Biblio
With the rapid development of artificial intelligence, video target tracking is widely used in the fields of intelligent video surveillance, intelligent transportation, intelligent human-computer interaction and intelligent medical diagnosis. Deep learning has achieved remarkable results in the field of computer vision. The development of deep learning not only breaks through many problems that are difficult to be solved by traditional algorithms, improves the computer's cognitive level of images and videos, but also promotes the progress of related technologies in the field of computer vision. This paper combines the deep learning algorithm and target tracking algorithm to carry out relevant experiments on basketball motion detection video, hoping that the experimental results can be helpful to basketball motion detection video target tracking.
The features of socio-cyber-physical systems are presented, which dictate the need to revise traditional management methods and transform the management system in such a way that it takes into account the presence of a person both in the control object and in the control loop. The use of situational control mechanisms is proposed. The features of this approach and its comparison with existing methods of situational awareness are presented. The comparison has demonstrated wider possibilities and scope for managing socio-cyber-physical systems. It is recommended to consider a wider class of types of relations that exist in socio-cyber-physical systems. It is indicated that such consideration can be based on the use of pseudo-physical logics considered in situational control. It is pointed out that it is necessary to design a classifier of situations (primarily in cyberspace), instead of traditional classifiers of threats and intruders.
Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.
Humans are a key part of software development, including customers, designers, coders, testers and end users. In this keynote talk I explain why incorporating human-centric issues into software engineering for next-generation applications is critical. I use several examples from our recent and current work on handling human-centric issues when engineering various `smart living' cloud- and edge-based software systems. This includes using human-centric, domain-specific visual models for non-technical experts to specify and generate data analysis applications; personality impact on aspects of software activities; incorporating end user emotions into software requirements engineering for smart homes; incorporating human usage patterns into emerging edge computing applications; visualising smart city-related data; reporting diverse software usability defects; and human-centric security and privacy requirements for smart living systems. I assess the usefulness of these approaches, highlight some outstanding research challenges, and briefly discuss our current work on new human-centric approaches to software engineering for smart living applications.
When communication about security to end users is ineffective, people frequently misinterpret the protection offered by a system. The discrepancy between the security users perceive a system to have and the actual system state can lead to potentially risky behaviors. It is thus crucial to understand how security perceptions are shaped by interface elements such as text-based descriptions of encryption. This article addresses the question of how encryption should be described to non-experts in a way that enhances perceived security. We tested the following within-subject variables in an online experiment (N=309): a) how to best word encryption, b) whether encryption should be described with a focus on the process or outcome, or both c) whether the objective of encryption should be mentioned d) when mentioning the objective of encryption, how to best describe it e) whether a hash should be displayed to the user. We also investigated the role of context (between subjects). The verbs "encrypt" and "secure" performed comparatively well at enhancing perceived security. Overall, participants stated that they felt more secure not knowing about the objective of encryption. When it is necessary to state the objective, positive wording of the objective of encryption worked best. We discuss implications and why using these results to design for perceived lack of security might be of interest as well. This leads us to discuss ethical concerns, and we give guidelines for the design of user interfaces where encryption should be communicated to end users.
Human emotion recognition plays a vital role in interpersonal communication and human-machine interaction domain. Emotions are expressed through speech, hand gestures and by the movements of other body parts and through facial expression. Facial emotions are one of the most important factors in human communication that help us to understand, what the other person is trying to communicate. People understand only one-third of the message verbally, and two-third of it is through non-verbal means. There are many face emotion recognition (FER) systems present right now, but in real-life scenarios, they do not perform efficiently. Though there are many which claim to be a near-perfect system and to achieve the results in favourable and optimal conditions. The wide variety of expressions shown by people and the diversity in facial features of different people will not aid in the process of coming up with a system that is definite in nature. Hence developing a reliable system without any flaws showed by the existing systems is a challenging task. This paper aims to build an enhanced system that can analyse the exact facial expression of a user at that particular time and generate the corresponding emotion. Datasets like JAFFE and FER2013 were used for performance analysis. Pre-processing methods like facial landmark and HOG were incorporated into a convolutional neural network (CNN), and this has achieved good accuracy when compared with the already existing models.
New research fields and applications on human computer interaction will emerge based on the recognition of emotions on faces. With such aim, our study evaluates the features extracted from faces to recognize emotions. To increase the success rate of these features, we have run several tests to demonstrate how age and gender affect the results. The artificial neural networks were trained by the apparent regions on the face such as eyes, eyebrows, nose, mouth, and jawline and then the networks are tested with different age and gender groups. According to the results, faces of older people have a lower performance rate of emotion recognition. Then, age and gender based groups are created manually, and we show that performance rates of facial emotion recognition have increased for the networks that are trained using these particular groups.
Security for authentication is required to give a superlative secure users' personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued-recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall-based n ×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space ( 2.4 ×104 bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone.