Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process

The proposed system illustrates that logic fuzzy can be used to aid management in assessing a supplier's environmental performance in the supplier selection process. A user-centred hierarchical system employing scalable fuzzy membership functions implement human priorities in the supplier selection process, with particular focus on a supplier's environmental performance. Traditionally, when evaluating supplier performance, companies have considered criteria such as price, quality, flexibility, etc. These criteria are of varying importance to individual companies pertaining to their own specific objectives. However, with environmental pressures increasing, many companies have begun to give more attention to environmental issues and, in particular, to their suppliers’ environmental performance. The framework presented here was developed to introduce efficiently environmental criteria into the existing supplier selection process and to reflect on its relevant importance to individual companies. The system presented attempts to simulate the human preference given to particular supplier selection criteria with particular focus on environmental issues when considering supplier selection. The system considers environmental data from multiple aspects of a suppliers business, and based on the relevant impact this will have on a Buying Organization, a decision is reached on the suitability of the supplier. This enables a particular supplier's strengths and weaknesses to be considered as well as considering their significance and relevance to the Buying OrganizationPeer reviewe

Planning, Organizing, and Hosting a Workshop: It’s All in the Details

A library training workshop is an effective way to teach and expand staff skills and, in the process, create interest in new library-related procedures. Hosting a workshop presents an opportunity to cultivate shared knowledge internally, and inviting outside participation provides a forum for strengthening external relationships and exchanging ideas. This article offers a detailed look at organizing a workshop—from budgeting and selecting a trainer to registering participants and making local arrangements. Additionally, it offers practical guidance for successfully planning and organizing a training workshop that will be a rewarding experience for participants, trainer, and host

Smart Transcription

The Intelligent Voice Smart Transcript is an interactive HTML5 document that contains the audio, a speech transcription and the key topics from an audio recording. It is designed to enable a quick and efficient review of audio communications by encapsulating the recording with the speech transcript and topics within a single HTML5 file. This paper outlines the rationale for the design of the SmartTranscript user experience. The paper discusses the difficulties of audio review, how there is large potential for misinterpretation associated with reviewing transcripts in isolation, and how additional diarization and topic tagging components augment the audio review process

Beryllium fastener technology

Program was conducted to develop, produce, and test optimum-configuration, beryllium prestressed and blind fasteners. The program was carried out in four phases - phase 1, feasibility study, phase 2, development, phase 3, evaluation of beryllium alloys, and phase 4, fabrication and testing

A Library and the Disciplines: A Collaborative Project Assessing the Impact of eBooks and Mobile Devices on Student Learning

With the proliferation of technology usage, it is essential to understand the effect of implementation of technology in the academic setting. Specifically, this article examines the impact of eBooks and mobile devices on student learning. A pilot study was conducted with three areas of interest. The first question of interest found that owning or having access to two or more mobile devices significantly increased respondents’ frequency of accessing eBooks. The second question examined the pros and cons of using mobile devices. Accessibility and cost savings were found as pros; while functionality and pedagogy were reported as drawbacks to mobile device usage. Furthermore, usability responses varied. The third question examined the effect of mobile device use on student learning. Findings show that eBooks and mobile device use in the classroom have a significant impact on the student’s educational experience

An Experimental Analysis of Deep Learning Architectures for Supervised Speech Enhancement

Recent speech enhancement research has shown that deep learning techniques are very effective in removing background noise. Many deep neural networks are being proposed, showing promising results for improving overall speech perception. The Deep Multilayer Perceptron, Convolutional Neural Networks, and the Denoising Autoencoder are well-established architectures for speech enhancement; however, choosing between different deep learning models has been mainly empirical. Consequently, a comparative analysis is needed between these three architecture types in order to show the factors affecting their performance. In this paper, this analysis is presented by comparing seven deep learning models that belong to these three categories. The comparison includes evaluating the performance in terms of the overall quality of the output speech using five objective evaluation metrics and a subjective evaluation with 23 listeners; the ability to deal with challenging noise conditions; generalization ability; complexity; and, processing time. Further analysis is then provided while using two different approaches. The first approach investigates how the performance is affected by changing network hyperparameters and the structure of the data, including the Lombard effect. While the second approach interprets the results by visualizing the spectrogram of the output layer of all the investigated models, and the spectrograms of the hidden layers of the convolutional neural network architecture. Finally, a general evaluation is performed for supervised deep learning-based speech enhancement while using SWOC analysis, to discuss the technique’s Strengths, Weaknesses, Opportunities, and Challenges. The results of this paper contribute to the understanding of how different deep neural networks perform the speech enhancement task, highlight the strengths and weaknesses of each architecture, and provide recommendations for achieving better performance. This work facilitates the development of better deep neural networks for speech enhancement in the future

A Mixed Reality Approach for dealing with the Video Fatigue of Online Meetings

Much of the issue with video meetings is the lack of naturalistic cues, together with the feeling of being observed all the time. Video calls take away most body language cues, but because the person is still visible, your brain still tries to compute that non-verbal language. It means that you’re working harder, trying to achieve the impossible. This impacts data retention and can lead to participants feeling unnecessarily tired. This project aims to transform the way online meetings happen, by turning off the camera and simplifying the information that our brains need to compute, thus preventing ‘Zoom fatigue’. The immersive solution we are developing, iVXR, consists of cutting-edge augmented reality technology, natural language processing, speech to text technologies and sub-real-time hardware acceleration using high performance computing

A New Secure and Lightweight Searchable Encryption Scheme over Encrypted Cloud Data

Searchable Encryption is an emerging cryptographic technique that enables searching capabilities over the encrypted data on the cloud. In this paper, a novel searchable encryption scheme for the client-server architecture has been presented. The scheme exploits the properties of modular inverse to generate a probabilistic trapdoor which facilitates the searching over the secure inverted index table. We propose indistinguishability that is achieved by using the property of a probabilistic trapdoor. We design and implement a proof of concept prototype and test our scheme onto a real dataset of files. We analyze the performance of our scheme against our claim of the scheme being light weight. The security analysis yields that our scheme assures higher level of security as compared to other existing schemes

Mapping and Masking Targets Comparison using Different Deep Learning based Speech Enhancement Architectures

Mapping and Masking targets are both widely used in recent Deep Neural Network (DNN) based supervised speech enhancement. Masking targets are proved to have a positive impact on the intelligibility of the output speech, while mapping targets are found, in other studies, to generate speech with better quality. However, most of the studies are based on comparing the two approaches using the Multilayer Perceptron (MLP) architecture only. With the emergence of new architectures that outperform the MLP, a more generalized comparison is needed between mapping and masking approaches. In this paper, a complete comparison will be conducted between mapping and masking targets using four different DNN based speech enhancement architectures, to work out how the performance of the networks changes with the chosen training target. The results show that there is no perfect training target with respect to all the different speech quality evaluation metrics, and that there is a tradeoff between the denoising process and the intelligibility of the output speech. Furthermore, the generalization ability of the networks was evaluated, and it is concluded that the design of the architecture restricts the choice of the training target, because masking targets result in significant performance degradation for deep convolutional autoencoder architecture