Embodying the Avatar in Videogames

Videogames are a pervasive part of lives of children and adults alike, with 73%of Americans older than 2 years engaging with them (Group, 2019). Playingvideogames can be seen as an activity that is done through our fingertips andwith our visual apparatus focused on a screen, without involvement of the restof our body, and it is usually considered as such from a cognitivist point ofview (Campbell, 2012; Gee, 2003; Klimmt and Hartmann, 2006) however thisraises the question of whether videogames can alternatively be thought of as anembodied experience, and if so, how can we formulate them as such, and whatfactors are at play

An energy-efficient and secure data inference framework for internet of health things: A pilot study

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices

Essay: 4E Cognition and Practical Ethical Implications

4E Cognition refers to a group of frameworks that propose an embodied, embedded, enacted and extended approach to cognition, in contrast with computational or brain-focused perspectives. Here I would like to explore questions of morality and ethics from the perspective of some such frameworks. In particular we will look at Varela (1999)'s exploration of ethics inspired by eastern traditions and on his own and Maturana's framework of autopoietic organisations (Maturana and Varela, 1987). We then look at Fuchs (2020)'s idea of relational values which gives us an insight into how might overarching values arise in a society where people are interconnected. Moreover, Urban (2014) points us towards a potential source of inspiration in works of Care Ethics, specifically Held et. al (2006) where we find overlapping conceptions of person as a relational being rather than the dominant individualistic view, and we try to draw from their developments in the field of ethics to our help

Cyber vulnerability intelligence for Internet of Things binary

Internet of Things (IoT) integrates a variety of software (e.g., autonomous vehicles and military systems) in order to enable the advanced and intelligent services. These software increase the potential of cyber-attacks because an adversary can launch an attack using system vulnerabilities. Existing software vulnerability analysis methods used to be relying on human experts crafted features, which usually miss many vulnerabilities. It is important to develop an automatic vulnerability analysis system to improve the countermeasures. However, source code is not always available (e.g., most IoT related industry software are closed source). Therefore, vulnerability detection on binary code is a demanding task. This article addresses the automatic binary-level software vulnerability detection problem by proposing a deep learning-based approach. The proposed approach consists of two phases: binary function extraction, and model building. First, we extract binary functions from the cleaned binary instructions obtained by using IDA Pro. Then, we employ the attention mechanism on top of a bidirectional long short-term memory for building the predictive model. To show the effectiveness of the proposed approach, we have collected datasets from several different sources. We have compared our proposed approach with a series of baselines including source code-based techniques and binary code-based techniques. We have also applied the proposed approach to real-world IoT related software such as VLC media player and LibTIFF project that used on Autonomous Vehicles. Experimental results show that our proposed approach betters the baselines and is able to detect more vulnerabilities

An Energy-Efficient and Secure Data Inference Framework for Internet of Health Things: A Pilot Study

Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices