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Medical Sieve: A Cognitive Assistant for Radiologists and Cardiologists

Medical Sieve: A Cognitive Assistant for Radiologists and Ca...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Syeda-Mahmood, T. Walach, E. Beymer, D. Gilboa-Solomon, F. Moradi, M. Kisilev, P. Kakrania, D. Compas, C. Wang, H. Negandar, R. Cao, Y. Baldwin, T. Guo, Y. Gur, Y. Rajan, D. Zlotnick, A. Rabinovici-Cohen, S. Ben-Ari, R. Guy, Amit Prasanna, P. Morey, J. Bokyo, O. Hashoul, S. IBM Res Almaden Labs 650 Harry Rd San Jose CA 95120 USA IBM Res Haifa Labs IL-3498825 Haifa Israel

ISBN: (纸本)9781510600201

Radiologists and cardiologists today have to view large amounts of imaging data relatively quickly leading to eye fatigue. Further, they have only limited access to clinical information relying mostly on their visual interpretation of imaging studies for their diagnostic decisions. In this paper, we present Medical Sieve, an automated cognitive assistant for radiologists and cardiologists designed to help in their clinical decision-making. The sieve is a clinical informatics system that collects clinical, textual and imaging data of patients from electronic health records systems. It then analyzes multimodal content to detect anomalies if any, and summarizes the patient record collecting all relevant information pertinent to a chief complaint. The results of anomaly detection are then fed into a reasoning engine which uses evidence from both patient-independent clinical knowledge and large-scale patient-driven similar patient statistics to arrive at potential differential diagnosis to help in clinical decision making. In compactly summarizing all relevant information to the clinician per chief complaint, the system still retains links to the raw data for detailed review providing holistic summaries of patient conditions. Results of clinical studies in the domains of cardiology and breast radiology have already shown the promise of the system in differential diagnosis and imaging studies summarization.

关键词： Clinical Decision Support Precision Medicine Cognitive Assistant data summarization Differential Diagnosis

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Efficient data Representation by Selecting Prototypes with Importance Weights 19

Efficient Data Representation by Selecting Prototypes with I...

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19th IEEE International Conference on data Mining (ICDM)

作者： Gurumoorthy, Karthik S. Dhurandhar, Amit Cecchi, Guillermo Aggarwal, Charu Amazon Dev Ctr Bangalore Karnataka India IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781728146034

Prototypical examples that best summarize and compactly represent an underlying complex data distribution, communicate meaningful insights to humans in domains where simple explanations are hard to extract. In this paper, we present algorithms with strong theoretical guarantees to mine these data sets and select prototypes, a.k.a. representatives that optimally describes them. Our work notably generalizes the recent work by Kim et al. (2016) where in addition to selecting prototypes, we also associate non-negative weights which are indicative of their importance. This extension provides a single coherent framework under which both prototypes and criticisms (i.e. outliers) can be found. Furthermore, our framework works for any symmetric positive definite kernel thus addressing one of the key open questions laid out in Kim et al. (2016). By establishing that our objective function enjoys a key property of that of weak submodularity, we present a fast ProtoDash algorithm and also derive approximation guarantees for the same. We demonstrate the efficacy of our method on diverse domains such as retail, digit recognition (MNIST) and on publicly available 40 health questionnaires obtained from the Center for Disease Control (CDC) website maintained by the US Dept. of Health. We validate the results quantitatively as well as qualitatively based on expert feedback and recently published scientific studies on public health, thus showcasing the power of our technique in providing actionability (for retail), utility (for MNIST), and insight (on CDC datasets), which arguably are the hallmarks of an effective interpretable machine learning method.

关键词： prototype selection submodularity outlier detection data summarization

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Semi-supervised Learning Algorithm for Online Electricity data Streams

Semi-supervised Learning Algorithm for Online Electricity Da...

