Book Review: Slavery and Liberation in Hotels, Restaurants and Bars

Conrad Lashley (Ed.), 2021. 1st edition. London: Routledge. 208 pages. eBook ISBN: 9780367855383

Teleworking and the jobs of tomorrow

Teleworking’s popularity as a flexible working arrangement has been on the rise. Today, it is a hot topic of discussion among employees and  employers alike. The COVID-19 pandemic has accelerated the popularity of this trend and has convinced many that teleworking is here to stay. This  review article aims to describe the characteristics of teleworking pre- and post-pandemic and shed light on future challenges and opportunities.  Empirical evidence promotes a favourable association between teleworking and benefits to employers’ profitability and employees’ health and well-  being. Nevertheless, some employees have experienced a negative impact on their health due to teleworking, primarily due to ergonomics, and  higher levels of stress, anxiety and loneliness. The overall conclusion shows that with proper job design, leadership and organisational support and  adequate information communication technology (ICT), teleworking will be central to the future of jobs

The future of hospitality jobs: The rise of the gig worker

This review article aims to analyse the different perspectives on the gig economy and gig workers, specifically in the hospitality industry, and establish a research base that contributes to future research. The article examines the perceptions of both employers and employees of the gig economy based on available literature. Current literature reveals that the impact of gig-style work is under-researched in many areas, including the hospitality sector. The COVID-19 pandemic and its restrictions have revitalised the gig economy and forced the hospitality industry to explore a sustainable long-term relationship with it. Many of today’s permanent hospitality jobs will be lost to gig workers. Governments and employers have to prepare and adapt to a future where the desire for flexibility is central. The article reviews the many advantages and disadvantages of the gig economy and offers an insight into the future of hospitality jobs. This review article is beneficial for both industrial and educational applications

The future of hospitality jobs

This article reviews the literature on artificial intelligence (AI)-driven technology and looks at its effects on the future of hospitality jobs, and the skills needed for the future. The purpose of this article is to understand and describe how developments in AI-driven robotics and automation will shape the future of hospitality jobs, the skills in demand, and their impact on the design of education and training. Various input parameters are significant in understanding the future of hospitality jobs. For an optimised understanding, literature has been critically reviewed and investigated from different angles, namely academics, technological advancements, developments in the industry, and governments and policymakers. The literature reveals that AI-driven technology is developing at a very high speed and shows its extensive application in tourism and hospitality  management and other related industries. Many of today’s jobs will be lost to AI, automation and robotics, and new jobs with new skill-set requirements will emerge. Education establishments will have to adopt a new futureproof educational system or risk becoming obsolete. This review article will be beneficial for industry and education. The article reveals the detailed literature review on jobs that are at high risk of disappearing and offers an insight into what future jobs might be and what skills and competences will be required. Keywords: artificial intelligence, employment, skills, hospitality, human resources, training and educatio

Employee psychological well-being, transformational leadership and the future of hospitality jobs

Employee psychological well-being is a central concern for hospitality establishments as it impacts talent retention. This empirical research explores the  relationship between transformational leadership and employee psychological well-being. This relationship is tested through a mediation model where  transformational leadership is proposed to explain the effect on the psychological well-being of hospitality employees (hedonic and eudaemonic well-  being) through the affective mediators thriving at work and employees’ amplification of pleasant emotions and employee engagement. The cross-  sectional data came from 133 5-star hotel employees in the Netherlands. Analysing the responses showed that eudaemonic well-being had to be split  into four new variables: growing and giving, liveliness, self-esteem and managing oneself. Furthermore, thriving at work and employee engagement fully  mediated between transformational leadership and hedonic well-being, thriving at work fully mediated between transformational leadership and  growing and giving, while thriving at work and employees’ amplification of pleasant emotions fully mediated between transformational leadership and  self-esteem. A direct relationship was found between transformational leadership and managing oneself. Practical and theoretical implications are  discussed in detail.&nbsp

MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment

The acoustic environment can degrade speech quality during communication (e.g., video call, remote presentation, outside voice recording), and its impact is often unknown. Objective metrics for speech quality have proven challenging to develop given the multi-dimensionality of factors that affect speech quality and the difficulty of collecting labeled data. Hypothesizing the impact of acoustics on speech quality, this paper presents MOSRA: a non-intrusive multi-dimensional speech quality metric that can predict room acoustics parameters (SNR, STI, T60, DRR, and C50) alongside the overall mean opinion score (MOS) for speech quality. By explicitly optimizing the model to learn these room acoustics parameters, we can extract more informative features and improve the generalization for the MOS task when the training data is limited. Furthermore, we also show that this joint training method enhances the blind estimation of room acoustics, improving the performance of current state-of-the-art models. An additional side-effect of this joint prediction is the improvement in the explainability of the predictions, which is a valuable feature for many applications.Comment: Submitted to Interspeech 202

Speaker Embeddings as Individuality Proxy for Voice Stress Detection

Since the mental states of the speaker modulate speech, stress introduced by cognitive or physical loads could be detected in the voice. The existing voice stress detection benchmark has shown that the audio embeddings extracted from the Hybrid BYOL-S self-supervised model perform well. However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages. Moreover, previous studies found strong individual differences in stress susceptibility. This paper presents the design and development of voice stress detection, trained on more than 100 speakers from 9 language groups and five different types of stress. We address individual variabilities in voice stress analysis by adding speaker embeddings to the hybrid BYOL-S features. The proposed method significantly improves voice stress detection performance with an input audio length of only 3-5 seconds.Comment: 5 pages, 2 figures. Accepted at Interspeech 202

BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping

Methods for extracting audio and speech features have been studied since pioneering work on spectrum analysis decades ago. Recent efforts are guided by the ambition to develop general-purpose audio representations. For example, deep neural networks can extract optimal embeddings if they are trained on large audio datasets. This work extends existing methods based on self-supervised learning by bootstrapping, proposes various encoder architectures, and explores the effects of using different pre-training datasets. Lastly, we present a novel training framework to come up with a hybrid audio representation, which combines handcrafted and data-driven learned audio features. All the proposed representations were evaluated within the HEAR NeurIPS 2021 challenge for auditory scene classification and timestamp detection tasks. Our results indicate that the hybrid model with a convolutional transformer as the encoder yields superior performance in most HEAR challenge tasks.Comment: Submitted to HEAR-PMLR 202

Proposal of an innovative method to implement, measure, and validate the security level of a system based on system modelling.

