Reviewerly/exHarmony huggingface dataset:
Representing a reference foundational ontology of events in SROIQ

In recent years, there has been a growing interest in the application of foundational ontologies, i.e., formal ontological theories in the philosophical sense, to provide a theoretically sound foundation for improving the theory and practice of conceptual modeling and knowledge representation. This paper addresses one particular foundational theory of events termed UFO-B, which has been successfully employed as a reference model for addressing problems from complex media management, enterprise architecture, software engineering, and modeling of events in petroleum exploration. Despite its success, there is still no formalization of UFO-B in a decidable knowledge representation language that could support reasoning about complex events and event relations. We address this gap by proposing a number of alternative translations from UFO-B’s original axiomatization (in first-order logic and in the Alloy formal language) to the description logic SROIQ, which is the formal underpinning of OWL 2 DL. Additionally, to support practical applications, we translated these SROIQ theories to OWL 2 DL TBoxes, which were validated by showing that all the intended models of UFO-B (the logical models of the UFO-B specification in Alloy) that we generated are consistent with these UFO-B TBoxes. In a sense, the specification in Alloy implements the specification in first-order logic, while the OWL 2 TBoxes implement the SROIQ specifications. Incidentally, the methodology that we designed for the translation from UFO-B’s original axiomatization in FOL and Alloy to SROIQ came to be a key contribution of this work by providing us evidence of the inadequacy of DLs for the specification of comprehensive foundational ontologies.
Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression

Semantic Backpropagation (SB) is a recent technique that promotes effective variation in tree-based genetic programming. The basic idea of SB is to provide information on what output is desirable for a specified tree node, by propagating the desired root-node output back to the specified node using inversions of functions encountered along the way. Variation operators then replace the subtree located at the specified node with a tree for which the output is closest to the desired output, by searching in a pre-computed library. In this paper, we propose two contributions to enhance SB specifically for symbolic regression, by incorporating the principles of Keijzer's Linear Scaling (LS). In particular, we show how SB can be used in synergy with the scaled mean squared error, and we show how LS can be adopted within library search. We test our adaptations using the well-known variation operator Random Desired Operator (RDO), comparing to its baseline implementation, and to traditional crossover and mutation. Our experimental results on real-world datasets show that SB enhanced with LS substantially improves the performance of RDO, resulting in overall the best performance among all tested GP algorithms.
Leveraging Small Software Engineering Data Sets with Pre-Trained Neural Networks

Many software engineering data sets, particularly those that demand manual labelling for classification, are necessarily small. As a consequence, several recent software engineering papers have cast doubt on the effectiveness of deep neural networks for classification tasks, when applied to these data sets. We provide initial evidence that recent advances in Natural Language Processing, that allow neural networks to leverage large amount of unlabelled data in a pre-training phase, can significantly improve performance.
Test-Driven Code Review: An Empirical Study

Test-Driven Code Review (TDR) is a code review practice in which a reviewer inspects a patch by examining the changed test code before the changed production code. Although this practice has been mentioned positively by practitioners in informal literature and interviews, there is no systematic knowledge of its effects, prevalence, problems, and advantages. In this paper, we aim at empirically understanding whether this practice has an effect on code review effectiveness and how developers' perceive TDR. We conduct (i) a controlled experiment with 93 developers that perform more than 150 reviews, and (ii) 9 semi-structured interviews and a survey with 103 respondents to gather information on how TDR is perceived. Key results from the experiment show that developers adopting TDR find the same proportion of defects in production code, but more in test code, at the expenses of fewer maintainability issues in production code. Furthermore, we found that most developers prefer to review production code as they deem it more critical and tests should follow from it. Moreover, general poor test code quality and no tool support hinder the adoption of TDR. Public preprint: [https: //doi.org/10.5281/zenodo.2551217], data and materials: [https:// doi.org/10.5281/zenodo.2553139].
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks

We propose feedback adversarial learning (FAL) framework that can improve existing generative adversarial networks by leveraging spatial feedback from the discriminator. We formulate the generation task as a recurrent framework, in which the discriminator’s feedback is integrated into the feedforward path of the generation process. Specifically, the generator conditions on the discriminator’s spatial output response, and its previous generation to improve generation quality over time – allowing the generator to attend and fix its previous mistakes. To effectively utilize the feedback, we propose an adaptive spatial transform layer, which learns to spatially modulate feature maps from its previous generation and the error signal from the discriminator. We demonstrate that one can easily adapt FAL to existing adversarial learning frameworks on a wide range of tasks, including image generation, image-to-image translation, and voxel generation.
GAN-Based virtual-to-real image translation for urban scene semantic segmentation

