样式: 排序: IF: - GO 导出 标记为已读
-
Janus: A Trusted Execution Environment Approach for Attack Detection in Industrial Robot Controllers IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-24 Stefano Longari, Jacopo Jannone, Mario Polino, Michele Carminati, Andrea Zanchettin, Mara Tanelli, Stefano Zanero
-
HARPOCRATES: An Approach Towards Efficient Encryption of Data-at-rest IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-17 Md Rasid Ali, Debranjan Pal, Abhijit Das, Dipanwita Roy Chowdhury
-
One-Spike SNN: Single-Spike Phase Coding With Base Manipulation for ANN-to-SNN Conversion Loss Minimization IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-16 Sangwoo Hwang, Jaeha Kung
-
LP-Star : Embedding Longest Paths into Star Networks with Large-Scale Missing Edges under an Emerging Assessment Model IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-16 Xiao-Yan Li, Jou-Ming Chang
-
FakeTracer: Catching Face-swap DeepFakes via Implanting Traces in Training IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-16 Pu Sun, Honggang Qi, Yuezun Li, Siwei Lyu
-
A Bio-inspired Implementation of A Sparse-learning Spike-based Hippocampus Memory Model IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-16 Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, J. P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
-
A Novel Privacy-Preserving Range Query Scheme with Permissioned Blockchain for Smart Grid IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-15 Kun-chang Li, Peng-bo Wang, Run-hua Shi
-
On the Privacy of the Count-Min Sketch: Extracting the Top-K Elements IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-04-05 Alfonso Sánchez-Macián, Jorge Martínez, Pedro Reviriego, Shanshan Liu, Fabrizio Lombardi
-
Guest Editorial IEEE Transactions on Emerging Topics in Special Section on Emerging In-Memory Computing Architectures and Applications IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-03-18 Alberto Bosio, Ronald F. DeMara, Deliang Fan, Nima TaheriNejad
Computer architecture stands at an important crossroad to surmount vital performance challenges. For more than four decades, the performance of general purpose computing systems has been improving by 20–50% per year [1]. In the last decade, this number has dropped to less than 7% per year. Most recently, that rate has slowed to only 3% per year. [1]. The demand for performance improvement, however
-
-
Guest Editorial IEEE Transactions on Emerging Topics in Computing Special Section on Advances in Emerging Privacy-Preserving Computing IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-03-18 Jinguang Han, Patrick Schaumont, Willy Susilo
Machine learning and cloud computing have dramatically increased the utility of data. These technologies facilitate our life and provide smart and intelligent services. Notably, machine learning algorithms need to learn from massive training data to improve accuracy. Hence, data is the core component of machine learning and plays an important role. Cloud computing is a new computing model that provides
-
Guest Editorial Emerging Trends and Advances in Graph-Based Methods and Applications IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-03-18 Alessandro D'Amelio, Jianyi Lin, Jean-Yves Ramel, Raffaella Lanzarotti
The integration of graph structures in diverse domains has recently garnered substantial attention, presenting a paradigm shift from classical euclidean representations. This new trend is driven by the advent of novel algorithms that can capture complex relationships through a class of neural architectures: the Graph Neural Networks (GNNs) [1], [2]. These networks are adept at handling data that can
-
-
IEEE Transactions on Emerging Topics in Computing Information for Authors IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-03-18
-
MiniFloats on RISC-V Cores: ISA Extensions with Mixed-Precision Short Dot Products IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-02-19 Luca Bertaccini, Gianna Paulin, Matheus Cavalcante, Tim Fischer, Stefan Mach, Luca Benini
-
A Design Framework for Hardware-Efficient Logarithmic Floating-Point Multipliers IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-02-19 Tingting Zhang, Zijing Niu, Jie Han
-
Adaptive Task Migration in Multiplex Networked Industrial Chains IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-02-16 Kai Di, Fulin Chen, Yuanshuang Jiang, Pan Li, Tianyi Liu, Yichuan Jiang
-
Engravings, Secrets, and Interpretability of Neural Networks IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-01-31 Nathaniel Hobbs, Periklis A. Papakonstantinou, Jaideep Vaidya
-
Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-01-31 Van-Tuan Tran, Huy-Hieu Pham, Kok-Seng Wong
