Reviewerly/exHarmony huggingface dataset:
A New Kernel Extreme Learning Machine Framework for Somatization Disorder Diagnosis

In this paper, we present a novel predictive model based on the kernel extreme learning machine (KELM) to predict the somatization disorder.Since the classification performance of KELM is largely affected by its two parameters, it is necessary to set two optimal parameters for it to ensure high prediction accuracy.In order to improve the accuracy of the prediction model, a new optimization strategy is used to optimize the parameters of KELM.The new optimization strategy adopted grey wolf optimization algorithm to generate high-quality initial populations for moth-flame optimization algorithm, called GWOMFO.The effectiveness of GWOMFO was first verified on the ten classic benchmark functions.The results show that the GWOMFO has provided consistently better results than other competitive algorithms.This reveals that high-quality initial populations can significantly improve the global search ability and convergence speed of search agents.Furthermore, the proposed GWOMFO-based KELM model was compared with other models, including a model based on GWO (GWO-KELM), a model based on MFO (MFO-KELM), a model based on genetic algorithm (GA-KELM), a model based on grid search method (Grid-KELM), a random forest, and the support vector machines, on the somatization disorder dataset.The simulation results show that the developed framework cannot only achieve higher prediction accuracy than other models but also has better robustness.
SoS TextVis: An Extended Survey of Surveys on Text Visualization

Text visualization is a rapidly growing sub-field of information visualization and visual analytics. There are many approaches and techniques introduced every year to address a wide range of challenges and analysis tasks, enabling researchers from different disciplines to obtain leading-edge knowledge from digitized collections of text. This can be challenging particularly when the data is massive. Additionally, the sources of digital text have spread substantially in the last decades in various forms, such as web pages, blogs, twitter, email, electronic publications, and digitized books. In response to the explosion of text visualization research literature, the first text visualization survey article was published in 2010. Furthermore, there are a growing number of surveys that review existing techniques and classify them based on text research methodology. In this work, we aim to present the first Survey of Surveys (SoS) that review all of the surveys and state-of-the-art papers on text visualization techniques and provide an SoS classification. We study and compare the 14 surveys, and categorize them into five groups: (1) Document-centered, (2) user task analysis, (3) cross-disciplinary, (4) multi-faceted, and (5) satellite-themed. We provide survey recommendations for researchers in the field of text visualization. The result is a very unique, valuable starting point and overview of the current state-of-the-art in text visualization research literature.
Ant colony optimization for text feature selection in sentiment analysis

In sentiment analysis, the high dimensionality of the feature vector is a key problem because it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimum subset of features. To solve this problem, this study proposes a new text feature selection method that uses a wrapper approach, integrated with ant colony optimization (ACO) to guide the feature selection process. It also uses the k-nearest neighbour (KNN) as a classifier to evaluate and generate a candidate subset of optimum features. To test the subset of optimum features, algorithm dependency relations were used to find the relationship between the feature and the sentiment word in customer reviews. The output of the feature subset, which was derived using the proposed ACO-KNN algorithm, was used as an input to identify and extract sentiment words from sentences in customer reviews. The resulting relationship between features and sentiment words was tested and evaluated to determine the accuracy based on precision, recall, and F-score. The performance of the proposed ACO-KNN algorithm on customer review datasets was evaluated and compared with that of two hybrid algorithms from the literature, namely, the genetic algorithm with information gain and information gain with rough set attribute reduction. The results of the experiments showed that the proposed ACO-KNN algorithm was able to obtain the optimum subset of features and can improve the accuracy of sentiment classification.
How Pair Programming Influences Team Performance: The Role of Backup Behavior, Shared Mental Models, and Task Novelty

Many organizations and software firms these days use pair programming to meet the challenges posed by digital transformations and to meet customer needs. They use pair programming to become more agile and responsive to their competitive environment. Although some studies suggest that pair programming can potentially increase the quality of developed software code and the satisfaction and confidence of developers, very little is known as to how pair programming affects team performance. Therefore, we conducted an empirical study at one of the largest enterprise software firms that serves a large number of Fortune 500 companies, and we collected data from the software developers, Scrum masters, and product owners of 62 software development teams. Our findings show that pair programming affects team performance positively by changing cognitive structures and behavioral patterns in software teams. Pair programming helps team members develop shared mental models and, as a consequence, increases backup behavior among team members. Backup behavior is particularly valuable for teams facing high task novelty. Our findings have critical implications for organizations and senior managers as they lead their digital transformation efforts where they often rely on autonomous teams for providing digital innovation. We show that pair programming can be a key ingredient for high-performance teams. Establishing the practice of pair programming promises to be particularly valuable for those teams that lack shared mental models and teams whose members fail to provide each other backup.
Walking with adaptive augmented reality workspaces

Mobile augmented reality may eventually replace our smartphones as the primary way of accessing information on the go. However, current interfaces provide little support to walking and to the variety of actions we perform in the real world. To achieve its full potential, augmented reality interfaces must support the fluid way we move and interact in the physical world. We explored how different adaptation strategies can contribute towards this goal. We evaluated design alternatives through contextual studies and identified the key interaction patterns that interfaces for walking should support. We also identified desirable properties of adaptation-based interface techniques, which can be used to guide the design of the next-generation walking-centered augmented reality workspaces.
Complete coverage path planning for aerial vehicle flocks deployed in outdoor environments

This paper presents a system for Complete Coverage Path Planning to survey an outdoor area with a flock of Unmanned Aerial Vehicles (UAVs). The proposed system is able to manage the presence of no-fly zones, by decomposing the area into smaller subregions via a Morse-based decomposition approach. Furthermore, negotiation for UAV-subregion assignment is managed, taking into account environment geometry and both starting and desired final positions for the UAVs, once the coverage mission is completed. The back-and-forth motion is chosen as coverage pattern inside each subregion. All these features have been implemented with an integrated software framework, capable of working with georeferenced maps and data structures, thus obtaining a software tool compatible with commonly used geographic information systems.
Deep Attributed Network Embedding by Preserving Structure and Attribute Information

Network embedding aims to learn distributed vector representations of nodes in a network. The problem of network embedding is fundamentally important. It plays crucial roles in many applications, such as node classification, link prediction, and so on. As the real-world networks are often sparse with few observed links, many recent works have utilized the local and global network structure proximity with shallow models for better network embedding. In reality, each node is usually associated with rich attributes. Some attributed network embedding models leveraged the node attributes in these shallow network embedding models to alleviate the data sparsity issue. Nevertheless, the underlying structure of the network is complex. What is more, the connection between the network structure and node attributes is also hidden. Thus, these previous shallow models fail to capture the nonlinear deep information embedded in the attributed network, resulting in the suboptimal embedding results. In this paper, we propose a deep attributed network embedding framework to capture the complex structure and attribute information. Specifically, we first adopt a personalized random walk-based model to capture the interaction between network structure and node attributes from various degrees of proximity. After that, we construct an enhanced matrix representation of the attributed network by summarizing the various degrees of proximity. Then, we design a deep neural network to exploit the nonlinear complex information in the enhanced matrix for network embedding. Thus, the proposed framework could capture the complex attributed network structure by preserving both the various degrees of network structure and node attributes in a unified framework. Finally, empirical experiments show the effectiveness of our proposed framework on a variety of network embedding-based tasks.
Digitalization and school leadership: on the complexity of leading for digitalization in school

Purpose Digitalization has permeated society and schools. In this process, focus has turned to the importance of school leaders in their leadership for digitalization. The purpose of this paper is to explore how school leaders understand digitalization and the digital competencies needed in leading for digitalization in Swedish schools. Design/methodology/approach Open questions from reflective learning journals ( n =32) and interviews ( n =8) conducted with school leaders were used to explore how school leaders understand digitalization and the digital competencies needed in leading for digitalization. Findings The findings show that school leaders see digitalization as a wide and complex concept including technical, pedagogical, administrational and organizational challenges at all levels of the school organization. Practical implications It appears that the role of the school leader, as a complex task, has become more complex as a result of digitalization. How time, resources and professional development are made available to support school leaders in their work with leadership for digitalization in order to support teachers’ and students’ learning. Originality/value This paper contributes to the area of school leadership and digitalization. The research contribution is of interest for school leaders and school organizers striving to implement and advance digitalization in schools. This also concerns the prioritization of digitalization as one of many important areas in schools as organizations.
Sliding Window Based Support Vector Machine System for Classification of Breast Cancer Using Histopathological Microscopic Images

