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This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm. Then the path planning algorithm based on Probability Dueling DQN is combined with FastSLAM to accomplish the autonomous navigation and map the environment.

This paper proposes an active simultaneous localization and mapping (SLAM) framework for autonomous navigation under an indoor environment with static and dynamic obstacles. It integrates a path planning algorithm with visual SLAM to decrease navigation uncertainty and build an environment map.

The result shows that the proposed method offers good performance over existing Dueling DQN for navigation uncertainty under the indoor environment with different numbers and shapes of the static and dynamic obstacles in the real world field.

This paper proposes a novel active SLAM framework composed of Probability Dueling DQN that is the improved path planning algorithm based on Dueling DQN and FastSLAM. This framework is used with the Monodepth depth image prediction method with faster prediction speed to realize autonomous navigation in the indoor environment with different numbers and shapes of the static and dynamic obstacles.

The purpose of this paper is to address three aspects of the managerial challenge that arises from the process of business interaction: the diversity of interaction; the dynamic effects of interaction; and the integration of interaction into company operations and strategy. The paper develops a typology which characterizes different types of interaction in order to identify the managerial issues they represent.

The paper is based on the empirical research programme of the IMP Group (impgroup.org) and on a recent large scale research project within that programme.

The paper seeks to identify the dynamic effects of each type of interaction on the resources and activities involved in it and address the implications that these effects have for management. Finally, the paper discusses how the challenges and opportunities presented by business interaction can be integrated into the general management of a company.

The paper is mainly based on detailed empirical observations focusing some special features. Focusing other aspects would give another result.

Important for all companies having important business relationships.

Gives a new view of managerial challenges related to the variety in business relationships.

“Affairs of honor,” which were contests between gentlemen of the 18th and 19th centuries, often ended with physical violence in the form of dueling. Such was the case in the notable showdown between Alexander Hamilton and Aaron Burr in 1801. These affairs were products of the code di duello, an intricate series of social rules and expectations through which adversaries negotiated their dispute. In this article, we examine the similarities between such “affairs” and the modern issue of “cyberbullying.” We compare disputes conducted under the code di duello to those which regularly occur in modern adolescents’ on-line lives while also considering the relevance of studying this topic in social studies classrooms.

Educational institutions have a special social responsibility to initiate processes of sustainability transformation in society, nevertheless, activities that effectively address students as well as employees are rather moderate. To initiate change alongside strategic and political decisions, this paper aims to present findings of a gamification intervention (hereafter referred to as climate duel), which was implemented in a field study and tested to assess its potential as a supporting and transformative approach in a university context.

A three-month field study was conducted with two universities of applied sciences. The study included an environmental impact analysis of the two participating university departments, the conception and testing of the intervention and associated surveys to evaluate the effectiveness of the intervention in order to and to obtain feedback that would allow the duel to be scaled up at other universities or institutions.

Three hundred seventy-five people took part and saved 2.6 tons of greenhouse gas emissions through their participation in the climate duel and their corresponding behavioural changes. In addition, feedback from the participants yielded positive results in terms of behavioural changes and generated valuable evidence for future implementations. Nevertheless, there is still room for improvement, especially in terms of supporting communication activities that promote social relatedness to motivate each other, share experiences or deal with implementation difficulties in everyday life.

Building on the promising effects of gamification, the study is a showcase for applied science. With the possibility of testing a theory-based intervention in practice, an implementable, effective and scalable measure for universities that helps to accelerate the transformation process is available.

In this research, the authors demonstrate the advantage of reinforcement learning (RL) based intrusion detection systems (IDS) to solve very complex problems (e.g. selecting input features, considering scarce resources and constrains) that cannot be solved by classical machine learning. The authors include a comparative study to build intrusion detection based on statistical machine learning and representational learning, using knowledge discovery in databases (KDD) Cup99 and Installation Support Center of Expertise (ISCX) 2012.

The methodology applies a data analytics approach, consisting of data exploration and machine learning model training and evaluation. To build a network-based intrusion detection system, the authors apply dueling double deep Q-networks architecture enabled with costly features, k-nearest neighbors (K-NN), support-vector machines (SVM) and convolution neural networks (CNN).

Machine learning-based intrusion detection are trained on historical datasets which lead to model drift and lack of generalization whereas RL is trained with data collected through interactions. RL is bound to learn from its interactions with a stochastic environment in the absence of a training dataset whereas supervised learning simply learns from collected data and require less computational resources.

All machine learning models have achieved high accuracy values and performance. One potential reason is that both datasets are simulated, and not realistic. It was not clear whether a validation was ever performed to show that data were collected from real network traffics.

The study provides guidelines to implement IDS with classical supervised learning, deep learning and RL.

The research applied the dueling double deep Q-networks architecture enabled with costly features to build network-based intrusion detection from network traffics. This research presents a comparative study of reinforcement-based instruction detection with counterparts built with statistical and representational machine learning.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

This paper examines the different processes that take place when companies engage in interactions with one another and the ultimate effect on the “players”. At the most basic “duel” level, these can be little more than transactions of goods and services. At the more complex “duet” level (such as engaging in research together), companies can develop sophisticated, complex relationships. This paper considers resources and activities involved and addresses the implications and challenges created for management.

The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

In this chapter I attempt to merge Athens’ conception of domination as a complex interactionist concept with Goffman’s notion of demeanor and deference as lynchpins of dramaturgical analysis. I ground the merger in an analysis of metaphorical duel between a superordinate and subordinate in the TV show Mad Men. The examination of this metaphorical dual also implies a connection between a radical interactionism as defined by Athens and a radical dramaturgy informed by Athens’ conception of domination. In particular, I propose an examination of civil domination within institutionalized settings in which use of shared pasts and concomitant acts of demeanor and deference enhance the construction of domination between superordinates and subordinates. The fictional representation of a metaphorical duel in the television show Mad Men depicts a struggle for control in which the superordinate demands that a willful subordinate sign a contract which will bind the subordinate to a particular place for an extended period of time. The examination of events leading to signing reveals a complex weave of social acts that combines the force of domination with the artistry of demeanor and deference.