-
Unsupervised Domain Adaptation Via Contrastive Adversarial Domain Mixup: A Case Study on COVID-19 IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-01-26 Huimin Zeng, Zhenrui Yue, Lanyu Shang, Yang Zhang, Dong Wang
-
Hardware-Aware DNN Compression via Diverse Pruning and Mixed-Precision Quantization IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-01-03 Konstantinos Balaskas, Andreas Karatzas, Christos Sad, Kostas Siozios, Iraklis Anagnostopoulos, Georgios Zervakis, J¨org Henkel
-
MFDS-STGCN: Predicting the Behaviors of College Students With Fine-Grained Spatial-Temporal Activities Data IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-01-01 Dongbo Zhou, Hongwei Yu, Jie Yu, Shuai Zhao, Wenhui Xu, Qianqian Li, Fengyin Cai
Mining and predicting college students behaviors from fine-grained spatial-temporal campus activity data play key roles in the academic success and personal development of college students. Most of the existing behavior prediction methods use shallow learning algorithms such as statistics, clustering, and correlation analysis approaches, which fail to mine the long-term spatial-temporal dependencies
-
Combining Trust Graphs and Keystroke Dynamics to Counter Fake Identities in Social Networks IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2024-01-01 Francesco Buccafurri, Gianluca Lax, Denis Migdal, Lorenzo Musarella, Christophe Rosenberger
-
A FeFET-Based ADC Offset Robust Compute-In-Memory Architecture for Streaming Keyword Spotting (KWS) IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-12-28 Yandong Luo, Johan Vanderhaegen, Oleg Rybakov, Martin Kraemer, Niel Warren, Shimeng Yu
Keyword spotting (KWS) on edge devices requires low power consumption and real-time response. In this work, a ferroelectric field-effect transistor (FeFET)-based compute-in-memory (CIM) architecture is proposed for streaming KWS processing. Compared with the conventional sequential processing scheme, the inference latency is reduced by 7.7 × ∼17.6× without energy efficiency loss. To make the KWS models
-
Sparsity-Oriented MRAM-Centric Computing for Efficient Neural Network Inference IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-10-26 Jia-Le Cui, Yanan Guo, Juntong Chen, Bo Liu, Hao Cai
Near-memory computing (NMC) and in- memory computing (IMC) paradigms show great importance in non-von Neumann architecture. Spin-transfer torque magnetic random access memory (STT-MRAM) is considered as a promising candidate to realize both NMC and IMC for resource-constrained applications. In this work, two MRAM-centric computing frameworks are proposed: triple-skipping NMC (TS-NMC) and analog-multi-bit-sparsity
-
A Chaotic Maps-Based Privacy-Preserving Distributed Deep Learning for Incomplete and Non-IID Datasets IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-10-05 Irina Arévalo, Jose L. Salmeron
Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data that wish to share their own knowledge without compromising the privacy of their data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and proposes a method for addressing the non-IID challenge
-
Scheduling Coflows by Online Identification in Data Center Network IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-29 Chang Ruan, Jianxin Wang, Wanchun Jiang, Tao Zhang
Recently, many scheduling schemes leverage coflows to improve the communication performance of jobs in distributed application frameworks deployed in data center networks, such as MapReduce and Spark. Most of them require application modification to obtain the coflow information such as the coflow ID. The latest work CODA suggests non-intrusively extracting coflow information via an identification
-
Quadtree-Based Adaptive Spatial Decomposition for Range Queries Under Local Differential Privacy IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-26 Huiwei Wang, Yaqian Huang, Huaqing Li
Nowadays, researchers have shown significant interest in geographic location-based spatial data analysis due to its wide range of application scenarios. However, the accuracy of the grid-based quadtree range query (GT-R) algorithm, which utilizes the uniform grid method to divide the data space, is compromised by the excessive noise introduced in the divided area. In addition, the private adaptive
-
Two Double-Node-Upset-Hardened Flip-Flop Designs for High-Performance Applications IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-25 Aibin Yan, Aoran Cao, Zhengfeng Huang, Jie Cui, Tianming Ni, Patrick Girard, Xiaoqing Wen, Jiliang Zhang
