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Articles 301 - 330 of 919
Full-Text Articles in Computer Sciences
Privacy-Preserving Data Falsification Detection In Smart Grids Using Elliptic Curve Cryptography And Homomorphic Encryption, Sanskruti Joshi, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana
Privacy-Preserving Data Falsification Detection In Smart Grids Using Elliptic Curve Cryptography And Homomorphic Encryption, Sanskruti Joshi, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana
Computer Science Faculty Research & Creative Works
In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt …
Toward Feature-Preserving Vector Field Compression, Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo
Toward Feature-Preserving Vector Field Compression, Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo
Computer Science Faculty Research & Creative Works
The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress …
Message From The Bits 2022 Workshop Co-Chairs, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee
Message From The Bits 2022 Workshop Co-Chairs, Sajal K. Das, Hayato Yamana, Keiichi Yasumoto, Shameek Bhattacharjee
Computer Science Faculty Research & Creative Works
No abstract provided.
Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das
Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the …
Rssafe: Personalized Driver Behavior Prediction For Safe Driving, Bhumika, Debasis Das, Sajal K. Das
Rssafe: Personalized Driver Behavior Prediction For Safe Driving, Bhumika, Debasis Das, Sajal K. Das
Computer Science Faculty Research & Creative Works
While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, sideslipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's …
An Icn-Based Secure Task Cooperation Scheme In Challenging Wireless Edge Networks, Ningchun Liu, Shuai Gao, Teng Liang, Xindi Hou, Sajal K. Das
An Icn-Based Secure Task Cooperation Scheme In Challenging Wireless Edge Networks, Ningchun Liu, Shuai Gao, Teng Liang, Xindi Hou, Sajal K. Das
Computer Science Faculty Research & Creative Works
Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking (ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, …
Spade: Multi-Stage Spam Account Detection For Online Social Networks, Federico Concone, Giuseppe Lo Re, Marco Morana, Sajal K. Das
Spade: Multi-Stage Spam Account Detection For Online Social Networks, Federico Concone, Giuseppe Lo Re, Marco Morana, Sajal K. Das
Computer Science Faculty Research & Creative Works
In recent years, Online Social Networks (OSNs) have radically changed the way people communicate. The most widely used platforms, such as Facebook, Youtube, and Instagram, claim more than one billion monthly active users each. Beyond these, news-oriented micro-blogging services, e.g., Twitter, are daily accessed by more than 120 million users sharing contents from all over the world. Unfortunately, legitimate users of the OSNs are mixed with malicious ones, which are interested in spreading unwanted, misleading, harmful, or discriminatory content. Spam detection in OSNs is generally approached by considering the characteristics of the account under analysis, its connection with the rest …
Sum-Rate Optimization For Visible-Light-Band Uav Networks Based On Particle Swarm Optimization, Yuwei Long, Nan Cen
Sum-Rate Optimization For Visible-Light-Band Uav Networks Based On Particle Swarm Optimization, Yuwei Long, Nan Cen
Computer Science Faculty Research & Creative Works
The mobility nature of unmanned aerial vehicles (UAVs) takes them into high consideration in military, public, and civilian applications in recent years. However, scaling out millions of UAVs in the air will inevitably lead to a more crowded radio frequency (RF) spectrum. Therefore, researchers have been focused on new technologies such as millimeter-wave, Terahertz, and visible light communications (VLCs) to alleviate the spectrum crunch problem. VLC has shown its great potential for UAV networking because of its high data rate, interference-free to legacy RF spectrum, and low-complex frontends. While the physical layer design of the VLC system has been extensively …
A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets, Francesco Betti Sorbelli, Federico Coro, Sajal K. Das, Emanuele Di Bella, Lara Maistrello, Lorenzo Palazzetti, Cristina M. Pinotti
A Drone-Based Application For Scouting Halyomorpha Halys Bugs In Orchards With Multifunctional Nets, Francesco Betti Sorbelli, Federico Coro, Sajal K. Das, Emanuele Di Bella, Lara Maistrello, Lorenzo Palazzetti, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
