Cybersecurity Analytics on a D-Wave Quantum Computer
Effective cybersecurity analysis requires frequent exploration of graphs of many types and sizes, the computational cost of which can be overwhelming if not carefully chosen. After briefly introducing the D-Wave quantum computing system, we describe an analytic for finding “lateral movement” in an enterprise network, i.e., an intruder or insider threat hopping from system to system to gain access to more information. This analytic depends on maximum independent set, an NP-hard graph kernel whose computational cost grows exponentially with the size of the graph and so has not been widely used in cyber analysis. The growing strength of D-Wave’s quantum computers on such NP-hard problems will enable new analytics. We discuss practicalities of the current implementation and implications of this approach.
Steve Reinhardt has built hardware/software systems that deliver new levels of performance usable via conceptually simple interfaces, including Cray Research’s T3E distributed-memory systems, ISC’s Star-P parallel-MATLAB software, and YarcData/Cray’s Urika graph-analytic systems. He now leads D-Wave’s efforts working with customers to map early applications to D-Wave systems.
Deep Reinforcement Learning: Q-LearningKai-Wen Zhao
This slide reviews deep reinforcement learning, specially Q-Learning and its variants. We introduce Bellman operator and approximate it with deep neural network. Last but not least, we review the classical paper: DeepMind Atari Game beats human performance. Also, some tips of stabilizing DQN are included.
deep reinforcement learning with double q learningSeungHyeok Baek
presentation for Lab seminar
Double DQN Algorithm of Deepmind
Van Hasselt, Hado, Arthur Guez, and David Silver. "Deep Reinforcement Learning with Double Q-Learning." AAAI. Vol. 2. 2016.
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.
Energy proportionality is the key in order to reduce the Total Cost of Ownership (TCO) of Warehouse Scale Computer (WSC) systems, yet is difficult to achieve in practice. Typical WSC hardware usually does not meet this principle. Furthermore, critical services (e.g. billing) require all servers to remain up regardless the current traffic intensity. These two issues make existing power management technique ineffective at reducing energy use in a WSC dimension. We present Hybrid Performance-aware Power-capping Orchestrator (HyPPO), a distributed Observe Decide Act (ODA) control loop for optimizing energy proportionality of a distribute containerized infrastructures. This first version of HyPPO uses Kubernetes resource metrics (e.g. milli-cpus consumption) in order to dynamically adjust node power consumption, while respecting the Service Level Agreement (SLA) agreement defined by the containerized application owners.
Adaptive Replication for Elastic Data Stream ProcessingZbigniew Jerzak
A major challenge for cloud-based systems is to be fault tolerant so as to cope with an increasing probability of faults in cloud environments. This is especially true for in-memory computing solutions like data stream processing systems, where a single host failure might result in an unrecoverable information loss.
In state of the art data streaming systems either active replication or upstream backup are applied to ensure fault tolerance, which have a high resource overhead or a high recovery time respectively. This paper combines these two fault tolerance mechanisms in one system to minimize the number of violations of a user-defined recovery time threshold and to reduce the overall resource consumption compared to active replication. The system switches for individual operators between both replication techniques dynamically based on the current workload characteristics. Our approach is implemented as an extension of an elastic data stream processing engine, which is able to reduce the number of used hosts due to the smaller replication overhead. Based on a real-world evaluation we show that our system is able to reduce the resource usage by up to 19% compared to an active replication scheme.
Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Scheduling of tasks based on real time requirement is a major issue in the heterogeneous multicore systemsfor micro-grid power management . Heterogeneous multicore processor schedules the serial tasks in the high performance core and parallel tasks are executed on the low performance cores. The aim of this paper is to implement a scheduling algorithm based on fuzzy logic for heterogeneous multicore processor for effective micro-grid application. Real – time tasks generally have different execution time and dead line. The main idea is to use two fuzzy logic based scheduling algorithm, first is to assign priority based on execution time and deadline of the task. Second , the task which has assigned higher priority get allotted for execution in high performance core and remaining tasks which are assigned low priority get allotted in low performance cores. The main objective of this scheduling algorithm is to increase the throughput and to improve CPU utilization there by reducing the overall power consumption of the micro-grid power management systems. Test cases with different task execution time and deadline were generated to evaluate the algorithms using MATLAB software.
Deep Reinforcement Learning: Q-LearningKai-Wen Zhao
This slide reviews deep reinforcement learning, specially Q-Learning and its variants. We introduce Bellman operator and approximate it with deep neural network. Last but not least, we review the classical paper: DeepMind Atari Game beats human performance. Also, some tips of stabilizing DQN are included.
deep reinforcement learning with double q learningSeungHyeok Baek
presentation for Lab seminar
Double DQN Algorithm of Deepmind
Van Hasselt, Hado, Arthur Guez, and David Silver. "Deep Reinforcement Learning with Double Q-Learning." AAAI. Vol. 2. 2016.