The continuous advancement of complementary metal-oxide-semiconductor technologies makes flip-flops (FFs) vulnerable to soft errors. Single-node upsets (SNUs), as well as double-node upsets (DNUs), are typical soft errors. This article proposes two radiation-hardened FF designs, namely DNU-tolerant FF (DUT-FF) and DNU-recoverable FF (DUR-FF). First, the DUT-FF which mainly consists of four dual-in
-
Hardware/Software Co-Design With ADC-Less In-Memory Computing Hardware for Spiking Neural Networks IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-22 Marco Paul E. Apolinario, Adarsh Kumar Kosta, Utkarsh Saxena, Kaushik Roy
Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for realizing energy-efficient implementations of sequential tasks on resource-constrained edge devices. However, commercial edge platforms based on standard GPUs are not optimized to deploy SNNs, resulting in high energy and latency. While analog In-Memory Computing (IMC) platforms can serve as energy-efficient inference
-
Adversarial Attacks Assessment of Salient Object Detection via Symbolic Learning IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-22 Gustavo Olague, Roberto Pineda, Gerardo Ibarra-Vazquez, Matthieu Olague, Axel Martinez, Sambit Bakshi, Jonathan Vargas, Isnardo Reducindo
Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention
-
Always on Voting: A Framework for Repetitive Voting on the Blockchain IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-22 Sarad Venugopalan, Ivana Stančíková, Ivan Homoliak
Elections repeat commonly after a fixed time interval, ranging from months to years. This results in limitations on governance since elected candidates or policies are difficult to remove before the next elections, if needed, and allowed by the corresponding law. Participants may decide (through a public deliberation) to change their choices but have no opportunity to vote for these choices before
-
Deadline-Aware and Energy-Efficient Dynamic Task Mapping and Scheduling for Multicore Systems Based on Wireless Network-on-Chip IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-20 Abbas Dehghani, Sadegh Fadaei, Bahman Ravaei, Keyvan RahimiZadeh
Hybrid Wireless Network-on-Chip (HWNoC) architecture has been introduced as a promising communication infrastructure for multicore systems. HWNoC-based multicore systems encounter extremely dynamic application workloads that are submitted at run-time. Mapping and scheduling of these applications are critical for system performance, especially for real-time applications. The existing resource allocation
-
Understanding Bulk-Bitwise Processing In-Memory Through Database Analytics IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-19 Ben Perach, Ronny Ronen, Benny Kimelfeld, Shahar Kvatinsky
Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are performed in parallel by the memory array itself, is an emerging form of computation with the potential to mitigate the memory wall problem. This article examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a fully-digital system based on memristive stateful logic, utilizing and focusing on in-memory bulk-bitwise
-
Guest Editorial Special Section on Applied Software Aging and Rejuvenation IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-09-05 Michael Grottke, Alberto Avritzer, Hironori Washizaki, Kishor Trivedi
Since the publication of the first paper on software aging and rejuvenation by Huang et al. in 1995 [1], considerable research has been devoted to this topic. It deals with the phenomenon that continuously-running software systems may show an increasing failure rate and/or a degrading performance, either because error conditions accumulate inside the running system or because the rate at which faults
-
Noise-Shaping Binary-to-Stochastic Converters for Reduced-Length Bit-Streams IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-08-01 Kleanthis Papachatzopoulos, Vassilis Paliouras
Stochastic computations have attracted significant attention for applications with moderate fixed-point accuracy requirements, as they offer minimal complexity. In these systems, a stochastic bit-stream encodes a data sample. The derived bit-stream is used for processing. The bit-stream length determines the computation latency for bit-serial implementations and hardware complexity for bit-parallel
-
An Edge-Cloud Collaboration Framework for Graph Processing in Smart Society IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-24 Jun Zhou, Masaaki Kondo
Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware way to provide the capacity-constrained portable terminals with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of the human activity in smart society, such as social networks, medical diagnosis
-
