Reviewerly/exHarmony huggingface dataset:
Understanding social perceptions towards interacting with on-skin interfaces in public

Wearable devices have evolved towards intrinsic human augmentation, unlocking the human skin as an interface for seamless interaction. However, the non-traditional form factor of these on-skin interfaces, as well as the gestural interactions performed on them may raise concerns for public wear. These perceptions will influence whether a new form of technology will eventually be accepted, or rejected by society. Therefore, it is essential for researchers to consider the societal implications of device design. In this paper, we investigate the third person perceptions of a user's interactions with an on-skin touch sensor. Specifically, we examine social perceptions towards the placement of the on-skin interface in different body locations, as well as gestural interactions performed on the device. The study was conducted in the United States and Taiwan to examine cross-cultural attitudes towards device usage. The results of this structured examination offer insight into the design of on-skin interfaces for public use.
ECDSA weak randomness in Bitcoin

Bitcoin security draws more and more attention recently. One of Bitcoin vulnerabilities is caused by ECDSA weak randomness. A random number is not cryptographically secure, which leads to private key leakage and even fund theft. This security problem has been well known in Bitcoin community and fixed by applying RFC 6979 update in 2013. In this paper, we systematically revisit the cases where random numbers are reused and evaluate them based on practical Bitcoin transactions. After analyzing Bitcoin transaction dataset from January 2009 to July 2017, we find that there are still approximately 0.48 percent of transactions involving this vulnerability, and 1331 private keys have been compromised. In addition, the transactions related to some involved addresses have a common pattern, which gives us a clue that a spam transaction attack may take advantage of ECDSA weak randomness. We also examine mainstream Bitcoin software wallets to check whether they are susceptible to ECDSA weak randomness. Even the result is quite optimistic, an example that one of the influenced addresses leaked in April 2014 is still in use again in August 2017 reflects that the severity of ECDSA weak randomness may not be paid enough attention even after its discovery and solution in 2013.
Jointgraph: A DAG‐based efficient consensus algorithm for consortium blockchains

Summary The blockchain is a distributed ledger that records all transactions and operations in a shared manner. Public blockchains such as Bitcoin realize decentralization at the cost of mining overhead, which is not suitable for real‐life scenarios requiring high throughput. Techniques such as the consortium blockchain improve efficiency through partial decentralization. However, the consensus algorithms used in the existing state‐of‐the‐art consortium blockchains face many challenges when dealing with commercial applications. For example, the high communication overhead hinders the scalability of PBFT‐based consensus algorithms even though they are efficient at small scale. Hashgraph, one of the most popular Directed Acyclic Graph‐based (DAG‐based) consensus algorithms, achieves good performance in scalability; however, it does not allow users' dynamic participation. To deal with these challenges, we propose Jointgraph, a Byzantine fault‐tolerance consensus algorithm for consortium blockchains based on DAG. In Jointgraph, transactions are packed into events and validated by no less than 2/3 of all members. A supervisor is introduced in our design, who monitors member behaviors and improves consensus efficiency. Simulation results demonstrate that Jointgraph outperforms Hashgraph in both throughput and latency.
Single and multiple outputs decision tree classification using bi-level discrete-continues genetic algorithm

Data classification with decision tree models is an attractive method in data analysis and data mining. However, compared to other classification methods, the quality of prediction of these models is lower when classic heuristics and local optimization training methods are employed. To improve the performance of these models for single output and multiple outputs data sets, an optimal tree construction method based on the genetic algorithm is presented. The presented bi-level discrete-continues genetic algorithm method is able to select effective features as well as construct optimal tree. For this purpose, new operators of selection, crossover, and mutation are designed in terms of continuous and discrete variables. Comparison of the proposed method with other well-known classification methods for some test data sets and real world data shows that the performance of the decision tree models has been upgraded to the best of prediction methods level.
Compact snapshot multiwavelength interferometer

We propose a snapshot and compact multiwavelength interferometer for real-time testing of optical surfaces with large slopes. A color camera with a micropolarizer array simultaneously captures the phase-shifted interferograms of multiple wavelengths, from which four interferograms with π/2 phase shift at each wavelength are extracted for multiwavelength phase-shifting interferometric measurement. The accuracy of the proposed system is validated experimentally, and the testing of freeform surface with large slopes is also demonstrated. The proposed system provides a feasible way to obtain the instantaneous online measurement of freeform surfaces while minimizing environmental disturbance.
Analysis of Promothee II Method in the Selection of the Best Formula for Infants Under Three Years

