Human Dynamics

The pursuit of sustainable fisheries requires a broad vision of fishery scienceas the scientilic study of fishery systems. This irnplies the incorporation into fishery science of research on fishery management, fishing processes, fi shcr bchaviour and the human dynamics of fishery systerns. Such rcscarch requires (a) more extensive rnultidisciplinary linkages, with an increased role for cconomic and social science research, and (b) more day-to-day linkages between people, namely thosc involved in fishery science and in managcmcnt, together with fishery stakeholders. This paper rcvicws potential directions for fisheries rcsearch, and suggests a corresponding range of multidisciplinary research priorities, focused on analysis of: (a) the fishery management system, at both strategic and operational levels, (b) fisher behaviour and response to regulations, (c) "human dynamics" in fisheries, and (d) fishery systems modelling.

The number of customers of a service for Internet access from cellular phones in Japan has been explosively increasing for some time. We analyze the relation between the number of customers and the volume of traffic, with a view to finding clues to the structure of human relations among the very large set of potential customers of the service. The traffic data reveals that this structure is a scale-free network, and we calculate the exponent that governs the distribution of node degree in this network. The data also indicates that people who have many friends tend to subscribe to the service at an earlier stage. These results are useful for investigating various fields, including marketing strategies, the propagation of rumors, the spread of computer viruses, and so on.

In medical emergencies, phoning the ambulance service constitutes a high-stakes interaction. Call-takers rely on
callers to provide information about the patient so they can promptly recognise the medical problem and take
swift action to remedy it. When a language barrier exists between the call-taker and caller, this can add a further
challenge, given that third-party interpreters are rarely engaged, especially for time-critical conditions such as
cardiac arrest. Research in cardiac arrest calls has found that language barrier calls experience longer delays to
critical points such as recognition of cardiac arrest and commencement of resuscitation. This study aimed to
understand, in the absence of interpreters, the interactional challenges that emerged in language barrier
emergency calls, as parties worked to communicate the nature of the medical problem. Based on a critical
conversation analysis approach, we conducted fine-grained analysis of interactions in audio recordings and
transcripts of 33 language barrier calls from an Australian ambulance service in 2019. We found that call takers
regularly failed to recognise that the patient had a cardiac arrest. Non-fluent-English callers often provided vital
information about the patient, which could have led to cardiac arrest recognition by the call-taker, however such
information was missed if it was delivered in an unsolicited or atypical way. Opportunities to recognise cardiac
arrest were also missed when call-takers did not probe further after such information was provided or did not
provide enough interactional space for callers to complete their turns. We found that the main reason for delays
in recognising cardiac arrest was a lack of mutual understanding, which most of the time seemed to remain
unbeknownst to participants. The study makes recommendations for emergency medical dispatch centres to cater
for language barrier calls, with the goal of fostering a more inclusive prehospital care system and addressing
health disparities for non-fluent-English speakers.

In social network markets, the act of consumer choice in these industries is governed not just by the set of incentives described by conventional consumer demand theory, but by the choices of others in which an individuals payoff is an explicit function of the actions of others. We observe two key empirical features of outcomes in social networked markets. First, a highly right-skewed, non-Gaussian distribution of the number of times competing alternatives are selected at a point in time. Second, there is turnover in the rankings of popularity over time. We show here that such outcomes can arise either when there is no alternative which exhibits inherent superiority in its attributes, or when agents find it very difficult to discern any differences in quality amongst the alternatives which are available so that it is as if no superiority exists. These features appear to obtain, as a reasonable approximation, in many social network markets. We examine the impact of network structure on both the rank-size distribution of choices at a point in time, and on the life spans of the most popular choices. We show that a key influence on outcomes is the extent to which the network follows a hierarchical structure. It is the social network properties of the markets, the meso-level structure, which determine outcomes rather than the objective attributes of the products.

