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Full-Text Articles in Physical Sciences and Mathematics

Enterprise Environment Modeling For Penetration Testing On The Openstack Virtualization Platform, Vincent Karovič Jr., Jakub Bartaloš, Vincent Karovič, Michal Greguš Dec 2021

Enterprise Environment Modeling For Penetration Testing On The Openstack Virtualization Platform, Vincent Karovič Jr., Jakub Bartaloš, Vincent Karovič, Michal Greguš

Journal of Global Business Insights

The article presents the design of a model environment for penetration testing of an organization using virtualization. The need for this model was based on the constantly increasing requirements for the security of information systems, both in legal terms and in accordance with international security standards. The model was created based on a specific team from the unnamed company. The virtual working environment offered the same functions as the physical environment. The virtual working environment was created in OpenStack and tested with a Linux distribution Kali Linux. We demonstrated that the virtual environment is functional and its security testable. Virtualizing ...


Using An Integrative Machine Learning Approach To Study Microrna Regulation Networks In Pancreatic Cancer Progression, Roland Madadjim May 2021

Using An Integrative Machine Learning Approach To Study Microrna Regulation Networks In Pancreatic Cancer Progression, Roland Madadjim

Computer Science and Engineering: Theses, Dissertations, and Student Research

With advances in genomic discovery tools, recent biomedical research has produced a massive amount of genomic data on post-transcriptional regulations related to various transcript factors, microRNAs, lncRNAs, epigenetic modifications, and genetic variations. In this direction, the field of gene regulation network inference is created and aims to understand the interactome regulations between these molecules (e.g., gene-gene, miRNA-gene) that take place to build models able to capture behavioral changes in biological systems. A question of interest arises in integrating such molecules to build a network while treating each specie in its uniqueness. Given the dynamic changes of interactome in chaotic ...


Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi May 2021

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated ...


Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, James Hendrickson May 2021

Dynamic Task Allocation In Partially Defined Environments Using A* With Bounded Costs, James Hendrickson

PhD Dissertations and Master's Theses

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering ...


Performance Implications Of Memory Affinity On Filesystem Caches In A Non-Uniform Memory Access Environment, Jacob Adams May 2021

Performance Implications Of Memory Affinity On Filesystem Caches In A Non-Uniform Memory Access Environment, Jacob Adams

Undergraduate Honors Theses

Non-Uniform Memory Access imposes unique challenges on every component of an operating system and the applications that run on it. One such component is the filesystem which, while not directly impacted by NUMA in most cases, typically has some form of cache whose performance is constrained by the latency and bandwidth of the memory that it is stored in. One such filesystem is ZFS, which contains its own custom caching system, known as the Adaptive Replacement Cache. This work looks at the impact of NUMA on this cache via sequential read operations, shows how current solutions intended to reduce this ...


Teachability And Interpretability In Reinforcement Learning, Jeevan Rajagopal May 2021

Teachability And Interpretability In Reinforcement Learning, Jeevan Rajagopal

Computer Science and Engineering: Theses, Dissertations, and Student Research

There have been many recent advancements in the field of reinforcement learning, starting from the Deep Q Network playing various Atari 2600 games all the way to Google Deempind's Alphastar playing competitively in the game StarCraft. However, as the field challenges more complex environments, the current methods of training models and understanding their decision making become less effective. Currently, the problem is partially dealt with by simply adding more resources, but the need for a better solution remains.

This thesis proposes a reinforcement learning framework where a teacher or entity with domain knowledge of the task to complete can ...


“The Revolution Will Not Be Supervised": An Investigation Of The Efficacy And Reasoning Process Of Self-Supervised Representations, Atharva Tendle May 2021

“The Revolution Will Not Be Supervised": An Investigation Of The Efficacy And Reasoning Process Of Self-Supervised Representations, Atharva Tendle

Computer Science and Engineering: Theses, Dissertations, and Student Research

Transfer learning technique enables training Deep Learning (DL) models in a data-efficient way for solving computer vision tasks. It involves pretraining a DL model to learn representations from a large and general-purpose source dataset, then fine-tuning the model using the task-specific target dataset. The dominant supervised learning (SL) approach for pretraining representations suffers from some limitations that include expensive labeling and poor generalizability. Recent advancements in the self-supervised learning (SSL) approach made it possible to learn effective representations from unlabeled data. The performance of the fine-tuned DL models based on pretrained SSL representations is on par with the state-of-the-art pretrained ...


