Biblio

One of the measures to prevent multi-pass social engineering attacks is to identify the chains of user, which are most susceptible to such attacks. The aim of the study is to combine a mathematical model for estimating the probability of success of the propagation of a multi-pass social engineering attack between users with a model for calculating information influence. Namely, it is proposed to include in estimating the intensity of interactions between users (which used in the model of the propagation of a multi-pass social engineering attack) estimating of power of influence actions of agents. The scientific significance of the work consists in the development of a mathematical structure for modeling the actions of an attacker-social engineer and creating a foundation for the subsequent analysis of the social graph of the organization's employees. The practical significance lies in the formation of opportunities for decision-makers. Therefore, they will be able to take more precise measures for increase the level of security as individual employees as the organization generally.

The design of systems with independent agency to act on the environment or which can act as persuasive agents requires consideration of not only the technical aspects of design, but of the psychological, sociological, and philosophical aspects as well. Creating usable, safe, and ethical systems will require research into human-computer communication, in order to design systems that can create and maintain a relationship with users, explain their workings, and act in the best interests of both users and of the larger society.

Multi-Agent system has the advantages of information sharing, knowledge accumulation and system stability, which is consistent with the concept of collaborative co-governance of public crisis management, and provides support for dealing with sudden public crises. Based on the background of the all-media environment, this study introduces the Internet-driven mass data management (“ crowdsourcing” crisis management) as a part of the crisis response system to improve the quality of information resource sharing. Crowdsourcing crisis management and Multi-Agent collaborative co-governance mechanism are combined with each other, so as to achieve a higher level of joint prevention and control mechanism, and explore how to effectively share information resources and emergency management resources across regions and departments in public crisis events.

Producing an editable model texture could be a challenging problem if the model is scanned from real world or generated by multi-view reconstruction algorithm. To solve this problem, we present a novel re-topology and UV remapping method based on neural network, which transforms arbitrary models with textured coordinates to a semi-regular meshes, and keeps models texture and removes the influence of lighting information. The main innovation of this paper is to use a neural network to find the appropriate location of the starting and ending points for models in the UV maps. Then each fragmented mesh is projected to the 2D planar domain. After calculating and optimizing the orientation field, a semi-regular mesh for each patch is then generated. Those patches can be projected back to three-dimension space and be spliced to a complete mesh. Experiments show that our method can achieve satisfactory performance.

This paper presents a study that examines whether social agents can elicit the social emotion shame as humans do. For that, we use job interviews, which are highly evaluative situations per se. We vary the interview style (shame-eliciting vs. neutral) and the job interviewer (human vs. social agent). Our dependent variables include observational data regarding the social signals of shame and shame regulation as well as self-assessment questionnaires regarding the felt uneasiness and discomfort in the situation. Our results indicate that social agents can elicit shame to the same amount as humans. This gives insights about the impact of social agents on users and the emotional connection between them.

In this paper we discuss building large scale virtual reality reconstructions of historical heritage sites and populating it with crowds of virtual agents. Such agents are capable of performing complex actions, while respecting the cultural and historical accuracy of agent behaviour. In many commercial video games such agents either have very limited range of actions (resulting primitive behaviour) or are manually designed (resulting high development costs). In contrast, we follow the principles of automatic goal generation and automatic planning. Automatic goal generation in our approach is achieved through simulating agent needs and then producing a goal in response to those needs that require satisfaction. Automatic planning refers to techniques that are concerned with producing sequences of actions that can successfully change the state of an agent to the state where its goals are satisfied. Classical planning algorithms are computationally costly and it is difficult to achieve real-time performance for our problem domain with those. We explain how real-time performance can be achieved with Case-Based Planning, where agents build plan libraries and learn how to reuse and combine existing plans to archive their dynamically changing goals. We illustrate the novelty of our approach, its complexity and associated performance gains through a case-study focused on developing a virtual reality reconstruction of an ancient Mesopotamian settlement in 5000 B.C.

With increasing interest in the development of intelligent agents capable of learning, proficiently automating tasks, and gaining world knowledge, the importance of integrating the ability to converse naturally with users is more crucial now than ever before. This thesis aims to understand and characterize different aspects of social language to facilitate the development of intelligent agents that are socially aware and able to engage users to a level that was not previously possible with language generation systems. Using various machine learning algorithms and data-driven approaches to model the nuances of social language in dialogue, such as factual and emotional expression, sarcasm and humor and the related subclasses of rhetorical questions and hyperbole, we can come closer to modeling the characteristics of the social language that allows us to express emotion and knowledge, and thereby exhibit these styles in the agents we develop.

Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.

Fast Growth of (ICT) Information and Communication Technology results to Innovation of Cloud Computing and is considered as a key driver for technological innovations, as an IT innovations, cloud computing had added a new dimension to that importance by increasing usage to technology that motivates economic development at the national and global levels. Continues need of higher storage space (applications, files, videos, music and others) are some of the reasons for adoption and implementation, Users and Enterprises are gradually changing the way and manner in which Data and Information are been stored. Storing/Retrieving Data and Information traditionally using Standalone Computers are no longer sustainable due to high cost of Peripheral Devices, This further recommends organizational innovative adoption with regards to approaches on how to effectively reduced cost in businesses. Cloud Computing provides a lot of prospects to users/organizations; it also exposes security concerns which leads to low adoption, implementation and usage. Therefore, the study will examine standard ways of improving cloud computing adoption, implementation and usage by proposing and developing a security model using a design methodology that will ensure a secured Cloud Computing and also identify areas where future regularization could be operational.

There are ongoing debates on whether learning involves the same mechanisms when it is mediated by social skills than when it is not [1]. Gaze cues serve as a strong communicative modality that is profoundly human. They have been shown to trigger automatic attentional orienting [2]. However, arrow cues have been shown to elicit similar effects [3]. Hence, gaze and arrow cues are often compared to investigate differences between social and non-social cognitive processes [4]. The present study sought to compare cued learning when the cue is provided by a social agent versus a nonsocial agent.

This work explores whether culture impacts the extent to which social dialogue can mitigate (or exacerbate) the loss of trust caused when agents make conversational errors. Our study uses an agent designed to persuade users to agree with its rankings on two tasks. Participants from the U.S. and Japan completed our study. We perform two manipulations: (1) The presence of conversational errors – the agent exhibited errors in the second task or not; (2) The presence of social dialogue – between the two tasks, users either engaged in a social dialogue with the agent or completed a control task. Replicating previous research, conversational errors reduce the agent's influence. However, we found that culture matters: there was a marginally significant three-way interaction with culture, presence of social dialogue, and presence of errors. The pattern of results suggests that, for American participants, social dialogue backfired if it is followed by errors, presumably because it extends the period of good performance, creating a stronger contrast effect with the subsequent errors. However, for Japanese participants, social dialogue if anything mitigates the detrimental effect of errors; the negative effect of errors is only seen in the absence of a social dialogue. Agent design should therefore take the culture of the intended users into consideration when considering use of social dialogue to bolster agents against conversational errors.

Social conflicts appearing in the media are increasing public awareness about security issues, resulting in a higher demand of more exhaustive environment monitoring methods. Automatic video surveillance systems are a powerful assistance to public and private security agents. Since the arrival of deep learning, object detection and classification systems have experienced a large improvement in both accuracy and versatility. However, deep learning-based object detection and classification systems often require expensive GPU-based hardware to work properly. This paper presents a novel deep learning-based foreground anomalous object detection system for video streams supplied by panoramic cameras, specially designed to build power efficient video surveillance systems. The system optimises the process of searching for anomalous objects through a new potential detection generator managed by three different multivariant homoscedastic distributions. Experimental results obtained after its deployment in a Jetson TX2 board attest the good performance of the system, postulating it as a solvent approach to power saving video surveillance systems.

The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological and sociological theory.

Inspired by the simple, yet effective, method of tweeting gibberish to attract automated social agents (bots), we attempt to create localised honeypots in the South African political context. We produce a series of defined techniques and combine them to generate interactions from users on Twitter. The paper offers two key contributions. Conceptually, an argument is made that honeypots should not be confused for bot detection methods, but are rather methods to capture low-quality users. Secondly, we successfully generate a list of 288 local low quality users active in the political context.

As many organizations deploy their chatbots on social network applications to interact with their customers, a person may switch among different chatbots for different services. To reduce the switching cost, this study proposed the Federated Chat Service Framework. The framework maintains user profiles and historical behaviors. Instead of deploying chatbots, organizations follow the rules of the framework to provide chat services. Therefore, the framework can organize service requests with context information and responses to emulate the conversations between users and chat services. Consequently, the study can hopefully contribute to reducing the cost for a user to communicate with different chatbots.

Aiming at the problems of long training time, high cost and high risk existing in the deep working oceanauts, this paper, based on virtual reality technology, designed and developed the simulation system of diving and underwater operation process of Jiaolong which possesses multiple functions and good interactivity. Through the research on the motion model of A-frame swing, use Unity3D engine to develop the interactive simulation of diving and underwater operation process of Jiaolong after the 3D model of Jiaolong and mother ship was built by 3DMax. On the basis of giving full consideration to user experience, the real situation of diving and underwater operation process of Jiaolong was simulated, and the interactive manipulation function was realized.

Whether an idea, information, disease, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in "bursts," active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns – some are bursty and others are not. We model diffusion on networks in which agents differ in their activity patterns. We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior.

We study the problem of designing a dynamic mechanism for security management in an interconnected multi-agent system with N strategic agents and one coordinator. The system is modeled as a network of N vertices. Each agent resides in one of the vertices of the network and has a privately known security state that describes its safety level at each time. The evolution of an agent's security state depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. Each agent's utility at time instant t depends on its own state, the states of its neighbors in the network and on actions taken by a network coordinator. The objective of the network coordinator is to take security actions in order to maximize the long-term expected social surplus. Since agents are strategic and their security states are private information, the coordinator needs to incentivize agents to reveal their information. This results in a dynamic mechanism design problem for the coordinator. We leverage the inter-temporal correlations between the agents' security states to identify sufficient conditions under which an incentive compatible expected social surplus maximizing mechanism can be constructed. We then identify two special cases of our formulation and describe how the desired mechanism is constructed in these cases.

In this study, we used a humanoid robot as a telepresence robot and compared with the basic telepresence robot which can only rotate the display in order to reveal the effect of embodiment. We also investigated the effect caused by changing the body size of the humanoid robot by using two different size of robots. Our experimental results revealed that the embodiment increases the remote person's social telepresence, familiarity, and directivity. The comparison between small and big humanoid robots showed no difference and both of them were effective.

Research has demonstrated promising benefits of applying virtual trainers to promote physical fitness. The current study investigated the value of virtual agents in the context of personal fitness, compared to trainers with greater levels of perceived agency (avatar or live human). We also explored the possibility that the effectiveness of the virtual trainer might depend on the affective tone it uses when trying to motivate users. Accordingly, participants received either positively or negatively valenced motivational messages from a virtual human they believed to be either an agent or an avatar, or they received the messages from a human instructor via skype. Both self-report and physiological data were collected. Like in-person coaches, the live human trainer who used negatively valenced messages were well-regarded; however, when the agent or avatar used negatively valenced messages, participants responded more poorly than when they used positively valenced ones. Perceived agency also affected rapport: compared to the agent, users felt more rapport with the live human trainer or the avatar. Regardless of trainer type, they also felt more rapport - and said they put in more effort - with trainers that used positively valenced messages than those that used negatively valenced ones. However, in reality, they put in more physical effort (as measured by heart rate) when trainers employed the more negatively valenced affective tone. We discuss implications for human–computer interaction.

In this paper, we present E-VOX, an emotionally enhanced semantic ECA designed to work as a virtual assistant to search information from Wikipedia. It includes a cognitive-affective architecture that integrates an emotion model based on ALMA and the Soar cognitive architecture. This allows the ECA to take into account features needed for social interaction such as learning and emotion management. The architecture makes it possible to influence and modify the behavior of the agent depending on the feedback received from the user and other information from the environment, allowing the ECA to achieve a more realistic and believable interaction with the user. A completely functional prototype has been developed showing the feasibility of our approach.

This paper describes a small experimental study into the use of avatars to remediate the lecturer's absence in voice-over-slide material. Four different avatar behaviours are tested. Avatar A performs all the upper-body gestures of the lecturer, which were recorded using a 3D depth sensor. Avatar B is animated using few random gestures in order to create a natural presence that is unrelated to the speech. Avatar C only performs the lecturer's pointing gestures, as these are known to indicate important parts of a lecture. Finally, Avatar D performs "lecturer-like" gestures, but these are desynchronised with the speech. Preliminary results indicate students' preference for Avatars A and C. Although the effect of avatar behaviour on learning did not prove statistically significant, students' comments indicate that an avatar that behaves quietly and only performs some of the lecturer's gestures (pointing) is effective. The paper also presents a simple empirical method for automatically detecting pointing gestures in Kinect recorded lecture data.

This paper examines audio-based social networking platforms and how their environments can affect the persistence of fake news and mis/disinformation in the whole information ecosystem. This is performed through an exploration of their features and how they compare to that of general-purpose multimodal platforms. A case study on Spotify and its recent issue on free speech and misinformation is the application area of this paper. As a supplementary, a demographic analysis of the current statistics of podcast streamers is outlined to give an overview of the target audience of possible deception attacks in the future. As for the conclusion, this paper confers a recommendation to policymakers and experts in preparing for future mis-affordance of the features in social environments that may unintentionally give the agents of mis/disinformation prowess to create and sow discord and deception.

By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.

Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) use advanced computing, sensors, control systems, and communication networks to monitor and control industrial processes and distributed assets. The increased connectivity of these systems to corporate networks has exposed them to new security threats and made them a prime target for cyber-attacks with the potential of causing catastrophic economic, social, and environmental damage. Recent intensified sophisticated attacks on these systems have stressed the importance of methodologies and tools to assess the security risks of Industrial Control Systems (ICS). In this paper, we propose a novel game theory model and Monte Carlo simulations to assess the cybersecurity risks of an exemplary industrial control system under realistic assumptions. We present five game enrollments where attacker and defender agents make different preferences and we analyze the final outcome of the game. Results show that a balanced defense with uniform budget spending is the best strategy against a look-ahead attacker.