引用

International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES)

作者： Patil, Pramod Fatangare, Yogita Kulkarni, Parag Coll Engn Dept Comp Engn Pune Maharashtra India

ISBN: (纸本)9788132221265;9788132221258

Recent developments in electricity market deregulation, the prices are not fixed. In such application, class labels are not available directly and potentially valuable information is lost. A learning model of electricity demand and prices needs to be adaptive for dynamic changes in massive data streams. This paper presents adaptive building of learning model for electricity demand supply and prices by detecting and adapting changes in trends and values. A proposed framework is build with four main challenges such as future assumptions, data stream summarization, change in data stream trend clusters, and learner adaptivity and model. A proposed online algorithm for not only considering data values by avoiding trends of the streams. A correlation-based similarity method is used to produce concept clusters to handle unlabeled data and trend analysis, change detection type in terms of variation between past concept clusters and current ones, and predict future assumptions. An adaptive classify algorithm for the predictive ability evaluation on the test set. Results of experiments using electricity data confirm applicability of methodology with more than 80-85 % unlabeled data.

关键词： Multiple data streams data summarization On-line update Synopsis Single scan Clustering

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Pub/Sub-Sum: A Content summarization Pub/Sub Protocol for Information-centric Networks

Pub/Sub-Sum: A Content Summarization Pub/Sub Protocol for In...

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IEEE Military Communications Conference (MILCOM)

作者： Lee, Jongdeog Hwang, Suk Min Abdelzaher, Tarek Marcus, Kelvin Chan, Kevin Univ Illinois Dept Comp Sci Urbana IL 61801 USA US Army Comp & Informat Sci Directorate Res Labs Adelphi MD USA

ISBN: (纸本)9781728142807

In an age of data overload and scenarios that require fast-distributed situational understanding, we envision that content summarization services will become a critical capability of underlying networked systems. Previous work, called InfoMax, proposed such a service in the transport layer to minimize semantic redundancy of transmitted content and maximize information coverage. Here, we extended this work in three ways. First, we adapted summarization to the needs of streaming content and developed a corresponding publish-subscribe protocol (called Pub/Sub-Sum) with on-the-fly extractive summarization of continuous content streams (as opposed to extractive summarization of fixed data sets). Next, we supported many-tomany communication between publishers and subscribers, as opposed to InfoMax, which was designed to disseminate data from one producer to multiple consumers. Lastly, we introduce a new type of congestion handling mechanism that adaptively controls the level of summarization by considering available network bandwidth. We conducted experiments for functionality and performance on Mininet (a network emulator) and on a real device testbed. Evaluation results indicated that the new protocol summarizes data appropriately to available network resources, offering an improved compromise between received data quality and resource consumption.

关键词： pub-sub protocol information-centric network (ICN) data summarization congestion control

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data AGGREGATION IN WIRELESS SENSOR NETWORK USING NODE CLUSTERING ALGORITHMS - A COMPARATIVE STUDY

DATA AGGREGATION IN WIRELESS SENSOR NETWORK USING NODE CLUST...

引用

IEEE Conference on Information and Communication Technologies (ICT)

作者： Nithyakalyani, S. Kumar, S. Suresh KSR Coll Engn Dept IT Tiruchengode India Vivekanandha Coll Technol Women Tiruchengode India

ISBN: (纸本)9781467357586;9781467357593

One of the most important parameters to be studied in Wireless Sensor Networks (WSNs) is its life time. There are two typical data mining processes that support to reduce the energy consumption of WSNs is clustering and data summarization. Several energy aware, communication aware, coverage aware, data dissemination and data aggregation/sensor fusion protocols and algorithms have been specifically designed for WSN to reduce the power consumption. One of the primary goals of Node clustering in WSN is in-network preprocessing that aims to obtain qualified information and to limit the energy consumed. A clustering algorithm is composed of three parts first electing cluster head (CH), selection of cluster membership and transferal data from members to *** relays only one of the aggregated or compressed data packet to base station or sink In this paper a brief comparative study is made from different research proposals, which suggests different cluster head selection approaches for data aggregation. The algorithms under study are data relay K-means clustering algorithm, Fuzzy C- means clustering algorithms and Voronoi based Genetic clustering algorithm. Significant factors for evaluating and comparing these algorithms are defined, analyzed and summarized. It has been assumed that the sensor nodes are randomly distributed and are not mobile, the coordinates of the base station (BS) and the dimensions of the sensor field are known.