Privacy-Preserving Authentication Protocols for IoT Devices Using the SiRF PUF IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-20 Jim Plusquellic, Eirini Eleni Tsiropoulou, Cyrus Minwalla
Authentication between IoT devices is important for maintaining security, trust and data integrity in an edge device ecosystem. The low-power, reduced computing capacity of the IoT device makes public-private, certificate-based forms of authentication impractical, while other lighter-weight, symmetric cryptography-based approaches, such as message authentication codes, are easy to spoof in unsupervised
-
GRAPHIC: Gather and Process Harmoniously in the Cache With High Parallelism and Flexibility IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-17 Yiming Chen, Mingyen Lee, Guohao Dai, Mufeng Zhou, Nagadastagiri Challapalle, Tianyi Wang, Yao Yu, Yongpan Liu, Yu Wang, Huazhong Yang, Vijaykrishnan Narayanan, Xueqing Li
In-memory computing (IMC) has been proposed to overcome the von Neumann bottleneck in data-intensive applications. However, existing IMC solutions could not achieve both high parallelism and high flexibility, which limits their application in more general scenarios: As a highly parallel IMC design, the functionality of a MAC crossbar is limited to the matrix-vector multiplication; Another IMC method
-
New Construction of Balanced Codes Based on Weights of Data for DNA Storage IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-13 Xiaozhou Lu, Sunghwan Kim
As maintaining a proper balanced GC content is crucial for minimizing errors in DNA storage, constructing GC-balanced DNA codes has become an important research topic. In this article, we propose a novel code construction method based on the weight distribution of the data, which enables us to construct GC-balanced DNA codes. Additionally, we introduce a specific encoding process for both balanced
-
CRAM-Based Acceleration for Intermittent Computing of Parallelizable Tasks IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-12 Khakim Akhunov, Kasım Sinan Yıldırım
There is an emerging requirement for performing data-intensive parallel computations, e.g., machine-learning inference, locally on batteryless sensors. These devices are resource-constrained and operate intermittently due to the irregular energy availability in the environment. Intermittent execution might lead to several side effects that might prevent the correct execution of computational tasks
-
3DL-PIM: A Look-Up Table Oriented Programmable Processing in Memory Architecture Based on the 3-D Stacked Memory for Data-Intensive Applications IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-12 Purab Ranjan Sutradhar, Sathwika Bavikadi, Sai Manoj Pudukotai Dinakarrao, Mark A. Indovina, Amlan Ganguly
Memory-centric computing systems have demonstrated superior performance and efficiency in memory-intensive applications compared to state-of-the-art CPUs and GPUs. 3-D stacked DRAM architectures unlock higher I/O data bandwidth than the traditional 2-D memory architecture and therefore are better suited for incorporating memory-centric processors. However, merely integrating high-precision ALUs in
-
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-11 Yong-Min Shin, Cong Tran, Won-Yong Shin, Xin Cao
We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs. Our study is motivated by the fact that GNNs cannot be straightforwardly adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this
-
A Graph-Incorporated Latent Factor Analysis Model for High-Dimensional and Sparse Data IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-11 Di Wu, Yi He, Xin Luo
A High-dimensional and s parse (HiDS) matrix is frequently encountered in Big Data-related applications such as e-commerce systems or wireless sensor networks. It is of great significance to perform highly accurate representation learning on an HiDS matrix due to the great desires of extracting latent knowledge from it. L atent f actor a nalysis (LFA), which represents an HiDS matrix by learning the
-
FINISH: Efficient and Scalable NMF-Based Federated Learning for Detecting Malware Activities IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-11 Yu-Wei Chang, Hong-Yen Chen, Chansu Han, Tomohiro Morikawa, Takeshi Takahashi, Tsung-Nan Lin
5G networks with the vast number of devices pose security threats. Manual analysis of such extensive security data is complex. Dark-NMF can detect malware activities by monitoring unused IP address space, i.e., the darknet. However, the challenges of cooperative training for Dark-NMF are immense computational complexity with Big Data, communication overhead, and privacy concern with darknet sensor
-
