scholarly journals Microgrid Communication and Security: State-Of-The-Art and Future Directions

2021 ◽  
Vol 1 (1) ◽  
pp. 37-52
Author(s):  
Farah Aqilah Bohani ◽  
Sitti Rachmawati Yahya ◽  
Siti Norul Huda Sheikh Abdullah
Keyword(s):  
Communication Network ◽  
State Of The Art ◽  
Future Directions ◽  
Different Types ◽  
Future Direction

The microgrid communication network with proper connectivity among microgrid resources is play important role to maintain a stability and reliability of the microgrid. Application of suitable communication network and protocol and highlighted the best security measurement is one of the elements to achieve those broad objectives.  The communication network and protocol that has been implemented in existing microgrid has different types and objective which is depend on specific application.  To secure the communication network and protocol, many security approaches is proposed.  In this paper, a review of microgrid communication and its security is shown and future direction of communication network and protocol with its security also provided.

scholarly journals Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges

2021 ◽  
Vol 297 ◽  
pp. 01074
Author(s):  
Achsha Babu ◽  
J. Andrew Onesimu ◽  
K. Martin Sagayam
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Future Directions ◽  
Human Essence ◽  
Research Challenges ◽  
Different Types ◽  
Precise Diagnosis ◽  
Clinical Practise ◽  
To Come ◽  
Applications Of Ai

Artificial Intelligence (AI) has the ability to process huge datasets, disclose human essence computationally, and perform like humans as technology advances. Because of the necessity for precise diagnosis and improved patient care, AI technology has greatly influenced the healthcare industry. In the domains of dentistry and medicine, artificial intelligence has yet to come a long way. As a result, dentists must be aware of the potential implications for a profitable clinical practise in the future. In this paper, we present the current applications of AI in dentistry. The different types of AI techniques are introduced and summarized. The state-of-the-art literature is studied analysed. A comparative analysis on the different AI techniques in dentistry is presented. Further, the research challenges in the field of dentistry and future directions are also provided.

Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube
Keyword(s):  
Anxiety Disorders ◽  
Neuronal Plasticity ◽  
Functional Neuroimaging ◽  
State Of The Art ◽  
Methodological Issues ◽  
Future Directions ◽  
Activation Patterns ◽  
New Learning ◽  
Analytical Strategies ◽  
Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

Targeting Cystathionine Beta-Synthase Misfolding in Homocystinuria by Small Ligands: State of the Art and Future Directions

2016 ◽  
Vol 17 (13) ◽  
pp. 1455-1470 ◽  
Author(s):  
Tomas Majtan ◽  
Angel L. Pey ◽  
June Ereño-Orbea ◽  
Luis Alfonso Martínez-Cruz ◽  
Jan P. Kraus
Keyword(s):  
State Of The Art ◽  
Future Directions ◽  
Cystathionine Beta Synthase ◽  
Small Ligands

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

Poly(hydroxyurethane) Adhesives and Coatings: State-of-the-Art and Future Directions

2021 ◽  
Author(s):  
Alvaro Gomez-Lopez ◽  
Satyannarayana Panchireddy ◽  
Bruno Grignard ◽  
Inigo Calvo ◽  
Christine Jerome ◽  
...  
Keyword(s):  
State Of The Art ◽  
Future Directions

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

Interpreting testing and assessment: A state-of-the-art review

2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han
Keyword(s):  
State Of The Art ◽  
Professional Certification ◽  
Automatic Assessment ◽  
Prospective Students ◽  
Assessment Practice ◽  
High Stakes ◽  
Future Directions ◽  
Assessment Tasks ◽  
Interpreter Education ◽  
Selection Of

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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