In this work, we consider the problem of using a drone to collect information within orchards in order to scout insect pests, i.e., the stink bug Halyomorpha halys. An orchard can be modeled as an aisle-graph, which is a regular and constrained data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone's energy is limited, only a subset of locations in the orchard can be visited with a fully charged …
Federated Secure Data Sharing By Edge-Cloud Computing Model*, Arijit Karati, Sajal K. Das
Federated Secure Data Sharing By Edge-Cloud Computing Model*, Arijit Karati, Sajal K. Das
Computer Science Faculty Research & Creative Works
Data sharing by cloud computing enjoys benefits in management, access control, and scalability. However, it suffers from certain drawbacks, such as high latency of downloading data, non-unified data access control management, and no user data privacy. Edge computing provides the feasibility to overcome the drawbacks mentioned above. Therefore, providing a security framework for edge computing becomes a prime focus for researchers. This work introduces a new key-aggregate cryptosystem for edge-cloud-based data sharing integrating cloud storage services. The proposed protocol secures data and provides anonymous authentication across multiple cloud platforms, key management flexibility for user data privacy, and revocability. Performance assessment …
Improving Age Of Information With Interference Problem In Long-Range Wide Area Networks, Preti Kumari, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
Improving Age Of Information With Interference Problem In Long-Range Wide Area Networks, Preti Kumari, Hari Prabhat Gupta, Tanima Dutta, Sajal K. Das
Computer Science Faculty Research & Creative Works
Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a …
Look-Up Table Based Fhe System For Privacy Preserving Anomaly Detection In Smart Grids, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana
Look-Up Table Based Fhe System For Privacy Preserving Anomaly Detection In Smart Grids, Ruixiao Li, Shameek Bhattacharjee, Sajal K. Das, Hayato Yamana
Computer Science Faculty Research & Creative Works
In advanced metering infrastructure (AMI), the customers' power consumption data is considered private but needs to be revealed to data-driven attack detection frameworks. In this paper, we present a system for privacy-preserving anomaly-based data falsification attack detection over fully homomorphic encrypted (FHE) data, which enables computations required for the attack detection over encrypted individual customer smart meter's data. Specifically, we propose a homomorphic look-up table (LUT) based FHE approach that supports privacy preserving anomaly detection between the utility, customer, and multiple partied providing security services. In the LUTs, the data pairs of input and output values for each function required …
Locality-Aware Qubit Routing For The Grid Architecture, Avah Banerjee, Xin Liang, R. Tohid
Locality-Aware Qubit Routing For The Grid Architecture, Avah Banerjee, Xin Liang, R. Tohid
Computer Science Faculty Research & Creative Works
Due to the short decohorence time of qubits available in the NISQ-era, it is essential to pack (minimize the size and or the depth of) a logical quantum circuit as efficiently as possible given a sparsely coupled physical architecture. In this work we introduce a locality-aware qubit routing algorithm based on a graph theoretic framework. Our algorithm is designed for the grid and certain 'grid-like' architectures. We experimentally show the competitiveness of algorithm by comparing it against the approximate token swapping algorithm, which is used as a primitive in many state-of-the-art quantum trans pilers. Our algorithm produces circuits of comparable …
Anomaly Based Incident Detection In Large Scale Smart Transportation Systems, Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Sayyed Mohsen Vazirizade, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das
Anomaly Based Incident Detection In Large Scale Smart Transportation Systems, Jaminur Islam, Jose Paolo Talusan, Shameek Bhattacharjee, Francis Tiausas, Sayyed Mohsen Vazirizade, Abhishek Dubey, Keiichi Yasumoto, Sajal K. Das
Computer Science Faculty Research & Creative Works
Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient trans-portation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a …
Extensive Thiol Profiling For Assessment Of Intracellular Redox Status In Cultured Cells By Hplc-Ms/Ms, Jiandong Wu, Anna Chernatynskaya, Annalise Pfaff, Huari Kou, Nan Cen, Nuran Ercal, Honglan Shi
Extensive Thiol Profiling For Assessment Of Intracellular Redox Status In Cultured Cells By Hplc-Ms/Ms, Jiandong Wu, Anna Chernatynskaya, Annalise Pfaff, Huari Kou, Nan Cen, Nuran Ercal, Honglan Shi
Computer Science Faculty Research & Creative Works
Oxidative stress may contribute to the pathology of many diseases, and endogenous thiols, especially glutathione (GSH) and its metabolites, play essential roles in the maintenance of normal redox status. Understanding how these metabolites change in response to oxidative insult can provide key insights into potential methods of prevention and treatment. Most existing methodologies focus only on the GSH/GSH disulfide (GSSG) redox couple, but GSH regulation is highly complex and depends on several pathways with multiple redox-active sulfur-containing species. In order to more fully characterize thiol redox status in response to oxidative insult, a high-performance liquid chromatography with tandem mass spectrometry …
An Energy-Efficient Smart Space System Using Lora Network With Deadline And Security Constraints, Preti Kumari, Hari Prabhat Gupta, Rahul Mishra, Sajal K. Das
An Energy-Efficient Smart Space System Using Lora Network With Deadline And Security Constraints, Preti Kumari, Hari Prabhat Gupta, Rahul Mishra, Sajal K. Das
Computer Science Faculty Research & Creative Works
In this paper, we develop techniques that create smart space in an efficient manner, wherein the efficiency is defined in terms of all-together: energy, security, delay, and cost. We design an energy-efficient smart space system using the Long-Range (LoRa) network. The system consists of various sensors that generate sensory data represented as Multi-dimensional Time Series (MTS). The sensors are connected with an Edge device and LoRa node for processing and transferring the MTS, respectively. The system first proposes a deep learning-based compression-decompression model for reducing the size of MTS at the Edge devices. Next, it uses game theory for finding …
Resilient Error-Bounded Lossy Compressor For Data Transfer, Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello
Resilient Error-Bounded Lossy Compressor For Data Transfer, Sihuan Li, Sheng Di, Kai Zhao, Xin Liang, Zizhong Chen, Franck Cappello
Computer Science Faculty Research & Creative Works
Todays exa-scale scientific applications or advanced instruments are producing vast volumes of data, which need to be shared/transferred through the network/devices with relatively low bandwidth (e.g., data sharing on WAN or transferring from edge devices to supercomputers). Lossy compression is one of the candidate strategies to address the big data issue. However, little work was done to make it resilient against silent errors, which may happen during the stage of compression or data transferring. In this paper, we propose a resilient error-bounded lossy compressor based on the SZ compression framework. Specifically, we design a new independentblock-wise model that decomposes the …
Online Optimization Of File Transfers In High-Speed Networks, Md Arifuzzaman, Engin Arslan
Online Optimization Of File Transfers In High-Speed Networks, Md Arifuzzaman, Engin Arslan
Computer Science Faculty Research & Creative Works
File transfers in high-speed networks require network and I/O parallelism to reach high speeds, however, creating arbitrarily large numbers of I/O and network threads overwhelms system resources and causes fairness issues. In this paper, we introduce Falcon that combines a novel utility function with state-of-the-art online optimization algorithms to discover the degree of I/O and network parallelism for file transfer that can maximize the throughput while keeping system overhead low and ensuring fairness among competing transfers. Our extensive evaluations in several dedicated and production high-speed networks show that Falcon can find near optimal solution in as little as 20 seconds …
Learning Transfers Via Transfer Learning, Md Arifuzzaman, Engin Arslan
Learning Transfers Via Transfer Learning, Md Arifuzzaman, Engin Arslan
Computer Science Faculty Research & Creative Works
Detecting performance anomalies is key to efficiently utilize network resources and improve the quality of service. Researchers proposed various approaches to identify the presence of anomalies by analyzing performance statistics using heuristic (e.g., change point detection) and Machine Learning (ML) models. Although these models yield high accuracy in the networks that they are trained for, their performance degrade severely when transferred to different network settings. This is because of the fact that existing models detect anomalies by capturing the changes in transfer throughput and observed RTT values, which are dependent to network settings. In this paper, we propose a novel …
