: A Centrality-aware Hybrid Caching Strategy in Information-centric Networking

CyHy: A Centrality-aware Hybrid Caching Strategy in Informat...

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IEEE international conference on Signal Processing, Information, Communication & systems (SPICSCON)

作者： Ashim Dey Asaduzzaman Mohammad Shamsul Arefin Department of Computer Science and Engineering Chittagong University of Engineering & Technology (CUET) Chattogram Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Network functionality has to be moved from the central server to the nearby user due to the recent explosion in multimedia applications' volume, worldwide accessibility, and rapid development. Inversely, typical Internet communications are inefficient as their main purpose is the transfer of data between hosts. As a result, a content-oriented networking paradigm known as information-centric networking (ICN) has surfaced. Researchers are trying to resolve implementation issues as ICN continues to develop. Allocating the space among cache-enabled nodes to maximize their benefits is a significantly difficult task because ICN has limited caching capability. In this work, a centrality-aware hybrid caching (CyHy) scheme has been proposed where cache storage distribution and content caching selection are made based on node relevance rather than uniformly dividing space among nodes. As node centrality metrics have a significant impact on caching decisions, we considered degree centrality which provides a local view, and betweenness centrality which provides a global view to find the important nodes in the network. Certain adjustments are suggested to the request packet and data packet structures to accommodate the proposed caching strategy. Finally, two real-world internet topologies are used for extensive modeling, and the results are compared with other standard caching techniques. The results of the simulation demonstrate that suggested caching outperforms others in terms of caching hit ratio, latency, and link load.

关键词： measurement Information-centric networking Signal processing Explosions Cache storage Servers Standards Load modeling Internet topology

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2021 IEEE 2nd international conference on Control, measurement and Instrumentation, CMI 2021 - Proceedings

2021 IEEE 2nd International Conference on Control, Measureme...

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2nd IEEE international conference on Control, measurement and Instrumentation, CMI 2021

ISBN: (纸本)9781728193427

The proceedings contain 41 papers. The topics discussed include: computer vision based obstacle identification using real-time illumination sensor data;computer aided diabetes diagnosis using textural features of saliva crystallogram images;realization of solid fractional order capacitor using common materials;fractional order intelligent controller for single tank liquid level system;meteorological data driven prediction of global solar radiation;application of triangular orthogonal function in chemical process simulation;effect of electrode thickness and spacing in the sensing capabilities of surface acoustic wave delay lines;motion control of an internally-actuated cubical robot balancing on its edge;and surface velocity estimation of streams using a 77 GHz radar unit.

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Attacking a joint Protection Scheme for Deep Neural Network Hardware Accelerators and Models

Attacking a Joint Protection Scheme for Deep Neural Network ...

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IEEE international conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Simon Wilhelmstätter Joschua Conrad Devanshi Upadhyaya Ilia Polian Maurits Ortmanns Institute of Microelectronics University of Ulm Ulm Germany Institute for Computer Architecture and Computer Engingeering University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

The tremendous success of artificial neural networks (NNs) in recent years, paired with the leap of embedded, low-power devices (e.g. IoT, wearables and smart sensors), gave rise to specialized NN accelerators that enable the inference of NNs in power-constrained environments. However, manufacturing or operating such accelerators in un-trusted environments poses risks of undesired model theft and hardware counterfeiting. One way to protect NN hardware against those threats is by locking both the model and the accelerator with secret keys that can only be supplied by entitled authorities (e.g. chip designer or distributor). However, current locking mechanisms contain severe drawbacks, such as required model retraining and vulnerability to the powerful satisfyability checking (SAT)-***, an approach for jointly protecting the model and the accelerator was proposed. Compared to previous locking mechanisms, it promises to avoid model retraining, not leak useful model information, and resist the SAT-attack, thereby securing the NN accelerator against counterfeiting and the model against intellectual property infringement. In this paper, those claims are thoroughly evaluated and severe issues in the technical evidence are identified. Furthermore, an attack is developed that does not require an expanded threat model but is still able to completely circumvent all of the proposed protection schemes. It allows to reconstruct all NN model parameters (i.e. model theft) and enables hardware counterfeiting.