Citizen Science is research undertaken by professional scientists and members of the public collaboratively. Despite numerous benefits of citizen science for both the advancement of science and the community of the citizen scientists, there is still no comprehensive knowledge of patterns of contributions, and the demography of contributors to citizen science projects. In this paper we provide a first overview of spatiotemporal and gender distribution of citizen science workforce by analyzing 54 million classifications contributed by more than 340 thousand citizen science volunteers from 198 countries to one of the largest online citizen science platforms, Zooniverse. First we report on the uneven geographical distribution of the citizen scientist and model the variations among countries based on the socio-economic conditions as well as the level of research investment in each country. Analyzing the temporal features of contributions, we report on high “burstiness” of participation i...

L'ambra del Mar Baltico, una delle varietà più preziose e conosciute di questa resina fossile, ha affascinato l'umanità per millenni. Questo materiale organico, spesso chiamato "l'oro del Nord", si forma dall'indurimento della resina di antiche conifere.

Air travelers' behavior is closely related to the operational performance of any airport terminal. Much of previous research has focused on how airport operators balance the number of facilities in a terminal and the Level of Service (LOS), while the behavior of passengers is less considered. Not much is known, however, about passenger's behavior during the entire departure process in an airport. In this study, we analyze empirical departure passenger's data to gain an insight into the regular patterns of their activities in an airport. We find that there exist two distinguished temporal patterns during two discretionary periods-post check-in and pre-security check, post security check and pre-boarding. The time that departure passengers spend in these two periods is well approximated by a double power-law distribution and an exponential truncated power-law distribution respectively. The two distinguished distributions suggest that there may be different mechanisms underlying passengers' behavior as indicated by previous studies on human mobility. We introduce a stochastic model that considers traveling experience and time pressure to capture the decision dynamics of human behavior. Simulation results suggest that traveling experience and time pressure dominate passenger's decisions before and after security respectively. Our findings contribute to a better understanding of human dynamics, and also offer the potential for optimizing and simulation of airport terminal operation.

Citizen Science is research undertaken by professional scientists and members of the public collaboratively. Despite numerous benefits of citizen science for both the advancement of science and the community of the citizen scientists, there is still no comprehensive knowledge of patterns of contributions, and the demography of contributors to citizen science projects. In this paper we provide a first overview of spatiotemporal and gender distribution of citizen science workforce by analyzing 54 million classifications contributed by more than 340 thousand citizen science volunteers from 198 countries to one of the largest online citizen science platforms, Zooniverse. First we report on the uneven geographical distribution of the citizen scientist and model the variations among countries based on the socio-economic conditions as well as the level of research investment in each country. Analyzing the temporal features of contributions, we report on high “burstiness” of participation i...

Human dynamics in real-world social networks has been a long lasting topic of research. Recently, the rapid growing of online social networks with their data made accessible has given researchers a door of opportunity to study large-scale social networks. Facebook has been the largest, most influential, and fastest growing social network on the Internet to date. As Facebook's userbase is extremely diverse, spreading across different countries, races, ages, professions, and interests, to name a few, this network is a good model to study our society. In this paper, we present an analysis of a regional Facebook dataset, with attention given to the correlation among user connectivity, activity, and similarity.

Long-standing results in urban studies have shown correlation of population and population density to a city’s pace of life, empirically tested by examining whether individuals in bigger cities walk faster, spend less time buying stamps, or make greater numbers of telephone calls. Contemporary social media presents a new opportunity to test these hypotheses. This study examines whether users of the social media platform Twitter in larger and denser American cities tweet at a faster rate than their counterparts in smaller and sparser ones. Contrary to how telephony usage and productivity scale superlinearly with city population, the total volume of tweets in cities scales sublinearly. This is similar to the economies of scale in city infrastructures like gas stations. When looking at individuals, however, greater population density is associated with faster tweeting. The discrepancy between the ecological correlation and individual behavior is resolved by noting that larger cities ha...

Human dynamics in real-world social networks has been a long lasting topic of research. Recently, the rapid growing of online social networks with their data made accessible has given researchers a door of opportunity to study large-scale social networks. Facebook has been the largest, most influential, and fastest growing social network on the Internet to date. As Facebook's userbase is extremely diverse, spreading across different countries, races, ages, professions, and interests, to name a few, this network is a good model to study our society. In this paper, we present an analysis of a regional Facebook dataset, with attention given to the correlation among user connectivity, activity, and similarity.

