Reviewerly/exHarmony huggingface dataset:
Performance of quantum error correction with coherent errors

We compare the performance of quantum error correcting codes when memory errors are unitary with the more familiar case of dephasing noise. For a wide range of codes we analytically compute the effective logical channel that results when the error correction steps are performed noiselessly. Our examples include the entire family of repetition codes, the 5-qubit, Steane, Shor, and surface codes. When errors are measured in terms of the diamond norm, we find that the error correction is typically much more effective for unitary errors than for dephasing. We observe this behavior for a wide range of codes after a single level of encoding, and in the thresholds of concatenated codes using hard decoders. We show that this holds with great generality by proving a bound on the performance of any stabilizer code when the noise at the physical level is unitary. By comparing the diamond norm error $D'_\diamond$ of the logical qubit with the same quantity at the physical level $D_\diamond$, we show that $D'_\diamond \le c D^d_\diamond $ where $d$ is the distance of the code and $c$ is constant that depends on the code but not on the error. This bound compares very favorably to the performance of error correction for dephasing noise and other Pauli channels, where an error correcting code of odd distance $d$ will exhibit a scaling $D'_\diamond \sim D_\diamond^{(d+1)/2}$.
Motivation and Satisfaction of Software Engineers

Context: The proper management of people can help software organisations to achieve higher levels of success. However, the limited attention paid to the appropriate use of theories to underpin the research in this area leaves it unclear how to deal with human aspects of software engineers, such as motivation and satisfaction. Objectives: This article aims to expose what drives the motivation and satisfaction of software engineers at work. Methods: A multiple case study was conducted at four software organisations in Brazil. For 11 months, data was collected using semi-structured interviews, diary studies, and document analyses. Results: The Theory of Motivation and Satisfaction of Software Engineers (TMS-SE), presented in this article, combines elements from well established theories with new findings, and translates them into the software engineering context. Conclusion: The TMS-SE advances the understanding of people management in the software engineering field and presents a strong conceptual framework for future investigations in this area.
TextWorld: A Learning Environment for Text-Based Games

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.
A method for detailed, short-term energy yield forecasting of photovoltaic installations

The global shift towards renewable energy production combined with the expected penetration of electric cars, increasing energy usage of cloud computing centers and the transformation of the electricity grid itself towards the “Smart Grid” requires novel solutions on all levels of energy production and management. Forecasting of energy production especially will become a major component for design and operation in all temporal and spatial scales, creating opportunities for optimized control of energy storage, local energy exchange etc. To this end, a method for the creation of detailed and accurate energy yield forecasts for PV installations is presented. Based on sky-imager information and using tailored neural networks, highly detailed energy yield forecasts are produced for a monitored test installation, for horizons up to 15 min and with a resolution of 1 s. Thermal effects are included in the calculations and error propagation is minimized by reducing the modeling steps. The described method manages to outperform state of the art models by up to 39% in forecast skill, while at the same time retaining temporal resolutions that enable control schemes and energy exchange in a local scale.
A virtual reality role-playing serious game for experiential learning

Educational systems can benefit from Virtual Reality’s (VR) ability to support experiential learning. In particular, VR based games, especially role-playing serious games (RPGs), can promote learning through the simulation of various educational scenarios. This study proposes an immersive VR-RPG to educate players about the behavior of honeybees. The player adopts the role of a honeybee and experiences a virtual world mimicking the real one from the honeybee’s perspective. Unlike most studies in educational VR, we assess the impact of immersion on knowledge gain by testing the players’ knowledge on the subject before, immediately after, and one week following the use of the system. We also compare the proposed system with both a conventional and a desktop VR-RPG approach. The results indicate that students significantly gained knowledge in all methods compared to the pre-test. We found that the immersion level for both tested VR-RPGs did not have a significant effect on learning. However, the study showed an improvement in knowledge retention for the desktop VR-RPG users compared to those of the conventional method. Moreover, the results revealed that users of the immersive and desktop VR-RPGs were more motivated and engaged compared to those of the conventional method.
A data locality based scheduler to enhance MapReduce performance in heterogeneous environments