Resource Allocation Optimization by Quantum Computing for Shared Use of Standalone IRS IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-11 Takahiro Ohyama, Yuichi Kawamoto, Nei Kato
Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective
-
PISA: A Non-Volatile Processing-in-Sensor Accelerator for Imaging Systems IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-07-11 Shaahin Angizi, Sepehr Tabrizchi, David Z. Pan, Arman Roohi
This work proposes a Processing-In-Sensor Accelerator, namely PISA, as a flexible, energy-efficient, and high-performance solution for real-time and smart image processing in AI devices. PISA intrinsically implements a coarse-grained convolution operation in Binarized-Weight Neural Networks (BWNNs) leveraging a novel compute-pixel with non-volatile weight storage at the sensor side. This remarkably
-
CANNON: Communication-Aware Sparse Neural Network Optimization IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-30 A. Alper Goksoy, Guihong Li, Sumit K. Mandal, Umit Y. Ogras, Radu Marculescu
Sparse deep neural networks (DNNs) have the potential to deliver compelling performance and energy efficiency without significant accuracy loss. However, their benefits can quickly diminish if their training is oblivious to the target hardware. For example, fewer critical connections can have a significant overhead if they translate into long-distance communication on the target hardware. Therefore
-
Rei: A Reconfigurable Interconnection Unit for Array-Based CNN Accelerators IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-30 Paria Darbani, Hakem Beitollahi, Pejman Lotfi-Kamran
Convolutional Neural Network (CNN) is used in many real-world applications due to its high accuracy. The rapid growth of modern applications based on learning algorithms has increased the importance of efficient implementation of CNNs. The array-type architecture is a well-known platform for the efficient implementation of CNN models, which takes advantage of parallel computation and data reuse. However
-
A Near-Sensor Processing Accelerator for Approximate Local Binary Pattern Networks IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-16 Shaahin Angizi, Mehrdad Morsali, Sepehr Tabrizchi, Arman Roohi
In this work, a high-speed and energy-efficient comparator-based N ear- S ensor L ocal B inary P attern accelerator architecture (NS-LBP) is proposed to execute a novel local binary pattern deep neural network. First, inspired by recent LBP networks, we design an approximate, hardware-oriented, and multiply-accumulate (MAC)-free network named Ap-LBP for efficient feature extraction, further reducing
-
Guest Editorial: IEEE Transactions on Emerging Topics in Computing Thematic Section on Memory- Centric Designs: Processing-in-Memory, In-Memory Computing, and Near-Memory Computing for Real-World Applications IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-06 Yuan-Hao Chang, Vincenzo Piuri
The von Neumann architecture has been the status quo since the dawn of modern computing. Computers built on the von Neumann architecture are composed of an intelligent master processor (e.g., CPU) and dumb memory/storage devices incapable of computation (e.g., memory and disk). However, the skyrocketing data volume in modern computing is calling such status quo into question. The excessive amounts
-
Graph Embedding Techniques for Predicting Missing Links in Biological Networks: An Empirical Evaluation IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-08 Binon Teji, Swarup Roy, Devendra Singh Dhami, Dinabandhu Bhandari, Pietro Hiram Guzzi
Network science tries to understand the complex relationships among entities or actors of a system through graph formalism. For instance, biological networks represent macromolecules such as genes, proteins, or other small chemicals as nodes and the interactions among the molecules as links or edges. Often potential links are guessed computationally due to the expensive nature of wet lab experiments
-
Private Delegated Computations Using Strong Isolation IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-05 Mathias Brossard, Guilhem Bryant, Basma El Gaabouri, Xinxin Fan, Alexandre Ferreira, Edmund Grimley Evans, Christopher Haster, Evan Johnson, Derek Miller, Fan Mo, Dominic P. Mulligan, Nick Spinale, Eric van Hensbergen, Hugo J. M. Vincent, Shale Xiong
Computations are now routinely delegated to third-parties. In response, Confidential Computing technologies are being added to microprocessors offering a trusted execution environment ( TEE ) that provides confidentiality and integrity guarantees to code and data hosted within—even in the face of a privileged attacker. TEEs, along with an attestation protocol, permit remote third-parties to establish
