Reviewerly/exHarmony huggingface dataset:
How to Write CHI Papers (Third Edition)

We base everything that we do as researchers on what we write. Primarily for graduate students and young researchers, it is hard to turn a research project into a successful CHI publication. This struggle continues for postdocs and young professors trying to author excellent reviews for the CHI community that pinpoint flaws and improvements in research papers. This third edition of the successful CHI paper writing course offers hands-on advice and more in-depth tutorials on how to write papers with clarity, substance, and style. It is structured into three 80-minute units with a focus on writing CHI papers.
VisCrimePredict

Open multidimensional data from social media and similar sources often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, it becomes easier for users to interact with and select meaningful information from large data sets. The prevention of crime is a crucial issue for law-enforcing agencies tasked with maintaining societal stability. The ability to visualise crime patterns and predict imminent incidents accurately opens new possibilities in crime prevention. In this paper, we present VisCrimePredict, a system that uses visual and predictive analytics to map out crimes that occurred in a region/neighbourhood. VisCrimePredict is underpinned by a novel algorithm that creates trajectories from heterogeneous data sources such as open data and social media with the aim to report incidents of crime. VisCrimePredict uses a Long Short Term Memory (LSTM) algorithm for trajectory prediction. A proof of concept implementation of VisCrimePredict and an experimental evaluation of crime trajectory prediction accuracy using LSTM neural network concludes the paper.
Identity and Access Management for Libraries

Linked open data will cause libraries to transition from authority control creating text strings to identity and access management using identifiers to select a single identity. The key step for libraries moving into linked open data for identity and access management is to correct thousands of incorrect names and subject access points in our online catalogs. This article describes a case study of the process of cleaning up unauthorized access points for personal and corporate names in the University of Illinois Library online catalog. This article is meant to help readers from other libraries prepare for linked open data environment.
Secure container orchestration in the cloud

In the late few years, cloud computing has been moving towards becoming the predominant infrastructure paradigm because of its scale economy advantages. Consequently, a great deal of sensitive and valuable information has begun to inhabit the cloud environment. At the current stage, a large amount of organizations is yet to step to the public cloud world because, primarily, the loss of infrastructure ownership gives away control of such information, and that is generally perceived as a severe security risk.
Dissent by Design

The past decade has seen a welcome rise in critical reflection in HCI [29,13,3,19,20,21]. But the use of manifestos - not to promote but to provoke - is still rare in comparison to more established disciplines. Digital activism has given new life to the manifesto, and the manifesto may in turn give new life to CHI - prompting new ideas by temporarily liberating scholars from the confines of careful speech and rational argument. We present a manifesto for manifestos; a chance for the CHI community to question its status quo and dream of its possible futures using our purpose-built authoring tools.
Visualization of Big Spatial Data Using Coresets for Kernel Density Estimates

The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatial data suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampling. We also introduce a method to ensure that thresholding of low values based on sampled data does not omit any regions above the desired threshold when working with sampled data. We demonstrate the effectiveness of our approach using both, artificial and real-world large geospatial datasets.
Fast Large Integer Modular Addition in GF(p) Using Novel Attribute-Based Representation

Addition is an essential operation in all cryptographic algorithms. Higher levels of security require larger key sizes and this becomes a limiting factor in GF(p) using large integers because of the carry propagation problem. We propose a novel and efficient attribute-based large integer representation scheme suitable for large integers commonly used in cryptography such as the five NIST primes and the Pierpont primes used in supersingular isogeny Diffie-Hellman (SIDH) for post-quantum cryptography. Algorithms are proposed for this new representation to implement arithmetic operations such as two's complement, addition/subtraction, comparison, sign detection, and modular reduction. Algorithms are also developed for converting binary numbers to attribute representation and vice versa. The extensive numerical simulations were done to verify the performance of the new number representation. Results show that addition is done faster in our proposed representation when compared with binary and residue number system (RNS)-based additions. Attribute addition outperformed RNS addition for all values of m where 128 ≤ m ≤ 32768 bits for all machine word sizes w where 4 ≤ w ≤ 128 bits. Attribute-based addition outperforms Kogge-Stone binary adders for a wide range of m when w is small. For increasing values of w, the speed advantages are evident only for large values of m. This makes the proposed number representation suitable for implementing cryptographic applications in embedded processors for IoT and consumer electronic devices where w is small.
Eyes

