Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: Application to children with asthma

Background: Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-The-Art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-Term memory networks (LSTMs). We extend the method to bidirectional LSTMs (BiLSTMs) and use it in the context of predicting future clinical outcomes using patients' EHR historical visits. Methods: We use a real EHR dataset comprising 11071 patients, to evaluate and compare CD interpretations from LSTM and BiLSTM models. First, we train LSTM and BiLSTM models for the task of predicting which pre-school children with respiratory system-related complications will have asthma at school-Age. After that, we conduct quantitative and qualitative analysis to evaluate the CD interpretations produced by the contextual decomposition of the trained models. In addition, we develop an interactive visualization to demonstrate the utility of CD scores in explaining predicted outcomes. Results: Our experimental evaluation demonstrate that whenever a clear visit-level pattern exists, the models learn that pattern and the contextual decomposition can appropriately attribute the prediction to the correct pattern. In addition, the results confirm that the CD scores agree to a large extent with the importance scores generated using logistic regression coefficients. Our main insight was that rather than interpreting the attribution of individual visits to the predicted outcome, we could instead attribute a model's prediction to a group of visits. Conclusion: We presented a quantitative and qualitative evidence that CD interpretations can explain patient-specific predictions using CD attributions of individual visits or a group of visits. - 2019 The Author(s).This work is supported in part by Sidra Medicine under grant (SDR200043). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Scopu

Security-aware service composition for end users of small enterprises

This paper focuses on the service composition based on security properties of services from an end user perspective. End users are usually not expert in computer security, but expert users of computer software. They typically either own or work for small and medium enterprises (SMEs). The proposed framework attempts to demonstrate that end users of small enterprises can compose a service based application based on the security profiles of software services. The paper argues that the security concerns of various stakeholders of services should be specified differently. The paper envisions a framework with which end users could select services consistent with their preferred security features suitable for their businesses. With the same token, consumers of such applications can easily understand the security profile of services in order to make a B2B transaction. This will provide end users more power to force the service developer to offer better security-aware services. The main contribution of this paper is a framework on which further work could be initiated.Scopu

Establishing trust in cloud computing

How can cloud providers earn their customers' trust when a third party is processing sensitive data in a remote machine located in various countries? Emerging technologies can help address the challenges of trust in cloud computing.Scopu

Performance Evaluation of Network-Parallel Data Storage

This paper presents a unique storage architecture that utilizes the multiplicity of storage nodes in LAN and WAN network environments to achieve high reliability, high-performance, scalability, and cost effectiveness. The paper discusses the NetSTOR engine as a storage framework that can be utilized and tuned by its applications. The paper presents performance evaluation results that demonstrate the effectiveness of the NetSTOR approach in achieving better response time, higher overall system throughput and perfect scalability.This work was supported in part by a grant from the National Science Foundation (SBIR Phase II Award # 0239034)

Efficient Channel Allocation Scheme with Triangle Communication

Radio is a valuable resource for wireless/mobile communication systems. In this paper, we present an efficient algorithm using a novel triangle communication model to synchronize processes effectively by searching for free channels for wireless mobile communication systems. A geographical area controlled by a mobile switching center (MSC) is divided into many small hexagonal regions called cells. Each cell in a sub-cluster can effectively collect channel information from the first tier of co-channel cells through the triangle communication model. The performance of the communication model is characterized in terms of message complexity, response time and failure locality. For fair evaluation, we introduce a novel metric, which is called accumulated failure locality (AFL). The triangle communication model improves message complexity,response time and AFL of the algorithm. With AFL, we examine our algorithm, and produce an AFL vector. We discuss the algorithm and prove its correctness. We also show that the algorithm requires at most O(Nsc) messages,where Nsc is the number of cells in a sub-cluster.This is compared to the algorithms (Boukerche et al., 2002) which requires O((Ng)2), where Ng is the number of channel groups in the large bandwidth allocated to the system

Anonymity and privacy in bitcoin escrow trades

As a decentralized cryptocurrency, Bitcoin has been in market for around a decade. Bitcoin transactions are thought to be pseudoanonymous, however, there were many attempts to deanonymize these transactions making use of public data. Escrow services have been introduced as a good private and secure way to handle Bitcoin payments between untrusted parties, where the escrow service acts as the arbitrator in case of disputes. In our work, we examine the privacy and anonymity level of trades done through one of the Bitcoin trading websites offering such escrow services and how using the data they provide for open access through their APIs along with some public scraped data can compromise the privacy and anonymity of trades in some cases. In this paper,we suggest some heuristics and methods to deanonymize Bitcoin escrow trades done on LocalBitcoins.com, a well-known escrow service used especially by people seeking anonymity, and link them to suspect sets of Bitcoin transactions in the blockchain and suspect sets of users. Our research spots privacy weakness points of using escrow services that affects the privacy and anonymity of their users trades and identities. It also shows how tracking down criminals activities across escrow services is possible even without any authority on the escrow service making it less attractive for criminals to use cryptocurrencies and leading it to gain more trust. - 2019 Association for Computing Machinery.This publication was made possible by a grant from the Qatar National Research Fund; project number NPRP X-063-1-014. Contents of the research are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund.Scopu

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability