Reviewerly/exHarmony huggingface dataset:
Creating Manageable Persona Sets from Large User Populations

Creating personas from actual online user information is an advantage of the data-driven persona approach. However, modern online systems often provide big data from millions of users that display vastly different behaviors, resulting in possibly thousands of personas representing the entire user population. We present a technique for reducing the number of personas to a smaller number that efficiently represents the complete user population, while being more manageable for end users of personas. We first isolate the key user behaviors and demographical attributes, creating thin personas, and we then apply an algorithmic cost function to collapse the set to the minimum needed to represent the whole population. We evaluate our approach on 26 million user records of a major international airline, isolating 1593 personas. Applying our approach, we collapse this number to 493, a 69% decrease in the number of personas. Our research findings have implications for organizations that have a large user population and desire to employ personas.
Study of Augmented Reality Applications Use in Education and Its Effect on the Academic Performance

Augmented reality (AR) applications can be used in almost all education and training environments. In this study, it reveals the relationship between perceived usefulness, utility and attitudes regarding the use of AR applications in educational environments as well as the relationship between attitude levels and academic achievements. It also reveals the effect of AR application use on academic achievement in education. According to the findings obtained in the study, the followings have been found: the perceived ease of use of students regarding AR applications in educational environments has a strong positive effect on perceived benefit; the perceived benefit and ease of use influence the attitude levels strongly in the positive direction; there is no semantic relation between attitude levels and academic achievement; and the use of AR applications in educational processes increases the academic achievement of students.
Neural sentence fusion for diversity driven abstractive multi-document summarization

The lack of multi-document based models and the inaccuracy in representing multiple long documents into a fixed size vector inspired us to solve abstractive multi-document summarization. Also, there is lack of good multi-document based human-authored datasets to train any encoder-decoder models. To overcome this, we have designed complementary models for two different tasks such as sentence clustering and neural sentence fusion. In this work, we minimize the risk of producing incorrect fact by encoding a related set of sentences as an input to the encoder. We have applied our complementary models to implement a full abstractive multi-document summarization system which simultaneously considers importance, coverage, and diversity under a desired length limit. We conduct extensive experiments for all the proposed models which bring significant improvements over the state-of-the-art methods across different evaluation metrics.
The Effect of Reading Code Aloud on Comprehension

In recent times, programming is increasingly taught to younger students in schools. While learning programming is known to be difficult, we can lighten the learning experience of this age group by adopting pedagogies that are common to them, but not as common in CS education. One of these pedagogies is Reading Aloud (RA), a familiar strategy when young children and beginners start learning how to read in their natural language. RA is linked with a better comprehension of text for beginner readers. We hypothesize that reading code aloud during introductory lessons will lead to better code comprehension. To this end, we design and execute a controlled experiment with the experimental group participants reading the code aloud during the lessons. The participants are 49 primary school students between 9 and 13 years old, who follow three lessons in programming in Python. The lessons are followed by a comprehension assessment based on Bloom's taxonomy. The results show that the students of the experimental group scored significantly higher in the Remembering-level questions compared to the ones in the control group. There is no significant difference between the two groups in their answers to the Understanding-level questions. Furthermore, the participants in both groups followed some of the instructed vocalizations more frequently such as the variable's assignment (is). Vocalizing the indentation spaces in a for -loop was among the least followed. Our paper suggests that using RA for teaching programming in schools will contribute to improving code comprehension with its effect on syntax remembering.
Providing Access to VR Through a Wheelchair

Individuals may use their wheelchair to play VR games, explore three-dimensional, visual worlds and take part in virtual social events, even if they do not master the hand- or head- inputs that are common for VR. We present the development of a low-cost, do-it-yourself, wheelchair locomotion-device, which allows navigation in VR. More than 50 people, including 9 wheelchair users, participated in the evaluations of three prototypes and a number of games developed for them. Initially, cybersickness turned out to be a problem, but when we changed to an electric wheelchair instead of a manual and fine-tuned the controls this discomfort was remarkably reduced. We suggest using this device for gaming, training, interaction design, accessibility studies and the operation of robots.
Korean sign language recognition based on image and convolution neural network

The purpose of this paper is to develop a convolution neural network based model for Korean sign language recognition. For this purpose, sign language videos were collected for 10 selected words of Korean sign language and these videos were converted into images to have 9 frames. The images with 9 frames were used as input data for the convolution neural network based model developed in this study. In order to develop the model for Korean sign language recognition, experiments for determining the number of convolution layers was first performed. Second, experiments for the pooling which intentionally reduces the features of the feature map was performed. Third, we conducted an experiment to reduce over fitting in the model learning process. Based on the experiments, we have developed a convolution neural network based model for Korean sign language recognition. The accuracy of the developed model was about 84.5% for the 10 selected Korean sign words.
A Sensing Technique for Data Glove Using Conductive Fiber

We demonstrate a sensing technique for data gloves using conductive fiber. This technique enables us to estimate hand shapes (bend of a finger and contact between fingers) and differentiates a grabbing tag. To estimate how far each finger bends, the electrical resistance of the conductive fiber is measured; this resistance decreases as the finger bends because the surface of the glove short circuits. To detect contact between fingers, we apply alternating currents with different frequencies to each finger and measure the signal propagation between the fingers. This principle is also used to differentiate a grabbing tag (each tag has an alternating current with a unique frequency). We developed a prototype data glove based on this technique.
A programming model and middleware for high throughput serverless computing applications

Serverless computing has introduced unprecedented levels of scalability and parallelism for the execution of High Throughput Computing tasks. This represents a challenge and an opportunity for different scientific workloads to be adapted to upcoming programming models that simplify the usage of such platforms. In this paper we introduce a serverless model for highly-parallel file-processing applications. We also describe a middleware implementation that supports the execution of customized execution environments based on Docker images on AWS Lambda, the leading serverless computing platform. Moreover, this middleware offers tools to manage the input/output of the serverless infrastructure and the creation of HTTP endpoints in a transparent way to the user. To test the programming model proposed and the middleware, this paper describes two case studies. The first one analyzes medical images with a high degree of parallelism. The second one presents an architecture to process video keyframes. The results from both case studies are discussed and a cost analysis of the medical image architecture comparing different Cloud options is carried out. The results show that the combination of a high-level programming model with the scalable capabilities of AWS Lambda makes it easy for end users to efficiently exploit serverless computing for the optimized and cost-effective execution of loosely-coupled tasks.
Integrating context-awareness and multi-criteria decision making in educational learning

Recommender system is a well-known information system which can capture user tastes and produce item recommendations to the end users. Context-aware recommender systems (CARS) additionally take contexts (e.g., location, time, weather, etc) into consideration, and multi-criteria recommender systems (MCRS) utilize user preferences in multiple criteria to better generate recommendations. Both CARS and MCRS have been widely applied in the real-world applications, such as tourism, movies, music and dining. However, there are no existing research which exploits the methods to integrate them together, not to mention the contributions in the area of educational learning. In this paper, we make the first attempt to integrate context-awareness and multi-criteria decision making in the recommender systems by using the educational data as a case study. Our experimental results reveal that it is able to help produce more accurate recommendations by taking advantage of these two recommendation strategies. We also perform experiments on a tourism data set to demonstrate that the proposed methods can also be generalized to other domains.
Predicting Visible Image Differences Under Varying Display Brightness and Viewing Distance

Numerous applications require a robust metric that can predict whether image differences are visible or not. However, the accuracy of existing white-box visibility metrics, such as HDR-VDP, is often not good enough. CNN-based black-box visibility metrics have proven to be more accurate, but they cannot account for differences in viewing conditions, such as display brightness and viewing distance. In this paper, we propose a CNN-based visibility metric, which maintains the accuracy of deep network solutions and accounts for viewing conditions. To achieve this, we extend the existing dataset of locally visible differences (LocVis) with a new set of measurements, collected considering aforementioned viewing conditions. Then, we develop a hybrid model that combines white-box processing stages for modeling the effects of luminance masking and contrast sensitivity, with a black-box deep neural network. We demonstrate that the novel hybrid model can handle the change of viewing conditions correctly and outperforms state-of-the-art metrics.
Possibilities for wider investment in solar energy implementation

Installing of new solar capacities for electricity production has slowed down worldwide. To explore this trend, to assess opportunities for implementing solar potential, and to determine whether wider investment in solar capacities is possible, a comprehensive model is developed and applied to 28 EU countries and Serbia. Holistic “triangulation” approach encompasses a correlational multidimensional model, an economic analysis, and societal perspective. First, in a linear regression analysis, number and types of massive input variables were adjusted to maximize the level of explanation of the phenomenon and the significance of the input variables. Calculating 255 combinations of eight types of input data for four years, possible production of solar electricity in the 29 countries was quantified. Calculated results were subjected to complex analyses of sensitivity. Second, the balance between the production cost and market price of solar electricity was analyzed by the economic method of Levelised Cost of Electricity. Third, questionnaires and interviews revealed public opinion and expert opinion. Discussion of current situation and positive practice internationally and in Serbia covers regulatory, financial, and economic aspects, including an assessment of investment risks. In conclusion, interests and opportunities favour wider implementation of solar energy facilities for clean and renewable production of electricity.
Multi-Modal Reflection Removal Using Convolutional Neural Networks

Although color images are easily interfered by glass, depth images captured by infrared sensors are robust to reflection. In this letter, we propose multi-modal reflection removal using convolutional neural networks (CNNs). We build a multi-modal CNN for reflection removal to separate transmission from reflection using depth information. The proposed network consists of two sub-networks: image restoration and depth adaptation. Image restoration sub-network (IRN) recovers transmission layer from the input image with reflection, whereas depth adaptation sub-network (DAN) guides reflection removal of the IRN. Moreover, to extract image details for reflection removal, we present a multi-scale loss function that penalizes non-similarity for multi-scale outputs. Experimental results demonstrate that the proposed method is robust to dominant reflections and outperforms state-of-the-art methods in terms of both peak signal-to-noise ratio (PSNR) and structural similarity.
Disciplining the Female Student Body: Consequential Transference in Arguments for School Dress Codes

Based on an examination of news coverage from 2013 to mid-2018, this article analyzes rhetoric in favor of school dress code policies. I illustrate how women's bodies are problematized in order to manifest grounds for regulating female attire. By employing pragmatic lines of argument, pro-dress code rhetors foster what I term consequential transference. Because pragmatic arguments rely on identifying consequences, questions of agency are implicit, as presuppositions of who is responsible for the consequences are embedded into the fabric of the discourse. In this case, dress code defenders paint female immodesty as responsible for several harmful potentialities, including negative social judgments, sexual harassment, and the distraction of male students in the classroom. I assert that pragmatic argumentation is a serviceable tool for underwriting misogynistic culture because consequential transference warrants female regulation and punishment by diminishing the accountability of other actors.
Enabling individualised health in learning healthcare systems

The rising burden of healthcare costs suggests that the healthcare system could benefit from novel methods that allow for continuous learning to provide more data-driven, individualised care at lower costs and with improved outcomes. Here, we present our synergistic Learning approach for Prediction, Interpretation/Inference and Communication (Learning PIC) framework to address the challenges hindering the successful implementation of learning healthcare systems and to enable the effective delivery of evidence-based medicine.
Mixed Reality Piano Tutor: A Gamified Piano Practice Environment

Learning to play piano has many benefits ranging from relieving stress to stimulating neural connections and language skills. However, the learning process is difficult and a lack of intrinsic motivation results in a high drop-out rate of novice piano students. Gamification has been proposed as a technique to overcome lack of intrinsic motivation, and has been successfully applied in education and health. In this paper we present a novel gamification technique for piano tutoring. The method is integrated with an AR approach in order to reduce cognitive load, and a continuous feedback system, to make the learning process less strenuous and more motivating. Our results show that users consider the system intuitive, interesting, and fun, and that users had a greater note playing accuracy than with Synthesia, a popular commercial piano learning system.
Alpaca

Developing cyber ranges for cybersecurity penetration testing and capture-the-flag challenges is normally a time-consuming process. A good cyber range challenges practitioners to find obscure paths to break into a system. The cyber range should encourage a "graph thinking" mindset, in which the attacker approaches the challenge from a variety of directions that may involve multiple steps before escalating privileges and solving the challenge. However, developing cyber ranges usually requires significant time and effort, and the solutions of many pre-made cyber ranges have already been published. We have developed Alpaca, a system that generates complex cyber ranges according to user-specified constraints. Using an AI planning engine and a database of vulnerabilities and machine configurations, the system is able to generate "vulnerability lattices," that is, sequences of vulnerabilities and exploits that achieve a user-specified goal. Alpaca also generates working virtual machines that include the vulnerabilities in the lattice. Constraints may be specified to require Alpaca to generate cyber ranges with a minimum or maximum complexity or require that certain vulnerabilities must be used to exploit the cyber range.
Stepwise transition to agile: From three agile practices to Kanban adaptation

