Reviewerly/exHarmony huggingface dataset:
Identification of Serious Illness Conversations in Unstructured Clinical Notes Using Deep Neural Networks

Advance care planning, which includes clarifying and documenting goals of care and preferences for future care, is essential for achieving end-of-life care that is consistent with the preferences of dying patients and their families. Physicians document their communication about these preferences as unstructured free text in clinical notes; as a result, routine assessment of this quality indicator is time consuming and costly. In this study, we trained and validated a deep neural network to detect documentation of advanced care planning conversations in clinical notes from electronic health records. We assessed its performance against rigorous manual chart review and rule-based regular expressions. For detecting documentation of patient care preferences at the note level, the algorithm had high performance; F1-score of 92.0 (95% CI, 89.1–95.1), sensitivity of 93.5% (95% CI, 90.0%–98.0%), positive predictive value of 90.5% (95% CI, 86.4%–95.1%) and specificity of 91.0% (95% CI, 86.4%–95.3%) and consistently outperformed regular expression. Deep learning methods offer an efficient and scalable way to improve the visibility of documented serious illness conversations within electronic health record data, helping to better quality of care.
New YARN Non-Exclusive Resource Management Scheme through Opportunistic Idle Resource Assignment

Efficiently managing resources and improving throughput in a large-scale cluster has become a crucial problem with the explosion of data processing applications in recent years. Hadoop YARN and Mesos, as two universal resource management platforms, have been widely adopted in the commodity cluster for co-deploying multiple data processing frameworks, such as Hadoop MapReduce and Apache Spark. However, in the existing resource management, a certain amount of resources are exclusively allocated to a running task and can only be re-assigned after that task is completed. This exclusive mode unfortunately leads to a potential problem that may under-utilize the cluster resources and degrade system performance. To address this issue, we propose a novel opportunistic and efficient resource allocation scheme, named OpERA, which breaks the barriers among the encapsulated resource containers by leveraging the knowledge of actual runtime resource utilizations to re-assign opportunistic available resources to the pending tasks. OpERA avoids incurring severe performance interference to active tasks by further using two approaches to efficiently balances the starvations of reserved tasks and normal queued tasks. We implement and evaluate OpERA in Hadoop YARN v2.5. Our experimental results show that OpERA significantly reduces the average job execution time and increases the resource (CPU and memory) utilizations.
A Novel Low Rank Spectral Clustering Method for Face Identification

&lt;P&gt;Background: Low rank is a recent significant model to explore the inner subspace structure of samples, which has been successfully used in many pattern recognition tasks. &lt;/P&gt;&lt;P&gt; Methods: In this paper, we proposed a novel spectral clustering method to address the face identification problem. There are three main contributions in our paper. Firstly, the sparse coding under a cluster-based learned dictionary is taken as the character sample of each face. Secondly, the collaborative low rank representation is incorporated in the comprehensive optimization framework to construct an effective affinity graph iteratively, which is different from the conventional ones to tackle the graph construction and spectral clustering independently. We revised all the patents relating to the face identification. Thirdly, a numerical algorithm is developed to solve the optimization framework and obtain a stable solution. &lt;/P&gt;&lt;P&gt; Results: The experimental results showed the superior performance of the proposed method on recognition ratio. &lt;/P&gt;&lt;P&gt; Conclusion: It means that our proposed low rank based identification algorithm outperforms the existed excellent methods.&lt;/P&gt;
Public Key Compression for Constrained Linear Signature Schemes

We formalize the notion of a constrained linear trapdoor as an abstract strategy for the generation of signature schemes, concrete instantiations of which can be found in MQ-based, code-based, and lattice-based cryptography. Moreover, we revisit and expand on a transformation by Szepieniec et al. [39] to shrink the public key at the cost of a larger signature while reducing their combined size. This transformation can be used in a way that is provably secure in the random oracle model, and in a more aggressive variant whose security remained unproven. In this paper we show that this transformation applies to any constrained linear trapdoor signature scheme, and prove the security of the first mode in the quantum random oracle model. Moreover, we identify a property of constrained linear trapdoors that is sufficient (and necessary) for the more aggressive variant to be secure in the quantum random oracle model. We apply the transformation to an MQ-based scheme, a code-based scheme and a lattice-based scheme targeting 128-bits of post quantum security, and we show that in some cases the combined size of a signature and a public key can be reduced by more than a factor 300.
A high-performance clustering algorithm based on searched experiences

Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to “reduce the computing time” of an “effective clustering algorithm” has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm.
Multi-player End-Nim games

W.O. Krawec (2012) [17] introduced a method of analyzing multi-player impartial games, and derived a recursive function capable of determining which of the n players has a winning strategy. The present paper is devoted to “Multi-player End-Nim games”, abbreviated by ENim( N , n ), assuming that the standard alliance matrix is adopted. The game values of ENim( N , n ) are completely determined for three cases n > N + 1 , n = N + 1 and n = N . For the case n < N , by letting d = N − n ≥ 1 , the game values of ENim( n + d , n ) are completely determined if n ≥ d + 4 . The case 3 ≤ n ≤ d + 3 is more complicated, we present the game values for d = 1 .
Document Image Classification on the Basis of Layout Information

In this chapter, a document image classification framework based on layout information is described. The proposed method does not use the optical character recognition (OCR) technique; hence, it is completely language independent. Nonetheless, text data are exploited by extracting text regions with a novel maximally stable extremal regions (MSER) approach. The Modified MSER formulation provides great robustness against text distortions in comparison to the existing approach. The two types of novel image descriptors are supplemented with Fisher vectors that are based on the Bernoulli mixture model. Classifiers, based on the aforementioned descriptors, are assembled in a meta-classification system that is able to classify the document in complex cases for which individual classifier accuracy is poor. The meta-classification system created has a low processing time comparable to a single classifier. It is also shown that the method outperforms the existing techniques for a wide range of documents from both well-known and machine-generated document datasets in terms of classification accuracy.
Version Control Systems: An Information Foraging Perspective

Version Control Systems (VCS) are an important source of information for developers. This calls for a principled understanding of developers' information seeking in VCS-both for improving existing tools and for understanding requirements for new tools. Our prior work investigated empirically how and why developers seek information in VCS: in this paper, we complement and enrich our prior findings by reanalyzing the data via a theory's lens. Using the lens of Information Foraging Theory (IFT), we present new insights not revealed by the prior empirical work. First, while looking for specific information, participants' foraging behaviors were consistent with other foraging situations in SE; therefore, prior research on IFT-based SE tool design can be leveraged for VCS. Second, in change awareness foraging, participants consumed similar diets, but in subtly different ways than in other situations; this calls for further investigations into change awareness foraging. Third, while committing changes, participants attempted to enable future foragers, but the competing needs of different foraging situations led to tensions that participants failed to balance: this opens up a new avenue for research at the intersection of IFT and SE, namely, creating forageable information. Finally, the results of using an IFT lens on these data provides some evidence as to IFT's scoping and utility for the version control domain.
Clustering as physically inspired energy minimization

We formulate the task of density based clustering as energy minimization, using both binary/pairwise energy term and unary/data energy term (the latter was largely ignored in previous clustering methods). Binary energy is defined in terms of inhomogeneity in local point density. While most previous methods use binary/pairwise energy only, the unary/data energy can represent the natural tendency of a given point belonging to a given cluster, which is also crucial for the clustering. Since our energy is expressed as the sum of a unary (data) term and a binary (pairwise or smoothness) term, we can thus make a perfect analogy with the energy model used in vision and borrow everything (such as the optimization algorithms) from vision field to clustering under this mapping. This correspondence provides an entirely new view point in handling the clustering problem, and in fact many mature methods and algorithms are already provided in the vision field and can be adopted by the clustering field readily. During our energy optimization, a sequence of energy minima are found to recursively partition the points, and thus find a hierarchical embedding of clusters that are increasingly homogeneous in density. Disjoint clusters with the same density are identified separately. Our clustering method is totally unsupervised (which is superior to most existing methods, as those listed below). It does not need any user input parameters (say number of segments, any bandwidth parameter, cutoff distance/scale, etc.), except one can specify the homogeneity criterion — the degree of acceptable fluctuation in density within a cluster (which is target-related), or let it be specified automatically in a hierarchical way. We conduct experiments on both synthetic datasets and real-image tasks. Experimental results on synthetic datasets show that our method is able to handle clusters of different shapes, sizes and densities. We present the performance of our approach using the commonly used energy optimization algorithms from vision such as ICM, LBP, Graph-cut, Mean field theory algorithm, as well as the integer programming algorithm. We also contrast our performance with several other commonly used clustering algorithms, such as k-means, fussy c-means, DBSCAN, as well as a recent state-of-the-art clustering method as reported in [40]. Our experiments on real-image tasks further validate the performance of the method. In addition, we show that the family of commonly used spectral, graph clustering algorithms (such as Normalized-cut) uses only the binary energy term while ignoring the unary energy term; therefore, their energy model is incomplete compared with ours.
A Project-based Learning Approach in Teaching Simulation to Undergraduate and Graduate Students

