Reviewerly/exHarmony huggingface dataset:
Modeling and Clustering Positive Vectors via Nonparametric Mixture Models of Liouville Distributions

In this article, we propose an effective mixture model-based approach to modeling and clustering positive data vectors. Our mixture model is based on the inverted Beta-Liouville (IBL) distribution which is extracted from the family of Liouville distributions. To cope with the problem of determining the appropriate number of clusters in our approach, a nonparametric Bayesian framework is used to extend the IBL mixture to an infinite mixture model in which the number of clusters is assumed to be infinite initially and will be inferred automatically during the learning process. To optimize the proposed model, we propose a convergence-guaranteed learning algorithm based on the averaged collapsed variational Bayes inference that can effectively learn model parameters with closed-form solutions. The effectiveness of the proposed infinite IBL mixture model for modeling and clustering positive vectors is validated through both synthetic and real-world data sets.
An Exploratory Study of the Relationship Between Software Test Smells and Fault-Proneness

Test smells have been defined as indicators of poorly designed tests. Their presence negatively affects the maintainability of a test suite as well as the production code. Despite the many studies that address the negative impacts of various test smells, until now there has been no empirical evidence considering the relation between the evolution of test smells and that of faults in the production code. This paper presents such evidence. It presents a case study of data collected from 28 versions of Apache Ant that include a total of 4,447 unit tests. Three key results arise from the data. First, the absolute number of test smells increases as Apache Ant evolves. Second, some test smells are positively correlated with the existence of faults in the production code. Finally, our results show that it is possible to predict faults in the production code based on the existence of test smells in the code’s unit tests. In addition, the resulting prediction model is more accurate at predicting high-severity faults than low-severity faults. This is an important result as it enables an engineer to focus preventative maintenance efforts, applied to the production code, using test smells found in the unit tests.
Principal component analysis of body measurements and body indices and their correlation with body weight in Katjang does of Indonesia

The present study was aimed to investigate the body measurements and body indices of Kacang do at Bone Bolango Regency of Indonesia.A total of 85 does (3years age) were recorded and analyzed by principal component analysis (PCA) to explain the body measurements and body indices.Eleven body measurements of face length (FL), face width (FW), face height (FH), ear length (EL), ear width (EW), body length (BL), withers height (WH), chest width (CW), chest depth (CD), chest girth (CG), cannon bone circumference (CC) and eleven body indices of cephalic index (CpI) length index (LI), depth index (DI), body index (BI), conformation index (CI), proportionality (Pr), relative depth of thorax (RDT), dactyl thorax index (DTI), thoracic development (TD), area index (AI), and relative cannon thickness index (RCTI) were calculated in this study.Four components of body measurements (FH, BL, CD, CG) and three components of body indices (CI, TD, RCTI) were identified as the first component (PC1) for Katjang does.The result suggests that the highly R2 value (0.61< R2<0.80) were obtained in linear regression of BW with CG (0.69) or PC1 (0.71) as predictors.The R2 value in linear regression of BW using body indices through PC1 and PC2 as predictors was showed moderate category (R2=0.72).
DCCL: A Benchmark for Cervical Cytology Analysis

Medical imaging analysis has witnessed impressive progress in recent years thanks to the development of large-scale labeled datasets. However, in many fields, including cervical cytology, a large well-annotated benchmark dataset remains missing. In this paper, we introduce by far the largest cervical cytology dataset, called Deep Cervical Cytological Lesions (referred to as DCCL). DCCL contains 14,432 image patches with around $$1{,}200\times 2{,}000$$ pixels cropped from 1,167 whole slide images collected from four medical centers and scanned by one of the three kinds of digital slide scanners. Besides patch level labels, cell level labels are provided, with 27,972 lesion cells labeled based on The 2014 Bethesda System and the bounding box by six board-certified pathologists with eight years of experience on the average. We also use deep learning models to generate the baseline performance for lesion cell detection and cell type classification on DCCL. We believe this dataset can serve as a valuable resource and platform for researchers to develop new algorithms and pipelines for advanced cervical cancer diagnosis and prevention.
Personas Design for Conversational Systems in Education

