Reviewerly/exHarmony huggingface dataset:
Argument Invention from First Principles

Competitive debaters often find themselves facing a challenging task -how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet?What they often do is rely on "first principles", commonplace arguments which are relevant to many topics, and which they have refined in past debates.In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP.The main goal of this work is to show that it is possible to define such a taxonomy.While the taxonomy suggested here should be thought of as a "first attempt" it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.
An Efficient Key Generation for the Internet of Things Based Synchronized Quantization

One solution to ensure secrecy in the Internet of Things (IoT) is cryptography. However, classical cryptographic systems require high computational complexity that is not appropriate for IoT devices with restricted computing resources, energy, and memory. Physical layer security that utilizes channel characteristics is an often used solution because it is simpler and more efficient than classical cryptographic systems. In this paper, we propose a signal strength exchange (SSE) system as an efficient key generation system and a synchronized quantization (SQ) method as a part of the SSE system that synchronizes data blocks in the quantization phase. The SQ method eliminates the signal pre-processing phase by performing a multi-bit conversion directly from the channel characteristics of the measurement results. Synchronization is carried out between the two authorized nodes to ensure sameness of the produced keys so it can eliminate the error-correcting phase. The test results at the IoT devices equipped with IEEE 802.11 radio show that SSE system is more efficient in terms of computing time and communication overhead than existing systems.
Mediating Relatedness for Adolescents with ME

This paper discusses how a networked object in the form of a small robot designed to mediate experiences of care, social connectedness, and intimacy, was used by adolescents with Myalgic Encephalomyelitis, a condition that reduces their normal functioning, including the ability to socialize. A study with nine adolescents, each using the robot for about a year in average, revealed that it was largely effective at mediating their everyday experiences of relatedness, triggering productive new habits and social practices. We interpret these findings to propose a set of strategies for designing technologies that support relatedness while requiring minimal interactivity and engagement. Balance, extension-of-self, coolness, and acts-of-care, in addition to commonly used physicalness, expressivity and awareness, enable the robot to extend the adolescents' ability to relate to others, people and animals.
Special Relativity in Immersive Learning

In this paper, we discuss our development, implementation and evaluation of an interactive, real-time, and real-scale virtual reality application used to understand the theory of special relativity. Since special relativity deals with non-trivial counter-intuitive subjects such as the twin paradox and the Lorentz contraction, we utilize an immersive VR experience to visualize these phenomena. In doing so, we attempt to teach the theory of special relativity in a manner different than conventional abstract methods. In this study, we tested a set of participants and examined their understanding of special relativity theory before and after engaging with the VR experience. Using the results, we inspected for any correlations between their perceived immersion during and after the simulation and their acquisition of special relativity theory. Our study has shown that visualizing the phenomena of special relativity in VR led to high immersion among participants and increased knowledge about the theory of special relativity. With this work, we hope to build upon the collective knowledge about the effects of learning in a strong, visually pronounced, and highly immersive environment.
Equivalence between non-Markovian dynamics and correlation backflows

The information encoded into an open quantum system that evolves under a Markovian dynamics is always monotonically non-increasing. Nonetheless, for a given quantifier of the information contained in the system, it is in general not clear if for all non-Markovian dynamics it is possible to observe a non-monotonic evolution of this quantity, namely a backflow. We address this problem by considering correlations of finite-dimensional bipartite systems. For this purpose, we consider a class of correlation measures and prove that if the dynamics is non-Markovian there exists at least one element from this class that provides a correlation backflow. Moreover, we provide a set of initial probe states that accomplish this witnessing task. This result provides the first one-to-one relation between non-Markovian dynamics of finite-dimensional quantum systems and correlation backflows.
Planning Poker in cost estimation in Agile methods: Averaging Vs. Consensus

Software cost estimation has been always a serious concern among the software experts. Although, a few estimation models and techniques have been provided in disciplined based software methodologies, Agile methodologies most often use Planning Poker technique for software cost estimation. This technique promotes coming to consensus when suggesting size of each user requirement (known as User Story). However, most often software experts ask whether it is necessary to reach to a consensus or not. They also, offer considering the average of the suggested sizes (cost) of User Stories instead of focusing on consensus on them. The main aim of this paper is to present the results of a case study research which has been carried out to compare the accuracy of both disciplines. The results show less accuracy when team get an average size of the User Stories compared to coming to consensus about the size of User Stories.
Expert knowledge recommendation systems based on conceptual similarity and space mapping