Pandemic Policymaking, Philip D. Waggoner Apr 2021

Pandemic Policymaking, Philip D. Waggoner

Journal of Social Computing

This study leverages a high dimensional manifold learning design to explore the latent structure of the pandemic policymaking space only based on bill-level characteristics of pandemic-focused bills from 1973 to 2020. Results indicate the COVID-19 era of policymaking maps extremely closely onto prior periods of related policymaking. This suggests that there is striking uniformity in Congressional policymaking related to these types of large-scale crises over time, despite currently operating in a unique era of hyperpolarization, division, and ineffective governance.


Deeppredict: A Zone Preference Prediction System For Online Lodging Platforms, Yihan Ma, Hua Sun, Yang Chen, Jiayun Zhang, Yang Xu, Xin Wang Apr 2021

Deeppredict: A Zone Preference Prediction System For Online Lodging Platforms, Yihan Ma, Hua Sun, Yang Chen, Jiayun Zhang, Yang Xu, Xin Wang

Journal of Social Computing

Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user’s booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (POIs) recommendation are mainly focused on users’ historical records in the same ...


Estimating Multiple Socioeconomic Attributes Via Home Location—A Case Study In China, Shichang Ding, Xin Gao, Yufan Dong, Yiwei Tong, Xiaoming Fu Apr 2021

Estimating Multiple Socioeconomic Attributes Via Home Location—A Case Study In China, Shichang Ding, Xin Gao, Yufan Dong, Yiwei Tong, Xiaoming Fu

Journal of Social Computing

Inferring people’s Socioeconomic Attributes (SEAs), including income, occupation, and education level, is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation. Previous works mainly focus on estimating SEAs from peoples’ cyberspace behaviors and relationships, such as the content of tweets or the social networks between online users. Besides cyberspace data, alternative data sources about users’ physical behavior, like their home location, may offer new insights. More specifically, in this paper, we study how to predict a person’s income level, family income level, occupation type, and education level from his/her ...


Learning Universal Network Representation Via Link Prediction By Graph Convolutional Neural Network, Weiwei Gu, Fei Gao, Ruiqi Li, Jiang Zhang Apr 2021

Learning Universal Network Representation Via Link Prediction By Graph Convolutional Neural Network, Weiwei Gu, Fei Gao, Ruiqi Li, Jiang Zhang

Journal of Social Computing

Network representation learning algorithms, which aim at automatically encoding graphs into low-dimensional vector representations with a variety of node similarity definitions, have a wide range of downstream applications. Most existing methods either have low accuracies in downstream tasks or a very limited application field, such as article classification in citation networks. In this paper, we propose a novel network representation method, named Link Prediction based Network Representation (LPNR), which generalizes the latest graph neural network and optimizes a carefully designed objective function that preserves linkage structures. LPNR can not only learn meaningful node representations that achieve competitive accuracy in node ...


Using Twitter Bios To Measure Changes In Self-Identity: Are Americans Defining Themselves More Politically Over Time?, Nick Rogers, Jason J. Jones Apr 2021

Using Twitter Bios To Measure Changes In Self-Identity: Are Americans Defining Themselves More Politically Over Time?, Nick Rogers, Jason J. Jones

Journal of Social Computing

Are Americans weaving their political views more tightly into the fabric of their self-identity over time? If so, then we might expect partisan disagreements to continue becoming more emotional, tribal, and intractable. Much recent scholarship has speculated that this politicization of Americans’ identity is occurring, but there has been little compelling attempt to quantify the phenomenon, largely because the concept of identity is notoriously difficult to measure. We introduce here a methodology, Longitudinal Online Profile Sampling (LOPS), which affords quantifiable insights into the way individuals amend their identity over time. Using this method, we analyze millions of "bios" on the ...