MapReduce is an essential framework for distributed storage and parallel processing for large-scale data-intensive jobs proposed in recent times. Hadoop default scheduler assumes homogeneous environment. This assumption of homogeneity does not work at all times in practice and limits the performance of MapReduce. Data locality is essentially moving computation closer (faster access) to the input data. Fundamentally, MapReduce does not always look into the heterogeneity from a data locality perspective. Improving data locality for MapReduce framework is an important issue to improve the performance of large-scale Hadoop clusters. This paper proposes a novel data locality based scheduler which allocates input data blocks to the nodes based on their processing capacity. Also schedulesmap andreduce tasks to the nodes based on their computing ability in the heterogeneous Hadoop cluster. We evaluate proposed scheduler using different workloads from Hi-Bench benchmark suite. The experimental results prove that our proposed scheduler enhances the MapReduce performance in heterogeneous environments. Minimizes job execution time, and also improves data locality for different parameters as compared to the Hadoop default scheduler, Matchmaking scheduler and Delay scheduler respectively.
Dictionary Alignment With Re-Ranking for Low-Resolution NIR-VIS Face Recognition

Recently, near-infrared (NIR) images are increasingly being captured for recognizing faces in low-light/night-time conditions. Matching these images against the controlled high-resolution visible facial images usually present in the database is a challenging task. In surveillance scenarios, the NIR images can have very low resolution and also non-frontal pose which makes the problem even more challenging. In this paper, we propose an orthogonal dictionary alignment approach for addressing this problem. We also propose a re-ranking approach to further improve the recognition performance for each probe by combining the rank list given by the proposed algorithm with that given by another complementary feature/algorithm. Finally, we have also collected our own database Heterogeneous face recognition across Pose and Resolution (HPR) that has facial images captured from two surveillance quality NIR cameras and one high-resolution visible camera, with significant variations in head pose and resolution. Extensive experiments on the modified CASIA NIR-VIS 2.0 database, the Surveillance Camera face database, and our HPR database show the effectiveness of the proposed approaches and the collected database.
On the Importance of Visual Context for Data Augmentation in Scene Understanding

Performing data augmentation for learning deep neural networks is known to be important for training visual recognition systems.By artificially increasing the number of training examples, it helps reducing overfitting and improves generalization.While simple image transformations can already improve predictive performance in most vision tasks, larger gains can be obtained by leveraging task-specific prior knowledge.In this work, we consider object detection, semantic and instance segmentation and augment the training images by blending objects in existing scenes, using instance segmentation annotations.We observe that randomly pasting objects on images hurts the performance, unless the object is placed in the right context.To resolve this issue, we propose an explicit context model by using a convolutional neural network, which predicts whether an image region is suitable for placing a given object or not.In our experiments, we show that our approach is able to improve object detection, semantic and instance segmentation on the PASCAL VOC12 and COCO datasets, with significant gains in a limited annotation scenario, i.e. when only one category is annotated.We also show that the method is not limited to datasets that come with expensive pixel-wise instance annotations and can be used when only bounding boxes are available, by employing weakly-supervised learning for instance masks approximation.
The Adoption of JavaScript Linters in Practice: A Case Study on ESLint

A linter is a static analysis tool that warns software developers about possible code errors or violations to coding standards. By using such a tool, errors can be surfaced early in the development process when they are cheaper to fix. For a linter to be successful, it is important to understand the needs and challenges of developers when using a linter. In this paper, we examine developers' perceptions on JavaScript linters. We study why and how developers use linters along with the challenges they face while using such tools. For this purpose we perform a case study on ESLint, the most popular JavaScript linter. We collect data with three different methods where we interviewed 15 developers from well-known open source projects, analyzed over 9,500 ESLint configuration files, and surveyed 337 developers from the JavaScript community. Our results provide practitioners with reasons for using linters in their JavaScript projects as well as several configuration strategies and their advantages. We also provide a list of linter rules that are often enabled and disabled, which can be interpreted as the most important rules to reason about when configuring linters. Finally, we propose several feature suggestions for tool makers and future work for researchers.
Forecasting solar irradiance at short horizons: Frequency and time domain models

