Reviewerly/exHarmony huggingface dataset:
Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs

Programs on a visual cognitive computer learned underlying concepts and generalized well to new settings without explicit demonstrations.
Regulation of the Crypto-Economy: Managing Risks, Challenges, and Regulatory Uncertainty

Distributed ledger technology, also known as the blockchain, is gaining traction globally. Blockchain offers a secure validation mechanism and decentralized mass collaboration. Cryptocurrencies make use of this technology as a new asset class for investors worldwide. Cryptocurrencies are being used by companies to raise capital via initial coin offerings (ICOs). The substantial inflow of unregulated capital into a transactional and transnational industry has aroused interest from not just investors, but also national securities and monetary regulatory agencies. In this paper, we review the Security and Exchange Commission’s initial statements and subsequent pronouncements on ICO’s to illustrate the potential problems with applying an older legal framework to an ever-evolving ecosystem. Recognizing the inability of enforcement within existing regulatory frameworks, we discuss the importance of regulation of the crypto asset class and internal collaboration between government agencies and developers in the establishment of an ecosystem that integrates investor protection and investments.
GAN-Based Semi-Supervised Learning Approach for Clinical Decision Support in Health-IoT Platform

With the development of the Internet of Things (IoT) technology, its application in the medical field becomes more and more extensive.However, with a dramatic increase in medical data obtained from the IoT-based health service system, labeling a large number of medical data requires high cost and relevant domain knowledge.Therefore, how to use a small number of labeled medical data reasonably to build an efficient and high-quality clinical decision support model in the IoT-based platform has been an urgent research topic.In this paper, we propose a novel semi-supervised learning approach in association with generative adversarial networks (GANs) for supporting clinical decision making in the IoT-based health service system.In our approach, GAN is adopted to not only increase the number of labeled data but also to compensate the imbalanced labeled classes with additional artificial data in order to improve the semi-supervised learning performance.Extensive evaluations on a collection of benchmarks and real-world medical datasets show that the proposed technique outperforms the others and provides a potential solution for practical applications.
A Novel Cryptographic Substitution Box Design Using Gaussian Distribution

In this paper, a novel method is being proposed to construct a substitution box or Boolean function for block ciphers using Gaussian distribution and linear fractional transform. The substitution box is constructed by employing a linear fractional transform based on Box-Muller transform, polarization decision, and central limit algorithm. The cryptographic strength of the proposed S-boxes is evaluated with standardized tests such as linear approximation probability, unified averaged changed intensity, bit independent criterion, histogram analysis, nonlinearity score, strict avalanche criterion, and differential approximation probability. The results show that the proposed substitution box achieves better cryptographic strength as compared with the state-of-the-art techniques.
Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance

Picking foraminifera from sediment samples is an essential, but repetitive and low-reward task that is well-suited for automation. The first step toward building a picking robot is the development of an automated identification system. We use machine learning techniques to train convolutional neural networks (CNNs) to identify six species of extant planktic foraminifera that are widely used by paleoceanographers, and to distinguish the six species from other taxa. We employ CNNs that were previously built and trained for image classification. Foraminiferal training and identification use reflected light microscope digital images taken at 16 different illumination angles using a light-emitting diode (LED) ring. Overall machine accuracy, as a combination of precision and recall, is better than 80% even with limited training. We compare machine performance to that of human pickers (six experts and five novices) by tasking each with the identification of 540 specimens based on images. Experts achieved comparable precision but poorer recall relative to the machine, with an average accuracy of 63%. Novices scored lower than experts on both precision and recall, for an overall accuracy of 53%. The machine achieved fairly uniform performance across the six species, while participants' scores were strongly species-dependent, commensurate with their past experience and expertise. The machine was also less sensitive to specimen orientation (umbilical versus spiral views) than the humans. These results demonstrate that our approach can provide a versatile ‘brain’ for an eventual automated robotic picking system.
Usability Measurement of Mobile Applications with System Usability Scale (SUS)