Le travail de cette thèse s'est concentré sur la création d'une solution de cybersécurité capable de protéger, d'identifier, de classer et de gérer la priorité des risques avec une efficacité temporelle. L'idée principale est de gérer les menaces et les vulnérabilités en se basant sur la séparation du système d'information en trois niveaux : niveau réseau, niveau équipement et niveau facteur humain. Pour chaque niveau, le système a été modélisé et une solution a été présentée et testée. Et au final, toutes les solutions ont été appliquées à un système d'information pour en vérifier l'utilisabilité et l'efficacité. Pour déterminer les solutions, une étude bibliographique a été réalisée afin de préciser quelle contribution peut être proposée. Et les résultats ont été croisés avec l'idée de la protection pour les trois niveaux évoqués précédemment. De l'étude, nous avons déduit qu'aucun travail n'a tenté de présenter une solution pour protéger un système d'information pour les trois niveaux. La plupart des travaux se concentraient sur un certain niveau seulement. Ainsi, notre travail dans la thèse a réussi à créer une solution pour les trois niveaux. Pour le niveau réseau, un périphérique réseau unidirectionnel A.K.A. une diode de données a été utilisée. La nouveauté du travail consiste à présenter une solution rentable avec des composants prêts à l'emploi. Nous avons développé un logiciel spécial pour utiliser cette diode de données et nous avons démontré son efficacité notamment dans la protection des données hautement sensibles en raison de sa nature physique. De plus, nous avons réussi à démontrer que sa présence n'affectait pas le flux de données tout en offrant la plus grande sécurité. Au niveau des appareils, nous avons travaillé sur la protection des flux de données en utilisant les algorithmes d'intelligence artificielle (IA) et l'apprentissage. Nous avons donc collecté un ensemble de données de 54 000 enregistrements. À l'aide d'un script python, un modèle a été créé à l'aide de l'algorithme KNN et ce modèle a été utilisé dans le logiciel médical pour étiqueter à la volée les données reçues. De plus, pour mieux protéger le niveau de l'appareil, nous avons développé un logiciel de gestion des cybermenaces qui comprend des méthodes et des outils d'évaluation des risques et de classification des attaques. Plusieurs classifications de menaces ont été développées mais la plus connue est la CVSS. Cependant, la notation des vulnérabilités n'est pas unique. Ainsi, choisir quelle vulnérabilité doit être corrigée en premier donnera lieu à un problème de prise de décision. De plus, la hiérarchisation manuelle peut être réalisée dans les petits réseaux où le nombre de menaces est limité, mais, dans les grands réseaux, l'automatisation est indispensable pour aider les responsables de la sécurité à prendre les bonnes décisions. Par conséquent, notre logiciel développé collectera les entrées de NVD, de la base de données Exploit et du Computer Incident Response Center Luxembourg pour les appliquer à une formule mathématique pondérée afin de fournir une nouvelle liste de priorités pour toutes les vulnérabilités connues. Tout ce que l'agent de sécurité a à faire est de le croiser avec les vulnérabilités repérées pour avoir une liste de priorités qu'il peut suivre pour corriger son système. Quant au niveau humain, nous nous sommes concentrés sur l'aspect de l'ignorance pour créer un pare-feu humain grâce à la cyber-sensibilisation. Un robot conversationnel basé sur l'IA a été créé. Il fournit des informations relatives aux politiques et procédures de l'entreprise, des informations sur la cybersécurité et un test pour évaluer le niveau de cyber sensibilisation. Ce bot est utilisé pour rendre l'interaction avec l'utilisateur attrayante et conviviale grâce à l'utilisation de WhatsApp comme moyen de communication. La mise en œuvre et la simplicité de l'interaction avec le bot ont été testées et évaluées.The work in this thesis focused on creating a cyber security solution with the ability to protect, identify, classify, and manage risk priority with time efficiency. The main idea is to handle the threats and vulnerabilities based on the separation of the information system into three levels: Network level, device level, and human factor level. For each level, the system was modeled and a solution was presented and tested. And at the end, all of the solutions were applied to an information system to verify its usability and effectiveness. To determine the solutions, a bibliographic study was made so we can specify what contribution can be offered. And the results were crossed with the idea of the protection for the three levels mentioned previously. From the study, we deduced that there was not a single work that tried to present a solution to protect an information system for the three levels. Most of the work was focused on a certain level only. Thus, our work in the thesis managed to create a solution for the three levels. For the network level, a unidirectional network device A.K.A. data diode was used. Data diodes are a paradigm of cyber security which have not been studied extensively even though they can be used to overcome some of the limitations that exist today with current approaches to cyber security such as basic firewalls, and intrusion detection systems. The novelty of the work consists of on presenting a cost-effective solution with off-the-shelf components. We developed special software to use this data diode and we demonstrated its effectiveness especially in protecting the highly sensitive data due to its physical nature. Furthermore, we managed to demonstrate how its presence didn’t affect the data flow yet provided the utmost security. For the device level, we worked on protecting the flow of data. Of course, Artificial Intelligence (AI) algorithms and machine learning became an important pillar in the “Cyber security revolution”. These technologies have already become part of everyday life and are being used by organizations for various purposes such as predictive maintenance, fraud detection etc. So, we collected a dataset of 54.000 records. Using a python script created a model using the KNN algorithm and that model was used in the medical software to the label on the fly the received data. Furthermore, to better protect the device level, we have developed software for managing cyber threats which includes methods and tools for risk assessment and attack classification. Several classifications of threats have been developed but the most renowned one is the CVSS. However, the scoring of the vulnerabilities is not unique. So, choosing which vulnerability must be remediated first will yield to a decision-taking problem. Additionally, manual prioritization can be achieved in small networks where the number of threats is limited, but, in large networks automation is a must to help security officers to take the right decisions. Consequently, our developed software will collect inputs from NVD, the Exploit Database, and Computer Incident Response Center Luxembourg to apply them onto a weighted mathematical formula in order to provide a new priority list for all known vulnerabilities. All what the security officer has to do is cross reference it with the spotted vulnerabilities to have a priority list that he can follow for remediating his system. As for the human level, we focused on the ignorance aspect to create a human firewall through cyber awareness. An AI-based conversational bot was created that provides information related to the company policies and procedures, information about cyber security and a test to evaluate the level of cyber awareness. This bot is used to make the interaction with the user appealing and friendly through the use of WhatsApp as a way of communication. The implementation and simplicity of the interaction with the bot were tested and evaluated