Breast cancer is one of the most common cancers in women's community, which is responsible for millions of death cases. The early diagnosis of breast cancer in its early stages increases the chances of healing; the most common current diagnosis methods are based on digital mammogram images and histopathological images. Automatic diagnosis of breast cancer and classifying the type of cancer is recommended to decrease the error that can be caused by humans. Recently, many systems have been developed to diagnose breast cancer by extracting textural and non-textural features from digital mammograms or histopathological images. This paper proposes a new sliding window technique for feature extraction where the local binary pattern (LBP) features are used. In this technique, each image produces 25 sliding windows. Features extracted from each window are saved and used to build a Support Vector Machine (SVM) classifier. The SVM classifier is used to classify each image into benign and malignant based on its most common windows classes. The system can be used to localize the cancerous tissues from the whole histopathological image. The proposed method achieved an overall accuracy of 91.12%, sensitivity of 85.22%, and specificity of 94.01%. Which is considered high when compared with other systems in the literature. The system can extend to extract more features and a comparison between different machine learning algorithms can performed.
COMB -- Shape as a Meaningful Element of Interaction

This paper introduces a tangible user interface (TUI) concept designed for child-oriented musical interaction and education by the name of COMB. The interaction concept of the interface is based upon the natural behavior and metaphors found in children's play during construction with building-blocks. This paradigm is used to increase the accessibility of the otherwise expert-focused digital and electronic music to children. We evaluated our prototype during two different study setups. We found preliminary indications that this concept fosters imitation during learning. Therefore, the usage of shape as a meaningful element of interaction could be a promising design strategy for interfaces addressing children. In this paper we present the theoretical foundation of the concept as well as technical details of the prototype. Furthermore, we discuss how this concept can be applied to increase accessibility of technology in various other domains.
Human-Robot-Interaction for mobile industrial robot teams

Due to high variants and small lot sizes, a conventional use of robots behind fences does not fulfill the requirements of today's production anymore. Therefore, the importance of mobile robot systems is increasing. In order to best use the capabilities of these robots, teams consisting of several robots or a robot and a human are built. For a cooperative work between robot and human, interaction systems are necessary. This paper describes an interaction concept, where the channel to the robot system is realized through gestures and eye tracking and the channel coming from the robot system is realized with projection.
Exploring Language Style in Chatbots to Increase Perceived Product Value and User Engagement

Chatbots that can automatically answer customer requests have become a common feature on e-commerce Web sites. There are many factors that might affect overall customer satisfaction with such services. We explore how adding language style to e-commerce chatbots can be used to increase user satisfaction, perceived product value, user interest in a product, and user engagement with a chatbot service. We conducted an experimental pilot study, where two chatbots were used to sell theatre tickets: one communicating in modern English and one in a Shakespearean-style dialect. 169 participants interacted with a randomly-assigned version of the chatbot. The results indicate that the bot talking in modern English showed a significantly higher user satisfaction, whereas the Shakespearean-styled chatbot showed higher user engagement and perceived product value. It was also found that the modern chatbot version was more often referred to as being 'easy to use', whereas the Shakespearean chatbot version was more often referred to as being 'fun to use'.
Parameter estimation of atmospheric continuous-variable quantum key distribution

Atmospheric effects are the chief threats to the quantum properties of propagating quantum signals and may degrade the performance of quantum key distribution seriously. As one of the most important parts of continuous-variable quantum key distribution (CVQKD), a parameter estimation method has not been specially proposed in an atmospheric channel, and usually the security analysis is based on the assumption that the relevant parameters, especially the excess noise, have been previously obtained. Here we propose a parameter estimation method for Gaussian modulated coherent state continuous-variable quantum key distribution over the atmospheric link, and we investigate the impact of the atmospheric channel on the estimated values of the parameters. Based on this method, we study theoretically the effect of link fluctuations on the achievable secret key rate of CVQKD under different practical transmitted conditions. The results show that this method is unified with the physical model and can effectively resist entanglement-distillation attack. The proposed method fills in the blank of parameter estimation for implementation of practical atmospheric CVQKD.
Decomposition as Design

HCI in recent years has shown an increasing interest in decentering humans in design. This decentering is a response to concerns about environmental sustainability, technology obsolescence, and consumerism. Scholars have introduced theoretical notions such as natureculture from feminist technoscience. Yet how such theories translate into material design practices remains an open question. This research seeks to broaden the repertoire of nonanthropocentric design practices in HCI. Specifically, it draws on the natural processes of decomposition as a creative approach to develop and test design tactics. To do so, we curate and critique hundreds of examples of decomposition in architecture, design, textile, crafting, and food making. We observe that decomposition often depends on what we call a "scaffold," and we further propose four variants of it as design tactics: fragmenting, aging, liberating, and tracing. We then tested the tactics over a period of four months in a ceramics studio using diverse materials, with a mixture of successes and failures. We conclude by reflecting on how the design tactics might be deployed in nonanthropocentric HCI/design.
A Study on the Interplay between Pull Request Review and Continuous Integration Builds

