scholarly journals On the Security and Privacy Challenges of Virtual Assistants

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2312
Author(s):  
Tom Bolton ◽  
Tooska Dargahi ◽  
Sana Belguith ◽  
Mabrook S. Al-Rakhami ◽  
Ali Hassan Sodhro
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Security And Privacy ◽  
Future Research ◽  
Research Directions ◽  
Privacy Concerns ◽  
Current Trends ◽  
Current State ◽  
Future Research Directions ◽  
Privacy Issues

Since the purchase of Siri by Apple, and its release with the iPhone 4S in 2011, virtual assistants (VAs) have grown in number and popularity. The sophisticated natural language processing and speech recognition employed by VAs enables users to interact with them conversationally, almost as they would with another human. To service user voice requests, VAs transmit large amounts of data to their vendors; these data are processed and stored in the Cloud. The potential data security and privacy issues involved in this process provided the motivation to examine the current state of the art in VA research. In this study, we identify peer-reviewed literature that focuses on security and privacy concerns surrounding these assistants, including current trends in addressing how voice assistants are vulnerable to malicious attacks and worries that the VA is recording without the user’s knowledge or consent. The findings show that not only are these worries manifold, but there is a gap in the current state of the art, and no current literature reviews on the topic exist. This review sheds light on future research directions, such as providing solutions to perform voice authentication without an external device, and the compliance of VAs with privacy regulations.

scholarly journals Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

2021 ◽  
Vol 9 ◽  
pp. 1061-1080
Author(s):  
Prakhar Ganesh ◽  
Yao Chen ◽  
Xin Lou ◽  
Mohammad Ali Khan ◽  
Yin Yang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Research Attention ◽  
Model Compression ◽  
Future Research Directions ◽  
Or Applications

Abstract Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted considerable research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models.

scholarly journals A Review of the State of the Art in Non-Contact Sensing for COVID-19

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5665
Author(s):  
William Taylor ◽  
Qammer H. Abbasi ◽  
Kia Dashtipour ◽  
Shuja Ansari ◽  
Syed Aziz Shah ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Advantages And Disadvantages ◽  
Non Invasive ◽  
Current State ◽  
Global Pandemic ◽  
Future Research Directions ◽  
Contact Sensing

COVID-19, caused by SARS-CoV-2, has resulted in a global pandemic recently. With no approved vaccination or treatment, governments around the world have issued guidance to their citizens to remain at home in efforts to control the spread of the disease. The goal of controlling the spread of the virus is to prevent strain on hospitals. In this paper, we focus on how non-invasive methods are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. Early detection of COVID-19 can allow for early isolation to prevent further spread. This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches. In addition, the paper highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

Modular Reconfigurable Robotics

2019 ◽  
Vol 2 (1) ◽  
pp. 63-88 ◽  
Author(s):  
Jungwon Seo ◽  
Jamie Paik ◽  
Mark Yim
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Future Research ◽  
Research Directions ◽  
Current State ◽  
Robot Systems ◽  
Technical Issues ◽  
Future Research Directions ◽  
Reconfigurable Robot ◽  
High Level

This article reviews the current state of the art in the development of modular reconfigurable robot (MRR) systems and suggests promising future research directions. A wide variety of MRR systems have been presented to date, and these robots promise to be versatile, robust, and low cost compared with other conventional robot systems. MRR systems thus have the potential to outperform traditional systems with a fixed morphology when carrying out tasks that require a high level of flexibility. We begin by introducing the taxonomy of MRRs based on their hardware architecture. We then examine recent progress in the hardware and the software technologies for MRRs, along with remaining technical issues. We conclude with a discussion of open challenges and future research directions.

scholarly journals Designing a solver competition: the QBFEVAL'10 case study

10.29007/cv3b ◽  
2018 ◽  
Author(s):  
Claudia Peschiera ◽  
Luca Pulina ◽  
Armando Tacchella
Keyword(s):  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Quantified Boolean Formulas ◽  
Current State ◽  
Boolean Formulas ◽  
Future Research Directions

In this paper we report about QBFEVAL'10, the seventh in a series of events established with the aim of assessing the advancements in reasoning about quantified Boolean formulas (QBFs). The paper discusses the results obtained and the evaluation setup, from the criteria used to select QBF instances down to the hardware infrastructure. We also discuss the current state-of-the-art in light of past challenges and we envision future research directions that are motivated by the results of QBFEVAL'10.

scholarly journals Digital Image Watermarking Techniques: A Review

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 110 ◽  
Author(s):  
Mahbuba Begum ◽  
Mohammad Shorif Uddin
Keyword(s):  
Digital Image ◽  
State Of The Art ◽  
Image Watermarking ◽  
Image Authentication ◽  
Future Research ◽  
Digital Image Watermarking ◽  
Research Directions ◽  
Digital Revolution ◽  
Current Trends ◽  
Future Research Directions

Digital image authentication is an extremely significant concern for the digital revolution, as it is easy to tamper with any image. In the last few decades, it has been an urgent concern for researchers to ensure the authenticity of digital images. Based on the desired applications, several suitable watermarking techniques have been developed to mitigate this concern. However, it is tough to achieve a watermarking system that is simultaneously robust and secure. This paper gives details of standard watermarking system frameworks and lists some standard requirements that are used in designing watermarking techniques for several distinct applications. The current trends of digital image watermarking techniques are also reviewed in order to find the state-of-the-art methods and their limitations. Some conventional attacks are discussed, and future research directions are given.

Emotional networks: The heart of brain design

2000 ◽  
Vol 23 (2) ◽  
pp. 198-199
Author(s):  
John C. Fentress
Keyword(s):  
Neural Networks ◽  
Single Unit ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Behavioral Issues ◽  
Current State ◽  
Adaptive Action ◽  
Future Research Directions

The concept of emotion as defined by Rolls is based upon reinforcement mechanisms and their underlying neural networks. He shows how these networks process signals at many levels, through both separate and convergent pathways essential for adaptive action. While many behavioral issues related to emotion are omitted from his review, he succeeds admirably in summarizing both the “current state of the art” in single unit analyses and in pointing out how future research directions may be crafted.

scholarly journals Deep Learning for Video Captioning: A Review

2019 ◽  
Author(s):  
Shaoxiang Chen ◽  
Ting Yao ◽  
Yu-Gang Jiang
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Video Captioning ◽  
Future Research Directions ◽  
Review State ◽  
Vision And Language

Deep learning has achieved great successes in solving specific artificial intelligence problems recently. Substantial progresses are made on Computer Vision (CV) and Natural Language Processing (NLP). As a connection between the two worlds of vision and language, video captioning is the task of producing a natural-language utterance (usually a sentence) that describes the visual content of a video. The task is naturally decomposed into two sub-tasks. One is to encode a video via a thorough understanding and learn visual representation. The other is caption generation, which decodes the learned representation into a sequential sentence, word by word. In this survey, we first formulate the problem of video captioning, then review state-of-the-art methods categorized by their emphasis on vision or language, and followed by a summary of standard datasets and representative approaches. Finally, we highlight the challenges which are not yet fully understood in this task and present future research directions.

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