Reviewerly/exHarmony huggingface dataset:
Test activities in the continuous integration and delivery pipeline

Abstract Based on 25 interviews with participants from four case study companies that develop large‐scale software embedded systems, this paper presents the Test Activity Stakeholders (TAS) model. The TAS model shows how the continuous integration and delivery pipeline can be designed to include test activities that support four stakeholder interests: “Check changes,” “Secure stability,” “Measure progress,” and “Verify compliance.” The model is developed to show how each of the stakeholder interests are best supported by unit/component tests or system tests, by automated testing or manual testing and by tests executed in simulated environments or on real hardware. The TAS model may serve as a starting point for companies when evaluating and designing their continuous integration and delivery pipeline. The validation of the TAS model included twelve individuals from three case study companies. The validation showed that the TAS model is actionable and useful in practice, enabling the identification of stakeholders and showing where improvement efforts should be focused in order to support all stakeholder interests in the continuous integration and delivery pipeline.
No reference quality assessment for Thangka color image based on superpixel

In view of the situation that a large number of Thangka images are missing part of color information because of time and environmental factors, the existing image evaluation methods are inconsistent with the result of subjective evaluation. This paper aims at evaluating the damaged Thangka color image, and proposes a new method of image quality evaluation based on superpixel and color entropy. In this algorithm, we use the uniformity of Thangka color image to extract color feature based on CIE 1976 L* a* b* (CIELAB) color space and superpixel. Therefore, the loss of color information in the complex area of Thangka images is well handled. The color entropy is used to quantify the color distribution and structure characteristics of each superpixel, and then we can get the preliminarily evaluation score. In the end, large amounts of data are obtained through some operations such as image deformation and rotating by the Generative Adversarial Nets (GANs), which makes the final evaluation score more reliable. Experimental results show that this method can obtain a good consistency with the subjective results, and Spearman rank order the correlation coefficient (SROCC) and Pearson linear correlation coefficient (PLCC) of the new method already exceed 0.9.
Virtual Portraitist

Smart photography carries the promise of quality improvement and functionality extension in making aesthetically appealing pictures. In this article, we focus on self-portrait photographs and introduce new methods that guide a user in how to best pose while taking a selfie. While most of the current solutions use a post processing procedure to beautify a picture, the developed tool enables a novel function of recommending a good look before the photo is captured. Given an input face image, the tool automatically estimates the pose-based aesthetic score, finds the most attractive angle of the face, and suggests how the pose should be adjusted. The recommendation results are determined adaptively to the appearance and initial pose of the input face. We apply a data mining approach to find distinctive, frequent itemsets and association rules from online profile pictures, upon which the aesthetic estimation and pose recommendation methods are developed. A simulated and a real image set are used for experimental evaluation. The results show the proposed aesthetic estimation method can effectively select user-favorable photos. Moreover, the recommendation performance for the vertical adjustment is moderately related to the degree of conformity among the professional photographers’ recommendations. This study echoes the trend of instant photo sharing, in which a user takes a picture and then immediately shares it on a social network without engaging in tedious editing.
Smart data mining algorithm for intelligent education

Smart education is one of the main applications of Smart cities which requires artificial intelligence-based models during the education process. Optimizing the educational contents to match the student’s capabilities and backgrounds is one of the core tasks in the smart educations. To improve the optimization of an education system, a data mining algorithm is used to classify and summarize the education data and improve the accuracy of teaching content. An optimization and updating scheme is put forward from the algorithm flow and evaluation model, and an adequate evaluation model is established by effectively evaluating the developing state of wisdom education. In the test of intelligent education model, the efficiency of AI education and the data accuracy of the data mining algorithm are tested. The test results show that the integration of AI can improve the efficiency of learning and teaching, and make the teaching content more accurate, which is worth to further promoting.
Deep learning-based scene-awareness approach for intelligent change detection in videos

