Reviewerly/exHarmony huggingface dataset:
Multipopulation Optimization for Multitask Optimization

Currently, the most of multitask evolutionary algorithms views multiple tasks as factors influencing the evolution of individuals. However, this consideration causes difficulty to assign fitness to individuals, because an individual which performs well on one task can have a bad performance on another task. To avoid this difficulty, this paper proposes a novel multipopulation technique for multitask optimization (MPMTO). The novelty of MPMTO is that it can solve the multiple tasks via a simple and straightforward method by corresponding each population to a task. By this way, the fitness assignment issue can be addressed by just assigning the objective value of the corresponding task to individuals. MPMTO is a general technique so that existing population-based optimization algorithms can be used in each population. This paper uses differential evolutionary algorithm in each population and develops a multipopulation multitask differential evolutionary optimization (mMTDE) based on the proposed multipopulation technique. mMTDE features that each population can use the other populations as the additional knowledge source to create an overlapping population, allowing the populations share information. By this way, the population can improve the efficacy and accuracy of solving multiple tasks. Moreover, the successful inter-task offspring can immigrate back to the corresponding population to fully utilize the inter-task knowledge. We have compared the proposed method with other state-of-the-art methods on benchmark multitask problems. The experimental results show the superiority of the proposed method which could utilizes efficiently the searching knowledge of multiple tasks.
Enhanced Switch Image-Based Visual Servoing Dealing with FeaturesLoss

In this paper, an enhanced switch image-based visual servoing controller for a six-degree-of-freedom (DOF) robot with a monocular eye-in-hand camera configuration is presented. The switch control algorithm separates the rotating and translational camera motions and divides the image-based visual servoing (IBVS) control into three distinct stages with different gains. In the proposed method, an image feature reconstruction algorithm based on the Kalman filter is proposed to handle the situation where the image features go outside the camera’s field of view (FOV). The combination of the switch controller and the feature reconstruction algorithm improves the system response speed and tracking performance of IBVS, while ensuring the success of servoing in the case of the feature loss. Extensive simulation and experimental tests are carried out on a 6-DOF robot to verify the effectiveness of the proposed method.
Ethnographic research in software engineering: a critical review and checklist

Software Engineering (SE) community has recently been investing significant amount of effort in qualitative research to study the human and social aspects of SE processes, practices, and technologies. Ethnography is one of the major qualitative research methods, which is based on constructivist paradigm that is different from the hypothetic-deductive research model usually used in SE. Hence, the adoption of ethnographic research method in SE can present significant challenges in terms of sufficient understanding of the methodological requirements and the logistics of its applications. It is important to systematically identify and understand various aspects of adopting ethnography in SE and provide effective guidance. We carried out an empirical inquiry by integrating a systematic literature review and a confirmatory survey. By reviewing the ethnographic studies reported in 111 identified papers and 26 doctoral theses and analyzing the authors' responses of 29 of those papers, we revealed several unique insights. These identified insights were then transformed into a preliminary checklist that helps improve the state-of-the-practice of using ethnography in SE. This study also identifies the areas where methodological improvements of ethnography are needed in SE.
Learning - based Adaptation Framework for Elastic Software Systems

Adaptation is a concern for elastic software systems.Conventional methods like Brownout try to deactivate optional computation per request after decoupling the software into different components, to lower the workload when peaks occur.However, resource-intensive components are not always easy to isolate, and some software systems are even not separable.In this paper, we propose a new paradigm that provides each core and mandatory component a corresponding alternative component, with similar function but lower resource consumption, and use reinforcement learning in a feedback loop control for the selfadaptation process.We modified the widely used benchmark RU-BiS to make it weakly coupled and add alternative components.Experiments show that our framework dramatically improves both the efficiency and effectiveness of self-adaptation.
The earthquake awareness levels of undergraduate students

Natural disasters cause serious effects on economic, social and sociological areas in Turkey. Earthquakes are one of the major natural disasters in Turkey. They affect social and economic life and education negatively. The more preparedness and awareness are provided against natural disasters, the safer society will be. This study was carried out in order to determine the views of undergraduate students about earthquake awareness attitudes and to examine them in terms of various variables. The research was conducted as a screening design from quantitative research models. In the study, a scale was applied as a data collection tool. The study group of the research consists of 619 undergraduate students studying at undergraduate level in various faculties at Düzce University in the academic year 2018-2019.
A New Adaptive High-Order Unscented Kalman Filter for Improving the Accuracy and Robustness of Target Tracking

