scholarly journals Conversational Agents: Goals, Technologies, Vision and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8448
Author(s):  
Merav Allouch ◽  
Amos Azaria ◽  
Rina Azoulay
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Building Blocks ◽  
Future Research ◽  
Conversational Agents ◽  
Daily Routines ◽  
Security Issues ◽  
Current State ◽  
Technological Developments ◽  
Emotional Aspects

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

Nano-Sized and Nanocrystalline Calcium Orhophosphates in Biomedical Engineering

2009 ◽  
Vol 3 ◽  
pp. 59-92 ◽  
Author(s):  
Sergey V. Dorozhkin
Keyword(s):  
Bone Repair ◽  
Dental Enamel ◽  
Building Blocks ◽  
Research Area ◽  
Future Research ◽  
Biological Origin ◽  
Current State ◽  
Calcium Orthophosphates ◽  
Recent Developments ◽  
Hard Tissue Engineering

There has been much recent activity in the research area of nanoparticles and nanocrystalline materials, in many fields of science and technology. This is due to their outstanding and unique physical, mechanical, chemical and biological characteristics. Recent developments in biomineralization have demonstrated that nano-sized particles play an important role in the formation of the hard tissues of animals. It is well established that the basic inorganic building blocks of bones and teeth of mammals are nano-sized and nanocrystalline calcium orthophosphates (in the form of apatites) of a biological origin. In mammals, tens to hundreds of nanocrystals of biological apatite are found to combine into self-assembled structures under the control of bio-organic matrixes. It was also confirmed experimentally that the structure of both dental enamel and bones could be mimicked by an oriented aggregation of nano-sized calcium orthophosphates, determined by the biomolecules. The application and prospective use of nano-sized and nanocrystalline calcium orthophosphates for clinical repair of damaged bones and teeth are also known. For example, a greater viability and a better proliferation of various cells were detected on smaller crystals of calcium orthophosphates. Furthermore, studies revealed that the differentiation of various cells was promoted by nano-sized calcium orthophosphates. Thus, the nano-sized and nanocrystalline forms of calcium orthophosphates have the potential to revolutionize the field of hard tissue engineering, in areas ranging from bone repair and augmentation to controlled drug delivery devices. This paper reviews the current state of knowledge and recent developments of various nano-sized and nanocrystalline calcium orthophosphates, covering topics from the synthesis and characterization to biomedical and clinical applications. This review also provides possible directions of future research and development.

scholarly journals Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

2020 ◽  
Author(s):  
Md Nazmus Saadat ◽  
Muhammad Shuaib
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Language Processing ◽  
Future Research ◽  
Learning In Context ◽  
Postgraduate Students ◽  
History Of ◽  
Learning Platforms ◽  
Future Research Directions ◽  
Learning Machine

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Moving the Customer Experience Field Forward: Introducing the Touchpoints, Context, Qualities (TCQ) Nomenclature

2020 ◽  
Vol 23 (4) ◽  
pp. 433-455 ◽  
Author(s):  
Arne De Keyser ◽  
Katrien Verleye ◽  
Katherine N. Lemon ◽  
Timothy L. Keiningham ◽  
Philipp Klaus
Keyword(s):  
Building Blocks ◽  
Customer Experience ◽  
Environmental Context ◽  
Future Research ◽  
Current Definition ◽  
Customer Experience Management ◽  
Current State ◽  
Methodological Rigor ◽  
Inductive Analysis ◽  
Available Resources

In response to initial voices that put the customer experience (management) (CX(M)) movement into question, this article aims to introduce a formal nomenclature to push the CX(M) field toward a more mature state. First, drawing from an inductive analysis of 143 CX(M) papers, the authors identify 12 basic CX components that aggregate into three overarching building blocks—touchpoints (T, i.e., points of interaction between the customer and brand/firm), context (C, i.e., situationally available resources internal and/or external to the customer), and qualities (Q, i.e., attributes that reflect the nature of customer responses and reactions to interactions with the brand/firm). The TCQ nomenclature offers a language to make CX actionable, moving beyond the breadth of the current definition and frameworks by disentangling CX into small bite-sized chunks (i.e., the CX components) that any academic and practitioner, regardless of their discipline, may understand and use to discuss and manage CX. Second, using the TCQ nomenclature, the authors assess the current state of the CX(M) literature and identify mature (e.g., firm-controlled touchpoints and cognitive and emotional qualities associated with CX) and underdeveloped (e.g., nonfirm controlled touchpoints and the market and environmental context in which CX emerges) areas ripe for future research. In addition, they also provide a set of recommendations to strengthen the methodological rigor of the field. Third, the TCQ nomenclature may support managers in auditing their current CXM practices and/or serve as a basis for CX design and innovation.

scholarly journals Recent advances of deep learning in psychiatric disorders

2020 ◽  
Vol 3 (3) ◽  
pp. 202-213
Author(s):  
Lu Chen ◽  
Chunchao Xia ◽  
Huaiqiang Sun
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Psychiatric Disorders ◽  
Language Processing ◽  
Visual Recognition ◽  
Learning Algorithm ◽  
Future Research ◽  
Academic World ◽  
Recent Advances ◽  
Conventional Machine

ABSTRACT Deep learning (DL) is a recently proposed subset of machine learning methods that has gained extensive attention in the academic world, breaking benchmark records in areas such as visual recognition and natural language processing. Different from conventional machine learning algorithm, DL is able to learn useful representations and features directly from raw data through hierarchical nonlinear transformations. Because of its ability to detect abstract and complex patterns, DL has been used in neuroimaging studies of psychiatric disorders, which are characterized by subtle and diffuse alterations. Here, we provide a brief review of recent advances and associated challenges in neuroimaging studies of DL applied to psychiatric disorders. The results of these studies indicate that DL could be a powerful tool in assisting the diagnosis of psychiatric diseases. We conclude our review by clarifying the main promises and challenges of DL application in psychiatric disorders, and possible directions for future research.

