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We introduce our explorative historical leveled approach that we use to understand drug debates in the Royal Dutch Library's digital newspaper archive. In this approach we alternate between distant reading and close reading. Furthermore, we use this approach to evaluate two text mining tools: AVResearcherXL and Texcavator.
Today, social media provide the means by which billions of people experience news and events happening around the world. However, the absence of traditional journalistic gatekeeping allows information to flow unencumbered through these platforms, often raising questions of veracity and credibility of the reported information. Here we ask: How do the dynamics of collective attention directed toward an event reported on social media vary with its perceived credibility? By examining the first large-scale, systematically tracked credibility database of public Twitter messages (47M messages corresponding to 1,138 real-world events over a span of three months), we established a relationship between the temporal dynamics of events reported on social media and their associated level of credibility judgments. Representing collective attention by the aggregate temporal signatures of an event reportage, we found that the amount of continued attention focused on an event provides information about its associated levels of perceived credibility. Events exhibiting sustained, intermittent bursts of attention were found to be associated with lower levels of perceived credibility. In other words, a
Cartograms are maps in which areas of geographic regions (countries, states) appear in proportion to some variable of interest (population, income). Cartograms are popular visualizations for geo-referenced data that have been used for over a century and that make it possible to gain insight into patterns and trends in the world around us. Despite the popularity of cartograms and the large number of cartogram types, there are few studies evaluating the effectiveness of cartograms in conveying information. Based on a recent task taxonomy for cartograms, we evaluate four major different types of cartograms: contiguous, non-contiguous, rectangular, and Dorling cartograms. Specifically, we evaluate the effectiveness of these cartograms by quantitative performance analysis, as well as by subjective preferences. We analyze the results of our study in the context of some prevailing assumptions in the literature of cartography and cognitive science. Finally, we make recommendations for the use of different types of cartograms for different tasks and settings.
The current research with EEG devices in the user authentication context has some deficiencies that address expensive equipment, the requirement of laboratory conditions and applicability. In this paper we address this issue by using widely available and inexpensive EEG device to verify its capability for authentication. As a part of this research, we developed two phase authentication that enables users to enhance their password with the mental state by breaking the password into smaller, marry them with mental state, and generate one time pad for a secure session.
Touch sensing, as a major human/machine interface, is widely used in various commercial products such as smart watches, mobile phones, tablets and TVs. State-of-the-art touch detections are mainly based on mutual capacitive sensing, which requires necessary contact-touch, limiting the mobile user experience. Recently, remote gesture sensing is widely reported in both academy and industry as it can provide additional user-experience for mobile interface. The capacitive remote gesture sensing is mainly based on detecting self-capacitance, achieving high resolution through eliminating the parasitic mutual capacitance. In this work, we overview the different generations of touchscreen technology, comparing the touch and remote gesture sensing technologies difference. In addition, different remote gesture sensing technologies are also compared. The limitations and potentials of different topologies are discussed and a final conclusion about the technology trends is summarized in the end.
Buildings form an essential part of modern life; people spend a significant amount of their time in them, and they consume large amounts of energy. A variety of systems provide services such as lighting, air conditioning and security which are managed using Building Management Systems (BMS) by building operators. To better understand the capability of current BMS and characterize common practices of building operators, we investigated their use across five institutions in the US. We interviewed ten operators and discovered that BMS do not address a number of key concerns for the management of buildings. Our analysis is rooted in the everyday work of building operators and highlights a number of design suggestions to help improve the user experience and management of BMS, ultimately leading to improvements in productivity, as well as buildings comfort and energy efficiency.
What makes a person pick certain tags over others when tagging an image? Does the order that a person presents tags for a given image follow an implicit bias that is personal? Can these biases be used to improve existing automated image tagging systems? We show that tag ordering, which has been largely overlooked by the image tagging community, is an important cue in understanding user tagging behavior and can be used to improve auto-tagging systems. Inspired by the assumption that people order their tags, we propose a new way of measuring tag preferences, and also propose a new personalized tagging objective function that explicitly considers a user's preferred tag orderings. We also provide a (partially) greedy algorithm that produces good solutions to our new objective and under certain conditions produces an optimal solution. We validate our method on a subset of Flickr images that spans 5000 users, over 5200 tags, and over 90,000 images. Our experiments show that exploiting personalized tag orders improves the average performance of state-of-art approaches both on per-image and per-user bases.
We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked in for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom.
Algorithms which sort lists of real numbers into ascending order have been studied for decades. They are typically based on a series of pairwise comparisons and run entirely on chip. However people routinely sort lists which depend on subjective or complex judgements that cannot be automated. Examples include marketing research; where surveys are used to learn about customer preferences for products, the recruiting process; where interviewers attempt to rank potential employees, and sporting tournaments; where we infer team rankings from a series of one on one matches. We develop a novel sorting algorithm, where each pairwise comparison reflects a subjective human judgement about which element is bigger or better. We introduce a finite and large error rate to each judgement, and we take the cost of each comparison to significantly exceed the cost of other computational steps. The algorithm must request the most informative sequence of comparisons from the user; in order to identify the correct sorted list with minimum human input. Our Discrete Adiabatic Monte Carlo approach exploits the gradual acquisition of information by tracking a set of plausible hypotheses which are updated a
This article investigates graph analysis for intelligent marketing in smart cities, where metatrails are crowdsourced by mobile sensing for marketing strategies. Unlike most works that focused on client sides, this study is intended for market planning, from the perspective of enterprises. Several novel crowdsourced features based on metatrails, including hotspot networks, crowd transitions, affinity subnetworks, and sequential visiting patterns, are discussed in the article. These smart footprints can reflect crowd preferences and the topology of a site of interest. Marketers can utilize such information for commercial resource planning and deployment. Simulations were conducted to demonstrate the performance. At the end, this study also discusses different scenarios for practical geo-conquesting applications.
The use of psychophysiologic signals in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of different physiological signals and analysis techniques. Cardiovascular signals such as heart rate variability and blood pressure variability are commonly used in psychophysiology in order to investigate phenomena such as mental workload. In this paper we present a short review of different cardiovascular metrics useful in the context of human-computer interaction. This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We emphasise everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, with 12 similar chapters, plus application guidelines and systematic review. Any citation to this paper should be made using the following reference: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M
Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech. We capitalize on the current breakthrough of Recurrent Neural Network (RNN) and seek to investigate its performance for emotion classification from noisy speech. We particularly focus on the recently proposed Gated Recurrent Unit (GRU), which is yet to be explored for emotion recognition from speech. Experiments conducted with speech compounded with eight different types of noises reveal that GRU incurs an 18.16% smaller run-time while performing quite comparably to the Long Short-Term Memory (LSTM), which is the most popular Recurrent Neural Network proposed to date. This result is promising for any embedded platform in general and will initiate further studies to utilize GRU to its full potential for emotion recognition on smartphones.
Advances in machine learning have produced systems that attain human-level performance on certain visual tasks, e.g., object identification. Nonetheless, other tasks requiring visual expertise are unlikely to be entrusted to machines for some time, e.g., satellite and medical imagery analysis. We describe a human-machine cooperative approach to visual search, the aim of which is to outperform either human or machine acting alone. The traditional route to augmenting human performance with automatic classifiers is to draw boxes around regions of an image deemed likely to contain a target. Human experts typically reject this type of hard highlighting. We propose instead a soft highlighting technique in which the saliency of regions of the visual field is modulated in a graded fashion based on classifier confidence level. We report on experiments with both synthetic and natural images showing that soft highlighting achieves a performance synergy surpassing that attained by hard highlighting.
People learn whenever and wherever possible, and whatever they like or encounter--Mathematics, Drama, Art, Languages, Physics, Philosophy, and so on. With the bursting of knowledge, evaluation of one's possession of knowledge becomes increasingly difficult. There are a lot of demands to evaluate one's understanding of a piece of knowledge. Assessment of understanding of knowledge is conventionally through tests or interviews, but they have some limitations such as low-efficiency and not-comprehensive. This paper proposes a method called Understanding Tree to estimate one's understanding of knowledge, by keeping track of his/her learning activities. It overcomes some limitations of traditional methods, hence complements traditional methods.
Technology has become an essential part in every aspect of our lives. However the key to a successful implementation of a technology depends on the acceptance by the general public. In order to increase the acceptance various approaches can be applied. In this paper, we will examine the human-robot emotional interaction by investigating the capabilities of a developed low-resolution RGB-LED display in the context of artificial emotions. We are focusing on four of the most representative human emotions which include happiness, anger, sadness and fear. We will work with colors and dynamic light patterns which are supposed to evoke various associations. In an experiment, the use these patterns as expressions of emotions are validated. The results of the conducted study show that some of the considered basic emotions can be recognized by human observers.
The P300 speller is a well known Brain-Computer Interface paradigm that has been used for over two decades. A new P300 speller paradigm (XP300) is proposed. It includes several characteristics: (i) the items are not intensified by using rows and columns, (ii) the order of the visual stimuli is pseudo-random, (iii) a visual feedback is added on each item to increase the stimulus meaning, which is the main novelty. XP300 has been tested on ten healthy subjects on copy spelling mode, with only eight sensors. It has been compared with the classical P300 paradigm (CP300). With five repetitions, the average recognition rate across subjects is 85.25% for XP300 and 77.25% for CP300. Single-trial detection is significantly higher with XP300 by comparing the AUC (Area Under Curve) of the ROC (Receiver Operating Characteristic) curve. The mean AUC is 0.86 for XP300, 0.80 for CP300. More importantly, XP300 has also been judged as more convenient and user-friendly than CP300, hence being able to allow longer sessions.