Change detection is the most fundamental component in video surveillance systems. Although many change detection approaches have been proposed, they are often only suitable for particular environments. This paper presents an approach that integrates several detection techniques with scene understanding capability, thereby overcoming the challenges of detecting various scene types and improving overall detection performance. First, a scene-awareness algorithm that incorporates a deep learning-based scene recognition model and support vector machine is developed to classify the monitored scene over time. Then, the appropriate detection technique is automatically adopted to perform scene-specific detection. Experimental results demonstrate that the performance of the proposed method is comparable to that of the state-of-the-art methods and satisfies the requirements of real-time practical applications. Hence, it can serve as an intelligent change detection approach for visual analytics in video surveillance systems.
Making the blockchain civic

column Share on Making the blockchain civic Authors: Chris Elsden Northumbria University Northumbria UniversityView Profile , Inte Gloerich Amsterdam University of Applied Sciences Amsterdam University of Applied SciencesView Profile , Anne Spaa Northumbria University Northumbria UniversityView Profile , John Vines Northumbria University Northumbria UniversityView Profile , Martijn de Waal Amsterdam University of Applied Sciences Amsterdam University of Applied SciencesView Profile Authors Info & Claims InteractionsVolume 26Issue 2March - April 2019 pp 60–65https://doi.org/10.1145/3305364Published:22 February 2019Publication History 4citation883DownloadsMetricsTotal Citations4Total Downloads883Last 12 Months187Last 6 weeks19 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Alignment Based Approach for Arabic Textual Entailment

Recognizing Textual Entailment (RTE) is a task of Natural language processing (NLP) in which two text fragments denoted Text (T) and Hypothesis (H) are processed by a system to determine whether the meaning of H is entailed from T or not. This task is necessary for numerous natural language processing applications. Thus, several approaches have been proposed to deal with the RTE task, running from shallow approaches based on measuring lexical similarity to advanced approaches based on semantic interpretation and deep application of machine learning algorithms. In this paper, we present an approach to deal with Textual Entailment for Arabic language based on machine learning and text alignment modeled as an optimization problem. The aim of this work is to experiment how well state of the art Arabic NLP tools, resources and alignment technique work when applied to Arabic RTE. We report the performance of our system on an existing Arabic RTE dataset and we achieve encouraging results.
Deep Learning for Driverless Vehicles

Automation is becoming a large component of many industries in the 21st century, in areas ranging from manufacturing, communications and transportation. Automation has offered promised returns of improvements in safety, productivity and reduced costs. Many industry leaders are specifically working on the application of autonomous technology in transportation to produce "driverless" or fully autonomous vehicles. A key technology that has the potential to drive the future development of these vehicles is deep learning. Deep learning has been an area of interest in machine learning for decades now but has only come into widespread application in recent years. While traditional analytical control systems and computer vision techniques have in the past been adequate for the fundamental proof of concept of autonomous vehicles, this review of current and emerging technologies demonstrates these short comings and the road map for overcoming them with deep learning.
A Cognitive Analysis of the Preposition<i>OVER</i>: Image-schema transformations and metaphorical extensions

Abstract Dewell (1994), following Brugman (1981) and Lakoff (1987), provides a semantic analysis of over by relying more exclusively on image-schema transformations than did Brugman and Lakoff. The Brugman-Lakoff-Dewell analysis, however, can be improved by using simpler image-schemas, more natural image-schema transformations, and metaphorical extensions. A key idea adopted in the present article is to capture both trajectors and landmarks three-dimensionally and topologically. This modification brings about the elimination of unessential features such as the shape and size of the trajector and the landmark, contact/non-contact between the trajector and the landmark, and physical properties of the trajector. Its main advantage is that a central image-schema for a semicircular path provides the basis for explaining all of the senses of over using natural image-schema transformations and metaphorical extensions. The proposed image-schema transformations include: segment profiling, profiling the endpoint of access paths, the profiled peak position of the semicircular path with the constraint that the rest of the semicircular path is excluded, and the extension of the semicircular path-trajectory to an image of covering. The proposed metaphorical senses are time, means, and control. In addition, the radial category relating each sense of over is presented.
Enhancing Utilization of SIMD-Like Accelerator for Sparse Convolutional Neural Networks

Although the existing single-instruction–multiple-data-like (SIMD) accelerators can handle the compressed format of sparse convolutional neural networks, the sparse and irregular distributions of nonzero elements cause low utilization of multipliers in a processing engine (PE) and imbalanced computation between PEs. This brief addresses the above issues by proposing a data screening and task mapping (DSTM) accelerator which integrates a series of techniques, including software refinement and hardware modules. An efficient indexing module is introduced to identify the effectual computation pairs and skip unnecessary computation in a fine-grained manner. The intra-PE load imbalance is alleviated with weight data rearrangement. An effective task sharing mechanism further balances the computation between PEs. When compared with the state-of-the-art SIMD-like accelerator, the proposed DSTM enhances the average PE utilization by $3.5\times $ . The overall processing throughput is 59.7% higher than the previous design.
Challenges in representing information with augmented reality to support manual procedural tasks