-
Construction of a Spike-Based Memory Using Neural-Like Logic Gates Based on Spiking Neural Networks on SpiNNaker IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-05 Alvaro Ayuso-Martinez, Daniel Casanueva-Morato, J. P. Dominguez-Morales, Angel Jimenez-Fernandez, Gabriel Jimenez-Moreno
Neuromorphic engineering concentrates the efforts of a large number of researchers due to its great potential as a field of research, in a search for the exploitation of the advantages of the biological nervous system and the brain as a whole for the design of more efficient and real-time capable applications. For the development of applications as close to biology as possible, Spiking Neural Networks
-
Coupled Attention Networks for Multivariate Time Series Anomaly Detection IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-02 Feng Xia, Xin Chen, Shuo Yu, Mingliang Hou, Mujie Liu, Linlin You
Multivariate time series anomaly detection (MTAD) plays a vital role in a wide variety of real-world application domains. Over the past few years, MTAD has attracted rapidly increasing attention from both academia and industry. Many deep learning and graph learning models have been developed for effective anomaly detection in multivariate time series data, which enable advanced applications such as
-
An Optimized Hardware Implementation of Modular Multiplication of Binary Ring LWE IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-06-01 Karim Shahbazi, Seok-Bum Ko
Providing end-to-end security is vital for most networks. Emerging quantum computers make it necessary to design secure crypto-systems against quantum attacks. Binary Ring Learning With Error (Ring-Bin LWE) is a Lattice-based cryptography that is hard to solve by quantum computers. Also, this algorithm does not have costly operations in terms of area, making Ring-Bin LWE a suitable algorithm for resource-constraint
-
An Energy-Efficient Generic Accuracy Configurable Multiplier Based on Block-Level Voltage Overscaling IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-05-30 Ali Akbar Bahoo, Omid Akbari, Muhammad Shafique
Voltage Overscaling (VOS) is one of the well-known techniques to increase the energy efficiency of arithmetic units. Also, it can provide significant lifetime improvements, while still meeting the accuracy requirements of inherently error-resilient applications. This paper proposes a generic accuracy-configurable multiplier that employs the VOS at a coarse-grained level (block-level) to reduce the
-
Predicting Aging-Related Bugs Using Network Analysis on Aging-Related Dependency Networks IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-05-30 Fangyun Qin, Zheng Zheng, Xiaohui Wan, Zhihao Liu, Zhiping Shi
Software aging, a phenomenon that exhibits an increasing failure rate or progressive performance degradation in long-running software systems, has caused serious cost damage or even loss of human lives. To aid aging-related bug (ARB, whose activation can result in software aging) detection and removal before software release, ARB prediction was proposed. Based on the prediction results, software teams
-
A Low-Cost Wireless Body Area Network for Human Activity Recognition in Healthy Life and Medical Applications IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-05-12 Florenc Demrozi, Cristian Turetta, Philipp H. Kindt, Fabio Chiarani, Ruggero Angelo Bacchin, Nicola Valè, Francesco Pascucci, Paola Cesari, Nicola Smania, Stefano Tamburin, Graziano Pravadelli
Moved by the necessity, also related to the ongoing COVID-19 pandemic, of the design of innovative solutions in the context of digital health, and digital medicine, Wireless Body Area Networks (WBANs) are more and more emerging as a central system for the implementation of solutions for well-being and healthcare. In fact, by elaborating the data collected by a WBAN, advanced classification models can
-
A Large Scale Characterization of Device Uptimes IEEE Trans. Emerg. Top. Comput. (IF 5.9) Pub Date : 2023-05-03 Mateus Nogueira, Erica da Cunha Ferreira, Pedro Tubenchlak Boechat, Felipe Assis, Estevão Rabello, Rafael Nascimento, Daniel Sadoc Menasché, Geraldo Xexéo, Abhishek Ramchandran, Katinka Wolter
Devices ages, also referred to as uptimes, convey information about systems, and are instrumental for patching and rejuvenation purposes. Knowing that a device is up for a long time suggests that it may be at risk or that degradation due to bugs may be in place. Nonetheless, there has been no systematic study of devices uptimes so far. The goal of this paper is to provide a large scale characterization