Abstract Many smaller companies cannot afford the time and effort to change their development processes all at once. In some cases, companies are not aware of the fact that many aspects of agile development are already in place. The aim of this work was to evaluate if an evolutionary transition to agile software development is suitable for small companies. We conducted a case study with a small company to start their step‐by‐step agile transition. Initially, three agile software development practices were introduced. Based on the assessment of improvements, the transition continued with three Kanban practices. Experiences with the improvement process and the selected practices were elicited by means of qualitative interviews for data collection. Our case study reports details on the implementation of the first practices as well as the way the resulting agile transition has taken so far. The positive experiences reported by all employees during the agile transition indicate that an evolutionary transition is suitable for small companies. The experiences concerning the transition process and the positive effects of the selected practices motivate practitioners to start their own agile transition.
A novel method for forecasting Construction Cost Index based on complex network

Construction Cost Index (CCI) forecasting has been paid great attention by civil engineers and cost analysts for its broad applications in construction industry. In this paper, for more accurate predictions of CCI, a novel method is proposed based on the analysis of complex network. Firstly, CCI data are mapped into a network by visibility graph. Afterwards, the link prediction method is combined to determine the node similarity in the network. Then initial predictions are made based on the analysis of node similarity. Finally the node distance is taken into account to improve the preliminary forecasting results. In the CCI prediction experiment, we illustrate the applicability and predictability of our method by error comparison and t test. It is believed that the method is able to predict CCI more accurately, which can contribute to saving costs and making budgets in construction industry.
Immersion or Diversion: Does Virtual Reality Make Data Visualisation More Effective?

When dealing with complex data, good visualisation tools are integral in efficiently inspecting trends and correlations. Virtual reality provides an opportunity to create a more immersive environment for users, utilising spatial awareness and depth perception. In this paper we investigate the effectiveness and usability of different systems using virtual reality and gesture control to visualise complex weather data. We propose three novel variants of our weather visualisation: a desktop-based application using an Xbox One Controller interface, and two virtual reality based applications, using an Xbox One Controller interface and a Leap Motion Interface. Our evaluation suggests that the Xbox One controller in combination with the VR display is most effective. Drawbacks of the gesture interface are its limited precision and reliably.
A New Projection-based Exhibition System for a Museum

In this article, we propose a new projection-based system to exhibit a realistic virtual cultural object in a museum. We introduce a new design by combining the multi-projection mapping technology with an optical see-through display that overcomes the limitations of the conventional projection-based exhibition by resolving the problems of ambient light and the occlusion by obstacles. We also introduce a mechanically moving projection surface that increases the degree of freedom of the projection content. Both geometric and radiometric calibration methods are used to correctly project a high-quality texture onto the moving 3D projection surface without perspective distortion. To evaluate the strength of the system and a potential for deployment in a general museum context, we conduct a user study that includes both the visitors and museum staff. The result shows that the proposed system can enrich the museum exhibition by creating a virtual cultural object with substantial 3D effect and high-fidelity appearance. We also demonstrate various applications of the system that provide dynamic content of a virtual cultural object by changing its appearance as well as the viewpoint.
Assessing mobile phone use by pregnant women in Nigeria: A capability perspective

Abstract This paper explores the use of mobile phones to access maternal health care in sub‐Saharan Africa and whether it enhances capability and human development. Analysing focus groups and interviews on mobile phone uses by pregnant women in Nigeria based on the Technology Augmented Capability Approach, we show that the mobile phone as a technical object facilitates three broad capabilities for pregnant women, namely, (a) enhances their voice and choice to push for health care quality, (b) enhances their access to emergency services while maintaining entrepreneurial activities, and (c) enhances their health literacy and social connectedness. However, personal, social, and environmental factors influence the conversion of the use of the mobile phone into capabilities by the pregnant women.
Processing images for red–green dichromats compensation via naturalness and information-preservation considered recoloring

Color vision deficiency (CVD) is caused by anomalies in the cone cells of the human retina. It affects approximately 200 million individuals throughout the world. Although previous studies have proposed compensation methods, contrast and naturalness preservation have not been adequately and simultaneously addressed in the state-of-the-art studies. This paper focuses on red–green dichromats' compensation and proposes a recoloring algorithm that combines contrast enhancement and naturalness preservation in a unified optimization model. In this implementation, representative color extraction and edit propagation methods are introduced to maintain global and local information in the recolored image. The quantitative evaluation results showed that the proposed method is competitive with state-of-the-art methods. A subjective experiment was also conducted and the evaluation results revealed that the proposed method obtained the best scores in preserving both naturalness and information for individuals with severe red–green CVD.