Human Computer Interaction

Sinha, Gaurav; Shahi, Rahul; Shankar, M. Gowri

doi:10.1109/icetet.2010.85

Cited by 43 publications

(23 citation statements)

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“…Despite the fact that ease of use is one of the criteria which facilitate user adapted interaction in MOODLE as argued by [9], however, there are several other characteristics suggesting future of HCI as pointed out by [10]. These characteristics include ubiquitous communication, high-functionality systems, mass availability of computer graphics, mixed media (images, voice, sounds, video, text, and formatted data), high-bandwidth interaction, large and thin displays (lightweight and low in power consumption) and information utilities.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Interaction and Interactivity in OUT MOODLE LMS:

Ally

2016

Int J Innov Educ Res

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Despite the wide spread adoption of MOODLE by Higher Learning Institutions (HLIs) in Tanzania, there is still lack of maximum utilization of the software by learners. This is due to lack of interactivity in the system caused by poor MOODLE customization and configuration, improper content design and integration with add-on multimedia files. On the other hand, instructors consider that uploading learning materials in MOODLE is enough without putting down the interactivity facilities. This paper assesses the interactivity in MOODLE from both design and usage. The paper discloses the extent of poor MOODLE customization that can likely hinder the inclusion of the multimedia facilities, extent of poor online course design and lack of virtual interactivity among learners. To undertake this study, a case study methodology was opted by investigating the MOODLE platform of the Open University of Tanzania. The respondents to this study were categorized as MOODLE Learners (MLs), MOODLE Instructors (MIs) and MOODLE Administrators (MA). In addition to interview, an intensive documentary review together with screening the design and configurations of MOODLE servers has been done. The study focused on four aspects of interactivity which are learner interaction to learner, instructor, content, and interface. The study reveals that there is a critical lack of interactivity between learners themselves enrolled in the same course, between learners and their respective tutors and between learners and system contents and interfaces. The synchronous interaction is less practiced compared to asynchronous interaction. This has been due to less insertion of real time multimedia and interactivity features. Generally, uncoordinated operations and ad hoc performance among key MOODLE stakeholders during interface design, software implementation, system configuration, and onsite content development form the basis of this.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing the Interaction and Interactivity in OUT MOODLE LMS:

Ally

2016

Int J Innov Educ Res

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“…The general structure of human-machine interaction is based on an easy interface between a human user and a technical system capable of interpreting the commands and information given during an interaction (Sinha et al, 2010). This was traditionally in the form of mechanical switches, like a keyboard, which allowed to convey formerly designated commands to be conferred.…”

Section: Introductionmentioning

confidence: 99%

Acoustic-Based Automatic Addressee Detection for Technical Systems: A Review

Siegert

Weißkirchen²,

Wendemuth³

2022

Front. Comput. Sci.

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ObjectiveAcoustic addressee detection is a challenge that arises in human group interactions, as well as in interactions with technical systems. The research domain is relatively new, and no structured review is available. Especially due to the recent growth of usage of voice assistants, this topic received increased attention. To allow a natural interaction on the same level as human interactions, many studies focused on the acoustic analyses of speech. The aim of this survey is to give an overview on the different studies and compare them in terms of utilized features, datasets, as well as classification architectures, which has so far been not conducted.MethodsThe survey followed the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. We included all studies which were analyzing acoustic and/or acoustic characteristics of speech utterances to automatically detect the addressee. For each study, we describe the used dataset, feature set, classification architecture, performance, and other relevant findings.Results1,581 studies were screened, of which 23 studies met the inclusion criteria. The majority of studies utilized German or English speech corpora. Twenty-six percent of the studies were tested on in-house datasets, where only limited information is available. Nearly 40% of the studies employed hand-crafted feature sets, the other studies mostly rely on Interspeech ComParE 2013 feature set or Log-FilterBank Energy and Log Energy of Short-Time Fourier Transform features. 12 out of 23 studies used deep-learning approaches, the other 11 studies used classical machine learning methods. Nine out of 23 studies furthermore employed a classifier fusion.ConclusionSpeech-based automatic addressee detection is a relatively new research domain. Especially by using vast amounts of material or sophisticated models, device-directed speech is distinguished from non-device-directed speech. Furthermore, a clear distinction between in-house datasets and pre-existing ones can be drawn and a clear trend toward pre-defined larger feature sets (with partly used feature selection methods) is apparent.

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“…• The hardware framework of our system is a Linux embedded system combined with a peripheral device. Moreover, a GUI based on Python and QT is employed to build a human-computer interaction (HCI) [22] system that facilitates the system manipulation and observations. The remainder of this work is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

Wei

et al. 2021

IEEE Access

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The dramatic increase in the amount of garbage and complex diversity of the materials in the garbage bring serious environmental pollution problems and wastes resources. Recycling reduces waste but manual pipeline waste sorting involves a harsh working environment at high labor intensity with low sorting efficiency. In our paper, a novel intelligent garbage classification system based on deep learning and an embedded Linux system is proposed. The system is divided into three parts. First, a Raspberry Pi 4B is utilized as the master board for the hardware system. The peripherals of the system consist of a touch panel, sensors, a 2-DOF (degree of freedom) servo, and a camera. Second, a new GNet model for garbage classification based on transfer learning and the improved MobileNetV3 model is proposed. Third, a GUI based on Python and QT is employed to build a human-computer interaction system to facilitate system manipulation and observation. A series of garbage classification experiments on the Huawei Garbage Classification Challenge Cup dataset were conducted. The proposed classification system's prediction accuracy was 92.62% at 0.63 s efficiency. The experimental results in this paper demonstrate that the proposed intelligent garbage classification system delivers high performance both in terms of accuracy and efficiency.

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Human Computer Interaction

Cited by 43 publications

References 0 publications

Assessing the Interaction and Interactivity in OUT MOODLE LMS:

Assessing the Interaction and Interactivity in OUT MOODLE LMS:

Acoustic-Based Automatic Addressee Detection for Technical Systems: A Review

A Novel Intelligent Garbage Classification System Based on Deep Learning and an Embedded Linux System

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