"Eyes" is an interactive biometric data art that transforms human's Iris data into musical sound and 3D animated image. The idea is to allow the audience to explore their own identities through unique visual and sound generated by their iris patterns based on iris recognition and image processing techniques. Selected iris images are printed in 3D sculptures, and it replays the sound and animated images on the sculptures. This research-based artwork has an experimental system generating distinct sounds for each different iris data using visual features such as colors, patterns, brightness and size of the iris. It has potentials to lead the new way of interpreting complicated dataset with the audiovisual output. Moreover, aesthetically beautiful, mesmerizing and uncanny valley-effected artwork can create personalized art experience and multimodal interaction. Multisensory interpretations of this data art can lead a new opportunity to reveal users' narratives and create their own "sonic signature."
Accurate Fashion Style Estimation with a Novel Training Set and Removal of Unnecessary Pixels

To improve the accuracy of fashion style estimation, this paper proposes a novel large-scale dataset named WEARStyle and two types of novel schemes that remove unnecessary pixels: SSD-based human detection and PSPNet-based pixel selection. The classification accuracy of the Hipster Wars dataset is improved to 78.8% by an SVM-based classifier when the WEARStyle dataset is used to train a ResNet50-based feature extractor. The accuracy is improved to 80.0% and 80.9%, when the SSD-based human detection and PSPNet-based pixel selection are applied, respectively. The achieved accuracy outperforms those of other existing schemes.
The full-reducing Krivine abstract machine KN simulates pure normal-order reduction in lockstep: A proof via corresponding calculus

Abstract We exploit the idea of proving properties of an abstract machine by using a corresponding semantic artefact better suited to their proof. The abstract machine is an improved version of Pierre Crégut’s full-reducing Krivine machine KN. The original version works with closed terms of the pure lambda calculus with de Bruijn indices. The improved version reduces in similar fashion but works on closures where terms may be open. The corresponding semantic artefact is a structural operational semantics of a calculus of closures whose reduction relation is purposely a reduction strategy. As shown in previous work, improved KN and the structural operational semantics ‘correspond’, i.e. both artefacts realise the same reduction strategy. In this paper, we prove in the calculus of closures that the reduction strategy simulates in lockstep (at every reduction step) the complete and standard normal-order strategy (i.e. leftmost reduction to normal form) of the pure lambda calculus. The simulation is witnessed by a substitution function from closures of the closure calculus to pure terms of the pure lambda calculus. Thus, KN also simulates normal-order in lockstep by the correspondence. This result is stronger than the known proof that KN is complete, for in the pure lambda calculus there are complete but non-standard strategies. The lockstep simulation proof consists of straightforward structural inductions, thanks to three properties of the closure calculus we call ‘index alignment’, ‘parameters-as-levels’ and ‘balanced derivations’. The first two come from KN. Thanks to these properties, a proof in a calculus of closures involving de Bruijn indices and de Bruijn levels is unproblematic. There is no lexical adjustment at binding lookup, on-the-fly alpha-conversion or recursive traversals of the term to deal with bound and free variables as in other calculi. This paper contributes to the framework for environment machines of Biernacka and Danvy a full-reducing open-terms closure calculus, its corresponding abstract machine, and a lockstep simulation proof via a substitution function.
Forecasting the yield of a semiconductor product using a hybrid-aggregation and entropy-consensus fuzzy collaborative intelligence approach

Forecasting the yield of each product is critical for a semiconductor manufacturer. To enhance the performance of forecasting the yield of a semiconductor product, a hybrid-aggregation and entropy-consensus fuzzy collaborative intelligence (FCI) approach is proposed in this study. The novelty of the proposed approach is in its use of a hybrid aggregation mechanism that first aggregates fuzzy yield forecasts by using a fuzzy weighted average (FWA) and then adjusts the FWA result by using fuzzy intersection (FI). In this way, both subjective and objective viewpoints are considered in forecasting the yield of a semiconductor product. In addition, the consensus among experts is measured with the entropy of the aggregation result. After consensus is reached, the aggregation result is defuzzified using a back propagation network (BPN). The effectiveness of the proposed methodology is validated by analyzing a real case. According to the analysis results, the forecasting accuracy, measured in terms of mean absolute error (MAE) or mean absolute percentage error (MAPE), improved considerably when using the proposed methodology.
Confabulation Radio

Material speculation through counterfactual artifacts has been an alternative approach to envisioning possible worlds in HCI. This paper further explores how false memory, or confabulation, can be a resource for enriching this constructive design approach. We conducted an explorative study to understand how personal experience of confabulation can contribute to the constitution of the body of possible worlds and to explore the potential of using mixed everyday soundscapes as a medium to trigger possible-world-experience. The result shows that these "counterfactual soundscapes" can create self-convinced experiences of counterpart self, which indicates a reflexive but fictional self across possible worlds. Based on these findings, we proposed the model of counterpart self that accounts for reflexive speculation in possible worlds and made a research artifact, the Confabulation Radio, as our attempt to inquire into this phenomenon.
Study of the Detection of Falls Using the SVM Algorithm, Different Datasets of Movements and ANOVA