Simulation, Additive Manufacturing, Process planning, and application of knowledge-based systems in manufacturing
Solving computational square jigsaw puzzles with a novel pairwise compatibility measure

The most challenging aspect of rebuilding the puzzle is finding out the right pair of pieces of the image. To do this, we need, an accurate estimation of the pairwise compatibility measure between local patches and the assembly strategy. In this paper, we propose a novel pairwise compatibility measure for the assembly of computational square jigsaw puzzles using Gist and color distance. This possible combination of gradient and color features improves the assembling pieces and can deal with the majority of the problems encountered in state-of-the-art. We also propose the rotation-based strategy to enable working on multiple parts and rebuild completed puzzles from local matching candidates. The experimental results on the frequently used data-sets sufficiently show that the suggested compatibility metric surpasses the latest approaches.
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation

This paper deals with a method for generating realistic labeled masses. Recently, there have been many attempts to apply deep learning to various bio-image computing fields including computer-aided detection and diagnosis. In order to learn deep network model to be well-behaved in bio-image computing fields, a lot of labeled data is required. However, in many bioimaging fields, the large-size of labeled dataset is scarcely available. Although a few researches have been dedicated to solving this problem through generative model, there are some problems as follows: (1) The generated bio-image does not seem realistic; (2) the variation of generated bio-image is limited; and (3) additional label annotation task is needed. In this study, we propose a realistic labeled bio-image generation method through visual feature processing in latent space. Experimental results have shown that mass images generated by the proposed method were realistic and had wide expression range of targeted mass characteristics.
NR‐IQA for noise‐affected images using singular value decomposition

This study presents an efficient no-reference image quality assessment (NR-IQA) technique to assess the quality of images affected by noise. The proposed technique is based on two characteristics of the human eye (retina), namely the presence of centre-surround receptive field and visualisation utilising different spatial frequency channels. In the proposed technique, the authors model centre-surround receptive field using difference of Gaussians (DoG), whereas to mimic multiple frequencies in the centre-surround receptive field, they compute multiple DoG images of different values of standard deviations generated for different frequencies. Furthermore, the singular value decomposition-based features are obtained from the generated DoG images to estimate the image quality. The proposed technique does not require any training, neither based on distorted/original images nor based on subjective human scores, to assess the image quality. The performance of the proposed technique is being analysed on LIVE, TID08, CSIQ and SD-IVL databases and it shows that the proposed technique outperforms recently proposed NR and no-training/training-based IQA techniques. Experimental validation of the proposed technique in the big-data scenario of 10,000 noisy images also shows encouraging results.
Code generator framework for smart TV platforms

In recent years, smart TVs have become more common, making them need to be included as targets for the software industry. In this study, the authors developed a code generator framework and demonstrated it in an architectural view. The proposed framework converts C# programming language based projects, in a Windows Forms or a Windows Phone Application project, into native smart TV Platform applications. The selected primary smart TV platforms assigned for application conversion were Android TV, Firefox OS, and Tizen OS. The authors enabled developers to generate native codes for all three platforms from a single code base using model to model conversion, as in the model driven architecture approach with the use of the open source Roslyn C# language compiler. The need for creating projects for every single platform to make them run on different platforms will thus be eliminated and development cycles shortened. By doing so, the time required to develop an application for each platform is reduced while keeping the generated applications’ quality as high as the original application. To show the functionality, the proposed approach is applied in three case studies. The success of the code conversion is satisfactory and converted applications are functional.
A mathematical model for the 3d location estimation of 2D echocardiography data

The recovery of 3D left ventricle(LV) shape using 2D echocardiography is very attractable topic in the field of ultrasound imaging. In this paper, we propose a mathematical model to determine the 3D position of LV contours extracted from multiple 2D echocardiography images. We formulate the proposed model as a non-convex constrained minimization problem. To solve it, we propose a proximal alternating minimization algorithm with a solver OPTI for quadratically constrained quadratic program. For validating the proposed model, numerical experiments are performed with real ultrasound data. The experimental results show that the proposed model is promising and available for real echocardiography data.
Enterprise Content Management (ECM) Maturity Models