In target tracking, the tracking process needs to constantly update the data information.For maneuvering target, model mismatch and loss of high-order moment information disrupt the accuracy of the state estimation.In this paper, an adaptive high-order unscented Kalman filter (AHUKF) algorithm is proposed for the case of errors occurring in the capturing of model dynamic behavior using the classical unscented Kalman filter (UKF) algorithm.By introducing the free parameter, the analytical solution of the high-order unscented transformation (UT) was obtained, the basis for choosing free parameters was analyzed, and the stability of the algorithm was discussed.A method for obtaining the optimal adaptive factor based on the prediction residual estimation covariance matrix was proposed, which reduces the influence of the dynamic model error and was applied to the target tracking model.In this paper, the proposed AHUKF is applied to a target tracking problem with state mutation, different sampling intervals, and different turn rates, respectively.Simulation results for target tracking illustrate that the proposed algorithm is more accurate and robust than the UKF, high-order unscented Kalman filter (HUKF) and adaptive unscented Kalman filter (AUKF).
Short-Term Load Forecasting of Virtual Machines Based on Improved Neural Network

Cloud computing allows applications to share resources on physical machines in an independent manner so as to effectively increase the utilization of physical machines. To enable cloud service users to obtain high-quality services, it is significant for cloud service providers to reduce the time required to allocate and deploy virtual machines, as well as to provide time windows for the deployment of physical servers. To ensure demand is met, one effective implementation method is to predict the workload of virtual machines in the future, as this contributes to the efficient deployment of a data center's requests from virtual machines to dynamically changing physical servers. However, due to the complexity of virtual machine requests, virtual machine workload prediction remains a significant challenge at present; on the one hand, it is difficult for standard recurrent neural networks to capture long-term dependencies because of the disappearance of gradients, while on the other hand, the long short-term memory method cannot handle irregular intervals. Accordingly, a new virtual machine workload prediction method that is capable of managing irregular time intervals is proposed in this paper. The proposed method combines the amount and time intervals of historical virtual machine work requests in order to accurately predict the virtual machine's future workload using a fixed length of time as a unit. Experiments show that the proposed model can generate more accurate prediction results than the long short-term memory method.
Pattern recognition and prognostic analysis of longitudinal blood pressure records in hemodialysis treatment based on a convolutional neural network

The aim of this study is to analyze and visualize blood pressure (BP) patterns during continuous hemodialysis (HD) sessions, referred to as multiple-session patterns (MSPs), and explore whether deep learning models with MSPs have better performance.Data from 3.79 million hemodialysis BP records collected from July 30, 2007, to August 25, 2016, were obtained from the health system's electronic health records. We analyzed BP patterns during 36 continuous HD sessions (approximately 3 months) and selected 1311 (survival: 1246; death: 65) end-stage renal disease patients to classify 1-year outcomes (survival or death). Convolution kernels of different sizes were used to construct convolutional neural networks to recognize MSPs and BP patterns during a single HD session, referred to as single-session patterns (SSPs). BP patterns corresponded to convolution kernels and were represented and visualized as the input patches that activate the feature maps most. We used global average pooling (GAP) to measure the overall response of the inputs to each convolution kernel (pattern). The weights of the fully connected layers after GAP can measure the correlations between the convolution kernels (patterns) and the classification results. We solved the problem of data imbalance with a two-phase training strategy.The F1_score was 0.782 ± 0.058 (95% CI) in the models with SSPs and was approximately 19.5% higher (0.977 ± 0.014, 95% CI) in the models with MSPs.The results indicated that consistent with previous studies, patients with lower BPs and longer HD sessions have better prognoses. BP patterns during continuous HD sessions can represent patients' 1-year mortality risk better than BP patterns during a single HD session and therefore improve the performance of prediction models.
The application of modified Chebyshev polynomials in asymmetric cryptography

Based on Chebyshev polynomials, you can create an asymmetric cryptosystem that allows secure communication. Such a cryptosystem uses the fact that these polynomials form a semi-group due to the composition operation. This article presents new cryptosystems that use other than semi-group property dependencies. Based on these dependencies as well as modifications of Chebyshev's polynomials, two cryptosystems have been proposed. The presented analysis shows that their security is the same as in the case of algorithms associated with the problem of discrete logarithms. The article also shows methods that allow faster calculation of Chebyshev polynomials.
EntEval: A Holistic Evaluation Benchmark for Entity Representations