Optimization of Continuous Queries in Federated Database and Stream Processin...Zbigniew Jerzak
The constantly increasing number of connected devices and sensors results in increasing volume and velocity of sensor-based streaming data. Traditional approaches for processing high velocity sensor data rely on stream processing engines. However, the increasing complexity of continuous queries executed on top of high velocity data has resulted in growing demand for federated systems composed of data stream processing engines and database engines. One of major challenges for such systems is to devise the optimal query execution plan to maximize the throughput of continuous queries.
In this paper we present a general framework for federated database and stream processing systems, and introduce the design and implementation of a cost-based optimizer for optimizing relational continuous queries in such systems. Our optimizer uses characteristics of continuous queries and source data streams to devise an optimal placement for each operator of a continuous query. This fine level of optimization, combined with the estimation of the feasibility of query plans, allows our optimizer to devise query plans which result in 8 times higher throughput as compared to the baseline approach which uses only stream processing engines. Moreover, our experimental results showed that even for simple queries, a hybrid execution plan can result in 4 times and 1.6 times higher throughput than a pure stream processing engine plan and a pure database engine plan, respectively.
Energy proportionality is the key in order to reduce the Total Cost of Ownership (TCO) of Warehouse Scale Computer (WSC) systems, yet is difficult to achieve in practice. Typical WSC hardware usually does not meet this principle. Furthermore, critical services (e.g. billing) require all servers to remain up regardless the current traffic intensity. These two issues make existing power management technique ineffective at reducing energy use in a WSC dimension. We present Hybrid Performance-aware Power-capping Orchestrator (HyPPO), a distributed Observe Decide Act (ODA) control loop for optimizing energy proportionality of a distribute containerized infrastructures. This first version of HyPPO uses Kubernetes resource metrics (e.g. milli-cpus consumption) in order to dynamically adjust node power consumption, while respecting the Service Level Agreement (SLA) agreement defined by the containerized application owners.
Adaptive Replication for Elastic Data Stream ProcessingZbigniew Jerzak
A major challenge for cloud-based systems is to be fault tolerant so as to cope with an increasing probability of faults in cloud environments. This is especially true for in-memory computing solutions like data stream processing systems, where a single host failure might result in an unrecoverable information loss.
In state of the art data streaming systems either active replication or upstream backup are applied to ensure fault tolerance, which have a high resource overhead or a high recovery time respectively. This paper combines these two fault tolerance mechanisms in one system to minimize the number of violations of a user-defined recovery time threshold and to reduce the overall resource consumption compared to active replication. The system switches for individual operators between both replication techniques dynamically based on the current workload characteristics. Our approach is implemented as an extension of an elastic data stream processing engine, which is able to reduce the number of used hosts due to the smaller replication overhead. Based on a real-world evaluation we show that our system is able to reduce the resource usage by up to 19% compared to an active replication scheme.
Auto-scaling Techniques for Elastic Data Stream ProcessingZbigniew Jerzak
An elastic data stream processing system is able to handle changes in workload by dynamically scaling out and
scaling in. This allows for handling of unexpected load spikes without the need for constant overprovisioning. One of the major challenges for an elastic system is to find the right point in time to scale in or to scale out. Finding such a point is difficult as it depends on constantly changing workload and system characteristics. In this paper we investigate the application of different auto-scaling techniques for solving this problem. Specifically: (1) we formulate basic requirements for an autoscaling technique used in an elastic data stream processing system, (2) we use the formulated requirements to select the best auto scaling techniques, and (3) we perform evaluation of the selected auto scaling techniques using the real world data. Our experiments show that the auto scaling techniques used in existing elastic data stream processing systems are performing worse than the strategies used in our work.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
Scheduling of tasks based on real time requirement is a major issue in the heterogeneous multicore systemsfor micro-grid power management . Heterogeneous multicore processor schedules the serial tasks in the high performance core and parallel tasks are executed on the low performance cores. The aim of this paper is to implement a scheduling algorithm based on fuzzy logic for heterogeneous multicore processor for effective micro-grid application. Real – time tasks generally have different execution time and dead line. The main idea is to use two fuzzy logic based scheduling algorithm, first is to assign priority based on execution time and deadline of the task. Second , the task which has assigned higher priority get allotted for execution in high performance core and remaining tasks which are assigned low priority get allotted in low performance cores. The main objective of this scheduling algorithm is to increase the throughput and to improve CPU utilization there by reducing the overall power consumption of the micro-grid power management systems. Test cases with different task execution time and deadline were generated to evaluate the algorithms using MATLAB software.
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...Otávio Carvalho
Work presented in partial fulfillment
of the requirements for the degree of
Bachelor in Computer Science - Federal University of Rio Grande do - Brazil
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
Exascale Computing for Autonomous DrivingLevent Gürel
Autonomous driving is one of the most computationally demanding technologies of modern times. Report here is a high-level roadmap towards satisfying the ever-increasing
computational needs and requirements of autonomous mobility that is easily at exascale level. We demonstrate that hardware solutions alone will not be sufficient, and exascale computing demands should be met with a combination of hardware and software. In that context, algorithms with reduced computational complexities will be crucial to provide software solutions that will be integrated with available hardware, which is also limited by various mobility restrictions, such as power, space, and weight.