关键词： Threat modeling Artificial neural networks Resists Robustness Manufacturing Security Protection

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A Study on Self-Configuring Intrusion Detection Model based on Hybridized Deep Learning Models

A Study on Self-Configuring Intrusion Detection Model based ...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： Sanchika Abhay Bajpai Archana B. Patankar Computer department Thadomal Sahani Engineering College Mumbai Maharashtra Computer Department Thadomal Sahani Engineering College

In order to achieve the necessary security guarantee, Intrusion Detection systems (IDSs) play a vital role in all networks and information systems worldwide. One of the best ways to prevent malicious assaults is to use an IDS. In order to keep up with attacker's constant evolution of attack strategies and discovery of new attack vectors, IDS must likewise advance by implementing more advanced detection techniques. New research in the deep learning sector, including intrusion detection, has been made possible by the enormous rise in data as well as the considerable advancements in computer hardware technology. Machine Learning (ML) techniques that are adapted on data learning representations include deep learning as a sub-field. This study analyses 25 research papers that discuss about machine learning techniques and intrusion detection models by highlighting numerous drawbacks and the advantages of the current methodologies, which provides the interpretation of the suitable method for most of the IDS systems. This case shows different ways to explore the journals, publication years, metrics, achievements of numerical evaluation techniques, and a number of variables support other factors. On the other hand, a technique analysis that considers the benefits and drawbacks of the methods is also presented. The relative assessment of the methodologies provides a thorough clarification of the proposed motivation.

关键词： Deep learning measurement Adaptation models Analytical models Computational modeling Intrusion detection Hardware

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Enhanced Diagnosis of Schizophrenia Using Integrated Node Embedding and Causal Discovery on Imbalanced fMRI Data

Enhanced Diagnosis of Schizophrenia Using Integrated Node Em...

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international conference of Signal Processing and Intelligent systems (ICSPIS)

作者： Sina Sabzevar Saeed Masoudnia Babak N Araabi MohammadReza NazemZadeh Hosna Tavakoli Department of Electrical and Computer Engineering The University of Tehran Tehran Iran Department of Neuroscience Monash University Melbourne Australia Computational and Artificial Intelligence Department Institute of Cognitive Science Studies Tehran Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

This study introduces a novel diagnostic method for schizophrenia using causal discovery and node embedding techniques on resting-state fMRI data. Data from 148 subjects (27 schizophrenia patients, 121 healthy controls) were analyzed across 116 brain regions, applying several causal discovery techniques to assess directional connectivity and uncover schizophrenia-related patterns. By combining advanced causal discovery with node embedding techniques and a rigorous data-balancing pipeline, our method improves sensitivity to subtle network variations critical for schizophrenia diagnosis. Our approach addresses key challenges in the literature: the difficulty of extracting effective neurobiological markers from noisy and complex fMRI data, the limited interpretability of connectivity networks, and the imbalance in datasets that often skews classification performance. Experimental results demonstrate the pipeline's effectiveness in overcoming imbalanced data challenges and extracting features from noisy and complex data, with Regression Dynamic Causal modeling (RDCM) achieving the highest classification metrics, including 90.06% accuracy and 89.09% sensitivity. This method identified 15 brain regions with strong diagnostic potential, offering improved sensitivity and specificity for schizophrenia diagnosis Compared to contemporary domain techniques.

关键词： Sensitivity Accuracy Pipelines Machine learning Schizophrenia Functional magnetic resonance imaging Feature extraction Brain modeling Noise measurement Data mining

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On the Accuracy-Energy Tradeoff for Hierarchical Federated Learning via Satisfaction Equilibrium

On the Accuracy-Energy Tradeoff for Hierarchical Federated L...

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international conference on Distributed Computing in Sensor systems (DCOSS)

作者： Panagiotis Charatsaris Maria Diamanti Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

Hierarchical Federated Learning (HFL) has recently emerged as a promising method to overcome the limitations of conventional Federated Learning (FL) in terms of communication inability of the end users with the cloud and increased backhaul network traffic when considering implementations over wireless networks. Nevertheless, to reap the benefits of HFL, proper user association with the different edge servers and wireless resource allocation is required. Unlike existing works - that in their majority treat unilaterally a single objective using centralized optimization techniques - in this paper, we particularly aim to explore the tradeoff induced between the users' local model training accuracy and personal energy consumption in a distributed manner. The joint problem of user-to-edge-server association and uplink power allocation for transmitting the users' local model parameters to the edge is formulated and solved as a game in satisfaction form. Each user autonomously seeks to achieve a target accuracy-energy ratio by selecting their edge association and uplink transmission power level, while their cumulative decisions result in a desired Satisfaction Equilibrium (SE) point. Specifically, to determine the respective SE of the formulated game, a Reinforcement Learning (RL) algorithm is utilized. Numerical results obtained via modeling and simulation demonstrate the operational and performance characteristics of the proposed framework in terms of achieving the users' personally desired tradeoff value.

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