Reviewerly/exHarmony huggingface dataset:
Searchers adjust their eye-movement dynamics to target characteristics in natural scenes

Abstract When searching a target in a natural scene, it has been shown that both the target’s visual properties and similarity to the background influence whether and how fast humans are able to find it. So far, it was unclear whether searchers adjust the dynamics of their eye movements (e.g., fixation durations, saccade amplitudes) to the target they search for. In our experiment, participants searched natural scenes for six artificial targets with different spatial frequency content throughout eight consecutive sessions. High-spatial frequency targets led to smaller saccade amplitudes and shorter fixation durations than low-spatial frequency targets if target identity was known. If a saccade was programmed in the same direction as the previous saccade, fixation durations and successive saccade amplitudes were not influenced by target type. Visual saliency and empirical fixation density at the endpoints of saccades which maintain direction were comparatively low, indicating that these saccades were less selective. Our results suggest that searchers adjust their eye movement dynamics to the search target efficiently, since previous research has shown that low-spatial frequencies are visible farther into the periphery than high-spatial frequencies. We interpret the saccade direction specificity of our effects as an underlying separation into a default scanning mechanism and a selective, target-dependent mechanism.
Comparison of Radial and Panel Menus in Virtual Reality

Although selection menus are widely used for interaction, their use on 3D virtual reality applications needs to be objectively assessed. The focus of this study is to evaluate a traditional panel and a radial menu in two distinct virtual environment placements (i.e. fixed on the wall and following the users' hands). Fifty-one participants used two different menus of the four possible combinations. To evaluate the menus' effectiveness and efficiency, we measured usability (System Usability Scale Questionnaire), user satisfaction (After-Scenario Questionnaire), time to finish the tasks (in seconds) and the number of unnecessary steps (errors) performed by the users. Overall results showed a clear preference for the traditional panel menu type and the fixed wall placement of the menu. We conclude that all menu types perform well, despite different user preferences, and that fixing the menu to the wall gives users a better overview of both the menu and the virtual environment, improving their ability to perceive their actions on the menu.
An Evaluation of a Key Innovation: Mobile Learning

Abstract This paper examines the impact of m-learning as an innovative tool that is still in its infancy in many countries, but that can significantly contribute to effective teaching and learning in the context of the digital age. Based on the theoretical principles of learning, the uses of m-learning are reviewed. To provide complete insights into its potential adoption in higher education institutions in countries such as Mauritius and Nigeria, the various emerging issues such as technical issues, usability issues, content security and copyright issues as well as issues relating to privacy and cyber bullying are discussed. The paper suggests some of the future trends in the adoption of m-learning, but it concluded that it may not be successful due to other challenges that are typical to many countries in Africa. African countries are faced with context-bound and technological constraints that are hindering the leaners of higher education institutions to benefit from the learning advantages that they may get to improve their learning and for the countries to give more access to higher education through the use of open education that may be technologically enhanced.
Game-theoretical model of cooperation between producers

A network model of manufacturing system is considered. This is a network formation game where players are participants of a production process and their actions are their's requests for interaction. Production networks are formed as a result of an interaction. Players' payoff functions are defined on the set of all possible networks. In this paper the special case of network formation games is considered. Payoff functions are supposed to be additive and depend on subsets of arcs. Two cases are considered. First, subsets of arcs are supposed to be not intersected. The necessary and sufficient conditions for equilibrium are given for this case.
&lt;p&gt;An automated mammogram classification system using modified support vector machine&lt;/p&gt;