关键词： Wireless sensor network protocols data summarization clustering algorithms Voronoi diagram energy aware coverage aware

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Distributed Time Series Analytics

Distributed Time Series Analytics

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作者： Tian GUO ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

学位级别：博士

In recent years time series data has become ubiquitous thanks to affordable sensors and ad- vances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitoring, finance, IoT applications, news feeds, social networks, data centre monitoring and so on. Availability of such large scale time series data highlights the importance of of scalable data management, efficient querying and analysis. Meanwhile, in the online setting time series carries invaluable information and knowledge about the real-time status of involved entities or monitored phe- nomena, which calls for online time series data mining for serving timely decision making or event detection. In addition, in many scenarios data generated from sensors (environmental, RFID, GPS, etc.) are noisy and non-stationary in nature. In this thesis we aim to address these important issues pertaining to scalable and distributed analytics techniques for massive time series data. Concretely, this thesis is centered around the following three topics: Managing and Querying Model-View Time Series. As the number of sensors that pervade our lives significantly increases (e. g., environmental sensors, mobile phone sensors, IoT applications, etc.), the efficient management of massive amount of time series from such sensors is becoming increasingly important. The infinite nature of sensor data poses a serious challenge for query processing even in a cloud infras- tructure. Traditional raw sensor data management systems based on relational databases lack scalability to accommodate large scale sensor data efficiently. Thus, distributed key-value stores in the cloud are becoming a prime tool to manage sensor data. On the other hand, model-view time series management, which stores the data in the form of modeled segments, brings the additional advantages of data compression and value interpolation. However, currently there are no techn

关键词： time series data mining distributed computing time series data management recurrent neural network robust regression decision tree data summarization

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Computing with Distributed Information

Computing with Distributed Information

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作者： Li, Yang University of Pennsylvania

学位级别：Ph.D.

The age of computing with massive data sets is highlighting new computational challenges. Nowadays, a typical server may not be able to store an entire data set, and thus data is often partitioned and stored on multiple servers in a distributed manner. A natural way of computing with such distributed data is to use distributed algorithms: these are algorithms where the participating parties (i.e., the servers holding portions of the data) collaboratively compute a function over the entire data set by sending (preferably small-size) messages to each other, where the computation performed at each participating party only relies on the data possessed by it and the messages received by it. We study distributed algorithms focused on two key themes: convergence time and data summarization. Convergence time measures how quickly a distributed algorithm settles on a globally stable solution, and data summarization is the approach of creating a compact summary of the input data while retaining key information. The latter often leads to more efficient computation and communication. The main focus of this dissertation is on design and analysis of distributed algorithms for important problems in diverse application domains centering on the themes of convergence time and data summarization. Some of the problems we study include convergence time of double oral auction and interdomain routing, summarizing graphs for large-scale matching problems, and summarizing data for query processing.

关键词： Convergence data summarization Distributed Computation

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Efficient Algorithms to Compute Hierarchical Summaries from Big data Streams

Efficient Algorithms to Compute Hierarchical Summaries from ...

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作者： Zubair Shah The University of New South Wales

学位级别：博士

Many data stream applications have hierarchical data; containing time, geographic locations, product information, clickstreams, server logs, IP addresses. A hierarchical summary of such volumous data offers multiple advantages including compactness, quick understanding, and abstraction. The goal of this thesis is to design algorithmic approaches for summarizing hierarchical data streams. First, this thesis provides a theoretical analysis of the benchmark hierarchical heavy hitters' algorithms and uncovers their shortcomings such as requiring high theoretical memory, updates and coverage problem. To address these shortcomings, this thesis proposes efficient algorithms which offer deterministic estimation accuracy using O( η? ) worst-case memory and O(η) worst-case time complexity per item, where? ∈ [0, 1] is a user defined parameter and η is a small constant derived from the data. The proposed hierarchical heavy hitters' algorithms are shown to have improved significantly over existing algorithms both theoretically as well as empirically. Next, this thesis introduces a new concept called hierarchically correlated heavy hitters, which is different from existing hierarchical summarization techniques. The thesis provides a formal definition of the proposed concept and compares it with existing hierarchical summarization approaches both at definition level and empirically. It also proposes an efficient hierarchy-aware algorithm for computing hierarchically correlated heavy hitters. The proposed algorithm offers deterministic estimation accuracy using O( η? p? s ) worst-case memory and O(η) worst-case time complexity per item, where η is as defined previously, and? p ∈ [0,1],? s ∈ [0,1] are other user defined parameters. Finally, the thesis proposes a special hierarchical data structure and algorithm to summarize spatiotemporal data. It can be used to extract interesting and useful pat- terns from high-speed spatiotemporal data streams at multiple spatial and temporal gra