Paper sharing_resource optimization scheduling and allocation for hierarchica...YOU SHENG CHEN
From Future Generation Computer Systems 107
Author:Jing Li (2020)
Declaration:There are some problems in this paper. I have tried my best to explain that the experimental data are not from the data of this paper.
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
In this deck from the HPC User Forum in Tucson, Steve Reinhardt from D-Wave Systems presents: Quantum Computing - Timing is Everything.
"Despite the incredible power of today’s supercomputers, there are many complex computing problems that can’t be addressed by conventional systems. Our need to better understand everything, from the universe to our own DNA, leads us to seek new approaches to answer the most difficult questions. While we are only at the beginning of this journey, quantum computing has the potential to help solve some of the most complex technical, scientific, national defense, and commercial problems that organizations face. We expect that quantum computing will lead to breakthroughs in science, engineering, modeling and simulation, healthcare, financial analysis, optimization, logistics, and national defense applications."
Watch the video: https://wp.me/p3RLHQ-ir5
Learn more: https://www.dwavesys.com/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
D-WaveQuantum ComputingAccess & applications via cloud deploymentRakuten Group, Inc.
Dr. Colin will give a brief overview of the current state of quantum computing and how D-wave is contributing to state-of-the-art quantum computing solutions and it's use cases.
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...Otávio Carvalho
Work presented in partial fulfillment
of the requirements for the degree of
Bachelor in Computer Science - Federal University of Rio Grande do - Brazil
Featuring a brief overview of fault-tolerant mechanisms across various Big Data systems such as Google File system (GFS), Amazon Dynamo, Bigtable, Hadoop - Map Reduce, Facebook Cassandra along with description of an existing fault tolerant model
Cloud Partitioning of Load Balancing Using Round Robin ModelIJCERT
Abstract: The purpose of load balancing is to look up the performance of a cloud environment through an appropriate
circulation strategy. Good load balancing will construct cloud computing for more stability and efficiency. This paper
introduces a better round robin model for the public cloud based on the cloud partitioning concept with a switch mechanism
to choose different strategies for different situations. Load balancing is the process of giving out of workload among
different nodes or processor. It will introduces a enhanced approach for public cloud load distribution using screening and
game theory concept to increase the presentation of the system.
Exascale Computing for Autonomous DrivingLevent Gürel
Autonomous driving is one of the most computationally demanding technologies of modern times. Report here is a high-level roadmap towards satisfying the ever-increasing
computational needs and requirements of autonomous mobility that is easily at exascale level. We demonstrate that hardware solutions alone will not be sufficient, and exascale computing demands should be met with a combination of hardware and software. In that context, algorithms with reduced computational complexities will be crucial to provide software solutions that will be integrated with available hardware, which is also limited by various mobility restrictions, such as power, space, and weight.
Paper sharing_resource optimization scheduling and allocation for hierarchica...YOU SHENG CHEN
From Future Generation Computer Systems 107
Author:Jing Li (2020)
Declaration:There are some problems in this paper. I have tried my best to explain that the experimental data are not from the data of this paper.
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud ComputingEswar Publications
Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load
balancing algorithms and their applicability in cloud environment.
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
Big data is a popular term used to define the exponential evolution and availability of data, includes both structured and unstructured data. The volatile progression of demands on big data processing imposes heavy burden on computation, communication and storage in geographically distributed data centers. Hence it is necessary to minimize the cost of big data processing, which also includes fault tolerance cost. Big Data processing involves two types of faults: node failure and data loss. Both the faults can be recovered using heartbeat messages. Here heartbeat messages acts as an acknowledgement messages between two servers. This paper depicts about the study of node failure and recovery, data replication and heartbeat messages.
In this deck from the HPC User Forum in Tucson, Steve Reinhardt from D-Wave Systems presents: Quantum Computing - Timing is Everything.
"Despite the incredible power of today’s supercomputers, there are many complex computing problems that can’t be addressed by conventional systems. Our need to better understand everything, from the universe to our own DNA, leads us to seek new approaches to answer the most difficult questions. While we are only at the beginning of this journey, quantum computing has the potential to help solve some of the most complex technical, scientific, national defense, and commercial problems that organizations face. We expect that quantum computing will lead to breakthroughs in science, engineering, modeling and simulation, healthcare, financial analysis, optimization, logistics, and national defense applications."
Watch the video: https://wp.me/p3RLHQ-ir5
Learn more: https://www.dwavesys.com/
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
D-WaveQuantum ComputingAccess & applications via cloud deploymentRakuten Group, Inc.
Dr. Colin will give a brief overview of the current state of quantum computing and how D-wave is contributing to state-of-the-art quantum computing solutions and it's use cases.
Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15MLconf
GraphMat: Bridging the Productivity-Performance Gap in Graph Analytics: With increasing interest in large-scale distributed graph analytics for machine learning and data mining, more data scientists and developers are struggling to achieve high performance without sacrificing productivity on large graph problems. In this talk, I will discuss our solution to this problem: GraphMat. Using generalized sparse matrix-based primitives, we are able to achieve performance that is very close to hand-optimized native code, while allowing users to write programs using the familiar vertex-centric programming paradigm. I will show how we optimized GraphMat to achieve this performance on distributed platforms and provide programming examples. We have integrated GraphMat with Apache Spark in a manner that allows the combination to outperform all other distributed graph frameworks. I will explain the reasons for this performance and show that our approach achieves very high hardware efficiency in both single-node and distributed environments using primitives that are applicable to many machine learning and HPC problems. GraphMat is open source software and available for download.
THE ENERGY GRID & Integration of IOT
Track 3 Session 3 Moderator: Mark Walker
Quantified results of an Energy Grid Management Use Case that explore grid performance boundaries in the face of proliferated residential solar array deployments is presented. The Use Case demonstrates how modern IT open source tools can be integrated into a grid simulation that provides a decision support tool for the utility industry to manage future change. GridLab-D is used as an agent based model to simulate energy consumer nodes in a complex inter-connected grid using a modern IBM SystemG graph computing engine. The resulting simulation environment executes the simulated grid network with structured and unstructured data results stored in the graph database. Big Data Analytics performed on the resulting simulation data using IBM Big Data Analytics tools and Sandia National Lab DAKOTA uncertainty quantification and statistical analysis tools allow for interrogation of the resulting performance database to establish performance characteristics visualized through graphs. The work is leverages DoD sponsored research in Uncertainty Quantification in complex System of System Modeling and Simulation environments and demonstrates future model based techniques for risk management, financial modeling, grid resiliency and critical infrastructure protection.
Design and testing of systolic array multiplier using fault injecting schemesCSITiaesprime
Nowadays low power design circuits are major important for data transmission and processing the information among various system designs. One of the major multipliers used for synchronizing the data transmission is the systolic array multiplier, low power designs are mostly used for increasing the performance and reducing the hardware complexity. Among all the mathematical operations, multiplier plays a major role where it processes more information and with the high complexity of circuit in the existing irreversible design. We develop a systolic array multiplier using reversible gates for low power appliances, faults and coverage of the reversible logic are calculated in this paper. To improvise more, we introduced a reversible logic gate and tested the reversible systolic array multiplier using the fault injection method of built-in self-test block observer (BILBO) in which all corner cases are covered which shows 97% coverage compared with existing designs. Finally, Xilinx ISE 14.7 was used for synthesis and simulation results and compared parameters with existing designs which prove more efficiency.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, CEO and Founder of Auviz Systems, presents the "Trade-offs in Implementing Deep Neural Networks on FPGAs" tutorial at the May 2015 Embedded Vision Summit.
Video and images are a key part of Internet traffic—think of all the data generated by social networking sites such as Facebook and Instagram—and this trend continues to grow. Extracting usable information from video and images is thus a growing requirement in the data center. For example, object and face recognition are valuable for a wide range of uses, from social applications to security applications. Deep neural networks are currently the most popular form of convolutional neural networks (CNN) used in data centers for such applications. 3D convolutions are a core part of CNNs. Nagesh presents alternative implementations of 3D convolutions on FPGAs, and discusses trade-offs among them.
The Sierra Supercomputer: Science and Technology on a Missioninside-BigData.com
In this deck from the Stanford HPC Conference, Adam Bertsch from LLNL presents: The Sierra Supercomputer: Science and Technology on a Mission.
"LLNL just celebrated its 65th anniversary. Since 1952, the laboratory has been at the forefront of high performance computing. Initially, HPC was used to accelerate the design and testing of the nation's nuclear stockpile. Since the last U.S. nuclear test in 1992, HPC has been used to validate the safety, security, and reliability of stockpile without nuclear testing.
Our next flagship HPC system at LLNL will be called Sierra. A collaboration between multiple government and industry partners, Sierra and its sister system Summit at ORNL, will pave the way towards Exascale computing architectures and predictive capability."
Watch the video: https://wp.me/p3RLHQ-i4K
Learn more: https://computation.llnl.gov/computers/sierra
and
http://hpcadvisorycouncil.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
FAST ALGORITHMS FOR UNSUPERVISED LEARNING IN LARGE DATA SETScsandit
The ability to mine and extract useful information automatically, from large datasets, is a
common concern for organizations (having large datasets), over the last few decades. Over the
internet, data is vastly increasing gradually and consequently the capacity to collect and store
very large data is significantly increasing.
Existing clustering algorithms are not always efficient and accurate in solving clustering
problems for large datasets.
However, the development of accurate and fast data classification algorithms for very large
scale datasets is still a challenge. In this paper, various algorithms and techniques especially,
approach using non-smooth optimization formulation of the clustering problem, are proposed
for solving the minimum sum-of-squares clustering problems in very large datasets. This
research also develops accurate and real time L2-DC algorithm based with the incremental
approach to solve the minimum
In present day MAC unit is demanded in most of the Digital signal processing. Function of addition and multiplication is performed by the MAC unit. MAC operates in two stages. Firstly, multiplier computes the given number output and the result is forwarded to second stage i.e. addition/accumulation operates. Speed of multiplier is important in MAC unit which determines critical path as well as area is also of great importance in designing of MAC unit. Multiplier plays an important roles in many digital signal processing (DSP) applications such as in convolution, digital filters and other data processing unit. Many research has been performed on MAC implementation. This paper provides analysis of the research and investigations held till now.