Semantic image segmentation requires large amounts of pixel-wise labeled training data. Creating such data generally requires labor-intensive human manual annotation. Thus, extracting training data from video games is a practical idea, and pixel-wise annotation can be automated from video games with near perfect accuracy. However, experiments show that models trained using raw video-game data cannot be directly applied to real-world scenes because of the domain shift problem. In this paper, we propose a domain-adaptive network based on CycleGAN that translates scenes from a virtual domain to a real domain in both the pixel and feature spaces. Our contributions are threefold: 1) we propose a dynamic perceptual network to improve the quality of the generated images in the feature spaces, making the translated images are more conducive to semantic segmentation; 2) we introduce a novel weighted self-regularization loss to prevent semantic changes in translated images; and 3) we design a discrimination mechanism to coordinate multiple subnetworks and improve the overall training efficiency. We devise a series of metrics to evaluate the quality of translated images during our experiments on the public GTA-V (a video game dataset, i.e., the virtual domain) and Cityscapes (a real-world dataset, i.e., the real domain) and achieved notably improved results, demonstrating the efficacy of the proposed model.
SolarData package update v1.1: R functions for easy access of Baseline Surface Radiation Network (BSRN)

Previously in “SolarData: An R package for easy access of publicly available solar datasets” [Sol. Energy 171 (2017) A3–A12], the R package SolarData was built for easy access of five publicly available solar datasets. In this update, code for reading data from the Baseline Surface Radiation Network (BSRN), the largest research-grade solar radiation monitoring network, is added to the package. BSRN comprises 66 stations (as of 2019-02) around the globe, which collect 1-min or 3-min radiation data since 1992. These data are stored in the so-called “station-to-archive” files, each containing records from one month and one station, and can be downloaded via ftp. The functions in SolarData v1.1 directly interact with these station-to-archive files, without using the BSRN-Toolbox. It should however be noted that SolarData is not a replacement of BSRN-Toolbox. Instead, it gives R users improved accessibility and freedom in BSRN data manipulation. Like the previous release, all contents herein described are made available on GitHub.
Neural query expansion for code search

Searching repositories of existing source code for code snippets is a key task in software engineering. Over the years, many approaches to this problem have been proposed. One recent tool called NCS, takes in a natural language query and outputs relevant code snippets, often being able to correctly answer Stack Overflow questions. But what happens when the developer doesn’t provide a query with a clear intent? What if shorter queries are used to demonstrate a more vague intent?
Educational Technology and Its Contributions in Students’ Focus and Attention Regarding Augmented Reality Environments and the Use of Sensors

In the course of a teaching and learning process, different cognitive skills are required for a student. Attention is an important variable to be measured, since it plays a fundamental role in the accumulation of information with the stimulus of the learner’s memory in the course of the assimilation of knowledge. The purpose of this article is to demonstrate the influence of augmented reality (AR) used as a tool for educational content in student concentration when compared with the use of traditional teaching and learning technologies. User attention was monitored through an electroencephalography sensor while performing an educational task using either an AR or a traditional interface. This presented favorable results, since it was possible to identify an increase in student attention during the interaction with the AR application, as opposed to its conventional counterpart.
Unified Collaborative Filtering over Graph Embeddings

Collaborative Filtering (CF) by learning from the wisdom of crowds has become one of the most important approaches to recommender systems research, and various CF models have been designed and applied to different scenarios. However, a challenging task is how to select the most appropriate CF model for a specific recommendation task. In this paper, we propose a Unified Collaborative Filtering framework based on Graph Embeddings (UGrec for short) to solve the problem. Specifically, UGrec models user and item interactions within a graph network, and sequential recommendation path is designed as a basic unit to capture the correlations between users and items. Mathematically, we show that many representative recommendation approaches and their variants can be mapped as a recommendation path in the graph. In addition, by applying a carefully designed attention mechanism on the recommendation paths, UGrec can determine the significance of each sequential recommendation path so as to conduct automatic model selection. Compared with state-of-the-art methods, our method shows significant improvements for recommendation quality. This work also leads to a deeper understanding of the connection between graph embeddings and recommendation algorithms.
Moments of Pleasure: A Preliminary Classification of Gustatory mmms and the Enactment of Enjoyment During Infant Mealtimes

The enjoyment of food and the sharing of mealtimes is a normative cultural and social practice. Empirical research on eating enjoyment has, however, been a rather neglected area across the social sciences, often marginalized in favor of health or focusing on individual preferences rather than shared enjoyment. Even with regards to children, their enjoyment of food is typically rated retrospectively via parental reports of mealtime behavior. What is missing is an understanding of how enjoyment becomes a normative, cultural practice during mealtimes. This paper examines this issue in the context of parents feeding their 5-8-month-old infants in the family home, since it is within this context that we can see the early emergence of such practices in often highly routinized situations. The enactment of eating as enjoyable, and of the food as appreciated or "liked" in some way, is a culturally normative practice that becomes recognizable through particular non-lexical ("mmm," "ooh") or lexical ("this is nice, isn't it?") utterances. The data comprise 66 infant mealtimes video-recorded over almost 19 h, from five families living in Scotland. The analysis uses discursive psychology and focuses on the sequential position of different types of parental gustatory mmms as produced during the infant meals. A classification of four types of mmm were identified in the corpus-announcement, receipting, modeling, and encouragement mmms-each associated with features of sequential and multimodal organization within the mealtime. In the majority of instances, mmms were uttered alone with no other assessment terms, and parents typically produced these as an orientation to the enjoyment of their infants', rather than their own, eating practices. The receipting mmms, for instance, occurred at the precise moment when the infant's mouth closed around the food. It is argued that eating enjoyment can be considered as much an interactional practice as an individual sensation, and that non-lexical vocalizations around food are an essential part of sensory practices. The paper thus aims to bridge the gap between cultural and psychological studies of eating enjoyment and contribute to developmental studies of infant feeding in everyday interaction.
Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations

Visual inspection is used in many areas due to the potential high costs of inspection error such as injury, fatality, loss of expensive equipment, scrapped items, rework, or failure to procure repeat business. This study presents an application of hidden Markov models (HMM) to fixations’ sequences analysis during visual inspection of front panels in a home appliance facility. The eye tracking data are gathered when quality control operators perform their tasks. The results support the difference between expert and novice operator. Moreover, the article demonstrates four HMMs with two and three hidden states both for novice and experienced operators and provides analysis and discussion of the outcomes.
News and subjective beliefs: A Bayesian approach to Bitcoin investments

The use of crypto-currencies in financial applications is receiving increasing interest. This paper relies on a Bayesian framework that combines market-neutral information with subjective beliefs to show an application of how Bitcoin can be exploited to build diversified investment strategies. By means of an intuitive procedure based on the Black and Litterman model, I propose to relate portfolio construction with the role of news in generating investors' subjective beliefs, which are computed according to market reactions occurred after similar announcement events in the recent past. To test this approach, the analysis refers to an extremely volatile market phase for Bitcoin such as the interval from mid-2017 to mid-2018. Results indicate that Bitcoin can contribute to improve the risk-adjusted performances of diversified portfolios and that investors' subjective beliefs can help to interpret the fundamental drivers of crypto-currencies' market behaviors. This approach may also stimulate the investigation of more sophisticated strategies built according to the relationships between news and investors' personal views on Bitcoin market dynamics.
What Do We Mean by “Interaction”? An Analysis of 35 Years of CHI

The notion of interaction is essential to human-computer interaction, yet rarely studied. We use quantitative and qualitative methods to investigate how this notion has been used across 35 years of proceedings from the ACM Conference on Human Factors in Computing (CHI). Using natural language processing, we extract 53,568 occurrences of the word “interaction” across 4,604 papers. In these occurrences, we categorize 2,668 unique words that modify how “interaction” is used in a sentence. We show that the use of “interaction” is both increasing and diversifying, suggesting the importance of the notion, but also the difficulty in developing theory about interaction. Our findings show that styles of interaction are closely associated with changes in technology and that modalities and characteristics of interaction are becoming more of a topic than specific devices or widgets. Interaction qualities, relating to structure, feel, effectiveness, and efficiency, are consistently prominent, and the quality of novelty is increasingly frequent. From this analysis, we identify open questions about interaction, including how to build knowledge across changing technologies, how to work toward a model of quality for interaction, and what the core of a science of interaction could be.
A Formal Treatment of Deterministic Wallets