This research aims to explore how to enhance student engagement in higher education institutions (HEIs) while using a novel conversational system (chatbots). The principal research methodology for this study is design science research (DSR), which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This paper focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, specifically the K-means clustering technique. Data analysis is conducted using two datasets. Three methods are used to find the K-values: the elbow, gap statistic, and silhouette methods. Subsequently, the silhouette coefficient is used to find the optimal value of K. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically K-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations. It will cover building SEFMs, building tailored interaction models for these personas and then evaluating them using chatbot technology.
Assisted Grasping in Individuals with Tetraplegia: Improving Control through Residual Muscle Contraction and Movement

Individuals who sustained a spinal cord injury often lose important motor skills, and cannot perform basic daily living activities. Several assistive technologies, including robotic assistance and functional electrical stimulation, have been developed to restore lost functions. However, designing reliable interfaces to control assistive devices for individuals with C4–C8 complete tetraplegia remains challenging. Although with limited grasping ability, they can often control upper arm movements via residual muscle contraction. In this article, we explore the feasibility of drawing upon these residual functions to pilot two devices, a robotic hand and an electrical stimulator. We studied two modalities, supra-lesional electromyography (EMG), and upper arm inertial sensors (IMU). We interpreted the muscle activity or arm movements of subjects with tetraplegia attempting to control the opening/closing of a robotic hand, and the extension/flexion of their own contralateral hand muscles activated by electrical stimulation. Two groups were recruited: eight subjects issued EMG-based commands; nine other subjects issued IMU-based commands. For each participant, we selected at least two muscles or gestures detectable by our algorithms. Despite little training, all participants could control the robot’s gestures or electrical stimulation of their own arm via muscle contraction or limb motion.
Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events.In this study, we aim to develop a deep-learning-based natural language processing (NLP) system to automatically detect hypoglycemic events from EHR notes. Our model is called the High-Performing System for Automatically Detecting Hypoglycemic Events (HYPE).Domain experts reviewed 500 EHR notes of diabetes patients to determine whether each sentence contained a hypoglycemic event or not. We used this annotated corpus to train and evaluate HYPE, the high-performance NLP system for hypoglycemia detection. We built and evaluated both a classical machine learning model (ie, support vector machines [SVMs]) and state-of-the-art neural network models.We found that neural network models outperformed the SVM model. The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03).Despite the challenges posed by small and highly imbalanced data, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE can be used for EHR-based hypoglycemia surveillance and population studies in diabetes patients.
CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines

Cloud Computing is becoming the leading paradigm for executing scientific and engineering workflows. The large-scale nature of the experiments they model and their variable workloads make clouds the ideal execution environment due to prompt and elastic access to huge amounts of computing resources. Autoscalers are middleware-level software components that allow scaling up and down the computing platform by acquiring or terminating virtual machines (VM) at the time that workflow tasks are being scheduled. In this work we propose a novel online multi-objective autoscaler for workflows denominated Cloud Multi-objective Intelligence (CMI), which aims at the minimization of makespan, monetary cost and the potential impact of errors derived from unreliable VMs. Besides, this problem is subject to monetary budget constraints. CMI is responsible for periodically solving the autoscaling problems encountered along with the execution of a workflow. Simulation experiments on four well-known workflows exhibit that CMI significantly outperforms a state-of-the-art autoscaler of similar characteristics called Spot Instances Aware Autoscaling (SIAA). These results convey a solid base for deepening in the study of other meta-heuristic methods for autoscaling workflow applications using cheap but unreliable infrastructures.
Representative elementary area of shale at the mesoscopic scale

The Representative Elementary Area (REA) of two shales (Callovo-Oxfordian claystone and Tournemire argillite) that are actively studied in the framework of the deep disposal of radioactive waste have been estimated from two mineral maps by classical methods (i.e., the box-counting and statistical approaches) and by different microstructural descriptors (i.e., the two-point probability function, lineal path function, percolation length, and variogram). The classical box-counting method provides estimates of the REA size of the clay fraction in the range from 129 μm to 441 μm, consistent with estimates obtained from the literature on other shales. However, these estimates show an extreme sensitivity to the chosen ε threshold or error and a wide scatter, thereby bringing the statistical homogeneity of both maps into question. Although the two-point probability function and lineal path function infer lower bounds of the REA size, these microstructural descriptors are relevant to demonstrate the microstructural anisotropy of both shales due to the alignment of nonclay grains parallel to bedding at the study scale. The results from the two-point probability function and variogram undoubtedly confirm that the Tournemire mineral map is not statistically homogeneous with regard to its mineral composition. This aspect it makes difficult to interpret the results and even questions the REA size determination of this particular map. Finally, our set of results allow us to recommend the use of the two-point probability function and variogram to preliminarily validate the statistical homogeneity of maps under study before calculating the REA size using conventional methods, e.g. the box-counting and statistical approaches.
Differential Privacy on Finite Computers

