Reviewerly/exHarmony huggingface dataset:
Analytical games for knowledge engineering of expert systems in support to Situational Awareness: The Reliability Game case study

Knowledge Acquisition (KA) methods are of paramount importance in the design of intelligent systems. Research is ongoing to improve their effectiveness and efficiency. Analytical games appear to be a promising tool to support KA. In fact, in this paper we describe how analytical games could be used for Knowledge Engineering of Bayesian networks, through the presentation of the case study of the Reliability Game. This game has been developed with the aim of collecting data on the impact of meta-knowledge about sources of information upon human Situational Assessment in a maritime context. In this paper we describe the computational model obtained from the dataset and how the card positions, which reflect a player belief, can be easily converted in subjective probabilities and used to learn latent constructs, such as the source reliability, by applying the Expectation-Maximisation algorithm.
Analysis of Decadal Climate Predictions with User‐guided Hierarchical Ensemble Clustering

Abstract In order to gain probabilistic results, ensemble simulation techniques are increasingly applied in the weather and climate sciences (as well as in various other scientific disciplines). In many cases, however, only mean results or other abstracted quantities such as percentiles are used for further analyses and dissemination of the data. In this work, we aim at a more detailed visualization of the temporal development of the whole ensemble that takes the variability of all single members into account. We propose a visual analytics tool that allows an effective analysis process based on a hierarchical clustering of the time‐dependent scalar fields. The system includes a flow chart that shows the ensemble members' cluster affiliation over time, reflecting the whole cluster hierarchy. The latter one can be dynamically explored using a visualization derived from a dendrogram. As an aid in linking the different views, we have developed an adaptive coloring scheme that takes into account cluster similarity and the containment relationships. Finally, standard visualizations of the involved field data (cluster means, ground truth data, etc.) are also incorporated. We include results of our work on real‐world datasets to showcase the utility of our approach.
DrunkSelfie: Intoxication Detection from Smartphone Facial Images

Drunk Driving killed over 10,000 million people in 2015, accounting for nearly a third of all traffic-related deaths in the US. In many cases, drivers do not know they are over the limit. Passive methods to detect intoxication so that drinkers can be warned proactively, are desirable. Many young people take selfportraits (selfies) while drinking. We explore whether user intoxication levels can be inferred by image analysis and classification of selfies. We analyzed a corpus of the facial images of 53 subjects after drinking 0-3 glasses of wine, extracted features from the photographs and used machine learning to classify subjects as either sober or drunk. We found that facial lines changed significantly after consuming alcohol and that facial landmark vectors were the most predictive features. We achieved a classification accuracy of 81% using Gradient Boosted Machines for classifying subjects as either "sober" (0 or 1 glasses of wine) or "non-sober" (2 or 3 glasses of wine). Augmenting the original dataset of studio images by blurring, rotating, and altering lighting in order to capture more realistic party/bar scenarios, also improved classification accuracy. We used our intoxication classifiers to build DrunkSelfie, an Android application that estimates the subject's drunkenness from a selfie.
Term evaluation metrics in imbalanced text categorization

Abstract This paper proposes four novel term evaluation metrics to represent documents in the text categorization where class distribution is imbalanced. These metrics are achieved from the revision of the four common term evaluation metrics: chi-square , information gain , odds ratio , and relevance frequency . While the common metrics require a balanced class distribution, our proposed metrics evaluate the document terms under an imbalanced distribution. They calculate the degree of relatedness of terms with respect to minor and major classes by considering their imbalanced distribution. Using these metrics in the document representation makes a better distinction between the documents of the minor and major classes and improves the performance of machine learning algorithms. The proposed metrics are assessed over three popular benchmarks (two subsets of Reuters-21578 and WebKB) by using four classification algorithms: support vector machines, naive Bayes, decision trees, and centroid-based classifiers. Our empirical results indicate that the proposed metrics outperform the common metrics in the imbalanced text categorization.
Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR

Room-scale 3D position tracking enables users to explore a virtual environment by physically walking, which improves comfort and the level of immersion. However, when users walk with their eyesight blocked by a head-mounted display, they may unexpectedly lose their balance and fall if they bump into real-world obstacles or unintentionally shift their center of mass outside the margin of stability. This paper evaluates balance recovery methods and intervention timing during the use of VR with the assumption that the onset of a fall is given. Our experiment followed the tether-release protocol during clinical research and induced a fall while a subject was engaged in a secondary 3D object selection task. The experiment employed a two-by-two design that evaluated two assistive techniques, i.e., video-see-through and auditory warning at two different timings, i.e., at fall onset and 500ms prior to fall onset. The data from 17 subjects showed that video-see-through triggered 500 ms before the onset of fall can effectively help users recover from falls. Surprisingly, video-see-through at fall onset has a significant negative impact on balance recovery and produces similar results to those of the baseline condition (no intervention).
DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

Abstract Adverse reactions caused by drug‐drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post‐marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e. , exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DTVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain‐specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in‐depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case‐studies and interviews by drug analysts at the US Food and Drug Administration ‐ both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug‐drug interactions.
Pedestrian detection using multi-channel visual feature fusion by learning deep quality model

Object detection has been widely applied in modern intelligent systems, especially using convolutional neural networks (CNNs). Pedestrian detection is a key technique in video surveillance, which could automatically locate special pedestrian. However, conventional CNN based methods such as Fast/Faster R-CNN cannot handle pedestrian detection effectively due to the extremely similar of positives and hard negatives. In this paper, in order to solve hard negative problem in pedestrian detection, we incorporate classifier enhancement and representational ability of CNNs. More specifically, we first fuse multi-channel visual features (color, texture, semantic) for quality assessment. Then, we propose “Reduction-adjustment” (RA) block which can enhance feature extraction and can be flexibly embedded into CNNs. In our implementation, we embed RA blocks into a base model such as VGG 16. Afterwards, we apply Faster R-CNN as a detection system to classify and locate pedestrians. Extensive experiments on Caltech, ETH and CityPersons datasets demonstrate that our deep model is feasible and effective for pedestrian detection.
Privacy-Preserving Interest-Ability Based Task Allocation in Crowdsourcing

Numerous crowdsourcing applications have emerged in our daily lives, which enable customers to outsource their complicated tasks to a crowd of workers. However, the information of task tags and worker profiles is explicitly obtained by the crowdsourcing server to recommend tasks effectively, which violates the privacy of both customers and workers. Moreover, the worker's ability to do the task should also be verified in a privacy-preserving way. To address these issues, we propose a privacy-preserving interest-ability based task allocation scheme in crowdsourcing, which protects both task and worker privacy and enables the crowdsourcing server to allocate tasks in a fine-grained way. Specifically, by utilizing attribute-based encryption (ABE) and proxy re-encryption based searchable encryption (PRE-SE) on the task content and task tags respectively, customers are able to enforce fine-grained ability requirements on their tasks, and workers can specify flexible interests to choose their desired tasks. Additionally, ElGamal signature enables workers to prove their abilities to the crowdsourcing server without revealing the task content. Numerical analysis and experiment results demonstrate that our proposed scheme is efficient in terms of computation and storage overhead and is practical to be implemented in crowdsourcing.
Passive solar tracker based in the differential thermal expansion of vertical strips

This paper presents a multiple axis passive solar tracking concept that takes advantage of the thermal expansion induced length variation of a material when exposed to sunshine. The differential expansion of three vertical thin flat strips with different orientations is amplified by a lever system to enable tracking of the apparent motion of the sun in the sky. A passive solar tracking system prototype supporting a photovoltaic (PV) module was built and tested. The model and experimental results show that the tracking system can correctly follow the azimuthal motion during the day. The tracking of solar elevation is less pronounced. The use of this tracking system leads to a 28% increase in PV generation during the testing period.
Oui! Outlier Interpretation on Multi‐dimensional Data via Visual Analytics