How To Better Identify Venture Capital Network Communities: Exploration Of A Semi-Supervised Community Detection Method, Hong Xiong, Ying Fan Apr 2021

How To Better Identify Venture Capital Network Communities: Exploration Of A Semi-Supervised Community Detection Method, Hong Xiong, Ying Fan

Journal of Social Computing

In the field of Venture Capital (VC), researchers have found that VC companies are more likely to jointly invest with other VC companies. This paper attempts to realize a semi-supervised community detection of the VC network based on the data of VC networking and the list of industry leaders. The main research method is to design the initial label of community detection according to the evolution of components of the VC industry leaders. The results show that the community structure of the VC network has obvious distinguishing characteristics, and the aggregation of these communities is affected by the type of ...


Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph Apr 2021

Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph

Digital Initiatives Symposium

Funded by a National Endowment for Humanities (NEH) Humanities Collections and Reference Resources Foundations Grant, the UA Little Rock Center for Arkansas History and Culture’s “Mapping Renewal” pilot project focused on creating access to and providing spatial context to archival materials related to racial segregation and urban renewal in the city of Little Rock, Arkansas, from 1954-1989. An unplanned interdisciplinary collaboration with the UA Little Rock Arkansas Economic Development Institute (AEDI) has proven to be an invaluable partnership. One team member from each department will demonstrate the Mapping Renewal website and discuss how the collaborative process has changed and ...


Student Academic Conference, Caitlin Brooks Apr 2021

Student Academic Conference, Caitlin Brooks

Student Academic Conference

No abstract provided.


Administrative Law In The Automated State, Cary Coglianese Apr 2021

Administrative Law In The Automated State, Cary Coglianese

Faculty Scholarship at Penn Law

In the future, administrative agencies will rely increasingly on digital automation powered by machine learning algorithms. Can U.S. administrative law accommodate such a future? Not only might a highly automated state readily meet longstanding administrative law principles, but the responsible use of machine learning algorithms might perform even better than the status quo in terms of fulfilling administrative law’s core values of expert decision-making and democratic accountability. Algorithmic governance clearly promises more accurate, data-driven decisions. Moreover, due to their mathematical properties, algorithms might well prove to be more faithful agents of democratic institutions. Yet even if an automated ...


Traffic Collision Avoidance System: False Injection Viability, John Hannah, Robert F. Mills, Richard A. Dill, Douglas D. Hodson Apr 2021

Traffic Collision Avoidance System: False Injection Viability, John Hannah, Robert F. Mills, Richard A. Dill, Douglas D. Hodson

Faculty Publications

Safety is a simple concept but an abstract task, specifically with aircraft. One critical safety system, the Traffic Collision Avoidance System II (TCAS), protects against mid-air collisions by predicting the course of other aircraft, determining the possibility of collision, and issuing a resolution advisory for avoidance. Previous research to identify vulnerabilities associated with TCAS’s communication processes discovered that a false injection attack presents the most comprehensive risk to veritable trust in TCAS, allowing for a mid-air collision. This research explores the viability of successfully executing a false injection attack against a target aircraft, triggering a resolution advisory. Monetary constraints ...


A Comprehensive Mapping And Real-World Evaluation Of Multi-Object Tracking On Automated Vehicles, Alexander Bassett Apr 2021

A Comprehensive Mapping And Real-World Evaluation Of Multi-Object Tracking On Automated Vehicles, Alexander Bassett

PhD Dissertations and Master's Theses

Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field.

This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of ...


Multivariate Deep Learning Approach For Electric Vehicle Speed Forecasting, Youssef Nait Malek, Mehdi Najib, Mohamed Bakhouya, Mohammed Essaaidi Mar 2021

Multivariate Deep Learning Approach For Electric Vehicle Speed Forecasting, Youssef Nait Malek, Mehdi Najib, Mohamed Bakhouya, Mohammed Essaaidi

Big Data Mining and Analytics

Speed forecasting has numerous applications in intelligent transport systems’ design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles’ speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles’ characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced ...


Improvement In Automated Diagnosis Of Soft Tissues Tumors Using Machine Learning, El Arbi Abdellaoui Alaoui, Stéphane Cédric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan Mar 2021

Improvement In Automated Diagnosis Of Soft Tissues Tumors Using Machine Learning, El Arbi Abdellaoui Alaoui, Stéphane Cédric Koumetio Tekouabou, Sri Hartini, Zuherman Rustam, Hassan Silkan

Big Data Mining and Analytics

Soft Tissue Tumors (STT) are a form of sarcoma found in tissues that connect, support, and surround body structures. Because of their shallow frequency in the body and their great diversity, they appear to be heterogeneous when observed through Magnetic Resonance Imaging (MRI). They are easily confused with other diseases such as fibroadenoma mammae, lymphadenopathy, and struma nodosa, and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients. Researchers have proposed several machine learning models to classify tumors, but none have adequately addressed this misdiagnosis problem. Also, similar studies that have proposed models for ...