The results of this pilot are of great importance for the entire Dutch government, because we have developed a best practice with which administrators can really get started, and actually incorporate ethical values into the algorithms used."-Rijks ICT Gilde-Ministry of the Interior and Kingdom Relations (BZK)

Тайната на Корпоративната Култура: Що за Животно е Тя и Има ли Почва У Нас?



Корпоративната култура често е сравнявана с мистериозно същество от легендите – всички говорят за нея, но малцина са я видели в истинската ѝ форма.



Тя може да бъде силата, която води една компания към успех, или пречката, която я дърпа назад.



Но какво всъщност представлява корпоративната култура и има ли тя почва у нас?



🌱 Да Хванем Животното за Рогата

Корпоративната култура е съвкупност от ценности, вярвания, нагласи и поведения, които оформят атмосферата в организацията. Тя е като диригентска палка, която задава темпото и хармонията на корпоративния оркестър.



🚀 Животното в Действие

Разгледайте примери от световни компании като Google, Apple и Toyota и вижте как те използват културата си за постигане на иновации и успех. Научете как тези принципи могат да се приложат и във вашата организация.



🇧🇬 Нашенски Реалности

Може ли корпоративната култура да процъфти в българските компании? Открийте уникалните предизвикателства и примери за успех у нас. Научете как лидерството, комуникацията и признанието могат да трансформират работната среда.



🔑 Тайната Съставка

Успешната корпоративна култура изисква ясна визия, постоянна комуникация и признание на правилните поведения. Разберете как вашата компания може да създаде подкрепяща и мотивираща работна атмосфера.



Открийте как да укротите това мистериозно животно, наречено корпоративна култура. Време е да посеем и отгледаме култура, която да вдъхновява и обединява екипа ви!

👉 Прочетете статията сега и открийте тайните на корпоративната култура!

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in...

The interactions among human beings represent the backbone of our societies. How people establish new connections and allocate their social interactions among them can reveal a lot of our social organisation. We leverage on a recent mathematical formalisation of the Adjacent Possible space to propose a microscopic model accounting for the growth and dynamics of social networks. At the individual’s level, our model correctly reproduces the rate at which people acquire new acquaintances as well as how they allocate their interactions among existing edges. On the macroscopic side, the model reproduces the key topological and dynamical features of social networks: the broad distribution of degree and activities, the average clustering coefficient and the community structure. The theory is born out in three diverse real-world social networks: the network of mentions between Twitter users, the network of co-authorship of the American Physical Society journals, and a mobile-phone-calls net...

Human mobility is differentiated by time scales. While the mechanism for long-time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analyzing the temporal and spatial trajectories of thousands of persons as individual networks. By using the concept of motifs from network theory only 17 unique networks are present in daily mobility, following simple rules. These networks, called here motifs, are sufficient to capture up to 90% of the population in surveys and mobile phone data sets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modeling periods of high frequency trips followed by periods of lower activity as the key ingredient.

Background Intergenerational family farm transfer is increasingly viewed as crucial to the survival, continuity and future prosperity of the agricultural sector, traditional family farm model and broader sustainability of rural society. While financial incentives designed to stimulate and entice this complex and highly topical phenomenon are important, there are many more facets to the farm succession and retirement decision-making process, which for the most part have been neglected. This has resulted in the formulation and implementation of largely unsuccessful policy strategies, which do not give proper and due consideration to the senior generation's attachment to their farm and occupation. One such key example happened in the Republic of Ireland, where an Early Retirement Scheme (ERS 3, June 2007), requested farmers retiring under the scheme to 'cease agricultural activity forever'. Essentially, older farmers were asked to revise their selfperceptions upon retirement. Challenging this one-dimensional approach, this research sets aside economic enticements and delves deeper into the mind-set and mannerism of farmers in later life to inform more appropriate, 'farmer-sensitive' farm transfer policy directions. Aim This thesis consists of three interrelated journal articles. Each interconnected article explores the various human dynamics influencing and hindering the older generation's decision-making processes surrounding farm succession and retirement from a different theoretical base. Article 1 theoretically pioneers the use of Pierre Bourdieu's notion of symbolic capital to comprehend the psychodynamic and sociodynamic factors influencing the unwillingness and reluctance amongst older farmers towards relinquishing management and ownership of the family farm and retirement. Article 2 explores the micro-politics and hierarchical power dynamics at play within family farm households through the analytical lens of Pierre Bourdieu's concept of symbolic power, and the exercise of symbolic violence. Finally, article 3 applies Graham Rowles' concept of insideness as a theoretical framework to present an insightful, nuanced analysis of the deeply embedded attachment older farmers have with their farms.

