Illusio and overwork: playing the game in the accounting field

Purpose The purpose of this paper is to understand: how and why do experienced professionals, who perceive themselves as autonomous, comply with organizational pressures to overwork? Unlike previous studies of professionals and overwork, the authors focus on experienced professionals who have achieved relatively high status within their firms and the considerable economic rewards that go with it. Drawing on the little used Bourdieusian concept of illusio, which describes the phenomenon whereby individuals are “taken in and by the game” (Bourdieu and Wacquant, 1992), the authors help to explain the “autonomy paradox” in professional service firms. Design/methodology/approach This research is based on 36 semi-structured interviews primarily with experienced male and female accounting professionals in France. Findings The authors find that, in spite of their levels of experience, success, and seniority, these professionals describe themselves as feeling helpless and trapped, and experience bodily subjugation. The authors explain this in terms of individuals enhancing their social status, adopting the breadwinner role, and obtaining and retaining recognition. The authors suggest that this combination of factors cause professionals to be attracted to and captivated by the rewards that success within the accounting profession can confer. Originality/value As well as providing fresh insights into the autonomy paradox the authors seek to make four contributions to Bourdieusian scholarship in the professional field. First, the authors highlight the strong bodily component of overwork. Second, the authors raise questions about previous work on cynical distancing in this context. Third, the authors emphasize the significance of the pursuit of symbolic as well as economic capital. Finally, the authors argue that, while actors’ habitus may be in a state of “permanent mutation”, that mutability is in itself a sign that individuals are subject to illusio

Which attacks lead to hazards? Combining safety and security analysis for cyber-physical systems

Cyber-Physical Systems (CPS) are exposed to a plethora of attacks and their attack surface is only increasing. However, whilst many attack paths are possible, only some can threaten the system's safety and potentially lead to loss of life. Identifying them is of essence. We propose a methodology and develop a tool-chain to systematically analyse and enumerate the attacks leading to safety violations. This is achieved by lazily combining threat modelling and safety analysis with formal verification and with attack graph analysis. We also identify the minimum sets of privileges that must be protected to preserve safety. We demonstrate the effectiveness of our methodology to discover threat scenarios by applying it to a Communication Based Train Control System. Our design choices emphasise compatibility with existing safety and security frameworks, whilst remaining agnostic to specific tools or attack graphs representations

An adaptive policy-based framework for network services management

This paper presents a framework for specifying policies for the management of network services. Although policy-based management has been the subject of considerable research, proposed solutions are often restricted to condition-action rules, where conditions are matched against incoming traffic flows. This results in static policy configurations where manual intervention is required to cater for configuration changes and to enable policy deployment. The framework presented in this paper supports automated policy deployment and flexible event triggers to permit dynamic policy configuration. While current research focuses mostly on rules for low-level device configuration, significant challenges remain to be addressed in order to:a) provide policy specification and adaptation across different abstraction layers; and, b) provide tools and services for the engineering of policy-driven systems. In particular, this paper focuses on solutions for dynamic adaptation of policies in response to changes within the managed environment. Policy adaptation includes both dynamically changing policy parameters and reconfiguring the policy objects. Access control for network services is also discussed.Accepted versio

Medical students mental health

University Center of Primary Health Care, Nicolae Testemitsanu State University of Medicine and Pharmacy, Chisinau, the Republic of MoldovaBackground: Youth is the driving force of society and they contribute to its economic and social development. Health maintenance and improvement activities focused on the younger generation present a major challenge for health policy at the global, regional and local levels. In these circumstances is increasing the role of youth health monitoring and particularly of medical students. Mental health, assessed by such psychological phenomena as anxiety and depression, is an essential component of health monitoring. Having studied the available specialized literature, national and international normative acts and PubMed medical database, we concluded that the prevalence of psychological phenomena in medical students is higher than in general population and peers. The prevalence of anxiety and depression of the medical students during their university training is higher in I-II and V-VI years, being lower in the fourth year. This fact highlights the importance of an ongoing assessment of the students’ mental health throughout the training period and the development of recommendations on medico-psychological assistance for the future doctors. Most studies found higher levels of anxiety and depression prevalence of non-medical students. Conclusions: Analysis of available data shows that the state of mental health of medical students in the South-East European countries is not fully studied. In the Republic of Moldova in this respect have been studied only some aspects of health of a small group