Falls are becoming a major public health problem, which is intensified by the aging of the population. Falls are one of the main causes of death among the elderly and in population groups that develop risk activities. In this sense, technologies can provide solutions to improve this situation. In this work we have analyzed different repositories of movements and falls designed to test decision algorithms in automatic fall detection systems. The objectives of the study are: firstly, to clarify what are the characteristics of the most significant accelerometry signals to identify a fall and secondly, to analyze the possibility of extrapolating the learning achieved with a certain database when tested with another one. As a novelty with respect to other works in the literature, the statistical significance of the results has been systematically evaluated by the analysis of variance (ANOVA).
Progressive Latent Models for Self-Learning Scene-Specific Pedestrian Detectors

The performance of offline learned pedestrian detectors significantly drops when they are applied to video scenes of various camera views, occlusions, and background structures. Learning a detector for each video scene can avoid the performance drop but it requires repetitive human effort on data annotation. In this paper, a self-learning approach is proposed, toward specifying a pedestrian detector for each video scene without any human annotation involved. Object locations in video frames are treated as latent variables and a progressive latent model (PLM) is proposed to solve such latent variables. The PLM is deployed as components of object discovery, object enforcement, and label propagation, which are used to learn the object locations in a progressive manner. With the difference of convex (DC) objective functions, PLM is optimized by a concave-convex programming algorithm. With specified network branches and loss functions, PLM is integrated with deep feature learning and optimized in an end-to-end manner. From the perspectives of convex regularization and error rate estimation, detailed optimization analysis and learning stability analysis of the proposed PLM are provided. The extensive experiments demonstrate that even without annotation involved the proposed self-learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer learning approaches.
Information Fusion via Deep Cross-Modal Factor Analysis

In this paper, we introduce Deep Cross-Modal Factor Analysis (DCFA) to identify complex nonlinear transformations of two variables for information fusion. DCFA is able to represent the coupled patterns between two different sets of variables by minimizing the Frobenius norm distance in the transformed domain. Unlike previous kernel methods, the feature mapping of DCFA is achieved with deep networks (DN) instead of the traditional kernel method. Therefore, the representation of DCFA method is not limited by the fixed kernel. Moreover, DCFA can be considered as a nonlinear extension of the linear Cross-Modal Factor Analysis (CFA), and an alternative to the nonparametric method Kernel Cross-Modal Factor Analysis (KCFA) and the recently proposed Deep Canonical Correlation Analysis (Deep CCA) method. The performance of DCFA is evaluated on MNIST handwritten digit dataset and two audio emotion datasets. Experimental results show that the proposed solution outperforms the methods of KCCA, KCFA, Deep CCA and the deep learning based method-Alexnet, in terms of accuracy.
Analysis of Basic-SegNet Architecture with Variations in Training Options

Deep learning techniques are becoming popular for vision-based automation applications. Recently, various deep convolutional neural network architectures have been evolved for image classification, object detection and semantic image segmentation tasks. SegNet is one of the successful encoder-decoder convolution architectures, implemented for semantic image segmentation. We simulated Basic-SegNet architecture using MATLAB R2017b. SegNet architecture is built layer by layer without using any pre-trained model. Multi-class images from Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes 2012 database, are used to train the architecture. The segmentation results obtained on test images are evaluated by calculating accuracy, intersection of union, boundary F1 measure and execution time. These evaluation parameters are computed over database as well as for individual object class. Training options like learning rate and its schedule, filter size, number of filters and number of epochs are varied to analyze their effects on the performance of architecture. This research work is focused on analysis of Basic-SegNet architecture with variations in training options.
A Multifactor Authentication Model to Mitigate the Phishing Attack of E-Service Systems from Bangladesh Perspective

A new multifactor authentication model has been proposed for Bangladesh taking cost-effectiveness in primary concern. We considered two-factor authentications in our previous e-service models which were proven to be insufficient in terms of phishing attack. Users often fail to identify phishing site and provide confidential information unintentionally, resulting in a successful phishing attempt. As a result, phishing can be considered as one of the most serious issues and required to be addressed and mitigated. Three factors were included to form multifactor authentication, namely, user ID, secured image with caption, and one-time password. Through the survey, the proposed multifactor model is proven to be better by 59% points for total users which comprises 55% points for technical users and 64% points for nontechnical users in comparison to traditional two-factor authentication model. Since the results and recommendations from the user were reflected in the model, user satisfaction was achieved.
Usability problems discovery based on the automatic detection of usability smells

Web applications usage has gone through a big and fast growth in the past few years, becoming part of our daily lives. Large companies use these applications to provide their services. So, it is necessary to ensure the development of high-quality applications. One of the main attributes of a Web application that directly determines its success or failure is their usability, and several methods were developed to evaluate it. The most popular method of usability evaluation is the laboratory testing because it can directly evaluate users' reactions while interacting with the final application. However, performing this test entails high cost and complexity. So, aiming to simplify the execution of this evaluation and to ease the discovery of usability problems, this work proposes an approach to detect indicators of problems (usability smells), based on the capture of the user interaction in the production environment and automatic analysis of this interaction. A study carried out with the approach showed that smells indicated by it were able to aid the detection and correction of usability problems in a real application.
Resolving data interoperability in ubiquitous health profile using semi-structured storage and processing

Advancements in the field of healthcare information management have led to the development of a plethora of software, medical devices and standards. As a consequence, the rapid growth in quantity and quality of medical data has compounded the problem of heterogeneity; thereby decreasing the effectiveness and increasing the cost of diagnostics, treatment and follow-up. However, this problem can be resolved by using a semi-structured data storage and processing engine, which can extract semantic value from a large volume of patient data, produced by a variety of data sources, at variable rates and conforming to different abstraction levels. Going beyond the traditional relational model and by re-purposing state-of-the-art tools and technologies, we present, the Ubiquitous Health Profile (UHPr), which enables a semantic solution to the data interoperability problem, in the domain of healthcare1.
Digital recognition of street view house numbers based on DCGAN

Deep learning algorithms have surpassed human resolution in applications such as face recognition and object classification. However, it can only produce very blurred, lack of details of the image. Generative Adversarial Network is a game training of minimax antagonism between generator G and discriminator D, and ultimately achieves Nash equilibrium. We use deep convolutional GAN that recognizes sequence numbers and without split characters. First we use convolution network to extract character features. Second we construct a convolution neural network to recognize digits of natural scene house number. DCGAN is used to improve the resolution of the number of fuzzy houses, so as to extract more abundant data features in data set training. It can better recognize the numbers in the natural street.
Of Mice and Pants

Withing the fields of HCI and game design, conventional design practices have been criticised for perpetuating the status quo and marginalising users beyond the norm [11], [1], e.g. through genderized assumptions about user interaction [13]. To solve this problem of conservatism in HCI, one recommended strategy has been queering; the use of mischiveous, spaceful, and oblique design principles [13]. This contribution focuses on the conventional computer mouse within videogames as an example for a conventional input device optimised for a limited set of interactions. The article first reviews HCI discourses on the mouse within technology studies, game culture, and queer game studies. In these three domains, the mouse has been consistently reduced to its functionality as high-precision point-and-click device, constructing it as conservative seemingly hard-wired to cater to male-centred pleasures. We then discuss three experimental game design strategies to queer the mouse controller in The Undie Game, a cooperative wearable mouse-based installation game by the Copenhagen Game Collective. The Undie Game speculates about ways to confront and disrupt conventional expectations about gaming by fa''silly''tating interaction for two players who wear a mouse controller in their panties and collectively steer a 3D high definition tongue on screen to achieve a mutual highscore. By creating a social, silly, and potentially daunting play experience, The Undie Game reinterprets the affordances of the computer mouse to bring subjects like consent, failure, and ambiguity into the picture.
Statistical reasoning in diagnostic problem-solving—The case of flow-rate measurements

There are various methods for measuring flow rates in rivers, but all of them have practical issues and challenges. A period of exceptionally high water levels revealed substantial discrepancies between two measurement setups in the same waterway. Finding a causal explanation of the discrepancies was important, as the problem might have ramifications for other flow-rate measurement setups as well. Finding the causes of problems is called diagnostic problem-solving. We applied a branch-and-prune strategy, in which we worked with a hierarchy of hypotheses, and used statistical analysis as well as domain knowledge to rule out options. We were able to narrow down the potential explanations to one main suspect and an alternative explanation. Based on the analysis, we discuss the role of statistical techniques in diagnostic problem-solving and reasoning patterns that make the application of statistics powerful. The contribution to theory in statistics is not in the individual techniques but in their application and integration in a coherent sequence of studies – a reasoning strategy.