Background Intergenerational family farm transfer is increasingly viewed as crucial to the survival, continuity and future prosperity of the agricultural sector, traditional family farm model and broader sustainability of rural society. While financial incentives designed to stimulate and entice this complex and highly topical phenomenon are important, there are many more facets to the farm succession and retirement decision-making process, which for the most part have been neglected. This has resulted in the formulation and implementation of largely unsuccessful policy strategies, which do not give proper and due consideration to the senior generation's attachment to their farm and occupation. One such key example happened in the Republic of Ireland, where an Early Retirement Scheme (ERS 3, June 2007), requested farmers retiring under the scheme to 'cease agricultural activity forever'. Essentially, older farmers were asked to revise their selfperceptions upon retirement. Challenging this one-dimensional approach, this research sets aside economic enticements and delves deeper into the mind-set and mannerism of farmers in later life to inform more appropriate, 'farmer-sensitive' farm transfer policy directions. Aim This thesis consists of three interrelated journal articles. Each interconnected article explores the various human dynamics influencing and hindering the older generation's decision-making processes surrounding farm succession and retirement from a different theoretical base. Article 1 theoretically pioneers the use of Pierre Bourdieu's notion of symbolic capital to comprehend the psychodynamic and sociodynamic factors influencing the unwillingness and reluctance amongst older farmers towards relinquishing management and ownership of the family farm and retirement. Article 2 explores the micro-politics and hierarchical power dynamics at play within family farm households through the analytical lens of Pierre Bourdieu's concept of symbolic power, and the exercise of symbolic violence. Finally, article 3 applies Graham Rowles' concept of insideness as a theoretical framework to present an insightful, nuanced analysis of the deeply embedded attachment older farmers have with their farms.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in...

With the rapid growth of cell phone networks during the last decades, call detail records (CDR) have been used as approximate indicators for large scale studies on human and urban mobility. Although coarse and limited, CDR are a real marker of human presence. In this paper, we use more than 800 million of CDR to identify weekly patterns of human mobility through mobile phone data. Our methodology is based on the classification of individuals into six distinct presence profiles where we focus on the inherent temporal and geographical characteristics of each profile within a territory. Then, we use an event-based algorithm to cluster individuals and we identify 12 weekly patterns. We leverage these results to analyze population estimates adjustment processes and as a result, we propose new indicators to characterize the dynamics of a territory. Our model has been applied to real data coming from more than 1.6 million individuals and demonstrates its relevance. The product of our work can be used by local authorities for human mobility analysis and urban planning.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in...

Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cellphone locations, [C. Song et al., Science 327, 1018 (2010)] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one's conversation partners is defined as the degree to which one's next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by longtailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual's predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community-in the sense of an abundance of surrounding triangles-tend to have low predictability, and those bridging different communities tend to have high predictability.

Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.