Support of manual procedural tasks such as repair or maintenance is one of the most promising areas for the application of augmented reality in industry. However, it is not yet fully understood how information like work instructions or CAD models must be represented in a way that users are optimally supported in accomplishing this kind of tasks. As an approach to this research challenge, a conceptual framework for modelling information representation in augmented reality is presented here. It introduces the idea of information objects, which are physical and virtual objects that provide relevant information for completing work steps of a task. These can be distinguished into multiple classes based on the levels of spatial connection that information in augmented reality can have. The classes are then used to identify possible sources of sensory or cognitive e ort for a user that is caused by the way information objects are included in an augmented reality system and not by the complexity of the task to be performed. Based on these sources, information representation challenges are formulated that must be addressed when creating augmented reality based support systems for procedural tasks. The five identified challenges are clarity, consistency, visibility, orientation and information linking. For each of them, a detailed explanation is given and literature is collected that provides indications of what can be done to overcome them.
Web-Based Embodied Conversational Agents and Older People

Within Human-Computer Interaction, there has recently been an important turn to embodied and voice-based interaction. In this chapter, we discuss our ongoing research on building online Embodied Conversational Agents (ECAs), specifically, their interactive 3D web graphics aspects. We present ECAs based on our technological pipeline, which integrates a number of free online editors, such as Adobe Fuse CC or MakeHuman, and standards, mainly BML (Behaviour Markup Language). We claim that making embodiment available for online ECAs is attainable, and advantageous over current alternatives, mostly desktop-based. In this chapter we also report on initial results of activities aimed to explore the physical appearance of ECAs for older people. A group of them (N = 14) designed female ECAs. Designing them was easy and great fun. The perspective on older-adult HCI introduced in this chapter is mostly technological, allowing for rapid online experimentations to address key issues, such as anthropomorphic aspects, in the design of ECAs with, and for, older people.
Graph Matching Based Semantic Search Engine

Explosive growth of the Web has made searching Web data a challenging task for information retrieval systems. Semantic search systems that go beyond the shallow keyword matching approaches and map words to their conceptual meaning representations offer better results to the users. On the other hand, a lot of representation formats have been specified to represent Web data into a semantic format. We propose a search engine for searching Web data represented in UNL (Universal Networking Language). UNL has numerous attractive features to support semantic search. One of the main features is that UNL does not depend on domain ontology. Our proposed search engine is based on semantic graph matching. It includes semantic expansion for graph nodes and relation matching based on relation meaning. The search results are ranked depending on the semantic similarity between the user query and the retrieved documents. We developed a prototype implementing the proposed semantic search engine, and our evaluations demonstrate its effectiveness across a wide-range of semantic search tasks.
Microfeatures influencing writing quality: the case of Chinese students’ SAT essays

This study investigates the extent to which microfeatures – such as basic text features, readability, cohesion, and lexical diversity based on specific word lists – affect Chinese EFL writing quality. Data analysis was conducted using natural language processing, correlation analysis and stepwise multiple regression analysis on a corpus of 268 Chinese students' SAT writing in response to a single prompt. The results show that word count, the number of words per sentence, the connecting word frequency, the number of commas per sentence, the stop word frequency and the Coleman-Liau readability index (CLI) contribute significantly towards predicting SAT essay scores of Chinese EFL students. The regression model explains 62.6% of the variance in predicting the SAT essay's score where the number of commas per sentence acts as a suppressor. Surprisingly, none of the word lists were found to be a significant predictor. Consequently, EFL teachers can adopt these simple lexical features to formulate writing strategies for beginner level Chinese EFL students to improve their SAT writing quality. Moreover, developers can include these features as a personalized option in automated writing assistance systems targeted toward beginner level Chinese EFL students.
Public Sphere 2.0: Targeted Commenting in Online News Media