Similar to Detecting Lateral Movement with a Compute-Intense Graph Kernel (20)
Data Journalism at The Baltimore BannerData Works MD
Data Works MD February 2023 - https://www.meetup.com/dataworks/events/290813196/
Video -
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Data Journalism at The Baltimore Banner
In this presentation, data journalism Nick Thieme will be presenting on what data journalism looks like at The Baltimore Banner. Nick will be discussing how data journalism dovetails with local news and will highlight several of the projects they have been working on. Several of The Baltimore Banner's recent articles featuring data journalism can be found here.
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Nick Thieme creates rigorous data journalism with the goal of exposing and undoing systemic inequities by using the tools of statistics to discover reliable information about Baltimore. He grew up in the D.C. area, moving to Baltimore in the 2010s. After a time creating data journalism for Atlanta at the Atlanta Journal-Constitution, he's excited to return home to use his work to make the city a more equitable place. Nick can be reached on Twitter.
Jolt’s Picks - Machine Learning and Major League Baseball Hit StreaksData Works MD
Data Works MD September 2022 - https://www.meetup.com/dataworks/events/288251332/
Video - https://www.youtube.com/watch?v=zzLJrMyCLik
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Jolt's Picks: Using Machine Learning to Predict Major League Baseball Hit Streaks
Can you beat baseball legend Joe DiMaggio’s 56 consecutive game hit streak? The short answer is no, you cannot. But, can you play Bet MGM’s “Beat The Streak” and win $5.6 million? Yes, you can! In this presentation FanGraphs writer and Data Scientist Lucas Kelly will present how he used machine learning in an attempt to predict a hitter most likely to get a hit in a major league baseball game each day, leveraging analytics to make more intelligent decisions. You will see how machine learning pipelines, API calls, and out-of-box thinking may help win an impossible game.
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Lucas Kelly is a writer for the baseball analytics website FanGraphs and a Data Scientist at the World Wildlife Fund. He is a baseball analytics hobbyist and loves to play and write about fantasy baseball. He uses his data science background and python coding skills to gain an edge in his fantasy leagues. He has not yet "Beat The Streak". If he did, he would be living on his own personal island somewhere.
Data Works MD July 2021 - https://www.meetup.com/DataWorks/events/278394107/
Video - https://youtu.be/WXA1yX8O3Lc
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Introducing Datawave: Scalable Data Ingest and Query on Apache Accumulo
Out of the box, Accumulo's strengths are difficult to appreciate without first building an application that showcases its capabilities to handle massive amounts of data. Unfortunately, building such an application is non-trivial for many would-be users, which affects Accumulo's adoption.
In this talk, we introduce Datawave, a complete ingest, query, and analytic framework for Accumulo. Datawave, recently open-sourced by the National Security Agency, capitalizes on Accumulo's capabilities, provides an API for working with structured and unstructured data, and boasts a robust, flexible, and scalable backend.
We'll do a deep dive into Datawave's project layout, table structures, and APIs in addition to demonstrating the Datawave quickstart—a tool that makes it incredibly easy to hit the ground running with Accumulo and Datawave without having to develop a complete application.
Datawave - https://code.nsa.gov/datawave/
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Hannah Pellón received her B.S. in Mathematics from the University of Maryland while working as a software engineering intern at Northrop Grumman conducting RF signal analysis and spectrometry. She spent 11 years at Northrop Grumman thereafter contributing to IR&D efforts and programs centered around Accumulo and Hadoop. She is currently a software developer and lead at Tiber Technologies focusing on Datawave and distributed computing technologies
Malware Detection, Enabled by Machine LearningData Works MD
Data Works MD January 2021 - https://www.meetup.com/DataWorks/events/274890810/
Video - https://www.youtube.com/watch?v=Bwy9aPOdDPE
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Malware Detection, Enabled by Machine Learning
With the scale of new malware being created each year growing, as well as the expanding market opportunities for malware reuse, protecting systems can’t rely solely on downloading a vendor’s updated virus signature files. Our customers need ways to detect and cordon likely threats, by using data retrieved from a combination of static and behavioral characteristics, and comparing it to other classes of “good” versus “bad” files. Optimally, the solution cordons risky files, force ranks them according to their likelihood of causing harm, correlates some metadata to help with further learning and to provide context to analysts, and lets an analyst “release” a file after further analysis and a request from a user. Oh, with that feedback relayed back into the model to support further tuning.
This talk will delve into IRAD efforts ClearEdge is doing on building and integrating malware detectors using machine learning algorithms.