Rich entity representations are useful for a wide class of problems involving entities.Despite their importance, there is no standardized benchmark that evaluates the overall quality of entity representations.In this work, we propose EntEval: a test suite of diverse tasks that require nontrivial understanding of entities including entity typing, entity similarity, entity relation prediction, and entity disambiguation.In addition, we develop training techniques for learning better entity representations by using natural hyperlink annotations in Wikipedia.We identify effective objectives for incorporating the contextual information in hyperlinks into state-of-the-art pretrained language models (Peters et al., 2018a) and show that they improve strong baselines on multiple EntEval tasks.
Content-Aware Image Resizing Detection Using Deep Neural Network

Content-aware image resizing is the process of adjusting the size of an image while preserving its important content. Image resizing is used to overcome diversity in resolutions between modules, such as display devices and applications, and can thus be deliberately exploited to distort or remove original content; therefore, detecting such tampering has become an important topic in forensics. This paper proposes a deep neural network architecture to capture subtle local artifacts caused by seam-based image resizing. Unlike past approaches that only classified two classes, our approach is the first attempt to solve a given forensic task with three-class classification: original, seam insertion, and seam carving. The experimental results show that our work performs better than the handcrafted feature-based method and networks designed for different forensic tasks.
Autoregressive Text Generation Beyond Feedback Loops

Autoregressive state transitions, where predictions are conditioned on past predictions, are the predominant choice for both deterministic and stochastic sequential models.However, autoregressive feedback exposes the evolution of the hidden state trajectory to potential biases from well-known train-test discrepancies.In this paper, we combine a latent state space model with a CRF observation model.We argue that such autoregressive observation models form an interesting middle ground that expresses local correlations on the word level but keeps the state evolution non-autoregressive.On unconditional sentence generation we show performance improvements compared to RNN and GAN baselines while avoiding some prototypical failure modes of autoregressive models. 1
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling

Yue Zhang, Rui Wang, Luo Si. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Data Acquisition System for Solar Panels

PV plants require monitoring to determine their performance ratio which is observed over time. Before constructing a solar energy system, it is important to know in advance how much energy such a project could potentially harvest from the sun and to identify the sources of losses which usually are pollution, cloudiness, and heat. For this reason it is essential to consider the solar irradiance. This paper describes the design and implementation of a data acquisition system used to determine the Global Horizontal Irradiance (GHI). It also uses and open-source clear sky model to estimate if the full potential of the solar energy is harvested on cloud free days. The necessary data travels with the aid of a Raspberry Pi from the pyranometer to a virtual machine hosted in the university. This VM uses a suite of available open-source solutions through its application of visualization, monitoring and analysis software. The collected solar radiation values from the pyranometer are cross referenced with the ones obtained from the solar radiation prediction model. This monitoring and analysis system is integrated alongside a PV plant to determine the performance of the energy harvested from the solar panels.
A Paraphrase Generation System for EHR Question Answering

This paper proposes a dataset and method for automatically generating paraphrases for clinical questions relating to patient-specific information in electronic health records (EHRs).Crowdsourcing is used to collect 10,578 unique questions across 946 semantically distinct paraphrase clusters.This corpus is then used with a deep learning-based question paraphrasing method utilizing variational autoencoder and LSTM encoder/decoder.The ultimate use of such a method is to improve the performance of automatic question answering methods for EHRs.
Research on Rock Mass Quality Classification Based on An Improved Rough Set–Cloud Model

Rock mass grading is a basic problem in the construction industry and underground engineering research.Because the index parameters that affect the rock mass quality are ambiguous and random, rock mass quality classification is often uncertain.Based on this issue, this paper selects the rock quality index RQD, rock uniaxial saturated compressive strength Rw, rock mass integrity coefficient Kv, structural surface strength coefficient K f and groundwater seepage quantity ω as quantitative evaluation indicators to construct an evaluation system.Thirty sets of data collected in China are selected as learning samples.Through the related concepts and finite interval cloud model, the characteristic parameters of the measured data are obtained, and a cloud model is generated with a forward cloud generator to achieve the transformation between qualitative and quantitative concepts.Combined with the basic knowledge of rough set theory, the weight determination problem is transformed into an attribute importance problem.To avoid zero weights in the traditional rough set approach, this paper introduces a calculation method based on the conditional information entropy, and the weight calculation method is modified to obtain the comprehensive weights.According to the principle of the maximum membership degree, the classification of rock mass quality is performed, and the rock mass quality data are determined to have different levels of comprehensive membership.A rock mass quality evaluation method based on the coupled improved rough set-cloud model is established and successfully applied for a rock mass quality evaluation of the 0+000∼0+560 test section of the second stage of underground engineering at the Guangdong Pump Storage Power Station.The results show that the model is reliable and practical and provides a new approach for uncertainty analysis and evaluation in rock engineering practice.
Very Deep Residual Network for Image Matting