Warmonger: Inflicting Denial-Of-Service Via Serverless Functions In The Cloud, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu
Warmonger: Inflicting Denial-Of-Service Via Serverless Functions In The Cloud, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu
Computer Science Faculty Research & Creative Works
We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the Warmonger attack, we ran months-long experiments, collected and analyzed the …
Towards Generalizable Network Anomaly Detection Models, Md Arifuzzaman, Shafkat Islam, Engin Arslan
Towards Generalizable Network Anomaly Detection Models, Md Arifuzzaman, Shafkat Islam, Engin Arslan
Computer Science Faculty Research & Creative Works
Finding the root causes of network performance anomalies is critical to satisfy the quality-of-service requirements. In this paper, we introduce machine learning (ML) models to process TCP socket statistics to pinpoint underlying reasons of performance issues such as packet loss and jitter. More importantly, we introduce a novel feature engineering method to transform network-dependent metrics (e.g., total packet count and round-trip time) in training datasets into network independent forms to be able to transfer the models to new network settings without requiring retraining them. Experimental results in various network settings show that the proposed feature engineering approach improves the performance …
Entity Retrieval Using Fine-Grained Entity Aspects, Shubham Chatterjee, Laura Dietz
Entity Retrieval Using Fine-Grained Entity Aspects, Shubham Chatterjee, Laura Dietz
Computer Science Faculty Research & Creative Works
Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval. Entity retrieval is the task of retrieving relevant entities for search queries, such as "Antibiotic Use in Livestock". Entity aspect linking is a new technique to refine the semantic information of entity links. For example, while passages relevant to the query above may mention the entity "USA", there are many aspects of the USA of which only few, such as "USA/Agriculture", are relevant for this query. By using entity aspect links that indicate which aspect of an entity is being referred to in the context of the …
Classification Of Mild Cognitive Impairment By Fusing Neuroimaging And Gene Expression Data: Classification Of Mild Cognitive Impairment By Fusing Neuroimaging And Gene Expression Data, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Dajiang Zhu
Classification Of Mild Cognitive Impairment By Fusing Neuroimaging And Gene Expression Data: Classification Of Mild Cognitive Impairment By Fusing Neuroimaging And Gene Expression Data, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Dajiang Zhu
Computer Science Faculty Research & Creative Works
As reversing the pathology of Alzheimer's disease (AD) is impossible, the diagnosis of mild cognitive impairment (MCI), which is considered as the precursor of AD, has become a more tractable goal. Because both brain structural and functional alterations have been observed in MCI patients, many multimodal fusion approaches have been proposed to classify MCI from normal controls (NC) in clinical studies. Given the complex relationships between brain structure and function, deep learning-based models can be helpful in revealing potential non-linear relationships buried in multimodal neuroimaging data. Meanwhile, RNA expression microarray profile can be a complementary feature in brain diseases analysis …
Machine Learning Models And Big Data Tools For Evaluating Kidney Acceptance, Lirim Ashiku, Md Al-Amin, Sanjay Kumar Madria, Cihan H. Dagli
Machine Learning Models And Big Data Tools For Evaluating Kidney Acceptance, Lirim Ashiku, Md Al-Amin, Sanjay Kumar Madria, Cihan H. Dagli
Computer Science Faculty Research & Creative Works
The rise of on-demand healthcare and the unprecedented growth of electronic health records has given rise to big data opportunities and data analysis using machine learning. The massive and disparate data management using conventional databases is incredibly challenging and expensive to manage. It often requires specialized analytical tools for developing advanced data-driven capabilities and performing data analytics. This paper explores the capability of an open-source framework 'Apache Spark' capable of processing large amounts of data on clusters of nodes to analyze Big data and integrate technologies to provide decision support systems in healthcare settings. Next, we propose machine learning models …
Accelerating Multigrid-Based Hierarchical Scientific Data Refactoring On Gpus, Jieyang Chen, Lipeng Wan, Xin Liang, Ben Whitney, For Full List Of Authors, See Publisher's Website.