Abstract Mother’s own milk is the best source of nutrition for almost all babies. Beyond somatic growth, breast milk as a biological fluid has various other benefits, including modulation of postnatal bowel function, ontogeny immunity, and brain development. Although breastfeeding is highly recommended, breastfeeding may not always be possible, appropriate or just adequate. However, following baby’s growth and development, breast milk alone is no longer enough to add nutrition to children. Because of that the baby must be given Mother’s Milk Complementary Food. One milk product that has the best market is a formula, but it is not easy to choose what formula is suitable to meet the nutritional content of babies. Baby formula milk is a substitute produced by the industry for baby consumption. Infant formula tries to mimic the nutritional composition of ASI as close as possible, and is based on cow’s milk or soy milk. A number of alternatives for cow milk-based formulas also exist. One of the SPK methods that can be used is PROMETHEE II. Based on the results of research using 9 criteria, namely: Energy, Fat, Sugar Protein, Carbohydrates, Sodium, Protein, Affordable Prices, More Variants of Flavor, Easy to Search and 5 Alternatives, namely: Dancow, SGM Eksplor, Vidoran Xmart, LactoGrow, Bebelac 3. The results of the assessment obtained the final result of the calculation of net flow is Vidoran Xmart (0.2075: Ranking 1), Bebelac 3 with net flow (0.0825: Ranking 2) and Dancow with net flow (0.0425: Ranking 3). It is hoped that this research can contribute to parents so that they will no doubt choose formula milk that is suitable for babies.
Factors of trust in data reuse

Purpose The purpose of this paper is to quantitatively examine factors of trust in data reuse from the reusers’ perspectives. Design/methodology/approach This study utilized a survey method to test the proposed hypotheses and to empirically evaluate the research model, which was developed to examine the relationship each factor of trust has with reusers’ actual trust during data reuse. Findings This study found that the data producer ( H1 ) and data quality ( H3 ) were significant, as predicted, while scholarly community ( H3 ) and data intermediary ( H4 ) were not significantly related to reusers’ trust in data. Research limitations/implications Further disciplinary specific examinations should be conducted to complement the study findings and fully generalize the study findings. Practical implications The study finding presents the need for engaging data producers in the process of data curation, preferably beginning in the early stages and encouraging them to work with curation professionals to ensure data management quality. The study finding also suggests the need for re-defining the boundaries of current curation work or collaborating with other professionals who can perform data quality assessment that is related to scientific and methodological rigor. Originality/value By analyzing theoretical concepts in empirical research and validating the factors of trust, this study fills this gap in the data reuse literature.
Language Learning Tool for Refugees: Identifying the Language Learning Needs of Syrian Refugees Through Participatory Design

A large number of refugees need to learn a new language in order to transition to, or settle in, a new country. Most refugees have access to smartphones with which they can access mobile language learning tools. However, refugees in different stages of their journeys have different language learning needs. Identifying the unique needs of refugees is essential to design language learning tools that support their language learning activities. Through a two-part participatory design study, we identify and compare the tacit and latent language learning needs of two different groups of Syrian refugees: a group in transition in Lebanon, and a group settling in Germany. The Syrian refugees we worked with in Lebanon are studying English to transition to another country with better living conditions. On the other hand, the Syrian refugees we worked with in Germany are trying to learn German to better integrate into German society. Our results show overlapping needs and specific needs for the two groups of refugees. We present our findings and discuss the opportunities and challenges for designing language learning tools to support the language learning activities of refugees.
Effects of Smart Virtual Assistants' Gender and Language

Smart virtual assistants (SVA) are becoming increasingly popular. Prominent SVAs, including Siri, Alexa, and Cortana, have female-gendered names and voices which raised the concern that combining female-gendered voices and submissive language amplifies gender stereotypes. We investigated the effect of gendered voices and the used language on the perception of SVAs. We asked participants to assess the performance, personality and user experience of an SVA while controlling the gender of the voice and the attributed status of the language. We show that low-status language is preferred but the voice's gender has a much smaller effect. Using low-status language and female-gendered voices might be acceptable but solely combining low-status language with female-gendered voices is not.
Semi-supervised learning for human activity recognition using adversarial autoencoders

SHL recognition challenge 2019 goal is to recognize eight locomotion and transportation (activities) from the inertial sensor data of a smartphone. The dataset contains information from different mobile-phones placement (torso, bag, hips, hand). Participants must provide their predictions based on test data that contains Hand phone sensors information. Only a small amount of Hand phone labeled data exists in the validation data. Train data consists only of torso, bag and hips placed mobile devices. Team DB proposes to apply deep semi-supervised learning. As the base for our model, we have chosen Adversarial Autoencoder (AAE) and employ Convolutional Networks for feature extraction. We prove that semi-supervised learning gives possibility to utilize test unlabeled data during AAE training with small amount of validation labeled data and achieve high model accuracy for Human Activity Recognition task.
Fuzzy cloud-fog computing approach application for human activity recognition in smart homes