With the increase in online news consumption, to maximize advertisement revenue, news media websites try to attract and retain their readers on their sites. One of the most effective tools for reader engagement is commenting, where news readers post their views as comments against the news articles. Traditionally, it has been assumed that the comments are mostly made against the full article. In this work, we show that present commenting landscape is far from this assumption. Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article. In this paper, we build a system which can automatically classify comments against relevant sections of an article. To implement that, we develop a deep neural network based mechanism to find comments relevant to any section and a paragraph wise commenting interface to showcase them. We believe that such a data driven commenting system can help news websites to further increase reader engagement.
Lifted Maximum Expected Utility

The lifted junction tree algorithm (LJT) answers multiple queries efficiently for relational models under uncertainties by building and then reusing a first-order cluster representation. We extend the underling model representation of LJT, which is called parameterised probabilistic model, to calculate a lifted solution to the maximum expected utility (MEU) problem. Specifically, this paper contributes (i) action and utility nodes for parameterised probabilistic models, resulting in parameterised probabilistic decision models and (ii) meuLJT, an algorithm to solve the MEU problem using parameterised probabilistic decision models efficiently, while also being able to answer multiple marginal queries.
Accessing Validity of Argumentation of Agents of the Internet of Everything

Agents of the Internet of Everything (IoE) exchange messages, make certain decisions, and provide arguments for them. In this IoE environment validation of the validity of argumentation patterns in messages, as well as their truthfulness, authenticity, and consistency, is essential. We formulate a problem of domain-independent assessment of argumentation validity based on rhetorical analysis of text. Argumentation structure is discovered in the form of discourse trees extended with edge labels for communicative actions. Extracted argumentation structure is represented as a defeasible logic program and is subject to dialectical analysis to establish the validity of the arguments for the main claim being communicated. We evaluate the accuracy of each step of this argumentation processing pipeline as well as its overall performance.
Data Source Selection Based on an Improved Greedy Genetic Algorithm

The development of information technology has led to a sharp increase in data volume. The tremendous amount of data has become a strategic capital that allows businesses to derive superior market intelligence or improve existing operations. People expect to consolidate and utilize data as much as possible. However, too much data will bring huge integration cost, such as the cost of purchasing and cleaning. Therefore, under the context of limited resources, obtaining more data integration value is our expectation. In addition, the uneven quality of data sources make the multi-source selection task more difficult, and low-quality data sources can seriously affect integration results without the desired quality gain. In this paper, we have studied how to balance data gain and cost in the source selection, specifically, maximizing the gain of data on the premise of a given budget. We proposed an improved greedy genetic algorithm (IGGA) to solve the problem of source selection, and carried out a wide range of experimental evaluations on the real and synthetic dataset. The empirical results show considerable performance in favor of the proposed algorithm in terms of solution quality.
A dynamic mosaicking method for finding an optimal seamline with Canny edge detector

Image mosaicking is a process of assembling multiple images to create an image with a larger field of view. Therefore, to avoid failures and to solve errors that could occur during this process, a new method for dynamic mosaicking is presented. This method finds regions that are similar between the images and regions that are not alike, then an optimal seamline is computed by going through the similar regions as much as possible and by avoiding the not common regions. In fact, this is done by combining Canny edge detector and the outliers and inliers from the RANSAC method to identify these areas. In addition, by highlighting these regions in an intensity difference, a map that reveals which areas to avoid and to frequent was created. Thus, an optimal seamline was found. The experimental results show that the proposed approach is capable of generating robust mosaics against ghosting and parallax effects.
Why is my code change abandoned?

Context: Software developers contribute numerous changes every day to the code review systems. However, not all submitted changes are merged into a codebase because they might not pass the code review process. Some changes would be abandoned or be asked for resubmission after improvement, which results in more workload for developers and reviewers, and more delays to deliverables. Objective: To understand the underlying reasons why changes are abandoned, we conduct an empirical study on the code review of four open source projects (Eclipse, LibreOffice, OpenStack, and Qt). Method: First, we manually analyzed 1459 abandoned changes. Second, we leveraged the open card sorting method to label these changes with reasons why they were abandoned, and we identified 12 categories of reasons. Next, we further investigated the frequency distribution of the categories across projects. Finally, we studied the relationship between the categories and time-to-abandonment. Results: Our findings include the following: (1) Duplicate changes are the majority of the abandoned changes; (2) the frequency distribution of abandoned changes across the 12 categories is similar for the four open source projects; (3) 98.39% of the changes are abandoned within a year. Conclusion: Our study concluded the root causes of abandoned changes, which will help developers submit high-quality code changes.