The mobile application market is expanding with the diversity in mobile devices, and competition among the mobile application developers becomes fierce. Usability of the mobile applications is crucial to gain a competitive advantage under these circumstances. This study aims to reveal the difference in terms of usability of four of the commonly used mobile applications (WhatsApp, Facebook, YouTube, and Mail). Furthermore, this study investigates the difference in terms of usability between iOS and Android operating systems. To measure the usability of the mobile applications, a System Usability Scale (SUS) with an adjective rating scale is applied to the young 222 participants, using the applications on their mobile phones. The result of the study shows that usability of all applications is satisfactory and above the standards. The comparison of mobile applications with each other shows that, WhatsApp has the highest usability score, whereas Facebook has the lowest one. In addition, according to the results, there is no significant difference between operating systems in terms of the usability of mobile applications.
Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm

Recently a number of algorithms under the theme of 'unbiased learning-to-rank' have been proposed, which can reduce position bias, the major type of bias in click data, and train a high-performance ranker with click data. Most of the existing algorithms, based on the inverse propensity weighting (IPW) principle, first estimate the click bias at each position, and then train an unbiased ranker with the estimated biases using a learning-to-rank algorithm. However, there has not been a method for unbiased pairwise learning-to-rank that can simultaneously conduct debiasing of click data and training of a ranker using a pairwise loss function. In this paper, we propose a novel framework to accomplish the goal and apply this framework to the state-of-the-art pairwise learning-to-rank algorithm, LambdaMART. Our algorithm named Unbiased LambdaMART can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker. Experiments on benchmark data show that Unbiased LambdaMART can significantly outperform existing algorithms by large margins. In addition, an online A/B Testing at a commercial search engine shows that Unbiased LambdaMART can effectively conduct debiasing of click data and enhance relevance ranking.
Visual Quality Assessment for Super-Resolved Images: Database and Method

Image super-resolution (SR) has been an active research problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR performance has hindered technology development. In this paper, we fill a gap in the literature by providing the first publicly available database as well as a new image quality assessment (IQA) method specifically designed for assessing the visual quality of super-resolved images (SRIs). In constructing the quality assessment database for SRIs (QADS), we carefully selected 20 reference images and created 980 SRIs using 21 image SR methods. Mean opinion score (MOS) for these SRIs is collected through 100 individuals participating in a suitably designed psychovisual experiment. Extensive numerical and statistical analysis is performed to show that the MOS of QADS has excellent suitability and reliability. The psychovisual experiment has led to the discovery that, unlike distortions encountered in other IQA databases, artifacts of the SRIs degenerate the image structure as well as the image texture. Moreover, the structural and textural degenerations have distinctive perceptual properties. Based on these insights, we propose a novel method to assess the visual quality of SRIs by separately considering the structural and textural components of images. Observing that textural degenerations are mainly attributed to dissimilar texture or checkerboard artifacts, we propose to measure the changes of textural distributions. We also observe that structural degenerations appear as blurring and jaggies artifacts in SRIs and develop separate similarity measures for different types of structural degenerations. A new pooling mechanism is then used to fuse the different similarities together to give the final quality score for an SRI. The experiments conducted on the QADS demonstrate that our method significantly outperforms the classical as well as current state-of-the-art IQA methods.
Enhancing L2 learning through a mobile assisted spaced-repetition tool: an effective but bitter pill?

We tested the effectiveness of the spaced-repetition flashcard application, Anki, on improving 62 university-level learners’ second language (L2) learning in a semester-long beginning Spanish course. Using effective study strategies is critical for L2 learning, but college students learning an L2 often study with strategies that are only useful for short-term performance. Prior research has shown that spaced-repetition testing increases long-term memory retention so we hypothesized that regularly studying L2 vocabulary with Anki would improve L2 learning. Participants were students enrolled in a beginning Spanish course who were assigned to study with Anki as a class requirement. The results showed a positive relationship between days studying with Anki and Spanish performance at the end of the semester even while controlling for baseline abilities and for motivation, self-efficacy, and beliefs. However, students were reluctant to use the app and reported low enjoyment. Regardless, participants also reported having higher motivation and more effort-based or incremental beliefs about learning at the end of the semester. The present study answers calls for experimental research on specific strategy interventions that also produce genuine gains in second language acquisition.
Ensemble of Classifiers Based on Multiobjective Genetic Sampling for Imbalanced Data