Fog Computing is an approach involving smart devices. These devices carry out data processing to provide collaborative services to reach a common goal, usually, in the cloud. In the fog computing paradigm, uncertainty and vagueness are inherent to the data processing due to the limitations of compu tational and communication capabilities of the smart devices. Fuzzy logic and protoforms represent a powerful tool to model and compute imprecise data presented within the fog-computing paradigm. In this paper, we present a fuzzy cloud-fog approach based on fuzzy temporal windows and fuzzy aggregation. The innovations of this paper are: i) to model the uncertainty involved in fog nodes linguistically, ii) to compute and distribute relevant linguistic information (protoforms), and iii) to publish the computed protoforms in the cloud to generate complex protoforms, which reach the common goal. This new fuzzy cloud-fog approach is applied to the problem of activity recognition in smart homes. In this context, the smart devices in a smart home are represented by fog nodes, which cooperate for activity recognition using a fuzzy cloud-fog computing approach to provide solutions in ambient-assisted living. Finally, to demonstrate the effectiveness of the proposal in handling situations/environments where multiple and heterogeneous devices are involved (such as UWB beacons, smart objects and smart wearable devices), a case study of the proposed fuzzy cloud-fog approach is implemented in the smart lab of the University of Jaén. So, the results obtained with the proposed approach in the case study are compared to the results obtained with a non-fuzzy approach with the aim of showing the advantages of the fuzzy methodology.
MUGGLE: MUlti-Stream Group Gaze Learning and Estimation

Being able to accurately predict the common gaze point of a group of persons is of particular interest to precise marketing and automatic group attention assessment. Group gaze estimation faces challenges including small face/head size and outlier observers. To address these challenges, we proposed a novel framework called Multi-stream Group Gaze Learning and Estimation (MUGGLE). The MUGGLE infrastructure includes two inference streams: 1) a holistic stream which utilizes fused attention map as input to a global deep convolutional structure to explore the global geometric configurations and contexts of interesting persons in the scene; and 2) an aggregative stream which robustly aggregates individual gazes via a recurrent structure (e.g., LSTM) to obtain outlier-tolerant estimation. Both streams are seamlessly integrated via a fusion network. Extensive experiments are performed on a fully annotated group gaze image dataset with 8,000+ images and 100,000+ faces (which is publicly releasable). The results demonstrate the effectiveness of the proposed MUGGLE framework in group gaze estimation.
Feature-Based Fusion Adversarial Recurrent Neural Networks for Text Sentiment Classification

Text sentiment classification is a fundamental task of natural language processing. In the past few years, many outstanding methods have attained favorable results in text sentiment classification. However, most of these methods do not make full use of the contextual information of the word embedding layer and attach less importance to the loss of information in the process of forwarding propagation. Hence, an ample room exists for further progress in enhancing the robustness of the model and the feature extraction of text. To tackle these problems, we propose a Feature-Based Fusion Adversarial Recurrent Neural Networks (FARNN-Att) integrated model with an attention mechanism. Firstly, we extract the long-term dependence of text using the BiLSTM network and put forward a novel contextual feature representation way. Subsequently, we combine the prediction results of two features vectors in the full connection layer, which can be captured through the feature connection and attention mechanism. Finally, a regularization method of adversarial training is used to improve the robustness and generalization ability of the model. Our proposed model was compared with other baseline methods such as TextCNN, BiLSTM, BiLSTM-Att, and RCNN on three different public datasets. The experimental results show that our model has state-of-the-art performance in text sentiment classification tasks in terms of accuracy, recall, and F1 score.
Improving information security risk analysis by including threat-occurrence predictive models

Protecting information is a crucial issue in today society, in both work and home environments. Over the years, different tools and technologies have contributed to safeguarding information, including risk analysis methodologies developed to evaluate the risk of threat materialization despite security measures. Traditional risk analysis methodologies base risk computation on, among other parameters, the frequency of occurrence of threats, which is gathered from available historical data. However, as new safeguards are implemented, and vulnerability potential changes, threat frequencies may also change. To take into account the current state of an organization's system as well as historical data, we propose to substitute past threat frequency by the probability of a threat occurring in the future. To compute this future threat probability, we use regression models, validated by a risk analysis for a Spanish SME based on Magerit (Spanish adaptation of ISO/IEC 27005). The results show that the future probability of each threat can be calculated with accuracy, precision, sensitivity and specificity rates above 70%. Obtaining a more realistic risk estimate (reflecting to the current state of vulnerabilities) is translated into the adoption of better and more efficient safeguards that reduce losses and improve information security in a business.
COCO_TS Dataset: Pixel–Level Annotations Based on Weak Supervision for Scene Text Segmentation

The absence of large scale datasets with pixel–level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel–level supervisions for a text detection dataset (i.e. where only bounding–box annotations are available) are generated. In particular, the COCO–Text–Segmentation (COCO_TS) dataset, which provides pixel–level supervisions for the COCO–Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed dataset can be used instead of synthetic data, allowing us to use only a fraction of the training samples and significantly improving the performances.
Fast Continuous User Authentication Using Distance Metric Fusion of Free-Text Keystroke Data

Keystroke dynamics are a powerful behavioral biometric capable of determining user identity and for continuous authentication. It is an unobtrusive method that can complement an existing security system such as a password scheme and provides continuous user authentication. Existing methods record all keystrokes and use n-graphs that measure the timing between consecutive keystrokes to distinguish between users. Current state-of-the-art algorithms report EER's of 7.5% or higher with 1000 characters. With 1000 characters it takes a longer time to detect an imposter and significant damage could be done. In this paper, we investigate how quickly a user is authenticated or how many digraphs are required to accurately detect an imposter in an uncontrolled free-text environment. We present and evaluate the effectiveness of three distance metrics individually and fused with each other. We show that with just 100 digraphs, about the length of a single sentence, we achieve an EER of 35.3%. At 200 digraphs the EER drops to 15.3%. With more digraphs, the performance continues to steadily improve. With 1000 digraphs the EER drops to 3.6% which is an improvement over the state-of-the-art.
Pathways to breast cancer screening artificial intelligence algorithm validation

<h2>Abstract</h2> As more artificial intelligence (AI)-enhanced mammography screening tools enter the clinical market, greater focus will be placed on external validation in diverse patient populations. In this viewpoint, we outline lessons learned from prior efforts in this field, the need to validate algorithms on newer screening technologies and diverse patient populations, and conclude by discussing the need for a framework for continuous monitoring and recalibration of these AI tools. Sufficient validation and continuous monitoring of emerging AI tools for breast cancer screening will require greater stakeholder engagement and the creation of shared policies and guidelines.
Practical Universal k-mer Sets for Minimizer Schemes

Minimizer schemes have found widespread use in genomic applications as a way to quickly predict the matching probability of large sequences. Most methods for minimizer schemes use randomized (or close to randomized) ordering of k-mers when finding minimizers, but recent work has shown that not all non-lexicographic orderings perform the same. One way to find k-mer orderings for minimizer schemes is through the use of universal k-mer sets, which are subsets of k-mers that are guaranteed to cover all windows. The smaller this set the fewer false positives (where two poorly aligned sequences are labeled as possible matches) are identified. Current methods for creating universal k-mer sets are limited in the length of the k-mer that can be considered, and cannot compute sets in the range of lengths currently used in practice. We take some of the first steps in creating universal k-mer sets that can be used to construct minimizer orders for large values of k that are practical. We do this using iterative extension of the k-mers in a set, and guided contraction of the set itself. We also show that this process will be guaranteed to never increase the number of distinct minimizers chosen in a sequence, and thus can only decrease the number of false positives over using the current sets on small k-mers.
Does a Smile Matter if the Person Is Not Real?: The Effect of a Smile and Stock Photos on Persona Perceptions

Does a Smile Matter if the Person Is Not Real?: The Effect of a Smile and a Stock Photo on Persona PerceptionsWe analyze the effect of using smiling/non-smiling and stock photo/non-stock photo pictures in persona profiles on four key persona perceptions, including credibility, likability, similarity, and willingness to use.For this, we collect data from an experiment with 2,400 participants using a 16-item survey instrument and multiple persona profile treatments of which half have a smiling photo/stock photo and half do not.The results from structural equation modeling, supplemented by a qualitative analysis, show that a smile enhances the perceived similarity with the persona, similar personas are more liked, and that likability increases the willingness to use a persona.In contrast, the use of stock photos decreases the perceived similarity with the persona as well as persona credibility, both of which are significant predictors to a willingness to use a persona.These professionally crafted stock-photos seem to diminish the sense of identification with the persona.The above effects are consistent across the tested ages, genders, and races of the persona picture, although the effect sizes tend to be small.The results suggest that persona creators should use smiling pictures of real people to evoke positive perceptions toward the personas.In addition to presenting quantitative evidence on the predictors of willingness to use a persona, our research has implications for the design of persona profiles, showing that the picture choice influences individuals' persona perceptions even when the other persona information is identical.