Reviewerly/exHarmony huggingface dataset:
A comparison of population segmentation methods

This paper presents the first comparison of descriptive segmentation methods for population health management. The aim of descriptive segmentation is to identify heterogeneous segments according to some target observed measure. In healthcare it can be used to understand how utilisation is distributed among a population, and to identify the patient attributes which explain the greatest differences (knowledge of which can help shape segment-tailored services). In reviewing a number of segmentation methods that are both employed on the ground and explored more experimentally within the academic literature, this paper aims to open up a range of options allowing clinicians and managers an informed choice on which approach to use for their situation. Results support the recommendation that decision tree approaches are on-the-whole most suitable, being configurable to local data and providing the best inter-segment discrimination. More basic judgemental splits on patient attributes can be powerful, with the count of chronic conditions being a key variable. Prescribed binning methods such as Bridges to Health are unlikely to achieve high levels of discrimination but do have easily interpretable segments and could be useful for benchmarking. Clustering methods are found to lack discriminative power, which can be attributed to a lack of conceptual appropriateness to the problem.
On Using Blockchain Based Workflows

We have been involved in the development of several blockchain-based solutions that largely utilize workflows. Workflows are used to guide users from independent organizations to process and manage transactions, data and documents in a trusted, immutable, and transparent manner for all relevant entities on a given blockchain network. This work discusses our approach to automate the process of creating, updating, and using workflows for blockchain-based solutions. In particular, we present a workflow definition schema using existing templates. We also show how the workflow definition is used to automate the generation of graphical user interfaces and the possibility of generating associated blockchain smart contracts in the future.
Differentially private data publishing via cross-moment microaggregation

Differential privacy is one of the most prominent privacy notions in the field of anonymization. However, its strong privacy guarantees very often come at the expense of significantly degrading the utility of the protected data. To cope with this, numerous mechanisms have been studied that reduce the sensitivity of the data and hence the noise required to satisfy this notion. In this paper, we present a generalization of classical microaggregation, where the aggregated records are replaced by the group mean and additional statistical measures, with the purpose of evaluating it as a sensitivity reduction mechanism. We propose an anonymization methodology for numerical microdata in which the target of protection is a data set microaggregated in this generalized way, and the disclosure risk limitation is guaranteed through differential privacy via record-level perturbation. Specifically, we describe three anonymization algorithms where microaggregation can be applied to either entire records or groups of attributes independently. Our theoretical analysis computes the sensitivities of the first two central cross moments; we apply fundamental results from matrix perturbation theory to derive sensitivity bounds on the eigenvalues and eigenvectors of the covariance and coskewness matrices. Our extensive experimental evaluation shows that data utility can be enhanced significantly for medium to large sizes of the microaggregation groups. For this range of group sizes, we find experimental evidence that our approach can provide not only higher utility but also higher privacy than traditional microaggregation.
A Comparison of Context-sensitive Models for Lexical Substitution

Word embedding representations provide good estimates of word meaning and give state-of-the art performance in semantic tasks.Embedding approaches differ as to whether and how they account for the context surrounding a word.We present a comparison of different word and context representations on the task of proposing substitutes for a target word in context (lexical substitution).We also experiment with tuning contextualized word embeddings on a dataset of sense-specific instances for each target word.We show that powerful contextualized word representations, which give high performance in several semantics-related tasks, deal less well with the subtle in-context similarity relationships needed for substitution.This is better handled by models trained with this objective in mind, where the inter-dependence between word and context representations is explicitly modeled during training.
A control microarchitecture for fault-tolerant quantum computing

Quantum computers can solve problems that are inefficiently solved by classical computers, such as integer factorization. A fully programmable quantum computer requires a quantum control microarchitecture that connects the quantum software and hardware. Previous research has proposed a Quantum Instruction Set Architecture (QISA) and a quantum control microarchitecture, which targets Noisy Intermediate-Scale Quantum (NISQ) devices without fault-tolerance. However, fault-tolerant (FT) quantum computing requires FT implementation of logical operations, and repeated quantum error correction, possibly at runtime. Though highly patterned, the amount of required (physical) operations to perform logical operations is ample, which cannot be well executed by existing quantum control microarchitectures. In this paper, we propose a control microarchitecture that can efficiently support fault-tolerant quantum computing based on the rotated planar surface code with logical operations implemented by lattice surgery. It highlights a two-level address mechanism which enables a clean compilation model for a large number of qubits, and microarchitectural support for quantum error correction at runtime, which can significantly reduce the quantum program codesize and present better scalability.
Learning a Classifier for Prediction of Maintainability Based on Static Analysis Tools

Static Code Analysis Tools are a popular aid to monitor and control the quality of software systems. Still, these tools only provide a large number of measurements that have to be interpreted by the developers in order to obtain insights about the actual quality of the software. In cooperation with professional quality analysts, we manually inspected source code from three different projects and evaluated its maintainability. We then trained machine learning algorithms to predict the human maintainability evaluation of program classes based on code metrics. The code metrics include structural metrics such as nesting depth, cloning information and abstractions like the number of code smells. We evaluated this approach on a dataset of more than 115,000 Lines of Code. Our model is able to predict up to 81% of the threefold labels correctly and achieves a precision of 80%. Thus, we believe this is a promising contribution towards automated maintainability prediction. In addition, we analyzed the attributes in our created dataset and identified the features with the highest predictive power, i.e. code clones, method length, and the number of alerts raised by the tool Teamscale. This insight provides valuable help for users needing to prioritize tool measurements.
Towards the Impact of Gamers' Adaptation to Delay Variation on Gaming Quality of Experience

Both online and cloud gaming services require a very low network delay to create a good Quality of Experience (QoE) for their users. The required network latency cannot be guaranteed due to the current best effort-nature of the network, and as a result, network latency often degrades the gamers performance and QoE. In this paper, the adaptability of gamers to different variations on delay is investigated both subjectively and objectively using three self-developed games. The results show that gamers can adapt to constant delay while they are playing and change their behavior if the actions in a game are predictable. Such adaptation leads to a significant increase in gamers performance and QoE. The paper also provides evidence that regardless of performance frequent delay switching annoys gamers. The result of this study can be used to create a network resource allocation technique which controls a congested network by giving more priority and resource to the unadaptable games than the adaptable games.
How Often and What StackOverflow Posts Do Developers Reference in Their GitHub Projects?

Stack Overflow (SO) is a popular Q&A forum for software developers, providing a large number of copyable code snippets. While GitHub is an independent code collaboration platform, developers often reuse SO code in their GitHub projects. In this paper, we investigate how often GitHub developers re-use code snippets from the SO forum, as well as what concepts they are more likely to reference in their code. To accomplish our goal, we mine SOTorrent dataset that provides connectivity between code snippets on the SO posts with software projects hosted on GitHub. We then study the characteristics of GitHub projects that reference SO posts and what popular SO discussions can be found in GitHub projects. Our results demonstrate that on average developers make 45 references to SO posts in their projects, with the highest number of references being made within the JavaScript code. We also found that 79% of the SO posts with code snippets that are referenced in GitHub code do change over time (at least ones) raising code maintainability and reliability concerns.
Automated Billing Code Retrieval from MRI Scanner Log Data

Although the level of digitalization and automation steadily increases in radiology, billing coding for magnetic resonance imaging (MRI) exams in the radiology department is still based on manual input from the technologist. After the exam completion, the technologist enters the corresponding exam codes that are associated with billing codes in the radiology information system. Moreover, additional billing codes are added or removed, depending on the performed procedure. This workflow is time-consuming and we showed that billing codes reported by the technologists contain errors. The coding workflow can benefit from an automated system, and thus a prediction model for automated assignment of billing codes for MRI exams based on MRI log data is developed in this work. To the best of our knowledge, it is the first attempt to focus on the prediction of billing codes from modality log data. MRI log data provide a variety of information, including the set of executed MR sequences, MR scanner table movements, and given a contrast medium. MR sequence names are standardized using a heuristic approach and incorporated into the features for the prediction. The prediction model is trained on 9754 MRI exams and tested on 1 month of log data (423 MRI exams) from two MRI scanners of the radiology site for the Swiss medical tariffication system Tarmed. The developed model, an ensemble of classifier chains with multilayer perceptron as a base classifier, predicts medical billing codes for MRI exams with a micro-averaged F1-score of 97.8% (recall 98.1%, precision 97.5%). Manual coding reaches a micro-averaged F1-score of 98.1% (recall 97.4%, precision 98.8%). Thus, the performance of automated coding is close to human performance. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding an administrative task, therefore enhance the MRI workflow, and prevent coding errors.
Enhanced News Retrieval

We observe that most relevant terms in unstructured news articles are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of news articles. Our experimentation, conducted using three publicly available news datasets, demonstrates that BM25P markedly outperforms BM25 in term of effectiveness by up to 17.44% in [email protected] and 85% in [email protected]
Behavior-Driven Dynamics in Agile Development: The Effect of Fast Feedback on Teams