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Tina Coleman is a Technical Director for ClearEdge. In that role, she’s accountable for furthering the company’s depth in cybersecurity, particularly in aspects that allow ClearEdge to build solutions that scale for customer needs using its strengths in software engineering, dev ops, and data science. In addition to her work on contract and as a Technical Director, Ms. Coleman leads the Women In Technology program for ClearEdge, which seeks to encourage the participation and retention of women in technology. Ms. Coleman graduated from UMBC with undergraduate degrees in Computer Science and Economics and is currently pursuing her Masters in Cybersecurity Technology from University of Maryland, Global Campus. Tina can be found on LinkedIn at https://www.linkedin.com/in/tinadcoleman/
Using AWS, Terraform, and Ansible to Automate Splunk at ScaleData Works MD
The DreamPort Splunk Project; How We Use AWS, Terraform, and Ansible to Automate Everything About a Splunk Cluster
At DreamPort, we use cloud platforms, infrastructure-as-code tooling, configuration tools, automation software, and container technologies to very quickly design, develop, and prototype projects. This particular talk focuses on the tools used to deploy and configure a Splunk cluster for a particular project we recently ran. We will cover the deployment, configuration, and orchestration of a large 16 node Splunk cluster using tools that are a core set to DreamPort's cloud infrastructure toolbox; AWS, Terraform, Ansible, and Docker.
It is recommended that attendees have a general understanding of AWS, Linux, Splunk, and Docker, and know about automation tools such as Terraform and Ansible.
Attendees will learn how to use AWS, Terraform, Ansible, and Docker to deploy a large Splunk cluster, how to use Ansible to orchestrate and manage the Splunk cluster, and how to use Ansible to orchestrate and manage the Splunk cluster.
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Bill Cawthra is a Principal Cloud Infrastructure Architect for CyberPoint, managing project-related cloud systems and platforms. He works primarily on the AWS platform, using various automation tools to rapidly deploy and manage infrastructure. Bill has over 18 years of experience in computers and technology, working in a range of fields, including construction, DoD, health care, and social media.
Data Works MD April 2020 - https://www.meetup.com/DataWorks/events/269772382/
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Video available at https://www.youtube.com/watch?v=RTy176hpr8Q
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A Day in the Life of a Data Journalist
Despite gaining prominence in recent years, “data journalism” is still a confusing term for many people. What does it mean to crunch numbers for the news? How does data journalism differ from data science and statistics, and where are the intersections?
Come hear all about the world of news nerdery from your friendly neighborhood data journalist.
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Christine Zhang just joined the Financial Times as a data journalist on the US elections team for 2020. Previously, she was a data journalist at The Baltimore Sun, where she used numbers, statistics and graphics to tell local news stories on a variety of topics, including police overtime, homicide patterns, population demographics, local and statewide politics — and even made a series of plots visualizing the impressive performance of Ravens quarterback Lamar Jackson. Prior to joining The Sun in 2018, she worked at Two Sigma in New York City, the Los Angeles Times in Los Angeles and the Brookings Institution in Washington, D.C. She has a B.A. from Smith College and an M.A. from Columbia University.
Christine's bylines: https://underthecurve.github.io/bylines/
Christine's LinkedIn: https://www.linkedin.com/in/christineyzhang/
Christine's author page: https://www.baltimoresun.com/bal-christine-zhang-20180802-staff.html
Robotics and Machine Learning: Working with NVIDIA Jetson KitsData Works MD
Data Works MD December 2019 - https://www.meetup.com/DataWorks/events/265823739/
Video is available at https://www.youtube.com/watch?v=EFHUdKTDRZM
Robotics and Machine Learning: Working with NVIDIA Jetson Kits
Interested in machine learning and AI? Do you want to learn more about high performance GPU programming and how it applies to Deep Learning? Patty Delafuente will introduce you to the Nvidia Jetson developer kits, discuss their applications, how to get started, and provide a live demonstration of NVIDIA® Jetson Nano™, an easy to use deep learning and robotics platform.
Patty Delafuente
Patty Delafuente, is a lead Data Scientist in the Advanced Data Analytics Lab at the Social Security Administration. She teaches evening classes for the University of Maryland Baltimore County’s graduate level Data Science Program. Patty is a member of the ‘MS in Analytics Advisory Board’ for Texas A&M University.
Patty holds a Master of Science in Analytics from Texas A&M University along with a Bachelor and Masters in Information Systems and holds numerous certifications. In 2017, she was awarded the Texas A&M Margaret Sheather Memorial Award in Analytics for her Capstone Project, “Using Decision Trees to Analyze Patterns in Disability Fraud.”
She has over twenty years of database engineering, business intelligence, and analytics experience.
Her interests include machine learning, text mining, and using GPUs to improve the performance of analyzing and processing big data. She is a certified Nvidia Instructor in the ‘Fundamentals of Deep Learning for Computer Vision’ and ‘Accelerated Computing with Python’. Patty can be reached LinkedIn at https://www.linkedin.com/in/pattydelafuente319/
Connect Data and Devices with Apache NiFiData Works MD
Data Works MD November 2019 - https://www.meetup.com/DataWorks/events/265433970/
Video is available at https://youtu.be/JklA7FNUVhY
Connect Data and Devices with Apache NiFi
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It comes with a wonderful management UI, a large marketplace of standard Processors, and a great Open Source Community behind it. This session will show you how to move data across servers & networks. It will show you how to manipulate data, enrich data, and stream data through custom enrichment processors.