The semantic analysis method of structured big data generated based on human knowledge is important in expert recommendation systems and scientific and technological information analysis. In these fields, the most important problem is the calculation of concept similarity. The study aims to explore the spatial mapping relationship between the general knowledge base and the professional knowledge base for the application of the general knowledge map in professional fields. With the core resource database (CRD) as the main body of the general knowledge and the institutional repository (IR) as the main body of the professional knowledge, the conceptual features of institutional expert knowledge were firstly abstracted from IR and inferred from small-scale datasets and the mathematical model was established based on the similarity of text concepts and related ranking results. Then, a two-set concept space mapping algorithm between CRD and IR was designed. In the algorithm, the more granular concept nodes were extracted from the information on the shortest paths among concepts to obtain a new knowledge set, the Expert Knowledge Recommendation System (EKRS). Finally, the simulation experiment was carried out with open datasets to verify the algorithm. The simulation results showed that the algorithm reduced the structural complexity in the calculation of large datasets. The proposed system model had a clear knowledge structure and the recommended accuracy of the text similarity was high. For small-scale knowledge base datasets with different sparsity, the system showed the stable performance, indicating the better convergence and robustness of the algorithm.
Analytical composite performance models for Big Data applications

Recent years witnessed a steep rise in data generation and, consequently, the widespread adoption of software solutions able to support data-intensive applications. Many companies currently engage in data-intensive processes, however, fully embracing a data-driven paradigm is still cumbersome, and establishing a production-ready and fine-tuned deployment is time-consuming. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. Moreover, many companies are using Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. Accurate and fast prediction of the execution time of a Big Data application helps improving design time decisions, reduces Cloud over allocation charges, and assists budget management. In this paper, analytical models based on Stochastic Activity Networks (SANs) are proposed to accurately model the execution of Hadoop, Tez and Spark applications, i.e., the most referred frameworks to support Big Data analyses. The proposed SANs model these applications together with the underlying cluster in order to accurately estimate the execution time. We evaluate the accuracy of the proposed models over the TPC-DS industry benchmark across different configurations. Results obtained by numerically solving the SAN models show an average error of 4.5%, 5.8%, and 2.7% in estimating the execution time of MR, Tez, and Spark applications, respectively, against the data gathered from the experiments demonstrating higher accuracy compared with the state-of-the-art. Moreover, the time required to solve the proposed models is lower than the simulation time of the previously presented approaches in this area.
Smart Lifelog Retrieval System with Habit-based Concepts and Moment Visualization

Lifelogging is a new trend of research in which we collect and analyze people's daily activities to gain insight into people daily basis and enhance people's life quality. This leads to many research in retrieval systems to be developed for lifelogging analysis. One of the most important factors of a successful lifelogging retrieval system is to enhance the user's ability to perform any queries, even arbitrary ones. This motivates our proposal of a retrieval system in which we focus on the two main properties. The first property is the ability to accurately annotate the lifelog dataset with metadata that is meaningful for the retrieving process. The second property focuses on a powerful user interface that supports a novice user to perform the retrieval efficiently.
Designing stories on social media: A corpus-assisted critical perspective on the mismatches of story-curation

At a time of increased monetization of social media, there is scope and need for critical-linguistic perspectives to go beyond the focus on users’ linguistic and multi-modal resources. Drawing on the project ‘Life-writing of the moment: The sharing and updating self on social media’ (www.ego-media.org), in this article, I make a case for a corpus-assisted critical perspective which allows the scrutiny of media companies’ design of stories as communication features in their apps. What definitions and views of stories underpin such story-features? What facilities are on offer for posting stories, how are they branded, and why? Who is positioned as an ideal story-creator and audience of those stories and why? I address these questions with reference to the recent story-designing spree on Snapchat and Instagram. The corpus analysis of how stories as an app feature are launched and discussed on online media, incl. keyword analysis and lexical and thematic associations of the term ‘story’, has brought to the fore three mismatches between the marketing rhetoric or branding of stories as a feature and the actual affordances offered for them. These involve tensions and trade-offs between: continuity of self and ephemerality; textuality and visuality; creativity and control for the user vs. pre-selection templates and customization. These mismatches are revealing of a re-designation of key-ingredients of stories, in particular time, memories and audience engagement, so as to suit the apps’ agenda of metricization of users’ lives and their ever closer links with advertising.
Systematic Mobile Device Usage Behavior and Successful Implementation of TPACK Based on University Students Need