In cryptocurrencies such as Bitcoin or Ethereum, users control funds via secret keys. To transfer funds from one user to another, the owner of the money signs a new transaction that transfers the funds to the new recipient. This makes secret keys a highly attractive target for attacks, and has lead to prominent examples where millions of dollars worth in cryptocurrency have been stolen. To protect against these attacks, a widely used approach are so-called hot/cold wallets. In a hot/cold wallet system, the hot wallet is permanently connected to the network, while the cold wallet stores the secret key and is kept without network connection. In this work, we propose the first comprehensive security model for hot/cold wallets and develop wallet schemes that are provable secure within these models. At the technical level our main contribution is to provide a new provably secure ECDSA-based hot/cold wallet scheme that can be integrated into legacy cryptocurrencies such as Bitcoin. Our construction and security analysis uses a modular approach, where we show how to generically build secure hot/cold wallets from signature schemes that exhibit a rerandomizing property of the keys.
Reinforcement Learning Algorithms in Global Path Planning for Mobile Robot

The paper is devoted to the research of two approaches for global path planning for mobile robots, based on Q-Learning and Sarsa algorithms. The study has been done with different adjustments of two algorithms that made it possible to learn faster. The implementation of two Reinforcement Learning algorithms showed differences in learning time and the methods of building path to avoid obstacles and to reach a destination point. The analysis of obtained results made it possible to select optimal parameters of the considered algorithms for the tested environments. Experiments were performed in virtual environments where algorithms learned which steps to choose in order to get a maximum payoff and reach the goal avoiding obstacles.
Content-adaptive reversible visible watermarking in encrypted images

The reversible visible watermark which serves as the perceptual ownership identifier can be extracted to losslessly recover the original cover media. This paper presents a novel content-adaptive reversible visible watermarking scheme in encrypted images. To achieve the tradeoff between watermark visibility and marked image quality, data embedding positions for accommodating the watermark are adaptively selected using the visual perceptual model before encryption. Due to weak spatial correlation in encrypted images, the data embedding room is vacated before encryption with a traditional reversible data hiding algorithm to contain pixel bits in data embedding positions. Therefore, it is convenient for the data hider to embed the visible watermark in encrypted images by substituting pixel bits in data embedding positions. If the receiver decrypts the marked encrypted image without extracting the embedded watermark, the visibly marked image can be obtained. In addition, if the receiver decrypts the marked encrypted image and extracts the embedded watermark, the original image can be perfectly recovered. Experimental results demonstrate the merits of the proposed scheme in terms of marked image quality, watermark visibility and watermark robustness.
Narrow Convolutional Neural Network for Arabic Dialects Polarity Classification

The complexities and tangles of Arabic dialect in orthography and morphology typically make the sentimental analysis quite challenging.Moreover, most of the classification approaches have addressed this problem based on hand-crafted features.Since the Arabic language has multi-dialects and the language has no word-based order, the extraction process and the classification tasks are more difficult and time consuming.Deep neural network approaches applied to the Arabic language colloquial are very limited.These deep learning approaches typically comprise a structure that is very complex for small quantities of data.The structures are based on wide convolutional networks that are not capable of capturing the entire semantic and sentiment features for Arabic dialects.In this paper, a narrow structure of the convolutional neural network (CNN) has been proposed in order to obtain the tweets representations and classify the Arabic tweets into five, three and two polarities.Sensitivity analysis has been conducted to evaluate the impact of various combination structural properties, such as the number of convolutional filters, pooling size, and filter size on the classification performances.The proposed Arabic narrow convolutional neural network (NCNN) has captured the entire semantic and sentiment information contained in the tweet by maximizing the features of the detector's range.The NCNN performances were estimated to be at its optimum when structured by three convolutional layers, each one followed by the max pooling layer.The model has been developed without using lexicon resources and lexical features or augmented the dataset with extra training data.The narrow model is the first baseline model for Arabic dialects sentiment classifications for a sentence level as it is the first narrow CNN model addressing the Arabic Dialect tweets.NCNN model achieved the lowest macro average mean absolute error (MAE M ) for five polarity and higher Macro average recall (P) for three and two polarities on the SemEval-2017 Arabic dialect Twitter datasets when compared to the other state-of-the-art approaches.
Prototype Design and Test of Blockchain Radio Access Network

The concept of blockchain radio access network (B-RAN) was proposed to integrate cross-network resources including spectra and infrastructures to meet the explosive growth of diverse connectivity needs. In this work, we consider the prototype design of B-RAN and address the issues arising in its implementation. To reduce the access delay, we develop a fast smart contract deployment (FSCD) for B-RAN. Also, we propose the concept of digitized spectrum assets (DSA) in B-RAN for efficient spectrum sharing and management. We further summarize and demonstrate the B-RAN prototype enhanced by the newly introduced FSCD and DSA, along with the overall workflow. We apply comprehensive simulations based on an inhouse experimental platform to illustrate the effectiveness of the proposed B-RAN prototype.