Imbalanced datasets may negatively impact the predictive performance of most classical classification algorithms. This problem, commonly found in real-world, is known in machine learning domain as imbalanced learning. Most techniques proposed to deal with imbalanced learning have been proposed and applied only to binary classification. When applied to multiclass tasks, their efficiency usually decreases and negative side effects may appear. This paper addresses these limitations by presenting a novel adaptive approach, E-MOSAIC (Ensemble of Classifiers based on MultiObjective Genetic Sampling for Imbalanced Classification). E-MOSAIC evolves a selection of samples extracted from training dataset, which are treated as individuals of a MOEA. The multiobjective process looks for the best combinations of instances capable of producing classifiers with high predictive accuracy in all classes. E-MOSAIC also incorporates two mechanisms to promote the diversity of these classifiers, which are combined into an ensemble specifically designed for imbalanced learning. Experiments using twenty imbalanced multi-class datasets were carried out. In these experiments, the predictive performance of E-MOSAIC is compared with state-of-the-art methods, including methods based on presampling, active-learning, cost-sensitive, and boosting. According to the experimental results, the proposed method obtained the best predictive performance for the multiclass accuracy measures mAUC and G-mean.
Cloud‐based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images

The advancement of computer- and internet-based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient-doctor diagnostics is extended to a more robust advanced concept of E-health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud-based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape-based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross-validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.
Geoscience keyphrase extraction algorithm using enhanced word embedding

A large amount of unstructured textual data about geoscience structures and minerals is buried in geoscience documents and is unused. Automatic keyphrase extraction provides opportunities to leverage this wealth of data for analysis and knowledge discovery. However, keyphrase extraction remains a complicated task, and the performance of state-of-the-art approaches is still low. Automatic discovery of high-quality and meaningful keyphrases requires the application of useful knowledge and suitable techniques. Seeing both challenges and opportunities in the situation described above, this paper proposes an ontology and enhanced word embedding-based (OEWE) methodology for automatic keyphrase extraction from geoscience documents. We first develop a quantitative analysis for keyphrase extraction evaluation based on conditional probability and the naive Bayesian model, which is valuable when human-annotated keyphrases are not available. The domain ontology is then performed on a multiway tree to enrich the domain-specific knowledge on certain concepts and relationships in a domain. Simultaneously, word2vec, a model of a word distribution using deep learning, is updated by applying the geological ontology, and it links domain background information and identifies infrequent but representative keyphrases. We use two homemade geoscience datasets to evaluate the performance of OEWE. We compare our method with frequency, term frequency-inverse document frequency (TF-IDF), TextRank and rapid automatic keyword extraction (RAKE), finding that our method achieves average F1 scores of 30.1% and 40.7% on two manually annotated datasets.
Pathways for the transition of the Polish power sector and associated risks

In Poland, coal has been a key driver of energy security and, despite its declining role, still accounts for most of the country’s power generation mix. Transition potential for the fossil fuel-based power sector can be reflected in a heated debate between a coal-oriented, evolutionary process of slowly reducing emissions; and a more ambitious renewable energy-driven pathway. Both pathways feature barriers; some are evident in standard economic analyses, while others are not. Here, we use a bottom-up energy model to identify the optimal energy mix for the two pathways, and a dynamic stochastic general equilibrium macroeconomic model to assess their socioeconomic implications. We then employ fuzzy cognitive maps to explore which agenda appears to be favoured by stakeholders, and identify omitted risk channels associated with both pathways. Finally, based on these insights, we reiterate the modelling analyses and assess the socioeconomic implications of some of the identified risk channels.
Co-Created Personas

