Self-Driving Networks

This chapter presents a new vision of network operations, the self-driving network, that takes automation to the next level. This is not a description of existing work; rather, it is a challenge to dramatically rethink how we manage networks (or rather, how we do not manage networks). It draws upon an analogy with the development of self-driving cars and presents motivations for this effort. It then describes the technologies needed to implement this and an overall architecture of the system. As this endeavor will cause a major shift in network management, the chapter offers an evolutionary path to the end goal. Some of the consequences and human impacts of such a system are touched upon. The chapter concludes with some research topics and a final message. Key takeaways are that machine learning and feedback loops are fundamental to the solution; a key outcome is to build systems that are adaptive and predictive, for the benefit of users.

Sustainability in transformation of maternal mortality by interaction based approach in Dairi, Indonesia

Introduction: Health and mortality problems are closely related to the maternal mortality rate (MMR). Efforts to reduce MMR have been carried out by many countries, including the Indonesian government. Materials and Methods: This research was conducted using two approaches, namely quantitative and qualitative or mixed methods and 149 respondents and 26 informants, to reduce MMR in Dairi Regency. This study was also carried out to determine the dominant variable that affects mother's behavior in an effort to reduce MMR in accordance with the existing theory. Result: Mother's intention to contribute to the reduction of MMR in this study was influenced by the good factor directly or indirectly. This is indicated by the score p<0.005. The study also shows that it is very important for the health workers to be able to communicate well with individuals and communities. Conclusion: The present study will help to reduce maternal fatalities, and will help to build systems and processes that will allow control the behaviour of the pregnant women and determine the cause of death as well as its contributing factors.

CybIQ: Secure Authentication Method

With increased vulnerabilities and vast technology landscapes, it is extremely critical to build systems which are highly resistant to cyber-attacks, to break into systems to exploit. It is almost impossible to build 100% secure authentication &amp; authorization mechanisms merely through standard password / PIN (With all combinations of special characters, numbers &amp; upper/lower case alphabets and by using any of the Graphical password mechanisms). The immense computing capacity and several hacking methods used, make almost every authentication method susceptible to cyber-attacks in one or the other way. Only proven / known system which is not vulnerable in spite of highly sophisticated computing power is, human brain. In this paper, we present a new method of authentication using a combination of computer&rsquo;s computing ability in combination with human intelligence. In fact this human intelligence is personalized making the overall security method more secure. Text based passwords are easy to be cracked [6]. There is an increased need for an alternate and more complex authentication and authorization methods. Some of the Methods [7] [8] in the category of Graphical passwords could be susceptible, when Shoulder surfing/cameras/spy devices are used.

Formalizing and guaranteeing human-robot interaction

As robots begin to interact closely with humans, we need to build systems worthy of trust regarding the safety and quality of the interaction.

Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias throughout the Machine Learning Lifecyle

Correctly resolving textual mentions of people fundamentally entails making inferences about those people. Such inferences raise the risk of systematic biases in coreference resolution systems, including biases that can harm binary and non-binary trans and cis stakeholders. To better understand such biases, we foreground nuanced conceptualizations of gender from sociology and sociolinguistics, and investigate where in the machine learning pipeline such biases can enter a coreference resolution system. We inspect many existing datasets for trans-exclusionary biases, and develop two new datasets for interrogating bias in both crowd annotations and in existing coreference resolution systems. Through these studies, conducted on English text, we confirm that without acknowledging and building systems that recognize the complexity of gender, we will build systems that fail for: quality of service, stereotyping, and over- or under-representation, especially for binary and non-binary trans users.

Attention mechanisms and deep learning for machine vision: A survey of the state of the art

With the advent of state of the art nature-inspired pure attention based models i.e. transformers, and their success in natural language processing (NLP), their extension to machine vision (MV) tasks was inevitable and much felt. Subsequently, vision transformers (ViTs) were introduced which are giving quite a challenge to the established deep learning based machine vision techniques. However, pure attention based models/architectures like transformers require huge data, large training times and large computational resources. Some recent works suggest that combinations of these two varied fields can prove to build systems which have the advantages of both these fields. Accordingly, this state of the art survey paper is introduced which hopefully will help readers get useful information about this interesting and potential research area. A gentle introduction to attention mechanisms is given, followed by a discussion of the popular attention based deep architectures. Subsequently, the major categories of the intersection of attention mechanisms and deep learning for machine vision (MV) based are discussed. Afterwards, the major algorithms, issues and trends within the scope of the paper are discussed.

Trust in Artificial Intelligence: Meta-Analytic Findings

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Future positive

The argument in this book rests on a simple proposition: understanding the reason why people prefer to take a chance on sickness and cure is the key to persuading them when and why they should choose prevention instead. This final chapter summarizes the means of persuasion: investigate rather than presuppose which criteria are used to make health choices; build systems for accounting (inclusive costs and benefits of prevention) and for accountability (liability and responsibility); offer ways to improve health, not merely ways to avoid losing it; evaluate, in order to manage, the perceptions linked to health hazards; exploit the logic of choice to insure against the risk of unlikely disasters, to increase the present value of future threats, to foster cooperation as a basis for prevention, to map out the practical pathways to prevention, and to remedy the under-investment in prevention research. The tools of prevention are the means to a greater end—health as a ‘state of complete physical, mental, and social well-being’.