We consider the problem of designing and analyzing differentially private algorithms that can be implemented on discrete models of computation in strict polynomial time, motivated by known attacks on floating point implementations of real-arithmetic differentially private algorithms (Mironov, CCS 2012) and the potential for timing attacks on expected polynomial-time algorithms.&#x0D; As a case study, we examine the basic problem of approximating the histogram of a categorical dataset over a possibly large data universe X.&#x0D; The classic Laplace Mechanism (Dwork, McSherry, Nissim, Smith, TCC 2006 and J. Privacy \&amp; Confidentiality 2017) does not satisfy our requirements, as it is based on real arithmetic, and natural discrete analogues, such as the Geometric Mechanism (Ghosh, Roughgarden, Sundarajan, STOC 2009 and SICOMP 2012), take time at least linear in |X|, which can be exponential in the bit length of the input.&#x0D; &#x0D; In this paper, we provide strict polynomial-time discrete algorithms for approximate histograms whose simultaneous accuracy (the maximum error over all bins) matches that of the Laplace Mechanism up to constant factors, while retaining the same (pure) differential privacy guarantee.&#x0D; One of our algorithms produces a sparse histogram as output.&#x0D; Its ``"per-bin accuracy" (the error on individual bins) is worse than that of the Laplace Mechanism by a factor of log|X|, but we prove a lower bound showing that this is necessary for any algorithm that produces a sparse histogram.&#x0D; A second algorithm avoids this lower bound, and matches the per-bin accuracy of the Laplace Mechanism, by producing a compact and efficiently computable representation of a dense histogram; it is based on an (n+1)-wise independent implementation of an appropriately clamped version of the Discrete Geometric Mechanism.
Popularity Prediction of Social Media based on Multi-Modal Feature Mining

Popularity prediction of social media becomes a more attractive issue in recent years. It consists of multi-type data sources such as image, meta-data, and text information. In order to effectively predict the popularity of a specified post in the social network, fusing multi-feature from heterogeneous data is required. In this paper, a popularity prediction framework for social media based on multi-modal feature mining is presented. First, we discover image semantic features by extracting their image descriptions generated by image captioning. Second, an effective text-based feature engineering is used to construct an effective word-to-vector model. The trained word-to-vector model is used to encode the text information and the semantic image features. Finally, an ensemble regression approach is proposed to aggregate these encoded features and learn the final regressor. Extensive experiments show that the proposed method significantly outperforms other state-of-the-art regression models. We also show that the multi-modal approach could effectively improve the performance in the social media prediction challenge.
Mediation role of responsible innovation between CSR strategy and competitive advantage: Empirical evidence for the case of Saudi Arabia enterprises

This study examines whether responsible innovation can be an important channel through which the corporate social responsibility (CSR) strategy influences the competitive advantage of the firm.Specifically, we tried to test the role of firm size and activity sector as conditional factors in the mediation of responsible innovation between CSR strategy and the competitive advantage for the case of Saudi Arabia enterprises.Based on structural equation modelling (SEM), our results show that responsible innovation is a key element of the sustainable business model since it determines the success of CSR strategy in boosting the competitive advantage of firms.In this model, the mediation of responsible innovation is moderated differently by the firm size and the activity sector, and it is dependent on the choice between strategic or reactive CSR strategy.Within this context, we also find that the indirect effects of the CSR strategy on competitive advantage are moderated by the firm size in the case of reactive CSR, whereas by the activity sector in case of strategic CSR.The originality of this paper is that it examines whether the competitive advantage of firms in a developing country is more dependent on the reactive CSR strategy or the strategic CSR strategy.It also proposes an empirical investigation into the conditional factors of responsible innovation and their effects on the relationship between CSR strategy and competitive advantage.
Modeling and prediction of sulfuric acid digestion analyses data from PXRF spectrometry in tropical soils