Mathematical Validation Of Proposed Machine Learning Classifier For Heterogeneous Traffic And Anomaly Detection, Azidine Guezzaz, Younes Asimi, Mourade Azrour, Ahmed Asimi Mar 2021

Mathematical Validation Of Proposed Machine Learning Classifier For Heterogeneous Traffic And Anomaly Detection, Azidine Guezzaz, Younes Asimi, Mourade Azrour, Ahmed Asimi

Big Data Mining and Analytics

The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data. Hence, automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes. The recognition of new elements is possible based on predefined classes. Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities. To overcome this problem, new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic. The main objective of ...


Iot-Based Data Logger For Weather Monitoring Using Arduino-Based Wireless Sensor Networks With Remote Graphical Application And Alerts, Jamal Mabrouki, Mourade Azrour, Driss Dhiba, Yousef Farhaoui, Souad El Hajjaji Mar 2021

Iot-Based Data Logger For Weather Monitoring Using Arduino-Based Wireless Sensor Networks With Remote Graphical Application And Alerts, Jamal Mabrouki, Mourade Azrour, Driss Dhiba, Yousef Farhaoui, Souad El Hajjaji

Big Data Mining and Analytics

In recent years, the monitoring systems play significant roles in our life. So, in this paper, we propose an automatic weather monitoring system that allows having dynamic and real-time climate data of a given area. The proposed system is based on the internet of things technology and embedded system. The system also includes electronic devices, sensors, and wireless technology. The main objective of this system is sensing the climate parameters, such as temperature, humidity, and existence of some gases, based on the sensors. The captured values can then be sent to remote applications or databases. Afterwards, the stored data can ...


Intelligent Monitoring System For Biogas Detection Based On The Internet Of Things: Mohammedia, Morocco City Landfill Case, Jamal Mabrouki, Mourade Azrour, Ghizlane Fattah, Driss Dhiba, Souad El Hajjaji Mar 2021

Intelligent Monitoring System For Biogas Detection Based On The Internet Of Things: Mohammedia, Morocco City Landfill Case, Jamal Mabrouki, Mourade Azrour, Ghizlane Fattah, Driss Dhiba, Souad El Hajjaji

Big Data Mining and Analytics

Mechanization is a depollution activity, because it provides an energetic and ecological response to the problem of organic waste treatment. Through burning, biogas from mechanization reduces gas pollution from fermentation by a factor of 20. This study aims to better understand the influence of the seasons on the emitted biogas in the landfill of the city Mohammedia. The composition of the biogas that naturally emanates from the landfill has been continuously analyzed by our intelligent system, from different wells drilled in recent and old waste repositories. During the rainy season, the average production of methane, carbon dioxide, and oxygen and ...


Hybrid Recommender System For Tourism Based On Big Data And Ai: A Conceptual Framework, Khalid Al Fararni, Fouad Nafis, Badraddine Aghoutane, Ali Yahyaouy, Jamal Riffi Mar 2021

Hybrid Recommender System For Tourism Based On Big Data And Ai: A Conceptual Framework, Khalid Al Fararni, Fouad Nafis, Badraddine Aghoutane, Ali Yahyaouy, Jamal Riffi

Big Data Mining and Analytics

With the development of the Internet, technology, and means of communication, the production of tourist data has multiplied at all levels (hotels, restaurants, transport, heritage, tourist events, activities, etc.), especially with the development of Online Travel Agency (OTA). However, the list of possibilities offered to tourists by these Web search engines (or even specialized tourist sites) can be overwhelming and relevant results are usually drowned in informational "noise", which prevents, or at least slows down the selection process. To assist tourists in trip planning and help them to find the information they are looking for, many recommender systems have been ...


