Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance

Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance

Integrating Visual Mnemonics and Input Feedback with Passphrases to Improve the Usability and Security of Digital Authentication

The need for both usable and secure authentication is more pronounced than ever before. Security researchers and professionals will need to have a deep understanding of human factors to address these issues. Due to their ubiquity, recoverability, and low barrier of entry, passwords remain the most common means of digital authentication. However, fundamental human nature dictates that it is exceedingly difficult for people to generate secure passwords on their own. System-generated random passwords can be secure but are often unusable, which is why most passwords are still created by humans. We developed a simple system for automatically generating mnemonic phrases and supporting mnemonic images for randomly generated passwords. We found that study participants remembered their passwords significantly better using our system than with existing systems. To combat shoulder surfing - looking at a user\u27s screen or keyboard as he or she enters sensitive input such as passwords - we developed an input masking technique that was demonstrated to minimize the threat of shoulder surfing attacks while improving the usability of password entry over existing methods. We extended this previous work to support longer passphrases with increased security and evaluated the effectiveness of our new system against traditional passphrases. We found that our system exhibited greater memorability, increased usability and overall rankings, and maintained or improved upon the security of the traditional passphrase systems. Adopting our passphrase system will lead to more usable and secure digital authentication

A Decentralized Recommender System for Effective Web Credibility Assessment

An overwhelming and growing amount of data is available online. The problem of untrustworthy online information is augmented by its high economic potential and its dynamic nature, e.g. transient domain names, dynamic content, etc. In this paper, we address the problem of assessing the credibility of web pages by a decentralized social recommender system. Specifically, we concurrently employ i) item-based collaborative filtering (CF) based on specific web page features, ii) user-based CF based on friend ratings and iii) the ranking of the page in search results. These factors are appropriately combined into a single assessment based on adaptive weights that depend on their effectiveness for different topics and different fractions of malicious ratings. Simulation experiments with real traces of web page credibility evaluations suggest that our hybrid approach outperforms both its constituent components and classical content-based classification approaches

INRISCO: INcident monitoRing in Smart COmmunities

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of the community. In the context of smart communities, the INRISCO [1] proposal intends for (i) the early detection of abnormal situations in cities (i.e., incidents), (ii) the analysis of whether, according to their impact, those incidents are really adverse for the community; and (iii) the automatic actuation by dissemination of appropriate information to citizens and authorities. Thus, INRISCO will identify and report on incidents in traffic (jam, accident) or public infrastructure (e.g., works, street cut), the occurrence of specific events that affect other citizens' life (e.g., demonstrations, concerts), or environmental problems (e.g., pollution, bad weather). It is of particular interest to this proposal the identification of incidents with a social and economic impact, which affects the quality of life of citizens.This work was supported in part by the Spanish Government through the projects INRISCO under Grant TEC2014-54335-C4-1-R, Grant TEC2014-54335-C4-2-R, Grant TEC2014-54335-C4-3-R, and Grant TEC2014-54335-C4-4-R, in part by the MAGOS under Grant TEC2017-84197-C4-1-R, Grant TEC2017-84197-C4-2-R, and Grant TEC2017-84197-C4-3-R, in part by the European Regional Development Fund (ERDF), and in part by the Galician Regional Government under agreement for funding the Atlantic Research Center for Information and Communication Technologies (AtlantTIC)

A NEW APPROACH TO ASSESS HIGH LEVEL PLANNING UNDERLYING COGNITIVE-MOTOR PERFORMANCE DURING COMPLEX ACTION SEQUENCES

While much work has examined low-level sensorimotor planning, only limited efforts have studied high-level motor planning processes underlying the cognitive-motor performance of complex action sequences. Such sequences can generally be successfully executed in a flexible manner and typically involve few constraints. In particular, no past study has examined the concurrent changes of high-level motor plans along with those of mental workload and confidence during practice of a novel complex action sequence. To address this gap, first a computational approach providing markers capturing performance dynamics of action sequences during practice had to be developed since past relevant works only employed fairly rough metrics. Such an approach should provide concise performance markers (e.g., distances, scalar) while still capturing accurately the changes of structure of high-level motor plans during the acquisition of novel complex action sequences. Thus, by adapting the Levenshtein distance (LD) and its operators to the motor domain, a computational approach was first proposed to assess in detail action sequences during an imitation practice task executed by various performers (humans, a humanoid robot) and with flexible success criteria. The results revealed that this approach i) could support accurately comparing the high-level plans generated between performers; ii) provides performance markers (LD, insertion operator) able to differentiate optimal (using a minimum of actions) from suboptimal (using more than a minimum of actions but still reaching the task goal) sequences; and iii) gives evidenced that the deletion operator is a marker of action sequence failure. This computational approach was then deployed to examine during practice the concurrent changes in high-level motor plans underlying action sequence execution with modulation of mental workload and an individual’s confidence in performing the task. The results revealed that as individuals practiced, performance improved (reduction of LD, insertion/substitution and movement time) while the level of mental workload and confidence decreased and increased, respectively. Also, by late practice the sequences were still suboptimal while being executed faster, possibly suggesting different dynamics between the generation of high-level motor plans and their execution. Overall, this work complements prior efforts to assess complex action sequences executed by humans and humanoid robots in the context of cognitive-motor practice, and it has the potential to inform not only human cognitive-motor mechanisms, but also human-robots interactions

非英語母語話者のためのインタラクティブな書き換え

Tohoku University博士（情報科学）thesi

A Neural Network-Based Situational Awareness Approach for Emergency Response

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