Purpose: Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer diagnosis process; hence this study. Methodology: The inputs to the proposed model are raw mammograms downloaded from the Mammographic Image Analysis Society database. Prior to the classification, the raw mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract fifteen textural features from the mammograms at four different angular directions: θ={0°, 45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector machine was used to classify the mammograms as normal, benign and malignant. Results: All of the 37 normal images used as test data were classified as normal (no false positive) and all 41 abnormal images were correctly classified to be abnormal (no false negative), meaning that the sensitivity and specificity of the model in detecting abnormality is 100%. After the detection of abnormality, the system further classified the abnormality on the mammograms to be either “benign” or “malignant”. Out of 23 benign images, 21 were truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be malignant. From these findings, the sensitivity, specificity, positive predictive value, and negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively. Conclusion: This article has further affirmed the prowess of automated CADx systems as a viable tool that could facilitate breast cancer diagnosis by radiologists. Keywords: cancer diagnosis, CADx systems, radiologists, diagnostic errors, GLCM
A Multi-objective Particle Swarm Optimization for Neural Networks Pruning

There is a ruling maxim in deep learning land, bigger is better. However, bigger neural network provides higher performance but also expensive computation, memory and energy. The simplified model which preserves the accuracy of original network arouses a growing interest. A simple yet efficient method is pruning, which cuts off unimportant synapses and neurons. Therefore, it is crucial to identify important parts from the given numerous connections. In this paper, we use the evolutionary pruning method to simplify the structure of deep neural networks. A multi-objective neural networks pruning model which balances the accuracy and the sparse ratio of networks is proposed and we solve this model with particle swarm optimization (PSO) method. Furthermore, we fine-tune the network which is obtained by pruning to obtain better pruning result. The framework of alternate pruning and fine-tuning operations is used to achieve more prominent pruning effect. In experimental studies, we prune LeNet on MNIST and shallow VGGNet on CIFAR-10. Experimental results demonstrate that our method could prune over 80% weights in general with no loss of accuracy.
An HCI Perspective on Distributed Ledger Technologies for Peer-to-Peer Energy Trading

Distributed Ledger Technologies (DLT), such as blockchain, are gaining increasing attention in the energy sector, where they can be used to support Peer-to-Peer (P2P) energy trading. Several proof-of-concept and pilot projects are running all over the world to test this specific use case. However, despite much work addressing the technical and regulatory aspects related to DLT for P2P energy trading, our understanding of the human aspects affecting the adoption of these systems and technologies is still minimal. The development of a decentralized energy market poses interesting challenges to the HCI community and raises important questions that need to be answered: do people trust a system which is, by definition, trust-free? How do they perceive P2P energy trading? What are their needs and motivations for engaging in energy trading? Moreover, are people willing to use cryptocurrencies as a medium of exchange for energy? And, to what extent is full-automation desirable? To shed light on these and related questions, we developed and tested PowerShare, a decentralized, P2P energy trading platform. In this paper, we report on our findings from interviews with nine families that have used PowerShare for a month. Motivated by our empirical findings we conclude by highlighting guidelines for designing P2P energy trading platforms and elaborate directions for further research.
Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels

Many machine learning methods depend on human supervision to achieve optimal performance. However, in tasks such as DensePose, where the goal is to establish dense visual correspondences between images, the quality of manual annotations is intrinsically limited. We address this issue by augmenting neural network predictors with the ability to output a distribution over labels, thus explicitly and introspectively capturing the aleatoric uncertainty in the annotations. Compared to previous works, we show that correlated error fields arise naturally in applications such as DensePose and these fields can be modeled by deep networks, leading to a better understanding of the annotation errors. We show that these models, by understanding uncertainty better, can solve the original DensePose task more accurately, thus setting the new state-of-the-art accuracy in this benchmark. Finally, we demonstrate the utility of the uncertainty estimates in fusing the predictions of produced by multiple models, resulting in a better and more principled approach to model ensembling which can further improve accuracy.
A Modular Architecture for Unsupervised Sarcasm Generation