The talk is designed to walk you through the NiFi basics, while showing practical examples you can follow-along with. The examples will include showing how to perform data manipulation using a custom java processor, the ExecuteScript processor, with JavaScript and Python, and the JoltTransformData processor. Open-source tools, such as Jolt, jQ, and JsonPath will be demonstrated. Finally, it will show how you could prototype a REST service with Standard Processors! There will even be a light-bulb flashing from things happening in NiFi.
Ryan Hendrickson is a Senior Software Engineer and Director of Innovation who joined Clarity Business Solutions in 2015. He is a Software Project Co-Lead, participates in the Maryland Data Works Meetup, attended OSCON 2018, presented at CodeMash 2019, and enjoys working on his 1966 MGB.
Bill Farmer is VP of Engineering and senior software engineer at Clarity Business Solutions where he is responsible for bringing innovative opportunities to solve some of the customer’s hardest data problems. Bill has over twenty years experience building data processing and visualization systems across a variety of domains including finance, transportation, and government.
Elli Schwarz is a Senior Software Engineer at Clarity Business Solutions. He has 15 years experience developing Java applications, creating custom data processing solutions, and applying specialized data models and ontologies to facilitate data exchange. An Apache Nifi enthusiast, he enjoys using Nifi to performing complex ETL tasks for his clients.
Data Works MD September 2019 - https://www.meetup.com/DataWorks/events/264711404/
Video is available at https://www.youtube.com/watch?v=Y3b4Cnnilfw
Introduction to Machine Learning
Machine Learning continues its’ rise in the common day vernacular and is used anywhere from automating mundane tasks to offering intelligent insights across many industries. You may already be using a device that utilizes it. For example, a wearable fitness tracker like Fitbit, or an intelligent home assistant like Google Home. But there are much more examples of ML in use.
• Predictive Analysis
• Image recognition
• Speech Recognition
• Medical diagnoses
• Cyber Security
This session will cover an introduction to Machine Learning to include data modeling, supervised/ unsupervised learning and visualizations.
Stephen Scarbrough, CISSP, C|EH
Joined the US Navy in 1990 and retired after 20 years as a CTNC(SW/AW/NAC). Early career was as Tactical Communications operator onboard surface ships and aircraft. Begin Network Administration and Network Security in late1998. In 2005,
Joined the NSA/CSS Red/Blue Team for several years. In 2010 I retired and joined IntelliGenesis LLC in which I am a Senior SIGINT Development Analyst and currently the lead contractor for the National Cryptologic Schools DATA Curriculum, which include Data Science and Advanced Analytics Tradecraft mentoring.
Data in the City: Analytics and Civic Data in BaltimoreData Works MD
Data Works MD August 2019 - https://www.meetup.com/DataWorks/events/263516699/
Data in the City: Analytics and Civic Data in Baltimore
Does Baltimore City government even know how to use data? Why is [insert service here] still paper-based? Where are the hotspots for illegal dumping? Is President Trump right about the rats?
Smart cities, civic data use, and urban data analytics are all hot topics, but what are the current capabilities and applications in our own city government? Justin Elszasz and Babila Lima from the City of Baltimore will showcase a few examples of how data is being used to improve city services for the residents of Baltimore, from simple performance management to predictive analytics.
Justin Elszasz
Justin served as the data scientist for the Bloomberg Philanthropies-funded Innovation Team in the Mayor’s Office before taking on his current role, where he manages the CitiStat program and leads analysis across CitiStat and the Innovation Team. With his previous organization, Navigant Consulting, Justin supported the U.S. Department of Energy’s appliance standards program – the unsung hero of the Obama administration’s climate change efforts – through analysis and developing Federal regulations. He also used data science to improve utility energy efficiency programs.
Justin holds an M.S. in mechanical engineering from Columbia University, where he researched applied data science in the energy sector and was a National Science Foundation fellow in the “Integrative Graduate Education and Research Traineeship”, with a program focus of “Solving Urbanization Challenges by Design”. He has prior experience as a design engineer in the aerospace and medical device industries. Justin can be reached at https://www.linkedin.com/in/justinelszasz/
Exploring Correlation Between Sentiment of Environmental Tweets and the Stock...Data Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Fortune 500 Company Performance Analysis Using Social Networks
Speaker: Yi-Shan Shir
This presentation focus on studying the correlation between financial performance and social media relationship and behavior of Fortune 500 companies. The findings from this research can assist in the prediction of Fortune 500 stock performance based on a number of social network analysis metrics.
Automated Software Requirements LabelingData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Machine Learning for Requirements Engineering
Speaker: Jon Patton
This project applies a number of machine learning, deep learning, and NLP techniques to solve challenging problems in requirements engineering.
Introduction to Elasticsearch for Business Intelligence and Application InsightsData Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: Elasticsearch for Business Intelligence and Application Insights
Speaker: Sean Donnelly
Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. In this talk, I’ll discuss the fundamentals of storage and retrieval in Elasticsearch, why we decided to use it for search in our applications, and how you can also leverage it for both business intelligence and application insights.