Accelerating Multigrid-Based Hierarchical Scientific Data Refactoring On Gpus, Jieyang Chen, Lipeng Wan, Xin Liang, Ben Whitney, For Full List Of Authors, See Publisher's Website.
Computer Science Faculty Research & Creative Works
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and …
Revisiting Huffman Coding: Toward Extreme Performance On Modern Gpu Architectures, Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao, Franck Cappello
Revisiting Huffman Coding: Toward Extreme Performance On Modern Gpu Architectures, Jiannan Tian, Cody Rivera, Sheng Di, Jieyang Chen, Xin Liang, Dingwen Tao, Franck Cappello
Computer Science Faculty Research & Creative Works
Today’s high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today’s HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, …
Automatically Selecting Follow-Up Questions For Deficient Bug Reports, Mia Mohammad Imran, Agnieszka Ciborowska, Kostadin Damevski
Automatically Selecting Follow-Up Questions For Deficient Bug Reports, Mia Mohammad Imran, Agnieszka Ciborowska, Kostadin Damevski
Computer Science Faculty Research & Creative Works
The availability of quality information in bug reports that are created daily by software users is key to rapidly fixing software faults. Improving incomplete or deficient bug reports, which are numerous in many popular and actively developed open-source software projects, can make software maintenance more effective and improve software quality. In this paper, we propose a system that addresses the problem of bug report incompleteness by automatically posing follow-up questions, intended to elicit answers that add value and provide missing information to a bug report. Our system is based on selecting follow-up questions from a large corpus of already posted …
An Iterative Hybrid Algorithm For Roots Of Non-Linear Equations, Chaman Lal Sabharwal
An Iterative Hybrid Algorithm For Roots Of Non-Linear Equations, Chaman Lal Sabharwal
Computer Science Faculty Research & Creative Works
Finding the roots of non-linear and transcendental equations is an important problem in engineering sciences. In general, such problems do not have an analytic solution; the researchers resort to numerical techniques for exploring. We design and implement a three-way hybrid algorithm that is a blend of the Newton–Raphson algorithm and a two-way blended algorithm (blend of two methods, Bisection and False Position). The hybrid algorithm is a new single pass iterative approach. The method takes advantage of the best in three algorithms in each iteration to estimate an approximate value closer to the root. We show that the new algorithm …
Ft-Cnn: Algorithm-Based Fault Tolerance For Convolutional Neural Networks, Kai Zhao, Sheng Di, Sihuan Li, Xin Liang, For Full List Of Authors, See Publisher's Website.
Ft-Cnn: Algorithm-Based Fault Tolerance For Convolutional Neural Networks, Kai Zhao, Sheng Di, Sihuan Li, Xin Liang, For Full List Of Authors, See Publisher's Website.
Computer Science Faculty Research & Creative Works
Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deployed in safety-critical systems, which may suffer from soft errors caused by high-energy particles, high temperature, or abnormal voltage. Of critical importance is ensuring the stability of the CNN inference process against soft errors. Traditional fault tolerance methods are not suitable for CNN inference because error-correcting code is unable to protect computational components, instruction duplication techniques incur high overhead, and existing algorithm-based fault tolerance (ABFT) techniques cannot protect all convolution implementations. In this paper, we focus …
Lanchester's Equations And Cyberwarfare, George Markowsky, Linda Markowsky
Lanchester's Equations And Cyberwarfare, George Markowsky, Linda Markowsky
Computer Science Faculty Research & Creative Works
In his classic book Aircraft in Warfare, F. W. Lanchester discussed different types of warfare and presented equations, called the Lanchester equations, that can be used to model the results of battles between two forces of different sizes or capabilities. This paper introduces the Lanchester equations and provides a theoretical discussion leading to an analysis of the relative value of increasing the effectiveness of military assets vs. increasing the quantity of those assets. In particular, we show that increasing the effectiveness contributes only linearly to the power of a combatant, but increasing the quantity contributes quadratically. This paper also presents …