关键词： data Streams data summarization Big data data Mining Network Traffic Monitor-ing Heavy Hitters Hierarchical Heavy Hitters Correlated Heavy Hitters Spatiotem-poral data Smart Grid Smart Meters

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Multicloud Resource Allocation: Cooperation, Optimization and Sharing

Multicloud Resource Allocation: Cooperation, Optimization an...

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作者： Hao ZHUANG ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

学位级别：博士

Nowadays our daily life is not only powered by water, electricity, gas and telephony but by "cloud" as well. Due to the high penetration of cloud-based services or applications into every aspect of our life and the unprecedented increase of digital data, cloud computing becomes the 5th utility that processes and stores these applications and data. Big cloud ven- dors such as Amazon, Microsoft and Google have built large-scale centralized data centers to achieve economies of scale, on-demand resource provisioning, high resource availability and elasticity. However, those massive data centers also bring about many other problems, e. g., bandwidth bottlenecks, privacy, security, huge energy consumption, legal and physical vulnerabilities. One of the possible solutions for those problems is to employ multicloud architectures. In this thesis, our work provides research contributions to multicloud resource allocation from three perspectives of cooperation, optimization and data sharing. We ad- dress the following problems in the multicloud: how resource providers cooperate in a mul- ticloud, how to reduce information leakage in a multicloud storage system and how to share the big data in a cost-effective way. More specifically, we make the following contributions: Cooperation in the decentralized cloud. Recently due to increasing concerns on the pri- vacy and data control, many small data centers (SDCs) established by different providers are emerging in an attempt to meet demand locally. However, SDCs can suffer from resource in-elasticity due to their relatively scarce resources, resulting in a loss of performance and revenue. In this work, we propose a decentralized cloud model in which a group of SDCs can cooperate with each other to improve performance. Moreover, we design a general strategy function for SDCs to evaluate the performance of cooperation based on different dimensions of resource sharing. Through extensive simulations using a realistic data center model,

关键词： Mutlicloud resource allocation decentralized cloud multicloud storage system multicloud optimization data sharing small data center data transfer information leakage system attackability data summarization social context

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datawindow: Python Library to Simplify the data Science Process 5

Datawindow: Python Library to Simplify the Data Science Proc...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Lawrence, Daniel Anu Barathi, B.U. Sreeji, S. Sathyabama Institute of Science & Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350383522

This paper is a documentation for datawindow, a Python package which has the goal of simplifying the data processing and analysis techniques. datawindow provides eight methods which allows users to quickly and easily handle activities in the data science process such as model building, data cleaning and summarization using user-friendly interfaces. The datawindow keeps user-friendliness and modularity as the main focus in the library's design. datawindow provides the user-friendly experience by utilizing Tkinter for graphical user interfaces. datawindow users Pandas for data processing and Tkinter for the interfaces to help provide clarity to the users. This ensures that the library satisfies user needs and aims to provide the user with a streamlined data science experience. datawindow seeks to refocus the focus of the data analysis and data science process from coding and programming to allowing data workers to focus more on the creative processes and analytical thinking part of the analysis using interfaces. © 2024 IEEE.

关键词： cluster analysis data analysis workflow data cleaning data processing data summarization deep learning exploratory data analysis machine learning modular design numpy pandas python library tkinter user interface

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