Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016

These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions

Proceedings of the 4th Workshop on Interacting with Smart Objects 2015

These are the Proceedings of the 4th IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Sketchography - Automatic Grading of Map Sketches for Geography Education

Geography is a vital classroom subject that teaches students about the physical features of the planet we live on. Despite the importance of geographic knowledge, almost 75% of 8th graders scored below proficient in geography on the 2014 National Assessment of Educational Progress. Sketchography is a pen-based intelligent tutoring system that provides real-time feedback to students learning the locations, directions, and topography of rivers around the world. Sketchography uses sketch recognition and artificial intelligence to understand the user’s sketched intentions. As sketches are inherently messy, and even the most expert geographer will draw only a close approximation of the river’s flow, data has been gathered from both novice and expert sketchers. This data, in combination with professors’ grading rubrics and statistically driving AI-algorithms, provide real-time automatic grading that is similar to a human grader’s score. Results show the system to be 94.64% accurate compared to human grading

Creating sparks: comparing search results using discriminatory search term word co-occurrence to facilitate serendipity in the enterprise.

Categories or tags that appear in faceted search interfaces which are representative of an information item, rarely convey unexpected or non-obvious associated concepts buried within search results. No prior research has been identified which assesses the usefulness of discriminative search term word co-occurrence to generate facets to act as catalysts to facilitate insightful and serendipitous encounters during exploratory search. In this study, 53 scientists from two organisations interacted with semi-interactive stimuli, 74% expressing a large/moderate desire to use such techniques within their workplace. Preferences were shown for certain algorithms and colour coding. Insightful and serendipitous encounters were identified. These techniques appear to offer a significant improvement over existing approaches used within the study organisations, providing further evidence that insightful and serendipitous encounters can be facilitated in the search user interface. This research has implications for organisational learning, knowledge discovery and exploratory search interface design

CupMar: A deep learning model for personalized news recommendation based on contextual user-profile and multi-aspect article representation

OnlinePublIn modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user’s historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user’s browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang, Nguyen H. Tran, Nguyen Lu Dang Kho

Exploratory information searching in the enterprise: a study of user satisfaction and task performance.

No prior research has been identified that investigates the causal factors for workplace exploratory search task performance. The impact of user, task, and environmental factors on user satisfaction and task performance was investigated through a mixed methods study with 26 experienced information professionals using enterprise search in an oil and gas enterprise. Some participants found 75% of high-value items, others found none, with an average of 27%. No association was found between self-reported search expertise and task performance, with a tendency for many participants to overestimate their search expertise. Successful searchers may have more accurate mental models of both search systems and the information space. Organizations may not have effective exploratory search task performance feedback loops, a lack of learning. This may be caused by management bias towards technology, not capability, a lack of systems thinking. Furthermore, organizations may not “know” they “don't know” their true level of search expertise, a lack of knowing. A metamodel is presented identifying the causal factors for workplace exploratory search task performance. Semistructured qualitative interviews with search staff from the defense, pharmaceutical, and aerospace sectors indicates the potential transferability of the finding that organizations may not know their search expertise levels

The Gestural Control of Audio Processing

Gesture enabled devices have become so ubiquitous in recent years that commands such as ‘pinch to zoom-in on an image’ are part of most people’s gestural vocabulary. Despite this, gestural interfaces have been used sparingly within the audio industry. The aim of this research project is to evaluate the effectiveness of a gestural interface for the control of audio processing. In particular, the ability of a gestural system to streamline workflow and rationalise the number of control parameters, thus reducing the complexity of Human Computer Interaction (HCI). A literature review of gestural technology explores the ways in which it can improve HCI, before focussing on areas of implementation in audio systems. Case studies of previous research projects were conducted to evaluate the benefits and pitfalls of gestural control over audio. The findings from these studies concluded that the scope of this project should be limited to two-dimensional gestural control. An elicitation of gestural preferences was performed to identify expert-user’s gestural associations. This data was used to compile a taxonomy of gestures and their most widely-intuitive parameter mappings. A novel interface was then produced using a popular tablet-computer. This facilitated the control of equalisation, compression and gating. Objective testing determined the performance of the gestural interface in comparison to traditional WIMP (Windows, Icons, Menus, Pointer) techniques, thus producing a benchmark for the system under test. Further testing is carried out to observe the effects of graphic user interfaces (GUIs) in a gestural system, in particular the suitability of skeuomorphic (knobs and faders) designs in modern DAWs (Digital Audio Workstations). A novel visualisation method, deemed more suitable for gestural interaction, is proposed and tested. Semantic descriptors are explored as a means of further improving the speed and usability of gestural interfaces, through the simultaneous control of multiple parameters. This rationalisation of control moves towards the implementation of gestural shortcuts and ‘continuous pre-sets’

Exploratory information searching in the enterprise: A study of user satisfaction and task performance

No prior research has been identified which investigates the causal factors for workplace exploratory search task performance. The impact of user, task and environmental factors on user satisfaction and task performance was investigated through a mixed methods study with 26 experienced information professionals using enterprise search in an oil and gas enterprise. Some participants found 75% of high value items, others found none with an average of 27%. No association was found between self-reported search expertise and task performance, with a tendency for many participants to overestimate their search expertise. Successful searchers may have more accurate mental models of both search systems and the information space. Organizations may not have effective exploratory search task performance feedback loops, a lack of learning. This may be caused by management bias towards technology not capability, a lack of systems thinking. Furthermore, organizations may not ‘know’ they ‘don’t know’ their true level of search expertise, a lack of knowing. A metamodel is presented identifying the causal factors for workplace exploratory search task performance. Semi-structured qualitative interviews with search staff from the Defence, Pharmaceutical and Aerospace sectors indicates the potential transferability of the finding that organizations may not know their search expertise levels

Exploratory information searching in the enterprise: A study of user satisfaction and task performance

No prior research has been identified which investigates the causal factors for workplace exploratory search task performance. The impact of user, task and environmental factors on user satisfaction and task performance was investigated through a mixed methods study with 26 experienced information professionals using enterprise search in an oil and gas enterprise. Some participants found 75% of high value items, others found none with an average of 27%. No association was found between self-reported search expertise and task performance, with a tendency for many participants to overestimate their search expertise. Successful searchers may have more accurate mental models of both search systems and the information space. Organizations may not have effective exploratory search task performance feedback loops, a lack of learning. This may be caused by management bias towards technology not capability, a lack of systems thinking. Furthermore, organizations may not ‘know’ they ‘don’t know’ their true level of search expertise, a lack of knowing. A metamodel is presented identifying the causal factors for workplace exploratory search task performance. Semi-structured qualitative interviews with search staff from the Defence, Pharmaceutical and Aerospace sectors indicates the potential transferability of the finding that organizations may not know their search expertise levels