47,110 research outputs found
An investigation of novel combined features for a handwritten short answer assessment system
© 2016 IEEE. This paper proposes an off-line automatic assessment system utilising novel combined feature extraction techniques. The proposed feature extraction techniques are 1) the proposed Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (WRL-MDGGF), 2) the proposed Gravity, Water Reservoir, Loop, Modified Direction and Gaussian Grid Feature (G-WRL-MDGGF). The proposed feature extraction techniques together with their original features and other combined feature extraction techniques were employed in an investigation of the efficiency of feature extraction techniques on an automatic off-line short answer assessment system. The proposed system utilised two classifiers namely, artificial neural networks and Support Vector Machines (SVMs), two type of datasets and two different thresholds in this investigation. Promising recognition rates of 94.85% and 94.88% were obtained when the proposed WRL-MDGGF and G-WRL-MDGGF were employed, respectively, using SVMs
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
Off-topic spoken response detection, the task aiming at predicting whether a
response is off-topic for the corresponding prompt, is important for an
automated speaking assessment system. In many real-world educational
applications, off-topic spoken response detectors are required to achieve high
recall for off-topic responses not only on seen prompts but also on prompts
that are unseen during training. In this paper, we propose a novel approach for
off-topic spoken response detection with high off-topic recall on both seen and
unseen prompts. We introduce a new model, Gated Convolutional Bidirectional
Attention-based Model (GCBiA), which applies bi-attention mechanism and
convolutions to extract topic words of prompts and key-phrases of responses,
and introduces gated unit and residual connections between major layers to
better represent the relevance of responses and prompts. Moreover, a new
negative sampling method is proposed to augment training data. Experiment
results demonstrate that our novel approach can achieve significant
improvements in detecting off-topic responses with extremely high on-topic
recall, for both seen and unseen prompts.Comment: ACL2020 long pape
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
Badger: Complexity Analysis with Fuzzing and Symbolic Execution
Hybrid testing approaches that involve fuzz testing and symbolic execution
have shown promising results in achieving high code coverage, uncovering subtle
errors and vulnerabilities in a variety of software applications. In this paper
we describe Badger - a new hybrid approach for complexity analysis, with the
goal of discovering vulnerabilities which occur when the worst-case time or
space complexity of an application is significantly higher than the average
case. Badger uses fuzz testing to generate a diverse set of inputs that aim to
increase not only coverage but also a resource-related cost associated with
each path. Since fuzzing may fail to execute deep program paths due to its
limited knowledge about the conditions that influence these paths, we
complement the analysis with a symbolic execution, which is also customized to
search for paths that increase the resource-related cost. Symbolic execution is
particularly good at generating inputs that satisfy various program conditions
but by itself suffers from path explosion. Therefore, Badger uses fuzzing and
symbolic execution in tandem, to leverage their benefits and overcome their
weaknesses. We implemented our approach for the analysis of Java programs,
based on Kelinci and Symbolic PathFinder. We evaluated Badger on Java
applications, showing that our approach is significantly faster in generating
worst-case executions compared to fuzzing or symbolic execution on their own
Explicit diversification of event aspects for temporal summarization
During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness
Analyzing collaborative learning processes automatically
In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in
The Jet Propulsion Laboratory Electric and Hybrid Vehicle System Research and Development Project, 1977-1984: A Review
The JPL Electric and Hybrid Vehicle System Research and Development Project was established in the spring of 1977. Originally administered by the Energy Research and Development Administration (ERDA) and later by the Electric and Hybrid Vehicle Division of the U.S. Department of Energy (DOE), the overall Program objective was to decrease this nation's dependence on foreign petroleum sources by developing the technologies and incentives necessary to bring electric and hybrid vehicles successfully into the marketplace. The ERDA/DOE Program structure was divided into two major elements: (1) technology research and system development and (2) field demonstration and market development. The Jet Propulsion Laboratory (JPL) has been one of several field centers supporting the former Program element. In that capacity, the specific historical areas of responsibility have been: (1) Vehicle system developments (2) System integration and test (3) Supporting subsystem development (4) System assessments (5) Simulation tool development
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery
Over the last years, most websites on which users can register (e.g., email
providers and social networks) adopted CAPTCHAs (Completely Automated Public
Turing test to tell Computers and Humans Apart) as a countermeasure against
automated attacks. The battle of wits between designers and attackers of
CAPTCHAs led to current ones being annoying and hard to solve for users, while
still being vulnerable to automated attacks.
In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies
on user interaction. This novel CAPTCHA leverages the innate human ability to
recognize shapes in a confused environment. We assess the effectiveness of our
proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency
to automated attacks. In particular, we evaluated the usability, carrying out a
thorough user study, and we tested the resiliency of our proposal against
several types of automated attacks: traditional ones; designed ad-hoc for our
proposal; and based on machine learning. Compared to the state of the art, our
proposal is more user friendly (e.g., only some 35% of the users prefer current
solutions, such as text-based CAPTCHAs) and more resilient to automated
attacks.Comment: 15 page
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