This paper analyzes the findings of a research study exploring the development of Technological Pedagogical Content Knowledge (TPACK) model in accordance with diverse university students' need for sustainable teaching and learning in different universities. Towards the aim of successful implementation of the TPACK model through systematic mobile device usage, the study included the participation of 201 university students in different undergraduate programs and 10 university lecturers. In order to collect their feedback on the TPACK model, the students and teachers were requested to complete a set of questionnaire. Furthermore, a focus group interview was also conducted to further investigate students' and tutors' scrutiny on the TPACK development with regular mobile device usage in accordance with diverse learners' needs. Even though the majority of the findings support the development of TPACK structure in accordance with diverse university students' need, a few limitations are found regarding the implementation. This research will be helpful to determine post-secondary learning and teaching with technology.
What Phishing E-mails Reveal: An Exploratory Analysis of Phishing Attempts Using Text Analysis

ABSTRACT Accountants and auditing firms are frequent phishing targets because of the proximity to organizational resources. Since phishing typically is done using emails, text analysis is used to explore differences between phishing e gmails and other emails. By analyzing and comparing a database of phishing messages to a database of the Enron emails, we find that the phishing data is statistically significantly different across a large number of univariate text variable categories. Further, we generate a model of phishing as “power,” based on independent variables of friend (who they pretend to be), achievement (of their goal), (to take your) money, and (typically done at) work. These variables are used as a basis to estimate power in both the phishing and non-phishing messages, where we find differences on the signs of the independent variables. Finally, using text analysis, we examine the ability of neural network models to differentiate between phishing emails and Enron emails.
A Dataset of Non-Functional Bugs

While several researchers have published bug data sets in the past, there has been less focus on bugs related to non-functional requirements. Non-functional requirements describe the quality attributes of a program. In this work, we introduce NFBugs, a data set of 133 non-functional bug fixes collected from 65 open-source projects written in Java and Python. NFBugs can be used to support code recommender systems focusing on non-functional properties.
Edge-aware deep image deblurring

Image deblurring is a fundamental and challenging low-level vision problem. Previous vision research indicates that edge structure in natural scenes is one of the most important factors to estimate the abilities of human visual perception. In this paper, we resort to human visual demands of sharp edges and propose a two-phase edge-aware deep network to improve deep image deblurring. An edge detection convolutional subnet is designed in the first phase and a residual fully convolutional deblur subnet is then used for generating deblur results. The introduction of the edge-aware network enables our model with the specific capacity of enhancing images with sharp edges. We successfully apply our framework on standard benchmarks and promising results are achieved by our proposed deblur model.
An Efficient Indoor Target Tracking Algorithm Using TDOA Measurements With Applications to Ultra-Wideband Systems

The ultra-wideband technique has shown its effectiveness for indoor target tracking.Various types of measurements have been applied to ultra-wideband systems for indoor target tracking, and the time difference of arrival (TDOA) measurement-based approaches are the most widely used methods due to their good accuracy and feasibility.Target tracking with the TDOA measurements usually encounters the problem of correlated measurement noises, as one sensor network utilizes the common reference sensor for measurement generation.The off-diagonal entries in the measurement error covariance matrix become non-zero values, which makes the standard target tracking algorithms inconvenient for practical installation of an ultra-wideband system.Another problem in sensor networks is properly exploiting the measurements obtained from multiple sensors considering practical conditions, such as storage limitations or computational resource consumption.The parallel update and the serial update are usually applied for the multi-sensor tracking problem.This paper presents a target tracking algorithm that integrates the Cholesky decomposition to decorrelate the measurement noises for the serial update, thus improving computational efficiency.The proposed algorithm is realized in an ultra-wideband system for real-time target tracking, and an experiment using real data is conducted to validate its practicability.
Verified compilation on a verified processor

Developing technology for building verified stacks, i.e., computer systems with comprehensive proofs of correctness, is one way the science of programming languages furthers the computing discipline. While there have been successful projects verifying complex, realistic system components, including compilers (software) and processors (hardware), to date these verification efforts have not been compatible to the point of enabling a single end-to-end correctness theorem about running a verified compiler on a verified processor.
On the complexity of inconsistency measurement

We survey a selection of inconsistency measures from the literature and investigate their computational complexity wrt. decision problems related to bounds on the inconsistency value and the functional problem of determining the actual value. Our findings show that those inconsistency measures can be partitioned into four classes related to their complexity. The first three classes contain measures whose complexities are located on the first three levels of the polynomial hierarchy, respectively. The final class is under standard complexity-theoretic assumptions located beyond the polynomial hierarchy. We provide membership results for all the investigated problems and completeness results for most of them. In addition, we undertake a preliminary study on the computational complexity of the measures on fragments of propositional logic.
The Quality and Readability of English Wikipedia Anatomy Articles

Forty anatomy articles were sampled from English Wikipedia and assessed quantitatively and qualitatively. Quantitatively, each article’s edit history was analyzed by Wikipedia X‐tools, references and media were counted manually, and two readability indices were used to evaluate article readability. This analysis revealed that each article was updated 8.3 ± 6.8 times per month, and referenced with 33.5 ± 24.3 sources, such as journal articles and textbooks. Each article contained on average 14.0 ± 7.6 media items. The readability indices including: (1) Flesch–Kincaid Grade Level Readability Test and (2) Flesch Reading Ease Readability Formula demonstrated that the articles had low readability and were more appropriate for college students and above. Qualitatively, the sampled articles were evaluated by experts using a modified DISCERN survey. According to the modified DISCERN, 13 articles (32.5%), 24 articles (60%), 3 articles (7.5%), were rated as “good,” “moderate,” and “poor,” respectively. There were positive correlations between the DISCERN score and the number of edits ( r = 0.537), number of editors ( r = 0.560), and article length ( r = 0.536). Strengths reported by the panel included completeness and coverage in 11 articles (27.5%), anatomical details in 10 articles (25%), and clinical details in 5 articles (12.5%). The panel also noted areas which could be improved, such as providing missing information in 28 articles (70%), inaccuracies in 10 articles (25%), and lack or poor use of images in 17 articles (42.5%). In conclusion, this study revealed that many Wikipedia anatomy articles were difficult to read. Each article’s quality was dependent on edit frequency and article length. Learners and students should be cautious when using Wikipedia articles for anatomy education due to these limitations.
Deep Learning Based Antenna Array Fault Detection

MmWave communication is a promising communication technique of future 5G system. Large bandwidth and high directional gain are the two advantages of mmWave communication solutions. High directional gain leads the usage of massive units. The system maintenance and status management play more and more important role in future wireless communication systems and contribute higher operation cost than before. In our paper we proposed a fault finding and location methods based on two deep neural network (DNN) with different complexity, the simple network is designed to fault finding with low cost, the other precision network start to fault location when the former one has detected the faults. Simulation results show that our network can work well at low SNR region without manual inspection involved, thus the operation cost is reduced.
Self-Adaptive Framework Based on MAPE Loop for Internet of Things

The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.
Efficient deep neural network for photo-realistic image super-resolution

Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly. However, despite their powerful performance, many methods are difficult to apply to real-world applications because of the heavy computational requirements. To facilitate the use of a deep model under such demands, we focus on keeping the network efficient while maintaining its performance. In detail, we design an architecture that implements a cascading mechanism on a residual network to boost the performance with limited resources via multi-level feature fusion. In addition, our proposed model adopts group convolution and recursive schemes in order to achieve extreme efficiency. We further improve the perceptual quality of the output by employing the adversarial learning paradigm and a multi-scale discriminator approach. The performance of our method is investigated through extensive internal experiments and benchmarks using various datasets. Our results show that our models outperform the recent methods with similar complexity, for both traditional pixel-based and perception-based tasks.