2,772 research outputs found
Report on the Information Retrieval Festival (IRFest2017)
The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017
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Learning at Scale: Using an Evidence Hub To Make Sense of What We Know
The large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
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Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time
We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales
HCI expertise needed! Personalisation and feedback optimisation in online education
Two key challenges in education relate to how traditional educational providers can personalise online provisions to the students’ skill level, optimise the use of tools and increase both the generation and utilisation of feedback (in terms of timing, content, and subsequent use by students). The application of traditional programmes in the online setting is often complicated by the legacy of traditional universities infrastructures, knowledge bases (or lack thereof in the human-computer-interaction/HCI realm), and pedagogical priorities. It is here that HCI experts (designers and researchers) can have real-world impact in line with macro-HCI, while also being able to test new innovations in collaboration with educators (e.g., the practitioners in such education settings). In this note, we make a case that the HCI community is in a situation where it can make a significant contribution to traditional providers in two prospective areas: personalisation, feedback generation and increased feedback utilisation
Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking
For exhaustive formal verification, industrial-scale cyber-physical systems
(CPSs) are often too large and complex, and lightweight alternatives (e.g.,
monitoring and testing) have attracted the attention of both industrial
practitioners and academic researchers. Falsification is one popular testing
method of CPSs utilizing stochastic optimization. In state-of-the-art
falsification methods, the result of the previous falsification trials is
discarded, and we always try to falsify without any prior knowledge. To
concisely memorize such prior information on the CPS model and exploit it, we
employ Black-box checking (BBC), which is a combination of automata learning
and model checking. Moreover, we enhance BBC using the robust semantics of STL
formulas, which is the essential gadget in falsification. Our experiment
results suggest that our robustness-guided BBC outperforms a state-of-the-art
falsification tool.Comment: Accepted to HSCC 202
Bivariate Beta-LSTM
Long Short-Term Memory (LSTM) infers the long term dependency through a cell
state maintained by the input and the forget gate structures, which models a
gate output as a value in [0,1] through a sigmoid function. However, due to the
graduality of the sigmoid function, the sigmoid gate is not flexible in
representing multi-modality or skewness. Besides, the previous models lack
modeling on the correlation between the gates, which would be a new method to
adopt inductive bias for a relationship between previous and current input.
This paper proposes a new gate structure with the bivariate Beta distribution.
The proposed gate structure enables probabilistic modeling on the gates within
the LSTM cell so that the modelers can customize the cell state flow with
priors and distributions. Moreover, we theoretically show the higher upper
bound of the gradient compared to the sigmoid function, and we empirically
observed that the bivariate Beta distribution gate structure provides higher
gradient values in training. We demonstrate the effectiveness of bivariate Beta
gate structure on the sentence classification, image classification, polyphonic
music modeling, and image caption generation.Comment: AAAI 202
Adaptive Architecture:Regulating human building interaction
In this paper, we explore the regulatory, technical and interactional implications of Adaptive Architecture (AA) and how it will recalibrate the nature of human-building interaction. We comprehensively unpack the emergence and history of this novel concept, reflecting on the current state of the art and policy foundations supporting it. As AA is underpinned by the Internet of Things (IoT), we consider how regulatory and surveillance issues posed by the IoT are manifesting in the built environment. In our analysis, we utilise a prominent architectural model, Stuart Brand’s Shearing Layers, to understand temporal change and informational flows across different physical layers of a building. We use three AA applications to situate our analysis, namely a smart IoT security camera; an AA research prototype; and an AA commercial deployment. Focusing on emerging information privacy and security regulations, particularly the EU General Data Protection Regulation 2016, we examine AA from 5 perspectives: physical & information security risks; challenges of establishing responsibility; enabling occupant rights over flows, collection, use & control of personal data; addressing increased visibility of emotions and bodies; understanding surveillance of everyday routine activities. We conclude with key challenges for AA regulation and the future of human–building interaction
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