A key issue in integrating solar power into the grid is short-term forecasting. Up to now, most solar forecasting has been run in the time domain. But since the data are dominated by the diurnal cycle, frequency domain methods naturally recommend themselves. Several time series models are tested over horizons of 15, 30 and 45 min, and 1–3 h. The methods include persistence, regressions in levels, ARIMAs, and a frequency domain model, based on Fourier transformation of the ARIMA. The model coefficients are time-varying, and are estimated using moving windows. Models are estimated on the actual irradiance data, the clear sky and clearness indexes. Forecasting tests are run using data from six sites in the continental United States. The persistence and regression forecasts achieve similar degrees of accuracy at the 15 min horizon, with the frequency domain model showing marginally higher errors. At the 45 min horizon, the performance of the persistence, regression and frequency domain forecasts is so close as to be nearly indistinguishable. At the 1 h horizon, the ARIMA as applied to the clear sky index achieves the most accurate forecast. At the 2 h horizon, the frequency domain model predicts more accurately. At 3 h, the ARIMA and frequency domain models are close. None of the models consistently dominate the others. Model performance differs as a function of the resolution of the data and the forecast horizon. The accuracy of the forecasts also depends on the degree of time variation in the coefficients. The optimal window width for estimating the coefficients was found to be 480 h.
BehaveSense: Continuous authentication for security-sensitive mobile apps using behavioral biometrics

With the emergence of smartphones as an essential part of our daily lives, continuous authentication becomes an urgent need which could efficiently protect user security and privacy. However, only a small percentage of apps contain sensitive data. To save energy and protect user security, we propose BehaveSense, an accurate and efficient continuous authentication method for security-sensitive mobile apps using touch-based behavioral biometrics. By exploring four different types of touch operations, we train the owner model using One-Class SVM (OCSVM) and isolation forest (iForest), and calculate the accuracy of each type with the model. Afterwards, we calculate the confidence level of each type using the Bayesian theorem. Finally, we obtain the accuracy of a touch operation sequence with an improved expectedprob algorithm. To validate the effectiveness of the proposed method, we conduct a series of experiments. We collect the WeChat app data of 45 volunteers during two weeks. Experimental results show that our method can recognize user identity efficiently. Specifically, our method achieves average accuracy of approaching 95.85% for touch operation sequence, when considering 9 touch operations. Our method is very promising to authenticate user.
Medical Video Coding Based on 2nd-Generation Wavelets: Performance Evaluation

The operations of digitization, transmission and storage of medical data, particularly images, require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. The 2nd-generation wavelets seeks to be positioned and confronted by the image and video coding methods currently used. It is in this context that we suggest a method combining bandelets and the SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage of capturing the geometrical complexity of the image structure. The second reason is the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements indicate that in some cases (for low bit rates) the performance of the proposed coding competes with the well-established ones (H.264 or MPEG4 AVC and H.265 or MPEG4 HEVC) and opens up new application prospects in the field of medical imaging.
User Satisfaction in Augmented Reality-Based Training Using Microsoft HoloLens

With the recent developments in augmented reality (AR) technologies comes an increased interest in the use of smart glasses for hands-on training. Whether this interest is turned into market success depends at the least on whether the interaction with smart AR glasses satisfies users, an aspect of AR use that so far has received little attention. With this contribution, we seek to change this. The objective of the article, therefore, is to investigate user satisfaction in AR applied to three cases of practical use. User satisfaction of AR can be broken down into satisfaction with the interaction and satisfaction with the delivery device. A total of 142 participants from three different industrial sectors contributed to this study, namely, aeronautics, medicine, and astronautics. In our analysis, we investigated the influence of different factors, such as age, gender, level of education, level of Internet knowledge, and the roles of the participants in the different sectors. Even though users were not familiar with the smart glasses, results show that general computer knowledge has a positive effect on user satisfaction. Further analysis using two-factor interactions showed that there is no significant interaction between the different factors and user satisfaction. The results of the study affirm that the questionnaires developed for user satisfaction of smart glasses and the AR application performed well, but leave room for improvement.
Embedded feature selection accounting for unknown data heterogeneity