Neutron diffraction studies of magnetostrictive Fe–Ga alloy ribbons

Melt-spun Fe–Ga ribbons were prepared and some ribbons were annealed at 1000 °C for 1 h then slowly cooled to room temperature. X-ray diffraction patterns revealed no evidence of texture and only bcc phase in the as-quenched ribbons. However, high-resolution neutron diffraction patterns gave more information on the structure of these ribbons. Only diffractions from the disordered bcc A2 phase were found in as-quenched ribbons with 15, 17.5, and 19.5 at. % Ga content, without any trace of satellite peaks or splitting peaks from the proposed Ga–Ga pairing superlattice structure. The broadening of the base of the �110� peaks for all samples except the as-quenched 15 at. % Ga ribbon might indicate the existence of some kind of short range ordering. Ribbons developed L12 phase after annealing especially in the Fe 19.5 at. % Ga ribbon where the formation of L12 phase reduced the Ga content in the remaining A2 phase and decreased its lattice parameter dramatically. D03 phase formed in the as-quenched 22.5 at. % Ga ribbon and the following annealing treatment transformed more A2 phase into D03 phase

Helping forensic analysts to attribute cyber-attacks: an argumentation-based reasoner

Discovering who performed a cyber-attack or from where it originated is essential in order to determine an appropriate response and future risk mitigation measures. In this work, we propose a novel argumentation-based reasoner for analyzing and attributing cyber-attacks that combines both technical and social evidence. Our reasoner helps the digital forensics analyst during the analysis of the forensic evidence by providing to the analyst the possible culprits of the attack, new derived evidence, hints about missing evidence, and insights about other paths of investigation. The proposed reasoner is flexible, deals with conflicting and incomplete evidence, and was tested on real cyber-attacks cases

Determining Resilience Gains from Anomaly Detection for Event Integrity in Wireless Sensor Networks

Measurements collected in a wireless sensor network (WSN) can be maliciously compromised through several attacks, but anomaly detection algorithms may provide resilience by detecting inconsistencies in the data. Anomaly detection can identify severe threats to WSN applications, provided that there is a sufficient amount of genuine information. This article presents a novel method to calculate an assurance measure for the network by estimating the maximum number of malicious measurements that can be tolerated. In previous work, the resilience of anomaly detection to malicious measurements has been tested only against arbitrary attacks, which are not necessarily sophisticated. The novel method presented here is based on an optimization algorithm, which maximizes the attack’s chance of staying undetected while causing damage to the application, thus seeking the worst-case scenario for the anomaly detection algorithm. The algorithm is tested on a wildfire monitoring WSN to estimate the benefits of anomaly detection on the system’s resilience. The algorithm also returns the measurements that the attacker needs to synthesize, which are studied to highlight the weak spots of anomaly detection. Finally, this article presents a novel methodology that takes in input the degree of resilience required and automatically designs the deployment that satisfies such a requirement

Universal adversarial robustness of texture and shape-biased models

Increasing shape-bias in deep neural networks has been shown to improve robustness to common corruptions and noise. In this paper we analyze the adversarial robustness of texture and shape-biased models to Universal Adversarial Perturbations (UAPs). We use UAPs to evaluate the robustness of DNN models with varying degrees of shape-based training. We find that shape-biased models do not markedly improve adversarial robustness, and we show that ensembles of texture and shape-biased models can improve universal adversarial robustness while maintaining strong performance

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection

Machine learning has become an important component for many systems and applications including computer vision, spam filtering, malware and network intrusion detection, among others. Despite the capabilities of machine learning algorithms to extract valuable information from data and produce accurate predictions, it has been shown that these algorithms are vulnerable to attacks. Data poisoning is one of the most relevant security threats against machine learning systems, where attackers can subvert the learning process by injecting malicious samples in the training data. Recent work in adversarial machine learning has shown that the so-called optimal attack strategies can successfully poison linear classifiers, degrading the performance of the system dramatically after compromising a small fraction of the training dataset. In this paper we propose a defence mechanism to mitigate the effect of these optimal poisoning attacks based on outlier detection. We show empirically that the adversarial examples generated by these attack strategies are quite different from genuine points, as no detectability constrains are considered to craft the attack. Hence, they can be detected with an appropriate pre-filtering of the training dataset