Abstract Outliers, the data instances that do not conform with normal patterns in a dataset, are widely studied in various domains, such as cybersecurity, social analysis, and public health. By detecting and analyzing outliers, users can either gain insights into abnormal patterns or purge the data of errors. However, different domains usually have different considerations with respect to outliers. Understanding the defining characteristics of outliers is essential for users to select and filter appropriate outliers based on their domain requirements. Unfortunately, most existing work focuses on the efficiency and accuracy of outlier detection, neglecting the importance of outlier interpretation. To address these issues, we propose Oui, a visual analytic system that helps users understand, interpret, and select the outliers detected by various algorithms. We also present a usage scenario on a real dataset and a qualitative user study to demonstrate the effectiveness and usefulness of our system.
Design Thinking and Scrum in Software Requirements Elicitation: A Case Study

Design Thinking is an innovative human-centered methodology that has gained visibility and importance for its great efficacy and efficiency in generating and testing innovative ideas. The present work seeks the application of this method in software development, more specifically in Software Requirements Elicitation. To this aim, the Design Thinking method was adapted by the team through the concepts of the Scrum framework for application in a case study that was conducted within a Brazilian state university. This study has verified this method to design a system for allocating and reserving resources for this university. Through the results obtained, this model can be considered positive, since it was possible to model a solution with 95% of average completeness and 100% of stakeholders’ satisfaction.
Summary of Association Rules

In recent years, scholars at home and abroad have conducted a large number of researches on association rules, in order to deeply understand the mining technology of association rules, and master its research status and current situation, The development trend, first of all, related definitions and classification methods of association rules are introduced. Secondly, the general methods of association rule mining are summarized from serial and parallel perspectives, and some typical association rule mining modes are summarized and analysed. Finally, the quality improvement of association rule mining and its field application are discussed.
On a Minimization of Variables to Represent Sparse Multi-Valued Input Decision Functions

A multiple-valued input decision function is a mapping f:P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> →{0,1}, where P={0,1, ..., p-1}. This paper considers the learning of such a function. That is, given the TRUE-set T ⊆ P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> and the FALSE-set F ⊆ P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> , obtain a function f such that f(→a)=1 for any →a ∈ T, and f(→b)=0 for any →b ∈ F. We show a method to find a function such that f depends on the least number of variables. Applications of such functions include detection of poisonous mushrooms, hepatitis and breast cancer.
Special issue on storytelling and information systems

Information Systems JournalVolume 29, Issue 6 p. 1122-1125 EDITORIAL Special issue on storytelling and information systems Gerhard Schwabe, Corresponding Author Gerhard Schwabe schwabe@ifi.uzh.ch Department of Informatics, University of Zurich, Zürich, Switzerland Correspondence Prof. Dr. Gerhard Schwabe, Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050 Zürich, Switzerland. Email: schwabe@ifi.uzh.chSearch for more papers by this authorAlexander Richter, Alexander Richter School of Information Management, Victoria University Wellington, Wellington, New ZealandSearch for more papers by this authorErik Wende, Erik Wende Department of Informatics, University of Zurich, Zürich, SwitzerlandSearch for more papers by this author Gerhard Schwabe, Corresponding Author Gerhard Schwabe schwabe@ifi.uzh.ch Department of Informatics, University of Zurich, Zürich, Switzerland Correspondence Prof. Dr. Gerhard Schwabe, Department of Informatics, University of Zurich, Binzmühlestrasse 14, 8050 Zürich, Switzerland. Email: schwabe@ifi.uzh.chSearch for more papers by this authorAlexander Richter, Alexander Richter School of Information Management, Victoria University Wellington, Wellington, New ZealandSearch for more papers by this authorErik Wende, Erik Wende Department of Informatics, University of Zurich, Zürich, SwitzerlandSearch for more papers by this author First published: 16 July 2019 https://doi.org/10.1111/isj.12258Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume29, Issue6Special Issue: Combined Special issues on ‘Storytelling and Information Systems’ and ‘Smart Service Systems’November 2019Pages 1122-1125 RelatedInformation
Effects of Character Guide in Immersive Virtual Reality Stories

Bringing cinematic experiences from traditional film screens into Virtual Reality (VR) has become increasingly popular in recent years. However, striking a balance between storytelling and user interaction can cause a big challenge for filmmakers. In this paper, we present a media review on the common strategies that constructed the existing framework of computer generated cinematic VR by evaluating over 80 real-time rendered interactive experiences across different media. We summarized the most-used methods, which creators applied to maintain a relative control when presenting a narrative experience in VR, that were associated with story-progression strategies and attention guidance techniques. We then approach the problem of guiding the audience through major events of a story in VR by using a virtual character as a travel guide providing assistance in directing viewers attention to the target. To assess the effectiveness of this technique, we performed a controlled experiment applying the method in three VR videos. The experiment compared three variations of the character guide: (1) no guide, (2) a guide with a matching art style to the video, and (3) a guide with a non-matching art style. The experiment results provided insights for future directors and designers into how to draw viewers attention to a target point within a narrative VE, such as what could be improved and what should be avoided.
Stateful human-centered visual captioning system to aid video surveillance

The study of Natural Language Generation (NLG), especially how human beings narrate the world, assists in understanding of the visual world for surveillance. Our research proposes an effective technique to axiomatically develop multi-line textual description of visual data by exploiting deep Convolution Neural Networks (CNN). Textual description of visual data aids in providing textual tags for visual information. A human can retrieve elected videos from a repository based on visual tags. Videos contain more complex and detailed information than images and provide more language data. The proposed feats-rich model encodes the visual contents to visual and facial features using CNN architecture. Encoded features are passed to two layer LSTM units with attention mechanism, reducing the number of parameters by encompassing relevant details. Experimental results on Trecvid 2016 and UET-Surveillance dataset depict that model outperforms state-of-the-art methods by scoring BLEU score of 0.35 and 0.52, respectively.
Soft computing methods for predicting daily global solar radiation

In this study, four artificial intelligence (AI) technologies, including extreme learning machine (ELM), a novel hybrid model which combines the least squares support vector machine with particle swarm optimization algorithm (PSO-LSSVM), back propagation neural network (BPNN) model and generalized regression neural network (GRNN) model, were developed for estimating daily global solar radiation (GSR) for Zhengzhou of China. Six meteorological variables are selected as the evaluating indices, while the daily GSR is the output. Three statistical indices were employed to measure and evaluate the prediction performance of these four AI models. By comparison of the results of these four AI models and an empirical model, it is concluded that the developed hybrid model which combines the least squares support vector machine with particle swarm optimization algorithm outperforms the other four models and it can be used for forecasting the daily GSR with high accuracy.
New Loan System Based on Smart Contract

The traditional lending system has problems such as the need to establish a trust foundation, the third-party trust media to participate in transactions, and the inevitable breach of contract behavior. The combination of smart contracts and blockchain technology has become a breakthrough point in solving the problem of lending systems. Taking the application of smart contract in the loan system as an example, this paper uses the blockchain as the storage characteristic of the distributed database, decentralizes and stably stores the smart contract on the blockchain for the purpose of directly controlling the transaction assets and their transfer. Through the rational design and application of intelligent contracts, the coded enforcement of the transaction process in the lending system is promoted, and the problems of insufficient supervision ability and low loan efficiency in the transaction process are improved. It also shows that there is a great space for innovation and practical prospects for the reasonable combination of smart contracts and blockchains.
Multi-agent Algorithms for Building Semantic Representations of Spatial Information in a Framework of Neurocognitive Architecture

The paper outlines the conceptual basics of multi-agent modeling of the semantics of subjective reflexive mapping of the interaction between real objects, space and time. A holistic view of the intellectual agent about the environment is needed to develop a learning system for internal representation of the events localization space to realize orientation and navigation of autonomous mobile systems. The paper proves that the multi-agent neurocognitive architecture is an effective formalism for describing the semantics of the spatial localization of events. Main theoretical foundations have been developed for the simulation of spatial relations using the so-called multi-agent facts, consisting of software agents-concepts, reflecting semantic categories corresponding to parts of speech. It is shown that locative software agents that describe the spatial location of objects and events, forming homogeneous connections, compose the so-called field locations.