In this paper, we propose a novel framework for sarcasm generation; the system takes a literal negative opinion as input and translates it into a sarcastic version.Our framework does not require any paired data for training.Sarcasm emanates from context-incongruity which becomes apparent as the sentence unfolds.Our framework introduces incongruity into the literal input version through modules that: (a) filter factual content from the input opinion, (b) retrieve incongruous phrases related to the filtered facts and (c) synthesize sarcastic text from the filtered and incongruous phrases.The framework employs reinforced neural sequence to sequence learning and information retrieval and is trained only using unlabeled non-sarcastic and sarcastic opinions.Since no labeled dataset exists for such a task, for evaluation, we manually prepare a benchmark dataset containing literal opinions and their sarcastic paraphrases.Qualitative and quantitative performance analyses on the data reveal our system's superiority over baselines, built using known unsupervised statistical and neural machine translation and style transfer techniques.
Contrastive Language Adaptation for Cross-Lingual Stance Detection

We study cross-lingual stance detection, which aims to leverage labeled data in one language to identify the relative perspective (or stance) of a given document with respect to a claim in a different target language.In particular, we introduce a novel contrastive language adaptation approach applied to memory networks, which ensures accurate alignment of stances in the source and target languages, and can effectively deal with the challenge of limited labeled data in the target language.The evaluation results on public benchmark datasets and comparison against current state-of-the-art approaches demonstrate the effectiveness of our approach.
Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension

In this work, we propose to use linguistic annotations as a basis for a Discourse-Aware Semantic Self-Attention encoder that we employ for reading comprehension on narrative texts.We extract relations between discourse units, events and their arguments as well as coreferring mentions, using available annotation tools.Our empirical evaluation shows that the investigated structures improve the overall performance (up to +3.4 Rouge-L), especially intra-sentential and cross-sentential discourse relations, sentence-internal semantic role relations, and long-distance coreference relations.We show that dedicating self-attention heads to intra-sentential relations and relations connecting neighboring sentences is beneficial for finding answers to questions in longer contexts.Our findings encourage the use of discourse-semantic annotations to enhance the generalization capacity of self-attention models for reading comprehension.
Rethinking the CSC Model for Natural Images

Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing. In recent years, the Convolutional Sparse Coding (CSC) model, in which the dictionary consists of shift invariant filters, has gained renewed interest. While this model has been successfully used in some image processing problems, it still falls behind traditional patch-based methods on simple tasks such as denoising. In this work we provide new insights regarding the CSC model and its capability to represent natural images, and suggest a Bayesian connection between this model and its patch-based ancestor. Armed with these observations, we suggest a novel feed-forward network that follows an MMSE approximation process to the CSC model, using strided convolutions. The performance of this supervised architecture is shown to be on par with state of the art methods while using much fewer parameters.
Cost-Efficient and Quality of Experience-Aware Provisioning of Virtual Machines for Multiplayer Cloud Gaming in Geographically Distributed Data Centers

Prompted by the remarkable progress in both cloud computing and GPU virtualization, cloud gaming has become increasingly more popular in the gaming industry. In cloud gaming, games are stored and run on cloud servers and the gamers interact with games through thin clients. Cloud gaming service providers generally employ multiple geographically distributed data centers to deliver their services. The main challenge for cloud gaming service providers is to find the best tradeoff between two contradicting objectives: reducing the infrastructure operating costs and increasing the quality of player's experience. In this paper, we address a virtual machine provisioning problem for multiplayer cloud gaming with the objective of minimizing both the inter-player delay among interacting players and the electricity costs of cloud gaming service providers, while providing the good-enough response delay to gamers. We formulate the problem into a constrained multiobjective optimization problem and propose an improved grey wolf algorithm to solve the problem. The performance of our proposed algorithm are assessed by simulation experiments based on the real-world parameters. The results show the superior performance of the proposed approach in comparison with the state-of-the-art approaches applied to similar problems.
Software Evolution in Time and Space: Unifying Version and Variability Management (Dagstuhl Seminar 19191)