Modern code review (MCR) is nowadays well-adopted in industrial and open source projects. Recent studies have investigated how developers perceive its ability to foster code quality, developers' code ownership, and team building. MCR is often being used with automated quality checks through static analysis tools, testing or, ultimately, through automated builds on a Continuous Integration (CI) infrastructure. With the aim of understanding how developers use the outcome of CI builds during code review and, more specifically, during the discussion of pull requests, this paper empirically investigates the interplay between pull request discussion and the use of CI by means of 64,865 pull request discussions belonging to 69 open source projects. After having analyzed to what extent a build outcome in uences the pull request merger, we qualitatively analyze the content of 857 pull request discussions. Also, we complement such an analysis with a survey involving 13 developers. While pull requests with passed build have a higher chance of being merged than failed ones, and while survey participants confirmed this quantitative finding, other process-related factors play a more important role in the pull request merge decision. Also, the survey participants point out cases where a pull request can be merged in presence of a CI failure, e.g., when a new pull request is opened to cope with the failure, when the failure is due to minor static analysis warnings. The study also indicates that CI introduces extra complexity, as in many pull requests developers have to solve non-trivial CI configuration issues.
Stereo-camera calibration for large-scale DIC measurements with active phase targets and planar mirrors

We propose an easy-to-implement yet accurate calibration method for large-scale 3D measurements that makes use of a regular-sized phase target and two planar mirrors. Being insensitive to severe defocus, the phase target is placed as to span a large depth within the field of view (FOV) of each camera for accurate intrinsic calibration. Extrinsic calibration is achieved by placing the phase target in the FOV of a short-range virtual stereo-system generated by the mirrors. Results from 3D shape and deformation measurements demonstrate that the proposed method is capable to operate within a working volume of 3 m × 2 m × 1.8 m with an error < 0.1% of the FOV thus opening to new possibilities for large-scale measurements in mechanical and civil engineering applications.
Wrapper Framework for Test-Cost-Sensitive Feature Selection

Feature selection is an optional preprocessing procedure and is frequently used to improve the classification accuracy of a machine learning algorithm by removing irrelevant and/or redundant features. However, in many real-world applications, the test cost is also required for making optimal decisions, in addition to the classification accuracy. To the best of our knowledge, thus far, few studies have been conducted on test-cost-sensitive feature selection (TCSFS). In TCSFS, the objectives are twofold: 1) to improve the classification accuracy and 2) to decrease the test cost. Therefore, in fact, it constitutes a multiobjective optimization problem. In this paper, we transformed this multiobjective optimization problem into a single-objective optimization problem by utilizing a new evaluation function and in this paper, we propose a new general wrapper framework for TCSFS. Specifically, in our proposed framework, we add a new term to the evaluation function of a wrapper feature selection method so that the test cost of measuring features is taken into account. We experimentally tested our proposed framework, using 36 classification problems from the University of California at Irvine (UCI) repository, and compared it to some other state-of-the-art feature selection frameworks. The experimental results showed that our framework allows users to select an optimal feature subset with the minimal test cost, while simultaneously maintaining a high classification accuracy.
Translation-Based Sequential Recommendation for Complex Users on Sparse Data

Sequential recommendation is one of the main tasks in recommender systems, where the next action (e.g., purchase, visit, and click) of the user is predicted based on his/her past sequence of actions. Translating Embeddings is a knowledge graph completion approach which was recently adapted to a translation-based sequential recommendation (TransRec) method. We observe a flaw of TransRec when handling complex translations, which hinders it from generating accurate suggestions. In view of this, we propose a translation-based recommender for complex users (CTransRec), which utilizes category-specific projection and temporal dynamic relaxation. Using our proposed Margin-based Pairwise Bayesian Personalized Ranking and Time-Aware Negative Sampling, CTransRec outperforms state-of-the-art methods for sequential recommendation on extremely sparse data. The superiority of CTransRec, which is confirmed by our extensive experiments on both public data and real data obtained from the industry, comes from not only the additional information used in training but also the fact that CTransRec makes good use of this additional information to model the complex translations.