Agile software development teams strive for fast and continuous feedback. Both the quality of the resulting software and the performance of the team require feedback. The performance of the team developments is often addressed in retrospectives which are not only part of the SCRUM framework, but also in general. Reflecting on incidents during the last sprint helps the team to increase performances, expressed by, e.g., efficiency and productivity. However, it is not only essential to identify volatile sprint performances, but also to characterize the primary cause to solve them. Main reasons for low performance are often not visible, primarily when they are related to social-driven team behavior, such as communication structures, mood, or satisfaction. In this paper, we analyze whether automated team feedback about retrospective sprint-behavior can help the team to increase performances due to additional awareness about the dynamic effects over time. In a comparative case study with 15 software projects and a total of 130 undergraduate students, we investigated the sustainable impact of feedback on human aspects. Our results indicate that automated feedback positively affects team performances - and customer satisfaction.
Single-Image Reflection Removal via a Two-Stage Background Recovery Process

The reflection problem often occurs when imaging through a semitransparent material such as glass. It degrades the image quality and affects the subsequent analyses on the image. Traditional single-image based reflection removal methods assume the reflection is blurry. Deep neural networks (DNNs) are, then, used to identify the blurry reflection and remove it. However, it is often that the blurry reflection still contains strong edges. They will be treated as the background and kept in the image. In this letter, we propose a novel two-stage DNN based reflection removal algorithm. In the first stage, we include a new feature reduction term in the loss function when training the network. Due to its strong reflection suppression ability, the reflection components in the image can be more effectively suppressed. However, it will also attenuate the gradient values of the background image. For recovering the background, in the second stage, we first estimate a reflection gradient confidence map based on the initial estimation result and use it to identify the strong background gradients. Then, we use a generative adversarial network to reconstruct the background image from its gradients. Experimental results show that the proposed two-stage approach can give a superior performance compared with the state-of-the-art DNN based methods.
Methods for Analyzing Polarity of the Kazakh Texts Related to the Terrorist Threats

In this work we described the rule-based method, using dictionary for sentiment analysis of texts in the Kazakh language related to the terrorist threats. It provides an overview of the methods for analyzing polarity, parser, which analyzes the pages on the content of keywords from the database, morphological, syntactic and sentiment analysis of the texts in the Kazakh language.
Practical Privacy-Preserving Frequent Itemset Mining on Supermarket Transactions

Data mining is widely applied to establish connections among the items in massive datasets nowadays. Association rule mining is one of the most popular methods to perform data mining, and a fundamental part of this is frequent itemset mining. Big-scale data are uploaded to the honest-but-curious cloud service provider (CSP). Therefore, it is imperative to protect the raw data from being obtained by the CSP and the third parties. Furthermore, because supermarket transactions are sparse, they are not suitable to be mined using the same methods used for most of the other data. The methods used for ordinary data will cost more computation power if they are applied on this special dataset. In this paper, we propose an efficient protocol to evaluate whether an itemset is frequent or not under the encrypted mining query on supermarket transactions. To improve the mining efficiency, we design a blocking algorithm. In this algorithm, we separate the encrypted transactions into blocks and only calculate bilinear pairings on ciphertexts of part blocks instead of all ciphertexts, which helps cut down the computation cost of the mining process. Finally, we evaluate the performance of our protocol by conducting theoretical analyses and simulator experiments in the aspects of computation cost, security, correctness, and running time. The results demonstrate that our protocol can output a correct mining result and clearly outperforms the previous solution in the aspect of efficiency under the same security level.
Bento Box: An Interactive and Zoomable Small Multiples Technique for Visualizing 4D Simulation Ensembles in Virtual Reality

We present Bento Box, a virtual reality data visualization technique and bimanual 3D user interface for exploratory analysis of 4D data ensembles. Bento Box helps scientists and engineers make detailed comparative judgments about multiple time-varying data instances that make up a data ensemble (e.g., a group of 10 parameterized simulation runs). The approach is to present an organized set of complementary volume visualizations juxtaposed in a grid arrangement, where each column visualizes a single data instance and each row provides a new view of the volume from a different perspective and/or scale. A novel bimanual interface enables users to select a sub-volume of interest to create a new row on-the-fly, scrub through time, and quickly navigate through the resulting virtual "bento box.'' The technique is evaluated through a real-world case study, supporting a team of medical device engineers and computational scientists using in-silico testing (supercomputer simulations) to redesign cardiac leads. The engineers confirmed hypotheses and developed new insights using a Bento Box visualization. An evaluation of the technical performance demonstrates that the proposed combination of data sampling strategies and clipped volume rendering is successful in displaying a juxtaposed visualization of fluid-structure-interaction simulation data (39 GB of raw data) at interactive VR frame rates.
SBST in the Age of Machine Learning Systems - Challenges Ahead