Data heterogeneity is one of the big challenges in modern data analysis caused by the effects of unknown/unwanted factors introduced during data collection procedures. It will cause spurious estimation of variable effects when traditional methods are applied for feature selection which simply assume that data samples are independently and identically distributed. Although some existing statistical models can evaluate more accurately the significance of each variable by estimating and including unknown factors as covariates, they are categorized as filter methods suffering from variable redundancy and lack of predictability. Therefore, we propose an embedded feature selection method from a sparse learning perspective capable of adjusting unknown heterogeneity. Its performance is investigated by evaluating the classification performance using the selected features in multi-class classification problems. Benefitting from the effective adjustment of unknown heterogeneity and model selection strategy, the experimental results on synthetic data and three real-world benchmark data sets have shown that our method can achieve consistent superiority over several conventional embedded methods and existing statistical models.
Bayesian Data Analysis in Empirical Software Engineering Research

Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics have traditionally dominated empirical data analysis, and certainly remain prevalent in empirical software engineering. This situation is unfortunate because frequentist statistics suffer from a number of shortcomings---such as lack of flexibility and results that are unintuitive and hard to interpret---that curtail their effectiveness when dealing with the heterogeneous data that is increasingly available for empirical analysis of software engineering practice. In this paper, we pinpoint these shortcomings, and present Bayesian data analysis techniques that provide tangible benefits---as they can provide clearer results that are simultaneously robust and nuanced. After a short, high-level introduction to the basic tools of Bayesian statistics, we present the reanalysis of two empirical studies on the effectiveness of automatically generated tests and the performance of programming languages. By contrasting the original frequentist analyses with our new Bayesian analyses, we demonstrate the concrete advantages of the latter. To conclude we advocate a more prominent role for Bayesian statistical techniques in empirical software engineering research and practice.
Predicting Invasive Disease-Free Survival for Early Stage Breast Cancer Patients Using Follow-Up Clinical Data

Objective: Chinese women are seriously threatened by breast cancer with high morbidity and mortality. The lack of robust prognosis models results in difficulty for doctors to prepare an appropriate treatment plan that may prolong patient survival time. An alternative prognosis model framework to predict invasive disease-free survival (iDFS) for early stage breast cancer patients, called MP4Ei, is proposed. MP4Ei framework gives an excellent performance to predict the relapse or metastasis breast cancer of Chinese patients in five years. Methods: MP4Ei is built based on statistical theory and gradient boosting decision tree framework. 5246 patients, derived from the clinical research center for breast in West China Hospital of Sichuan University, with early-stage (stage I-III) breast cancer are eligible for inclusion. Stratified feature selection, including statistical and ensemble methods, is adopted to select 23 out of the 89 patient features about the patient' demographics, diagnosis, pathology, and therapy. Then, 23 selected features as the input variables are imported into the XGBoost algorithm, with Bayesian parameter tuning and cross validation, to find out the optimum simplified model for five-year iDFS prediction. Results: For eligible data, with 4196 patients (80%) for training, and with 1050 patients (20%) for testing, MP4Ei achieves comparable accuracy with AUC 0.8451, which has a significant advantage (p <; 0.05). Conclusion: This work demonstrates the complete iDFS prognosis model with very competitive performance. Significance: The proposed method in this paper could be used in clinical practice to predict patients' prognosis and future surviving state, which may help doctors make treatment plan.
The Effect of Humanizing <scp>Robo‐Advisors</scp> on Investor Judgments*

ABSTRACT We examine the effect of humanizing (naming) robo‐advisors on investor judgments, which has taken on increased importance as robo‐advisors have become increasingly common and there is currently little SEC regulation governing key aspects of their use. In our first experiment, we predict and find that investors are more likely to rely on the investment recommendation of an unnamed robo‐advisor, whereas they are more likely to rely on the investment recommendation of a named human advisor. Theory suggests one reason that naming a robo‐advisor may have drawbacks pertains to the complexity of the task the robo‐advisor performs. We explore the importance of task complexity in our second experiment. We predict and find that investors are less likely to rely on a named robo‐advisor when the advisor is perceived to be performing a relatively complex task, consistent with our first experiment, and more likely to rely on a named robo‐advisor when the advisor is perceived to be performing a relatively simple task, consistent with prior research on human‐computer interactions. Our findings contribute to the literature examining how technology influences the acquisition and use of financial information and the general literature on human‐computer interactions. Our study also addresses a call by the SEC to learn more about robo‐advisors. Lastly, our study has practical implications for wealth management firms by demonstrating the potentially negative effects of making robo‐advisors more humanlike in an attempt to engage and attract users.