Neuronal Diversity In The Retina

e-Neuroforum ◽  
2017 ◽  
Vol 23 (2) ◽  
Author(s):  
Philipp Berens ◽  
Thomas Euler
Keyword(s):  
Building Blocks ◽  
Cell Types ◽  
Relevant Information ◽  
Future Research ◽  
Neuronal Diversity ◽  
Form Complex ◽  
Current State ◽  
Complex Circuits ◽  
Different Cell Types ◽  
The Brain

AbstractThe retina in the eye performs complex computations, to transmit only behaviourally relevant information about our visual environment to the brain. These computations are implemented by numerous different cell types that form complex circuits. New experimental and computational methods make it possible to study the cellular diversity of the retina in detail – the goal of obtaining a complete list of all the cell types in the retina and, thus, its “building blocks”, is within reach. We review the current state of this endeavour and highlight possible directions for future research.

scholarly journals RESEARCH IN-DEPTH TRAINING OF NEURAL NETWORKS

2020 ◽  
Vol 70 (2) ◽  
pp. 234-238
Author(s):  
K.S. Imanbaev ◽  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Language Processing ◽  
Back Propagation ◽  
Fine Tuning ◽  
Current State ◽  
Large Numbers ◽  
History Of ◽  
Propagation Of Errors

Currently, deep learning of neural networks is one of the most popular methods for speech recognition, natural language processing, and computer vision. The article reviews the history of deep learning of neural networks and the current state in General. We consider algorithms for training neural networks used for deep training of neural networks, followed by fine-tuning using the method of back propagation of errors. Neural networks with large numbers of hidden layers, frequently occurring and disappearing gradients are very difficult to train. In this paper, we consider methods that successfully implement training of neural networks with large numbers of layers (more than one hundred) and vanishing gradients. A review of well-known libraries used for successful deep learning of neural networks is conducted.

scholarly journals Artificial Intelligence-Based Conversational Agents for Chronic Conditions: Systematic Literature Review

10.2196/20701 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e20701 ◽  
Author(s):  
Theresa Schachner ◽  
Roman Keller ◽  
Florian v Wangenheim
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Chronic Diseases ◽  
Language Processing ◽  
Systematic Literature Review ◽  
Chronic Conditions ◽  
Experimental Studies ◽  
Future Research ◽  
Conversational Agents ◽  
Quasi Experimental

Background A rising number of conversational agents or chatbots are equipped with artificial intelligence (AI) architecture. They are increasingly prevalent in health care applications such as those providing education and support to patients with chronic diseases, one of the leading causes of death in the 21st century. AI-based chatbots enable more effective and frequent interactions with such patients. Objective The goal of this systematic literature review is to review the characteristics, health care conditions, and AI architectures of AI-based conversational agents designed specifically for chronic diseases. Methods We conducted a systematic literature review using PubMed MEDLINE, EMBASE, PyscInfo, CINAHL, ACM Digital Library, ScienceDirect, and Web of Science. We applied a predefined search strategy using the terms “conversational agent,” “healthcare,” “artificial intelligence,” and their synonyms. We updated the search results using Google alerts, and screened reference lists for other relevant articles. We included primary research studies that involved the prevention, treatment, or rehabilitation of chronic diseases, involved a conversational agent, and included any kind of AI architecture. Two independent reviewers conducted screening and data extraction, and Cohen kappa was used to measure interrater agreement.A narrative approach was applied for data synthesis. Results The literature search found 2052 articles, out of which 10 papers met the inclusion criteria. The small number of identified studies together with the prevalence of quasi-experimental studies (n=7) and prevailing prototype nature of the chatbots (n=7) revealed the immaturity of the field. The reported chatbots addressed a broad variety of chronic diseases (n=6), showcasing a tendency to develop specialized conversational agents for individual chronic conditions. However, there lacks comparison of these chatbots within and between chronic diseases. In addition, the reported evaluation measures were not standardized, and the addressed health goals showed a large range. Together, these study characteristics complicated comparability and open room for future research. While natural language processing represented the most used AI technique (n=7) and the majority of conversational agents allowed for multimodal interaction (n=6), the identified studies demonstrated broad heterogeneity, lack of depth of reported AI techniques and systems, and inconsistent usage of taxonomy of the underlying AI software, further aggravating comparability and generalizability of study results. Conclusions The literature on AI-based conversational agents for chronic conditions is scarce and mostly consists of quasi-experimental studies with chatbots in prototype stage that use natural language processing and allow for multimodal user interaction. Future research could profit from evidence-based evaluation of the AI-based conversational agents and comparison thereof within and between different chronic health conditions. Besides increased comparability, the quality of chatbots developed for specific chronic conditions and their subsequent impact on the target patients could be enhanced by more structured development and standardized evaluation processes.

scholarly journals The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review (Preprint)

2020 ◽  
Author(s):  
Madison Milne-Ives ◽  
Caroline de Cock ◽  
Ernest Lim ◽  
Melissa Harper Shehadeh ◽  
Nick de Pennington ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Health Care ◽  
Language Processing ◽  
Health Care Services ◽  
Interactive Voice Response ◽  
Future Research ◽  
Conversational Agents ◽  
Privacy And Security ◽  
Care Services

BACKGROUND The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption. OBJECTIVE This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents. METHODS PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another. RESULTS A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback. CONCLUSIONS The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents. INTERNATIONAL REGISTERED REPORT RR2-10.2196/16934

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