# computer.org > AI-optimized mirror of computer.org containing 25 pages totalling 23,803 words of clean markdown content, structured data, and semantic HTML. Original source: https://computer.org/. Last updated: 2026-06-15T10:27:32.853Z. Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG). ## Homepage - [Cache Index - computer.org](/content/index.html): Index of 6 cached pages and 0 generated product pages ## Articles & Blog Posts - [csdl/proceedings/2015/icnp/12omnx3q6ue/index.html](/content/csdl/proceedings/2015/icnp/12omnx3q6ue/index.html) (1 words) - [publications/tech-news/insider-membership-news/2025-charles-babbage-award-winner/index.html](/content/publications/tech-news/insider-membership-news/2025-charles-babbage-award-winner/index.html) (1 words) - [csdl/magazine/an/2012/01/man2010010020/13rruy0qnim/index.html](/content/csdl/magazine/an/2012/01/man2010010020/13rruy0qnim/index.html) (1 words) - [The Role of CRM in Digital Transformation: Driving Business Growth and Innovation](/content/publications/tech-news/trends/crm-role-in-digital-transformation/index.html): Learn why CRM software is essential for businesses. Discover how to drive engagement, optimize resources, and increase revenue. (1,277 words) - [Disaggregating LLM Infrastructure: Solving the Hidden Bottleneck in AI Inference](/content/publications/tech-news/community-voices/llm-infrastructure-ai-inference/index.html): Learn how disaggregated inference improves LLM efficiency by splitting tasks, boosting performance, and lowering cost. (896 words) - [Toward High-Resolution Regional Atmospheric Reanalysis for Japan: An Overview of the ClimCORE Project](/content/csdl/proceedings-article/big-data/2022/10020656/1kfst6nrn3o/index.html): An overview is provided of on-going production of regional atmospheric reanalysis data RRJ-ClimCORE for Japan and its surrounding maritime domain. The data can reproduce the past atmospheric conditions four-dimensionally with high spatial and temporal resolutions, through assimilating fine-resolution observational data into a state-of-the-art operational regional forecast system. The reanalysis data will have wide-ranging potential applications in society, including disaster prevention and risk management planning, and system designing for renewable energy through deep learning. (402 words) - [Sense4FL: Vehicular Crowdsensing Enhanced Federated Learning for Object Detection in Autonomous Driving](/content/csdl/journal/tm/5555/01/11434993/2eufspujnb2/index.html): To accommodate constantly changing road conditions, real-time vision model training is essential for autonomous driving (AD). Federated learning (FL) serves as a promising paradigm to enable autonomous vehicles to train models collaboratively with their onboard computing resources. However, existing vehicle selection schemes for FL all assume predetermined and location-independent vehicles' datasets, neglecting the fact that vehicles collect training data along their routes, thereby resulting in suboptimal vehicle selection. In this paper, we focus on the fundamental perception problem and propose Sense4FL, a vehicular crowdsensing-enhanced FL framework featuring trajectory-dependent vehicular training data collection to improve the object detection quality in AD for a region. To this end, we first derive the convergence bound of FL by considering the impact of both vehicles' uncertain trajectories and uploading probabilities, from which we discover that minimizing the training loss is equivalent to minimizing a weighted sum of local and global earth mover's distance (EMD) between vehicles' collected data distribution and global data distribution. Based on this observation, we formulate the trajectory-dependent vehicle selection and data collection problem for FL in AD. Given that the problem is NP-hard, we develop an efficient algorithm to find the solution with an approximation guarantee. Extensive simulation results have demonstrated the effectiveness of our approach in improving object detection performance compared with existing benchmarks. (663 words) - [IEEE Transactions on Software Engineering](/content/csdl/journal/ts/2024/08/10596932/1yw9ktc84xo/index.html): During software development and maintenance, vulnerability detection is an essential part of software quality assurance. Even though many program-analysis-based and machine-learning-based approaches have been proposed to automatically detect vulnerabilities, they rely on explicit rules or patterns defined by security experts and suffer from either high false positives or high false negatives. Recently, an increasing number of studies leverage deep learning techniques, especially Graph Neural Network (GNN), to detect vulnerabilities. These approaches leverage program analysis to represent the program semantics as graphs and perform graph analysis to detect vulnerabilities. However, they suffer from two main problems: (i) Existing GNN-based techniques do not effectively learn the structural and semantic features from source code for vulnerability detection. (ii) These approaches tend to ignore fine-grained information in source code. To tackle these problems, in this paper, we propose a novel vulnerability detection approach, named MGVD (M ultiple-G raph-Based V ulnerability D etection), to detect vulnerable functions. To effectively learn the structural and semantic features from source code, MGVD uses three different ways to represent each function into multiple forms, i.e., two statement graphs and a sequence of tokens. Then we encode such representations to a three-channel feature matrix. The feature matrix contains the structural feature and the semantic feature