Machine Learning, and especially Deep Neural Network (DNN), is being rapidly adopted by various software systems, including applications in safety-critical systems such as autonomous driving and medical imaging. This calls for an urgent need to test these AI/ML techniques as part of larger systems. However, this task can be very different from testing of traditional software systems. We will briefly examine the fundamentals of software testing as well as the state of the art in Search Based Software Testing (SBST), and try to outline the challenges ahead while highlighting areas where SBST can shine.
A Virtual Reality Assembly Assessment Benchmark for Measuring VR Performance &amp; Limitations

With an increasing product complexity in manufacturing industry, virtual reality (VR) offers the possibility to immersively assess assembly processes already in early product development stages. Within production validation phases, engineers visually assess product part assembly and interactively validate corresponding production processes. Nevertheless, by now research does not give answers on how VR assembly system's performance can be measured with respect to its technical limitations. The proposed Virtual Reality Assembly Assessment (VR2A) benchmark is an open, standardized experiment design for evaluating the overall VR assembly assessment performance in terms of sizes and clearances instead of measuring single technical impact factors within the interaction cycle, such as tracking, rendering and visualization limitations. VR2A benchmark focusses on the overall production engineer's assessment objective generating quantifiable metrics. Using VR2A, users gain practical insights on their overall VR assessment system's performance and limitations. An in-depth evaluation with production engineers (N=32) revealed, that negative clearances can be detected more easily than positive ones, part sizes directly correlate with the assessment performance. Additionally, the evaluation showed that VR2A is easy to use, universally usable and generates objective insights on the applied VR system.
3D Pose Detection of Closely Interactive Humans Using Multi-View Cameras

We propose a method to automatically detect 3D poses of closely interactive humans from sparse multi-view images at one time instance. It is a challenging problem due to the strong partial occlusion and truncation between humans and no tracking process to provide priori poses information. To solve this problem, we first obtain 2D joints in every image using OpenPose and human semantic segmentation results from Mask R-CNN. With the 3D joints triangulated from multi-view 2D joints, a two-stage assembling method is proposed to select the correct 3D pose from thousands of pose seeds combined by joint semantic meanings. We further present a novel approach to minimize the interpenetration between human shapes with close interactions. Finally, we test our method on multi-view human-human interaction (MHHI) datasets. Experimental results demonstrate that our method achieves high visualized correct rate and outperforms the existing method in accuracy and real-time capability.
Time Series Analysis of Online Public Opinions in Colleges and Universities and its Sustainability

With the quick penetration of Internet applications, online media have become an important carrier of public opinions. The opinions and comments expressed by young college students—one of the most active netizen groups—on the Internet have turned out to be an essential part of the online public opinions in colleges and universities. However, the existing systems generally employ simple statistical methods to analyze the effect of online public opinions on the image and reputation development of colleges and universities without taking account of other factors, such as the hotness characteristics of online public opinions and semantic information. Therefore, on the basis of Public Opinion Hotness Index and time series-based trend analysis, as well as the topics extracted using the latent Dirichlet allocation (LDA) topic model, this study aims to improve the analysis performance on the online public opinions in colleges and universities using short-term trend prediction results. The experience and lessons learned from a real case may provide strong data support and feasible suggestions for colleges and universities in analyzing and guiding the online public opinions.
Cryptanalysis and Improvement of a Chaos-Based Watermarking Scheme

In the past decade, a number of chaos-based watermarking schemes have been proposed.Generally, it is widely accepted that the introduction of chaos effectively enhances the security of digital watermarking.Yet, taking a representative algorithm as an example, this paper reveals vulnerabilities of the chaos-based watermarking schemes against various attacks under Kerckhoff's principle, and the watermark can be successfully removed and decrypted.To achieve higher security, a sub key generation mechanism based on the feature codes of images (SKGM-FCI) is designed.The obtained watermarking key is related to the original images, such that individual sub keys are obtained for different host images.Benefiting from the SKGM-FCI, a blind watermarking scheme based on Discrete Cosine Transform domain of the scrambled image is further proposed and investigated.The theoretical analysis and simulation results validate the effectiveness and the high security level of the proposed scheme.