A maturity model in the enterprise content management (ECM) sphere serves to evaluate an organization's performance against an assessment framework, and to determine roadmaps for optimizing that performance. Such tools are developed typically by academics, informed by considerable research, or by consulting firms that use their maturity model tool to promote the sale of their own products and services. Therefore, the experience of practitioners with using ECM maturity models is underrepresented in the literature. Practitioners require tools that are quick to implement whilst providing useful insights. This chapter explores two case studies in which the author conducted quick assessments using a maturity model as a guide. The case studies indicate that while quick assessments are useful, they need to be included as part of a maturity tool to ensure that the implementation method is repeatable and the results consistent.
An Information Governance Policy Is Required for My Institution, What to Do?

Effective business management within organizations depends, among other factors, on the availability and proper management of appropriate resources. Information resources are one of those resources. This chapter offers practical answers to the many questions that information professionals in an institution may have about how to ensure performing secure and rational management for corporate informational assets. After a brief presentation and discussion of main concepts, it defines and describes the information governance policy, which is the key tool of an advanced information governance approach. It specifies how and when a maturity model should help to develop and update a corporate information governance policy. In addition, it presents the main practical guidelines including specific recommendations on the structure, content, and format of an information governance policy. A discussion of the development and implementation process is then proposed.
Process diversity in software development: an industrial study

Software companies need to employ state-of-the-art technologies to meet customer requirements. Owing to the complexity of contemporary software products and uncertainty concerning the budget for the required resources, companies use process-oriented quality management techniques in order to guarantee appropriate product quality. In this light, standardised quality assurance system that uses the capability maturity model integration-development (CMMI-DEV) program are incorporated into software development processes, and software companies aim to enhance productivity by acquiring CMMI-DEV certificates. In this study, an approach based on CMMI-DEV is used to develop software process diversity for an international company. The results show that the proposed software process diversity model can be used to assess and improve processes in middle-sized software companies that have identical technical frameworks and similar business structures.
Automorphisms of even unimodular lattices over number fields

We describe the powers of irreducible polynomials occurring as characteristic polynomials of automorphisms of even unimodular lattices over number fields. This generalizes results of Gross & McMullen and Bayer-Fluckiger & Taelman.
Certifying multi‐power RSA

In this study, the authorspresent two rigorous algorithms to certify the trapdoor permutation property of the RSAfunction , where is a multi-power RSA modulus with unknown factorisation and r is a known positive integer. Their work gives effective certification for a prime exponent e when and for a composite integer when for , where is a known prime, is a positive integer, and is some small enough constant. The algorithms apply Coppersmith's method for solving univariate modular polynomial equations and run in time , where is a constant number.
A practical streaming approximate matrix multiplication algorithm

Approximate Matrix Multiplication (AMM) has emerged as a useful and computationally inexpensive substitute for actual multiplication of large matrices. Randomized as well as deterministic solutions to AMM were provided in the past. The latest work provides a deterministic algorithm that solves AMM more accurately than the other works. It is a streaming algorithm that is both fast and accurate. But, it is less robust to noise and is also liable to have less than optimal performance in the presence of concept drift in the input matrices. We propose an algorithm that is more accurate, robust to noise, invariant to concept drift in the data, while having almost the same running time as the state-of-the-art algorithm. We also prove that theoretical guarantees exist for the proposed algorithm. An empirical performance improvement of up to 90% is obtained over the previous algorithm. We also propose a general framework for parallelizing the proposed algorithm. The two parallelized versions of the algorithm achieve up to 1.9x and 3.6x speedups over the original version of the proposed algorithm.
The Complete Set of Independent Affine Moment Invariants of Color Images

Moment invariants of images are important features for pattern recognition and image processing. Many methods have been proposed to derive moment invariants of images under different group actions. However, the completeness and independence of a set of moment invariants are two open problems. In this paper, we use the moving frame method to derive affine moment invariants of color images. The moving frame for the normalized color moment space under the action of the affine group is presented. Using this moving frame, we obtain a complete and independent set of affine moment invariants of color images. This system of affine moment invariants is also invariant under diagonal photometric changes. Experimental results are provided to validate the correctness of the derivation.