Many of today's most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others' posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users' online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user's motivation to use the app and tak
Usability is a key quality attribute of successful software systems. Unfortunately, there is no common understanding of the factors influencing usability and their interrelations. Hence, the lack of a comprehensive basis for designing, analyzing, and improving user interfaces. This paper proposes a 2-dimensional model of usability that associates system properties with the activities carried out by the user. By separating activities and properties, sound quality criteria can be identified, thus facilitating statements concerning their interdependencies. This model is based on a tested quality meta-model that fosters preciseness and completeness. A case study demonstrates the manner by which such a model aids in revealing contradictions and omissions in existing usability standards. Furthermore, the model serves as a central and structured knowledge base for the entire quality assurance process, e.g. the automatic generation of guideline documents.
Driving under the influence of alcohol is a widespread phenomenon in the US where it is considered a major cause of fatal accidents. In this research we present a novel approach and concept for detecting intoxication from motion differences obtained by the sensors of wearable devices. We formalize the problem of drunkenness detection as a supervised machine learning task, both as a binary classification problem (drunk or sober) and a regression problem (the breath alcohol content level). In order to test our approach, we collected data from 30 different subjects (patrons at three bars) using Google Glass and the LG G-watch, Microsoft Band, and Samsung Galaxy S4. We validated our results against an admissible breathalyzer used by the police. A system based on this concept, successfully detected intoxication and achieved the following results: 0.95 AUC and 0.05 FPR, given a fixed TPR of 1.0. Applications based on our system can be used to analyze the free gait of drinkers when they walk from the car to the bar and vice-versa, in order to alert people, or even a connected car and prevent people from driving under the influence of alcohol.
This article presents a new proposal design of GUI and new technology in programming Namely "Super Technology" which can be applied for supporting the proposal design of GUI
Our work is generally focused on recommending for small or medium-sized e-commerce portals, where explicit feedback is absent and thus the usage of implicit feedback is necessary. Nonetheless, for some implicit feedback features, the presentation context may be of high importance. In this paper, we present a model of relevant contextual features affecting user feedback, propose methods leveraging those features, publish a dataset of real e-commerce users containing multiple user feedback indicators as well as its context and finally present results of purchase prediction and recommendation experiments. Off-line experiments with real users of a Czech travel agency website corroborated the importance of leveraging presentation context in both purchase prediction and recommendation tasks.
Wikipedia articles about the same topic in different language editions are built around different sources of information. For example, one can find very different news articles linked as references in the English Wikipedia article titled "Annexation of Crimea by the Russian Federation" than in its German counterpart (determined via Wikipedia's language links). Some of this difference can of course be attributed to the different language proficiencies of readers and editors in separate language editions, yet, although including English-language news sources seems to be no issue in the German edition, English references that are listed do not overlap highly with the ones in the article's English version. Such patterns could be an indicator of bias towards certain national contexts when referencing facts and statements in Wikipedia. However, determining for each reference which national context it can be traced back to, and comparing the link distributions to each other is infeasible for casual readers or scientists with non-technical backgrounds. Wikiwhere answers the question where Web references stem from by analyzing and visualizing the geographic location of exter
We present RFexpress! the first-ever network-edge based system to recognize emotion from movement, gesture and pose via Device-Free Activity Recognition (DFAR). With the proliferation of the IoT, also wireless access points are deployed at increasingly dense scale. in particular, this includes vehicular nodes (in-car WiFi or Bluetooth), office (Wlan APs, WiFi printer or projector) and private indoor domains (home WiFi mesh, Wireless media access), as well as public spaces (City/open WiFi, Cafes, shopping spaces). Processing RF-fluctuation at such edge-devices, enables environmental perception. In this paper, we focus on the distinction between neutral and agitated emotional states of humans from RF-fluctuation at the wireless network edge in realistic environments. In particular, the system is able to detect risky driving behaviour in a vehicular setting as well as spotting angry conversations in an indoor environment. We also study the effectiveness of edge-based DFAR emotion and activity recognition systems in real environments such as cafes, malls, outdoor and office spaces. We measure radio characteristics in these environments at different days and times and analyse the impact
We present a novel open-source framework for visualizing electromagnetic articulography (EMA) data in real-time, with a modular framework and anatomically accurate tongue and palate models derived by multilinear subspace learning.
People in this topic
Researcher
Yang Liu contributes to research discovery and scholarly infrastructure.
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Wei Zhang contributes to research discovery and scholarly infrastructure.
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Yu Zhang contributes to research discovery and scholarly infrastructure.
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Wei Li contributes to research discovery and scholarly infrastructure.
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Lei Li contributes to research discovery and scholarly infrastructure.
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Wei Liu contributes to research discovery and scholarly infrastructure.
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Yang Yang contributes to research discovery and scholarly infrastructure.
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Xin Zhang contributes to research discovery and scholarly infrastructure.
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Jun Wang contributes to research discovery and scholarly infrastructure.
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Hao Wang contributes to research discovery and scholarly infrastructure.
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Yoshua Bengio contributes to research discovery and scholarly infrastructure.
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Yan Wang contributes to research discovery and scholarly infrastructure.
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