Introduzione Le ricerche di superficie e gli scavi condotti dall'insegnamento di Topografia Antica della Sapienza-Università di Roma nell'ultimo decennio nel bacino di Piediluco hanno permesso il rinvenimento di alcuni ornamenti in ambra 1 dal sito di Paduli 2 e Ara Marina (fig. 1). Si tratta di un vago troncopiramidale schiacciato (da Paduli), un bottone sferico e una placchetta multiforata (entrambi da Ara Marina, già identificato come Podere Colle D'Agnano 3). I materiali si inquadrano tra il Bronzo Tardo gli inizi dell'età del ferro.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behavior into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. Here we provide direct evidence that for five human activity patterns, such as email and letter based communications, web browsing, library visits and stock trading, the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times. In contrast, priority blind execution is well approximated by uniform interevent statistics. We discuss two queueing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can hadle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution P (τw) ∼ τ −α w with α = 3/2. The second model imposes limitations on the queue length, resulting in a heavy tailed waiting time distribution characterized by α = 1. We provide empirical evidence supporting the relevance of these two models to human activity patterns, showing that while emails, web browsing and library visitation display α = 1, the surface mail based communication belongs to the α = 3/2 universality class. Finally, we discuss possible extension of the proposed queueing models and outline some future challenges in exploring the statistical mechanisms of human dynamics. These findings have important implications not only for our quantitative understanding of human activity patterns, but also for resource management and service allocation in both communications and retail.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission

Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use case, namely an AI-powered CDSS currently used in the emergency call setting to identify patients with life-threatening cardiac arrest. More specifically, we performed a normative analysis using socio-technical scenarios to provide a nuanced account of the role of explainability for CDSSs for the concrete use case, allowing for abstractions to a more general level. Our analysis focused on three layers: technical considerations, human factors, and the designated system role in decision-making. Our findings suggest that whether explainability can provide added value to CDSS depends on several key questions: technical feasibility, the level of validation in case of explainable algorithms, the characteristics of the context in which the sy...

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in...

Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.

The NATO Human View is a system architectural viewpoint that focuses on the human as part of a system. Its purpose is to capture the human requirements and to inform on how the human impacts the system design. The viewpoint contains seven static models that include different aspects of the human element, such as roles, tasks, constraints, training and metrics. It also includes a Human Dynamics component to perform simulations of the human system under design. One of the static models, termed Human Networks, focuses on the human-to-human communication patterns that occur as a result of ad hoc or deliberate team formation, especially teams distributed across space and time. Parameters of human teams that effect system performance can be captured in this model. Human centered aspects of networks, such as differences in operational tempo (sense of urgency), priorities (common goal), and team history (knowledge of the other team members), can be incorporated. The information captured in ...

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms “trustworthy” AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in...

Human dynamic models are useful in design of physical human-robot and human-robot-environment interaction: informing choice of robot impedance, motivating relaxations to passivity-based safety constraints, and allowing online inference to user intent. Designing for performance objectives such as stable well-damped contact transitions also requires nominal models, but the use of human models in controller design is limited. Established approaches to identify human dynamics apply position or force perturbation and measure the corresponding response, mostly to validate neuromuscular hypotheses on motor control, which raises questions about their transferability to human-led collaboration. Here, human dynamics are identified in a task which closely resembles the final application, where the human leads the robot into contact with a (virtual) wall. This paper investigates the impact of human dynamics on coupled system behavior, and establishes a general framework for identification in humanled scenarios, making consideration of unmeasured human input. Experiments with different stiffness environments allow inference to human dynamics, and characterize the range of human dynamics which can be modulated by the user.

In this work we propose a new experimental paradigm in the context of human motor control. Human subjects track a target with a mouse-pointer on a computer screen while the underlying dynamics is similar to a stick-balancing problem. This approach gives wide control over system parameters. We show that there are two scaling regions in the power spectrum of the distance r between mouse and target and ÿnd a power law in the laminar phases distribution of r. We propose a model for this dynamics and compare the model results to the experimental ÿndings.

The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.

Coupled human balancing tasks, performed by a pair of artificial controllers and a pair of human subjects, have been studied by the present authors based on the coupled inverted pendula (CIP) model. On the contrary, in this paper, we examine another type of combination, the artificial controller and the human subject. We experimentally estimate Lyapunov and sub-Lyapunov exponents of balancing errors of the system of human subject and artificial controller, in which the human subject is in cooperation with the artificial controller having several different feedback gains. The result implies that the human subject seems to try to make the artificial controller minimally or neutrally stable.