An Asynchronous Distributed Deep Learning Based Intrusion Detection System fo...Data Works MD
Video of the presentation is available here: https://youtu.be/L6EMnvALYtU
Talk: An Asynchronous Distributed Deep Learning Based Intrusion Detection System for IoT Devices
Speaker: Pu Tian
Intrusion Detection Systems (IDS) in IoT devices are crucial for cybersecurity. Existing models may fail due to increased traffic pattern complexity and data complexity. To address these challenges, we propose an asynchronous distributed deep learning based IDS in which only training weights are shared and devices of heterogeneous computing power can train asynchronously. Empirical results on a large network intrusion dataset show that the system achieves high detection accuracy.
RAPIDS – Open GPU-accelerated Data ScienceData Works MD
RAPIDS – Open GPU-accelerated Data Science
RAPIDS is an initiative driven by NVIDIA to accelerate the complete end-to-end data science ecosystem with GPUs. It consists of several open source projects that expose familiar interfaces making it easy to accelerate the entire data science pipeline- from the ETL and data wrangling to feature engineering, statistical modeling, machine learning, and graph analysis.
Corey J. Nolet
Corey has a passion for understanding the world through the analysis of data. He is a developer on the RAPIDS open source project focused on accelerating machine learning algorithms with GPUs.
Adam Thompson
Adam Thompson is a Senior Solutions Architect at NVIDIA. With a background in signal processing, he has spent his career participating in and leading programs focused on deep learning for RF classification, data compression, high-performance computing, and managing and designing applications targeting large collection frameworks. His research interests include deep learning, high-performance computing, systems engineering, cloud architecture/integration, and statistical signal processing. He holds a Masters degree in Electrical & Computer Engineering from Georgia Tech and a Bachelors from Clemson University.
Two Algorithms for Weakly Supervised Denoising of EEG DataData Works MD
Video of the presentation is available here: https://www.youtube.com/watch?v=XdigjHEnFGM
Two Algorithms for Weakly Supervised Denoising of EEG Data
Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to noise so that they can be removed. However, these approaches are artifact specific. In this talk, I will discuss two algorithms for solving this problem that uses a weak supervisory signal to indicate that some noise is occurring, but not what the source of the noise is or how it is manifested in the EEG signal. In the first algorithm, the EEG data is decomposed into independent components using Independent Components Analysis, and these components form bags that are labeled and classified by a multi-instance learning algorithm that can identify the noise components for removal to reconstruct a clean EEG signal. The second algorithm is a novel Generative Adversarial Network (GAN) formulation. I’ll present empirical results on EEG data gathered by the Army Research Lab, and discuss pros and cons of both algorithms.
Dr. Tim Oates
Dr. Tim Oates is an Oros Family Professor in the Computer Science Department at the University of Maryland, Baltimore County. His Ph.D. from the University of Massachusetts Amherst was in the areas of artificial intelligence and machine learning with a focus on situated
language learning. After working as a postdoctoral researcher in the MIT Artificial Intelligence Lab, he joined UMBC where he has taught extensively in core areas of Computer Science, including data structures, discrete math, compiler design, artificial intelligence, machine learning, and robotics. Dr. Oates has published more than 150 peer-reviewed papers in areas such as time series analysis, natural language processing, relational learning, and social media analysis. He has developed systems to determine operating room state from video streams, predict the need for blood transfusions and emergency surgery for traumatic brain injury patients based on vital signs data, detect seizures from scalp EEG, and find story chains (causal connections) joining news articles, among many others. Recently Dr. Oates served as the Chief Scientist of a Virgina-based startup where he developed architectures and algorithms for managing contact data, including entity linking, fuzzy record matching, and connected components on billion node graphs stored in a columnar database. He has extensive knowledge of machine learning algorithms, implementations, and usage. Dr. Oates can be contacted at oates@umbc.edu
Predictive Analytics and Neighborhood HealthData Works MD
After the 2008 recession, Kansas City, MO, experienced waves of unemployment and foreclosures that led many properties to fall into disrepair. Faced with this growing issue during a period of decreased funding, the city’s code enforcement officials were unable to keep up with the workload, creating an enormous backlog and doubling the workload for each inspector. Together with the JHU Center for Government Excellence (GovEx), the city developed an algorithm to predict how long a given violation will take to resolve based on internal and public data that will help inspectors proactively schedule follow-up inspections and connect more serious cases to community programs earlier.
Matt is the Chief Data Scientist at the Johns Hopkins University Center for Government Excellence, where he and his team help governments apply data to performance challenges and improve the quality of life of their constituents. Prior to joining GovEx, Matt led the data, GIS, and targeting programs for national and state political campaigns, labor unions, and non-profits as they sought to register, persuade, and motivate voters. He was also the lead GIS analyst for Delaware’s State House of Representatives redistricting project in 2010.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Fundamentals of Electric Drives and its applications.pptx
Detecting Lateral Movement with a Compute-Intense Graph Kernel
1. Detecting Lateral Movement with a
Compute-Intense Graph Kernel
<anonymous collaborator> <US government agency>
Steve Reinhardt D-Wave Government spr@dwavesys.com