Matting is a fundamental computer vision problem, which has wide applications from daily life to professional fields. To get more precise matting result, we propose a deep learning based algorithm. The network called very deep residual network (VDRN). The first stage is designed to capture entire foreground object by an improved encoder-decoder architecture. It consists of a deep residual encoder and a sophisticated decoder. The second stage is a fully residual convolutional network used for recovering fine structure like hair. Experimental results show our algorithm can tackle complicated foreground textures even it has similar color with background. We evaluate our algorithm on alphamatting.com online benchmark, and Composition-1k dataset. The results demonstrate our method outperforms previous methods, especially in tackling fine structure.
Robust principal component analysis with adaptive neighbors

Suppose certain data points are overly contaminated, then the existing principal component analysis (PCA) methods are frequently incapable of filtering out and eliminating the excessively polluted ones, which potentially lead to the functional degeneration of the corresponding models. To tackle the issue, we propose a general framework namely robust weight learning with adaptive neighbors (RWL-AN), via which adaptive weight vector is automatically obtained with both robustness and sparse neighbors. More significantly, the degree of the sparsity is steerable such that only exact k well-fitting samples with least reconstruction errors are activated during the optimization, while the residual samples, i.e., the extreme noised ones are eliminated for the global robustness. Additionally, the framework is further applied to PCA problem to demonstrate the superiority and effectiveness of the proposed RWL-AN model.
Arabic Dialect Identification for Travel and Twitter Text

This paper presents the results of the experiments done as a part of MADAR Shared Task in WANLP 2019 on Arabic Fine-Grained Dialect Identification.Dialect Identification is one of the prominent tasks in the field of Natural language processing where the subsequent language modules can be improved based on it.We explored the use of different features like char, word n-gram, language model probabilities, etc on different classifiers.Results show that these features help to improve dialect classification accuracy.Results also show that traditional machine learning classifier tends to perform better when compared to neural network models on this task in a low resource setting.
The University of Maryland’s Kazakh-English Neural Machine Translation System at WMT19

This paper describes the University of Maryland's submission to the WMT 2019 Kazakh to English news translation task.We study the impact of transfer learning from another lowresource but related language.We experiment with different ways of encoding lexical units to maximize lexical overlap between the two language pairs, as well as back-translation and ensembling.The submitted system improves over a Kazakh-only baseline by +5.45 BLEU on newstest2019.
Coding Prior Based High Efficiency Restoration for Compressed Video

Lossy compression introduces complex compression artifacts such as the blocking, ringing and blurring artifacts, making decoded videos unpleasant for human visual system. In this paper, we propose a coding prior based high efficiency restoration algorithm to remove these compression artifacts. To improve the quality of the restored compressed videos, we take full advantage of side information from coding streams as coding prior which is ignored or not fully exploited by most existing post-processing methods. In particular, the unfiltered frames and the prediction frames are derived from coding streams which are utilized as coding priors and a high efficiency neural network is designed for these information to improve the overall quality of restored videos. Extensive experimental results on HEVC coding streams demonstrate that our proposed method can significantly improve both the objective and subjective quality of compressed videos.
Separating the Wheat from the Chaff: Comparative Visual Cues for Transparent Diagnostics of Competing Models

Experts in data and physical sciences have to regularly grapple with the problem of competing models. Be it analytical or physics-based models, a cross-cutting challenge for experts is to reliably diagnose which model outcomes appropriately predict or simulate real-world phenomena. Expert judgment involves reconciling information across many, and often, conflicting criteria that describe the quality of model outcomes. In this paper, through a design study with climate scientists, we develop a deeper understanding of the problem and solution space of model diagnostics, resulting in the following contributions: i) a problem and task characterization using which we map experts' model diagnostics goals to multi-way visual comparison tasks, ii) a design space of comparative visual cues for letting experts quickly understand the degree of disagreement among competing models and gauge the degree of stability of model outputs with respect to alternative criteria, and iii) design and evaluation of MyriadCues, an interactive visualization interface for exploring alternative hypotheses and insights about good and bad models by leveraging comparative visual cues. We present case studies and subjective feedback by experts, which validate how MyriadCues enables more transparent model diagnostic mechanisms, as compared to the state of the art.