The ethical and societal implications of artificial intelligence systems raise concerns. In this article, we outline a novel process based on applied ethics, namely, Z-Inspection , to assess if an AI system is trustworthy. We use the definition of trustworthy AI given by the high-level European Commission's expert group on AI. Z-Inspection is a general inspection process that can be applied to a variety of domains where AI systems are used, such as business, healthcare, and public sector, among many others. To the best of our knowledge, Z-Inspection is the first process to assess trustworthy AI in practice.

Armed conflict and forced migration are associated with an increase in intimate partner violence (IPV) against women. Yet as risks of IPV intensify, familiar options for seeking help dissipate as families and communities disperse and seek refuge in a foreign country. The reconfiguration of family and community systems, coupled with the presence of local and international humanitarian actors, introduces significant changes to IPV response pathways. Drawing from intensive fieldwork, this article examines response options available to women seeking help for IPV in refugee camps against the backdrop of efforts to localize humanitarian assistance. This study employed a qualitative approach to study responses to IPV in three refugee camps: Ajuong Thok (South Sudan), Dadaab (Kenya), and Domiz (Iraqi Kurdistan). In each location, data collection activities were conducted with women survivors of IPV, members of the general refugee community, refugee leaders, and service providers. The sample...

Countries around the world spend substantial amounts of money on programmes designed to address social issues such as place-based disadvantage, health and aged care. Despite such huge investments, evidence shows that many of these social problems are complex and remain far from being resolved, and in some situations, they are worsening. To face these challenges, many organizations have turned to interorganizational collaboration as a more effective means of dealing with social issues. This exploratory qualitative study investigates a relatively new framework for tackling complex social challenges – Collective Impact. We show that while the interpretation and application of the Collective Impact framework varies, broad similarities can be found. We conclude that Collective Impact is best conceptualized as a method for network-based collaboration rather than a distinct methodology or philosophy, and emphasize the importance of the relational aspects of interorganizational collaboration.

The geographic position, isolation, and the long and dynamic history of colonization created a human context in Sicily that allows for a particular anthropological study; information about "migratory flow" and "population influx" could be investigated in the cranial morphology of a localized geographical region. The research goals are the identification of temporal trends in facial morphology in order to assess the adaptations and the microevolutionary trends and to verify if the cranial morphology of humans was modified by the various genetic contributions and more or less related to the intense and significant migratory flows. This work includes a diachronic morphometrics study of 3D models of 95 Sicilian skulls coming from 19 populations (from the Paleolithic to the Contemporary Age), providing an overview of human biodiversity and variability in Sicily. To achieve this, a geometric morphometrics analysis of the facial features of adult human skulls was performed. The approach used allows for the identification of the main micro-anatomical and micro-evolutionary features. Considering sample size/composition, it has been possible to discriminate between prehistorical and historical populations. The results highlight a series of morphological changes related to different migratory flows that have followed one another with different intensities and effectiveness starting from the Prehistory up to the Contemporary Age. The human peopling of Sicily is a subject of continuous debate; however, this study points to the coexistence of microevolutionary patterns and population dynamics, with the latter being one of the main causes of the morphological variations.

The analysis of vehicle's GPS traces such as taxis can help better understand urban mobility and flow. In this paper we present a spatiotemporal analysis of taxis GPS traces collected in Lisbon, Portugal during the course of five month. We also show that trip distance can be represented with a Gamma distribution, and discuss the taxi driving strategies and respective income.

Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from ‘Baidu Map’, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The resul...

Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient.

Open Web-based and social platforms dramatically influenced models for work. The emergence of service-oriented systems has paved the way for a new computing paradigm that not only applies to software services but also human actors. This work introduces a novel programming model for Open Enterprise Systems whose interactions are governed by dynamics. Compositions of humans and services often expose unexpected behavior because of sudden changes in load conditions or unresolved dependencies. We present a middleware for programming and adapting complex serviceoriented systems. Our approach is based on monitoring and real-time intervention to regulate interactions based on behavior policies. A further challenge addressed by our approach is how to simulate and adapt behavior rules prior to deploy polices in the real system. We outline a testing approach to analyze and evaluate the behavior of services.