Sulfuric acid digestion analyses (SAD) provide useful information to environmental studies, in terms of the geochemical balance of nutrients, parent material uniformity, nutrient reserves for perennial crops, and mineralogical composition of the soil clay fraction. Yet, these analyses are costly, time consuming, and generate chemical waste. This work aimed at predicting SAD results from portable X-ray fluorescence (pXRF) spectrometry, which is proposed as a "green chemistry" alternative to the current SAD method. Soil samples developed from different parent materials were analyzed for soil texture and SAD, and scanned with pXRF. The SAD results were predicted from pXRF elemental analyses through simple linear regressions, stepwise multiple linear regressions, and random forest algorithm, with and without incorporation of soil texture data. The modeling was developed with 70 % of the data, while the remaining 30 % was used for validation through calculation of R2, adjusted R2, root mean square error, and mean error. Simple linear regression can accurately predict SAD results of Fe2O3 (R2 0.89), TiO2 (R2 0.96), and P2O5 (R2 0.89). Stepwise regressions provided accurate predictions for Al2O3 (R2 0.87) and Ki - molar weathering index (SiO2/Al2O3) (R2 0.74) by incorporating soil texture data, as well as for SiO2 (R2 0.61). Random forest also provided adequate predictions, especially for Fe2O3 (R2 0.95), and improved results of Kr - molar weathering index (SiO2/(Al2O3 + Fe2O3)) (R2 0.66), by incorporation of soil texture data. Our findings showed that the SAD results could be accurately predicted from pXRF data, decreasing costs, time and the production of laboratory waste.
Development of an In-Situ Laser Machining System Using a Three-Dimensional Galvanometer Scanner

In this study, a three-dimensional (3D) in-situ laser machining system integrating laser measurement and machining was built using a 3D galvanometer scanner equipped with a side-axis industrial camera. A line structured light measurement model based on a galvanometer scanner was proposed to obtain the 3D information of the workpiece. A height calibration method was proposed to further ensure measurement accuracy, so as to achieve accurate laser focusing. In-situ machining software was developed to realize time-saving and labor-saving 3D laser processing. The feasibility and practicability of this in-situ laser machining system were verified using specific cases. In comparison with the conventional line structured light measurement method, the proposed methods do not require light plane calibration, and do not need additional motion axes for 3D reconstruction; thus they provide technical and cost advantages. The in-situ laser machining system realizes a simple operation process by integrating measurement and machining, which greatly reduces labor and time costs.
Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.
Detecting Code Smells using Deep Learning

A smell in software refers to a symptom introduced in software artifacts such as architecture, design or code. A code smell can potentially cause deeper and serious problems, while dealing with mainly non-functional requirements such as testability, maintainability, extensibility and scalability. The detection of code smell is an essential step in the refactoring process, which facilitates non functional requirements in a software. The existing approaches for detecting code smells use detection rules or standards using a combination of different object-oriented metrics. Although a variety of code smell detection tools have been developed, they still have limitations and constraints in their capabilities. The most well-known object-oriented metrics are considered to identify the presence of smells in software. This paper proposes a deep learning based approach to detect two code smells (Brain Class and Brain Method). The proposed system uses thirty open source Java projects, which are shared by many users in GitHub repositories. The dataset of these Java projects is partitioned into mutually exclusive training and test sets. Our experiments have demonstrated high accuracy results for both the code smells.
Privacy-Aware Text Rewriting

Biased decisions made by automatic systems have led to growing concerns in research communities.Recent work from the NLP community focuses on building systems that make fair decisions based on text.Instead of relying on unknown decision systems or human decisionmakers, we argue that a better way to protect data providers is to remove the trails of sensitive information before publishing the data.In light of this, we propose a new privacy-aware text rewriting task and explore two privacyaware back-translation methods for the task, based on adversarial training and approximate fairness risk.Our extensive experiments on three real-world datasets with varying demographical attributes show that our methods are effective in obfuscating sensitive attributes.We have also observed that the fairness risk method retains better semantics and fluency, while the adversarial training method tends to leak less sensitive information.