of the function. And we add a weight allocation layer to distribute the weights between structural and semantic features. To overcome the second problem, MGVD constructs each graph representation of the input function using multiple different graphs instead of a single graph. Each graph focuses on one statement in the function and its nodes denote the related statements and their fine-grained code elements. Finally, MGVD leverages CNN to identify whether this function is vulnerable based on such feature matrix. We conduct experiments on 3 vulnerability datasets with a total of 30,341 vulnerable functions and 127,931 non-vulnerable functions. The experimental results show that our method outperforms the state-of-the-art by 9.68% – 10.28% in terms of F1-score. (724 words) - [STELLA: a domain-specific tool for structured grid methods in weather and climate models](/content/csdl/proceedings-article/sc/2015/2807627/12omnzxghow/index.html): Many high-performance computing applications solving partial differential equations (PDEs) can be attributed to the class of kernels using stencils on structured grids. Due to the disparity between floating point operation throughput and main memory bandwidth these codes typically achieve only a low fraction of peak performance. Unfortunately, stencil computation optimization techniques are often hardware dependent and lead to a significant increase in code complexity. We present a domain-specific tool, STELLA, which eases the burden of the application developer by separating the architecture dependent implementation strategy from the user-code and is targeted at multi- and manycore processors. On the example of a numerical weather prediction and regional climate model (COSMO) we demonstrate the usefulness of STELLA for a real-world production code. The dynamical core based on STELLA achieves a speedup factor of 1.8× (CPU) and 5.8× (GPU) with respect to the legacy code while reducing the complexity of the user code. (415 words) - [Deep Learning for Human Affect Recognition: Insights and New Developments](/content/csdl/journal/ta/2021/02/08598999/17d45xh13tx/index.html): Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications. (511 words) - [Password-Based Authentication: A System Perspective](/content/csdl/proceedings-article/hicss/2004/205670170b/12omnyo8tt1/index.html): User authentication in computer systems has been a cornerstone of computer security for decades. The concept of a user id and password is a cost effective and efficient method of maintaining a shared secret between a user and a computer system. One of the key elements in the password solution for security is a reliance on human cognitive ability to remember the shared secret. In early computing days with only a few computer systems and a small select group of users, this model proved effective. With the advent of the Internet, e-commerce, and the proliferation of PCs in offices and schools, the user base has grown both in number and in demographic base. Individual users no longer have single passwords for single systems, but are presented with the challenge of remembering numerous passwords for numerous systems, from email, to web accounts, to banking and financial services. This paper presents a conceptual model depicting how users and systems work together in this function and examines the consequences of the expanding user base and the use of password memory aids. A system model of the risks associated with password-based authentication is presented from a user centric point of view including the construct of user password memory aids. When confronted with too much data to remember, users will develop memory aids to assist them in the task of remembering important pieces of information. These user password memory aids form a bridge between otherwise unconnected systems and have an effect on system level security across multiple systems interconnected by the user. A preliminary analysis of the implications of this user centric interconnection of security models is presented. (6,090 words) - [SocketWatch: An autonomous appliance monitoring system](/content/csdl/proceedings-article/percom/2014/06813941/12omnbe7moy/index.html): A significant amount of energy is wasted by electrical appliances when they operate inefficiently either due to anomalies and/or incorrect usage. To address this problem, we present SocketWatch - an autonomous appliance monitoring system. SocketWatch is positioned between a wall socket and an appliance. SocketWatch learns the behavioral model of the appliance by analyzing its active and reactive power consumption patterns. It detects appliance malfunctions by observing any marked deviations from these patterns. SocketWatch is inexpensive and is easy to use: it neither requires any enhancement to the appliances nor to the power sockets nor any communication infrastructure. Moreover, the decentralized approach avoids communication latency and costs, and preserves data privacy. Real world experiments with multiple appliances indicate that SocketWatch can be an effective and inexpensive solution for reducing electricity wastage. (391 words) - [Accuracy-Efficiency Optimization for Multi-Stage Small Object Detection in Surveillance Video with Collaborative Frame Sampling](/content/csdl/proceedings-article/cluster/2024/587100a403/21hprqqymnu/index.html): In video analytics, accuracy and efficiency are two important metrics and there tend to be a tradeoff between each other. In this paper, we consider accuracy-efficiency optimization for small object detection in surveillance video, which is important and has been widely used in many scenarios such as license plate