New Enhanced Authentication Protocol For Internet Of Things, Mourade Azrour, Jamal Mabrouki, Azedine Guezzaz, Yousef Farhaoui Mar 2021

New Enhanced Authentication Protocol For Internet Of Things, Mourade Azrour, Jamal Mabrouki, Azedine Guezzaz, Yousef Farhaoui

Big Data Mining and Analytics

Internet of Things (IoT) refers to a new extended network that enables to any object to be linked to the Internet in order to exchange data and to be controlled remotely. Nowadays, due to its multiple advantages, the IoT is useful in many areas like environment, water monitoring, industry, public security, medicine, and so on. For covering all spaces and operating correctly, the IoT benefits from advantages of other recent technologies, like radio frequency identification, wireless sensor networks, big data, and mobile network. However, despite of the integration of various things in one network and the exchange of data among ...


On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead Mar 2021

On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead

Engineering Faculty Articles and Research

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can ...


Multi-Step-Ahead Exchange Rate Forecasting For South Asian Countries Using Multi-Verse Optimized Multiplicative Functional Link Neural Networks, Kishore Kumar Sahu, Sarat Chandra Nayak, Himansu Sekhar Behera Mar 2021

Multi-Step-Ahead Exchange Rate Forecasting For South Asian Countries Using Multi-Verse Optimized Multiplicative Functional Link Neural Networks, Kishore Kumar Sahu, Sarat Chandra Nayak, Himansu Sekhar Behera

Karbala International Journal of Modern Science

The dynamic nonlinearity approach, coupled with the exchange rate data series, makes its future predictions difficult. Sophisticated methods are highly desired for effective prediction of such data. Artificial neural networks (ANNs) have shown their ability to model and predict such data. This article presents a multi-verse optimizer (MVO) based multiplicative functional link neural network (MV-MFLN) model to forecast the exchange rate data. Functional link neural network (FLN) makes use of functional expansion for input data with a fewer number of adjustable neuron weights, which makes it capable of learning the uncertainties accompanying the exchange rate data. In contrast to the ...


Computation And Data Driven Discovery Of Topological Phononic Materials, Jiangxu Li, Jiaxi Liu, Stanley A. Baronett, Mingfeng Liu, Lei Wang, Ronghan Li, Yun Chen, Dianzhong Li, Qiang Zhu, Xing Qiu Chen Feb 2021

Computation And Data Driven Discovery Of Topological Phononic Materials, Jiangxu Li, Jiaxi Liu, Stanley A. Baronett, Mingfeng Liu, Lei Wang, Ronghan Li, Yun Chen, Dianzhong Li, Qiang Zhu, Xing Qiu Chen

Physics & Astronomy Faculty Publications

© 2021, The Author(s). The discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link ...


Exploring The Efficiency Of Self-Organizing Software Teams With Game Theory, Clay Stevens, Jared Soundy, Hau Chan Feb 2021

Exploring The Efficiency Of Self-Organizing Software Teams With Game Theory, Clay Stevens, Jared Soundy, Hau Chan

CSE Conference and Workshop Papers

Over the last two decades, software development has moved away from centralized, plan-based management toward agile methodologies such as Scrum. Agile methodologies are founded on a shared set of core principles, including self-organizing software development teams. Such teams are promoted as a way to increase both developer productivity and team morale, which is echoed by academic research. However, recent works on agile neglect to consider strategic behavior among developers, particularly during task assignment–one of the primary functions of a self-organizing team. This paper argues that self-organizing software teams could be readily modeled using game theory, providing insight into how ...


Addressing Multiple Bit/Symbol Errors In Dram Subsystem, Ravikiran Yeleswarapu, Arun K. Somani Feb 2021

Addressing Multiple Bit/Symbol Errors In Dram Subsystem, Ravikiran Yeleswarapu, Arun K. Somani

Electrical and Computer Engineering Publications

As DRAM technology continues to evolve towards smaller feature sizes and increased densities, faults in DRAM subsystem are becoming more severe. Current servers mostly use CHIPKILL based schemes to tolerate up-to one/two symbol errors per DRAM beat. Such schemes may not detect multiple symbol errors arising due to faults in multiple devices and/or data-bus, address bus. In this article, we introduce Single Symbol Correction Multiple Symbol Detection (SSCMSD)—a novel error handling scheme to correct single-symbol errors and detect multi-symbol errors. Our scheme makes use of a hash in combination with Error Correcting Code (ECC) to avoid silent ...