Proposition d'une nouvelle technique pour implémenter, mesurer et valider le niveau de sécurité d'un système basé sur la modélisation du système

Le travail de cette thèse s'est concentré sur la création d'une solution de cybersécurité capable de protéger, d'identifier, de classer et de gérer la priorité des risques avec une efficacité temporelle. L'idée principale est de gérer les menaces et les vulnérabilités en se basant sur la séparation du système d'information en trois niveaux : niveau réseau, niveau équipement et niveau facteur humain. Pour chaque niveau, le système a été modélisé et une solution a été présentée et testée. Et au final, toutes les solutions ont été appliquées à un système d'information pour en vérifier l'utilisabilité et l'efficacité. Pour déterminer les solutions, une étude bibliographique a été réalisée afin de préciser quelle contribution peut être proposée. Et les résultats ont été croisés avec l'idée de la protection pour les trois niveaux évoqués précédemment. De l'étude, nous avons déduit qu'aucun travail n'a tenté de présenter une solution pour protéger un système d'information pour les trois niveaux. La plupart des travaux se concentraient sur un certain niveau seulement. Ainsi, notre travail dans la thèse a réussi à créer une solution pour les trois niveaux. Pour le niveau réseau, un périphérique réseau unidirectionnel A.K.A. une diode de données a été utilisée. La nouveauté du travail consiste à présenter une solution rentable avec des composants prêts à l'emploi. Nous avons développé un logiciel spécial pour utiliser cette diode de données et nous avons démontré son efficacité notamment dans la protection des données hautement sensibles en raison de sa nature physique. De plus, nous avons réussi à démontrer que sa présence n'affectait pas le flux de données tout en offrant la plus grande sécurité. Au niveau des appareils, nous avons travaillé sur la protection des flux de données en utilisant les algorithmes d'intelligence artificielle (IA) et l'apprentissage. Nous avons donc collecté un ensemble de données de 54 000 enregistrements. À l'aide d'un script python, un modèle a été créé à l'aide de l'algorithme KNN et ce modèle a été utilisé dans le logiciel médical pour étiqueter à la volée les données reçues. De plus, pour mieux protéger le niveau de l'appareil, nous avons développé un logiciel de gestion des cybermenaces qui comprend des méthodes et des outils d'évaluation des risques et de classification des attaques. Plusieurs classifications de menaces ont été développées mais la plus connue est la CVSS. Cependant, la notation des vulnérabilités n'est pas unique. Ainsi, choisir quelle vulnérabilité doit être corrigée en premier donnera lieu à un problème de prise de décision. De plus, la hiérarchisation manuelle peut être réalisée dans les petits réseaux où le nombre de menaces est limité, mais, dans les grands réseaux, l'automatisation est indispensable pour aider les responsables de la sécurité à prendre les bonnes décisions. Par conséquent, notre logiciel développé collectera les entrées de NVD, de la base de données Exploit et du Computer Incident Response Center Luxembourg pour les appliquer à une formule mathématique pondérée afin de fournir une nouvelle liste de priorités pour toutes les vulnérabilités connues. Tout ce que l'agent de sécurité a à faire est de le croiser avec les vulnérabilités repérées pour avoir une liste de priorités qu'il peut suivre pour corriger son système. Quant au niveau humain, nous nous sommes concentrés sur l'aspect de l'ignorance pour créer un pare-feu humain grâce à la cyber-sensibilisation. Un robot conversationnel basé sur l'IA a été créé. Il fournit des informations relatives aux politiques et procédures de l'entreprise, des informations sur la cybersécurité et un test pour évaluer le niveau de cyber sensibilisation. Ce bot est utilisé pour rendre l'interaction avec l'utilisateur attrayante et conviviale grâce à l'utilisation de WhatsApp comme moyen de communication. La mise en œuvre et