detection in the traffic domain. Given that small objects tend to being attached to big objects, multi-stage object detection is supposed to be an effective approach to achieve high accuracy for small objects by detecting big objects first and then small objects within the ROIs (Region of interests) of big objects. However, existing studies considered the accuracy-efficiency optimization for small object detection only within the single-stage scenario by changing the frame resolution or sampling rate configuration of video data, which are not suitable for multi-stage detection given that its accuracy-efficiency result is determined by the results of all stages jointly. In this paper, we propose an Adaptive and Collaborative frame Sampling approach named ACS for accuracy-efficiency optimization in the multi-stage small object detection. To improve the efficiency significantly while guaranteeing a given accuracy threshold, ACS dynamically adjusts the sampling rates of all stages collaboratively and periodically using the Karush-Kuhn-Tucker (KKT) condition based on the Lagrangian multiplier method. Additionally, we introduce a tuning knob to allow users to flexibly balance accuracy and efficiency, while ensuring a given accuracy threshold λ. Extensive experiments demonstrate the effectiveness of our approach in improving detection efficiency while guaranteeing diverse accuracy requirements. (569 words) - [Simon S. Lam](/content/profiles/simon-lam/index.html): Simon S. Lam is Professor and Regents Chair in Computer Sciences at the University of Texas at Austin. He received the BSEE degree with Distinction from Washington State University, Pullman, in 1969, and the M.S. and Ph.D. degrees from the University of California at Los Angeles in 1970 and 1974, respectively. From 1971 to 1974, he was with the ARPA Network Measurement Center at UCLA, where he worked on satellite and radio packet switching networks. From 1974 to 1977, he was a Research Staff Member at the IBM T.J. Watson Research Center, Yorktown Heights, New York. Since 1977, he has been on the faculty of the University of Texas at Austin, where he served as Department Chair from 1992 to 1994. Professor Lam has served on the editorial boards of six journals (IEEE/ACM Transactions on Networking, IEEE Transactions on Software Engineering, IEEE Transactions on Communications, Proceedings of the IEEE, Performance Evaluation, and Computer Networks). He was editor-in-chief of IEEE/ACM Transactions on Networking from 1995 to 1999. He is a Founding Steering Committee member of the Computer Society's International Conference on Network Protocols. He co-founded the ACM SIGCOMM Symposia by organizing, as Program Chair, the inaugural SIGCOMM Symposium held on the campus of UT-Austin in 1983. He was a panelist of the NSF Airlie House Workshops in 1989, 1992, and 1994 and co-authored the Workshop reports on "Research Priorities in Networking and Communications" for the National Science Foundation. Professor Lam is a co-recipient of the 1975 Leonard G. Abraham Prize for the best paper published in IEEE Transactions on Communications and the 2001 William R. Bennett Prize for the best paper published in IEEE/ACM Transactions on Networking, both from the IEEE Communications Society. He is a Fellow of IEEE (elected 1985) and a Fellow of ACM (elected 1998). Professor Lam's research… (707 words) - [High Performance Householder QR Factorization on Emerging GPU Architectures Using Tensor Cores](/content/csdl/journal/td/2025/03/10816084/22y5dseuv3q/index.html): Since 2017, NVIDIA GPUs have been equipped with specialized units known as Tensor Cores, which demonstrate remarkable efficiency in processing matrix multiplications (GEMMs). Beyond GEMMs, researchers have explored the potential applications of Tensor Cores in matrix factorization, such as QR factorization. However, the inside GEMMs in QR factorization are typically tall and skinny. Compared to compute-bound square GEMMs, these tall and skinny GEMMs are memory bound, leading to suboptimal performance on Tensor Cores. To solve this problem, we indicate the recursive QR factorization can convert the tall and skinny GEMMs to relatively square and large GEMMs, resulting in better performance on Tensor Cores. Besides, we extend the FP16 Tensor-Cores-based QR factorization to accommodate FP32 and FP64 on FP16 and INT8 Tensor Cores, respectively. Additionally, to address the issue of orthogonality loss in the preceding Tensor Cores-based QR factorization, we transition from the Gram-Schmidt to the Householder algorithm while preserving high performance. According to our experimental evaluation conducted on NVIDIA's A100 and GeForce RTX 3090 GPU, the precision levels of FP64, FP32, and FP16 are up to 6.22x, 8.67x, and 4.03x faster, respectively, than the current state-of-the-art implementations. (521 words) - [Blockchain Compliance With Federal Cryptographic Information-Processing Standards](/content/csdl/magazine/sp/2020/01/08965252/1gm05bhx2ms/index.html): Under current Federal Information Security Management Act of 2002 (FISMA) requirements, all new federal IT programs and modernization efforts using blockchain must meet National Institute of Standards and Technology (NIST) cryptographic standards. This article evaluates four major blockchain technologies-Ethereum, Hyperledger Fabric, R3's Corda, and Multichain-to determine their level of NIST compliance. (304 words) - [Scoring Metrics of Assessing Voiceprint Distinctiveness Based on Speech Content and Rate](/content/csdl/journal/tq/2024/06/10478153/1vwbceobtos/index.html): A voiceprint is the distinctive pattern of human voices widely used for authentication in voice assistants. This article investigates the impact of speech contents and speech rates on the distinctiveness of voiceprint, and has obtained answers to three questions by studying 2457 speakers and 21,500,000 test samples: 1) What are the influential factors that users can control to affect the distinctiveness of voiceprints? 2) How to quantify the distinctiveness for given speeches, e.g., the speech of wake-up words when activating voice assistants? 3) How to help users select wake-up words and adjust the speech rate to improve distinctiveness levels? To answer those questions, we break down speeches into phones, and experimentally obtain the correlation between false recognition rates and the richness, order, length, and elements of the phones. Then, we define the PROLE Score that can reflect the voice distinctiveness, and evaluate 30 wake-up words of 19 commercial voice assistant products to provide recommendations on selecting secure voiceprint words. We also measure the correlation between false recognition rates and speech rates, and define the TER Score that reveals the distance of distinctiveness from the secure voiceprint, and it guides users to adjust their speech rate to a secure value. (618 words) - [Predicting defects using network analysis on dependency graphs](/content/csdl/proceedings-article/icse/2008/04814164/12omnxgsm9j/index.html): In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10% points higher than for models built from complexity metrics. In addition, network measures could identify 60% of the binaries that the Windows developers considered as critical-twice as many as identified by complexity metrics. (361 words) - [FitFuzz: Depth-Oriented Coverage-Guided Fuzzing via Fitness-Based Seed Scheduling](/content/csdl/proceedings-article/iccd/2025/034600a315/2crueadyjkk/index.html): Coverage-guided greybox fuzzing (CGF) has emerged as a powerful technique for identifying software vulnerabilities by leveraging lightweight code coverage feedback. However, existing fuzzers often treat seeds independently, overlooking the structural and evolutionary relationships among inputs. This lack of relational insight can limit the fuzzer's ability to prioritize inputs collectively capable of revealing deeper or more complex program behaviors. We present FitFuzz, a novel fuzzing framework that improves seed scheduling through a fitness-guided strategy. It constructs a mutation-aware seed relation tree and assigns each node a dynamic fitness score based on execution performance, structural depth, and complexity. By integrating fitness-aware seed generation and selection, FitFuzz achieves more effective path exploration while maintaining efficient resource usage. On the UniFuzz benchmark suite, FitFuzz triggered 221 crashes in tiffsplit and 490 in infotocap, while also achieving leading results in seed scheduling efficiency, with top performance in edge discovery for 6 of 11 benchmarks and favored path efficiency in 7 of 11. These results demonstrate that FitFuzz consistently outperforms state-of-the-art fuzzers in crash discovery, particularly by uncovering deeper and less frequently explored execution paths. (406 words) - [VPN scalability over high performance backbones Evaluating MPLS VPN against traditional approaches](/content/csdl/proceedings-article/iscc/2003/19610975/12omnqizgz7/index.html): The rapid growth of the Internet and the widespread deployment of networks built around the Internet Protocol suite are creating a demand for new capabilities in IP networks. The IP-based virtual private network (VPN) technology is rapidly becoming the foundation for the delivery of future Internet services, and many service providers are offering value-added applications on top of their VPN transport networks. Two unique and complementary architectures based on traditional industry standard encrypted tunnels (IPSec) and still developing Multiprotocol Label Switching (MPLS) technologies are emerging to form the predominant framework for delivery of high performance VPN services. We analyzed the strengths and the weaknesses of both the approaches, and compared their performance and scalability features by carefully testing them against the requirements of the future optical high performance backbones. Our technical considerations and experimental results strongly emphasized the better scalability and reliability of the MPLS/BGP model that seems to be the most promising approach for the provisioning of VPN services on the future Giga-speed optical backbones. (416 words) - [2019 IEEE International Conference on Data Mining (ICDM)](/content/csdl/proceedings/icdm/2019/1h5xdp5eock/index.html): Browse the leading magazines in computing offering topical peer-reviewed current research, developments, and timely information. (2,878 words) - [404](/content/csdl/journals/tifs/index.html) (41 words) - [2010 IEEE International Conference on Data Mining](/content/csdl/proceedings/icdm/2010/12omnzbohwk/index.html): Browse the leading magazines in computing offering topical peer-reviewed current research, developments, and timely information. (2,738 words) - [Third IEEE International Conference on Data Mining](/content/csdl/proceedings/icdm/2003/12omnzdvsne/index.html): Browse the leading magazines in computing offering topical peer-reviewed current research, developments, and timely information. (2,872 words) ## Resources - [Full Page Index](/index.html): Browse all cached pages with rich metadata - [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines - [XML Sitemap](/sitemap.xml): Machine-readable sitemap for crawler discovery - [Robots.txt](/robots.txt): Crawler directives