Effectively managing versions and variants of software systems are among the main challenges of software engineering. Over the last decades, two large research fields, Software Configuration Management (SCM) and Software Product Line Engineering (SPLE), have focused on addressing the version and the variant management, respectively. Yet, large-scale systems require addressing both challenges in a unified way. The SCM community regularly faces the need to support variants, while SPLE needs versioning support. However, neither community has been successful in producing unified version and variant management techniques that are effective in practice. This seminar aimed at establishing a body of knowledge of version and variant management techniques. Together with industrial practitioners, we invited researchers from both fields to conceive an ontology of SCM and SPLE concepts, to identify open problems, and to elicit and synthesize practitioners' challenges and requirements. These outcomes provided the basis to create a research agenda, research infrastructure, and working groups, and finally, to establish a benchmark for evaluating future research results. As such, the seminar enabled research on enhanced version and variant management techniques that will ultimately be adopted in practice.
CSN-LAUSD Network: A Dense Accelerometer Network in Los Angeles Schools

Abstract The Community Seismic Network-Los Angeles Unified School District is a network of 300 low-cost microelectromechanical systems accelerometers located in schools in the Los Angeles, California, region. They are capable of accurately recording strong motion up to ±2g and are sufficiently spatially dense that they provide unaliased measurements of strong motions up to 1 Hz following a major earthquake. They are used to provide state-of-health monitoring for the schools and surrounding communities to guide the emergency response. As a research tool, they can be used to provide estimates of the site response at the schools and, therefore, provide a much denser set of site responses for ground-motion prediction than is currently available.
Dislocated Boardgames: Design Potentials for Remote Tangible Play

Conventional digital and remote forms of play lack the physicality associated with analog play. Research on the materiality of boardgames has highlighted the inherent material aspects to this analog form of play and how these are relevant for the design of digital play. In this work, we analyze the inherent material qualities and related experiences of boardgames, and speculate how these might shift in remote manifestations. Based on that, we depict three lenses of designing for remote tangible play: physicality, agency, and time. These lenses present leverage points for future designs and illustrate how the digital and the physical can complement each other following alternative notions of hybrid digital–physical play. Based on that, we illustrate the related design space and discuss how boardgame qualities can be translated to the remote space, as well as how their characteristics might change. Thereby, we shed light on related design challenges and reflect on how designing for shared physicality can enrich dislocated play by applying these lenses.
Toward a Blockchain-based Platform to Manage Cybersecurity Certification of IoT devices

The goal of this paper is to propose a blockchain-based platform to enhance transparency and traceability of cybersecurity certification information motivated by the recently adopted EU Cybersecurity Act. The proposed platform is generic and intended to support the trusted exchange of cybersecurity certification information for any electronic product, service, or process. However, for the purposes of this paper, we focus on the case study of the cybersecurity certification of IoT devices, which are explicitly referenced in the recently adopted Cybersecurity Act as one of the main domains where it is highlighted the need for an increased level of trust.
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.
Designing Punjabi Poetry Classifiers Using Machine Learning and Different Textual Features

Analysis of poetic text is very challenging from computational linguistic perspective. Computational analysis of literary arts, especially poetry, is very difficult task for classification. For library recommendation system, poetries can be classified on various metrics such as poet, time period, sentiments and subject matter. In this work, content-based Punjabi poetry classifier was developed using Weka toolset. Four different categories were manually populated with 2034 poems Nature and Festival (NAFE), Linguistic and Patriotic (LIPA), Relation and Romantic (RORE), Philosophy and Spiritual (PHSP) categories consists of 505, 399, 529 and 601 numbers of poetries, respectively. These poetries were passed to various pre-processing sub phases such as tokenization, noise removal, stop word removal, and special symbol removal. 31938 extracted tokens were weighted using Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme. Based upon poetry elements, three different textual features (lexical, syntactic and semantic) were experimented to develop classifier using different machine learning algorithms. Naive Bayes (NB), Support Vector Machine, Hyper pipes and K-nearest neighbour algorithms were experimented with textual features. The results revealed that semantic feature performed better as compared to lexical and syntactic. The best performing algorithm is SVM and highest accuracy (76.02%) is achieved by incorporating semantic information associated with words.