la simplicité de l'interaction avec le bot ont été testées et évaluées.The work in this thesis focused on creating a cyber security solution with the ability to protect, identify, classify, and manage risk priority with time efficiency. The main idea is to handle the threats and vulnerabilities based on the separation of the information system into three levels: Network level, device level, and human factor level. For each level, the system was modeled and a solution was presented and tested. And at the end, all of the solutions were applied to an information system to verify its usability and effectiveness. To determine the solutions, a bibliographic study was made so we can specify what contribution can be offered. And the results were crossed with the idea of the protection for the three levels mentioned previously. From the study, we deduced that there was not a single work that tried to present a solution to protect an information system for the three levels. Most of the work was focused on a certain level only. Thus, our work in the thesis managed to create a solution for the three levels. For the network level, a unidirectional network device A.K.A. data diode was used. Data diodes are a paradigm of cyber security which have not been studied extensively even though they can be used to overcome some of the limitations that exist today with current approaches to cyber security such as basic firewalls, and intrusion detection systems. The novelty of the work consists of on presenting a cost-effective solution with off-the-shelf components. We developed special software to use this data diode and we demonstrated its effectiveness especially in protecting the highly sensitive data due to its physical nature. Furthermore, we managed to demonstrate how its presence didn’t affect the data flow yet provided the utmost security. For the device level, we worked on protecting the flow of data. Of course, Artificial Intelligence (AI) algorithms and machine learning became an important pillar in the “Cyber security revolution”. These technologies have already become part of everyday life and are being used by organizations for various purposes such as predictive maintenance, fraud detection etc. So, we collected a dataset of 54.000 records. Using a python script created a model using the KNN algorithm and that model was used in the medical software to the label on the fly the received data. Furthermore, to better protect the device level, we have developed software for managing cyber threats which includes methods and tools for risk assessment and attack classification. Several classifications of threats have been developed but the most renowned one is the CVSS. However, the scoring of the vulnerabilities is not unique. So, choosing which vulnerability must be remediated first will yield to a decision-taking problem. Additionally, manual prioritization can be achieved in small networks where the number of threats is limited, but, in large networks automation is a must to help security officers to take the right decisions. Consequently, our developed software will collect inputs from NVD, the Exploit Database, and Computer Incident Response Center Luxembourg to apply them onto a weighted mathematical formula in order to provide a new priority list for all known vulnerabilities. All what the security officer has to do is cross reference it with the spotted vulnerabilities to have a priority list that he can follow for remediating his system. As for the human level, we focused on the ignorance aspect to create a human firewall through cyber awareness. An AI-based conversational bot was created that provides information related to the company policies and procedures, information about cyber security and a test to evaluate the level of cyber awareness. This bot is used to make the interaction with the user appealing and friendly through the use of WhatsApp as a way of communication. The implementation and simplicity of the interaction with the bot were tested and evaluated