Personas are powerful tools for designing technology and envisioning its usage. They are widely used to imagine archetypal users around whom to orient design work. We have been exploring co-created personas as a technique to use in co-design with users who have diverse needs. Our vision was that this would broaden the demographic and liberate co-designers of their personal relationship with a health condition. This paper reports three studies where we investigated using co-created personas with people who had Parkinson's disease, dementia or aphasia. Observational data of co-design sessions were collected and analysed. Findings revealed that the co-created personas encouraged users with diverse needs to engage with co-designing. Importantly, they also afforded additional benefits including empowering users within a more accessible design process. Reflecting on the outcomes from the different user groups, we conclude with a discussion of the potential for co-created personas to be applied more broadly.
On post-processing day-ahead NWP forecasts using Kalman filtering

Kalman filtering is an important concept in engineering and statistics. In the field of solar forecasting, it is well known as a numerical weather prediction (NWP) post-processing technique. However, it appears that this acknowledged post-processing technique needs some revisit. Since Kalman filtering is a sequential procedure, i.e., actual measurement from t is required to filter the forecast made for time t+1, it changes the forecast horizon of NWP from day-ahead to hour-ahead. Hence, the previously claimed improvements over NWP forecasts are not interpretable. Two simple remedies are proposed, which address the forecast horizon problem, but the effectiveness of the remedies is thought to be minimal.
Toward Practical Volumetric Video Streaming on Commodity Smartphones

Volumetric videos offer six degree-of-freedom (DoF) as well as 3D rendering, making them highly immersive, interactive, and expressive. In this paper, we design Nebula, a practical and resource-efficient volumetric video streaming system for commodity mobile devices. Our design leverages edge computing to reduce the computation burden on mobile clients. We also introduce various optimizations to lower the perceived "motion-to-photon" delay, to dynamically adapt to the fluctuating network bandwidth, and to reduce the system's resource consumption while maintaining a high QoE.
Predicting susceptibility to social influence in phishing emails

To reduce the threat caused by phishing attacks, it is vital to investigate why some phishing attacks are successful, and why some people are more susceptible to them than others. To examine this, we used a social influence framework, and applied the Susceptibility to Persuasion Strategies scale within a dual-process model of persuasion framework. A total of 985 participants took part in a role-play scenario-based phishing study. Results indicated that phishing emails utilising scarcity and social proof principles were least successful, whereas those applying consistency and reciprocity principles were most successful. The same principles were also considered least and most persuasive according to the Susceptibility to Persuasion Strategies scale. For the majority of principles, participants who were susceptible to a specific principle were significantly more susceptible to emails containing that principle. Further results revealed that age; the percentage of time spent using a computer; susceptibility to the social proof principle; and, both dispositional and situational impulsivity, were significant predictors in people's ability to detect phishing emails. Practical implications of these findings as well as future directions are discussed.
Spotify Guilds: How to Succeed With Knowledge Sharing in Large-Scale Agile Organizations

The new generation of software companies has revolutionized the way companies are designed. While bottom-up governance and team autonomy improve motivation, performance, and innovation, managing agile development at scale is a challenge. We describe how Spotify cultivates guilds to help the company share knowledge, align, and make collective decisions.
Inferencing underspecified natural language utterances in visual analysis

Handling ambiguity and underspecification of users' utterances is challenging, particularly for natural language interfaces that help with visual analytical tasks. Constraints in the underlying analytical platform and the users' expectations of high precision and recall require thoughtful inferencing to help generate useful responses. In this paper, we introduce a system to resolve partial utterances based on syntactic and semantic constraints of the underlying analytical expressions. We extend inferencing based on best practices in information visualization to generate useful visualization responses. We employ heuristics to help constrain the solution space of possible inferences, and apply ranking logic to the interpretations based on relevancy. We evaluate the quality of inferred interpretations based on relevancy and analytical usefulness.