Context-Aware Personalised Service Delivery

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Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.

Social machines dictating social behaviors – When context is missing what is the fallout of uberveillance?

Abstract: In the mid-1990s when I worked for a telecommunications giant I struggled to gain access to basic geodemographic data. It cost hundreds of thousands of dollars at the time to simply purchase a tile of satellite imagery from Marconi, and it was often cheaper to create my own maps using a digitizer and A0 paper maps. Everything from granular administrative boundaries to right-of-ways to points of interest and geocoding capabilities were either unavailable for the places I was working in throughout Asia or very limited. The control of this data was either in a government’s census and statistical bureau or was created by a handful of forward thinking corporations. Twenty years on we find ourselves inundated with data (location and other) that we are challenged to amalgamate, and much of it still “dirty” in nature. Open data initiatives such as ODI give us great hope for how we might be able to share information together and capitalize not only in the crowdsourcing behavior but in the implications for positive usage for the environment and for the advancement of humanity. We are already gathering and amassing a great deal of data and insight through excellent citizen science participatory projects across the globe. In early 2015, I delivered a keynote at the Data Made Me Do It conference at UC Berkeley, and in the preceding year an invited talk at the inaugural QSymposium. In gathering research for these presentations, I began to ponder on the effect that social machines (in effect, autonomous data collection subjects and objects) might have on social behaviors. I focused on studying the problem of data from various veillance perspectives, with an emphasis on the shortcomings of uberveillance which included the potential for misinformation, misinterpretation, and information manipulation when context was entirely missing. As we build advanced systems that rely almost entirely on social machines, we need to ponder on the risks associated with following a purely technocratic approach where machines devoid of intelligence may one day dictate what humans do at the fundamental praxis level. What might be the fallout of uberveillance? Bio: Dr Katina Michael is a professor in the School of Computing and Information Technology at the University of Wollongong. She presently holds the position of Associate Dean – International in the Faculty of Engineering and Information Sciences. Katina is the IEEE Technology and Society Magazine editor-in-chief, and IEEE Consumer Electronics Magazine senior editor. Since 2008 she has been a board member of the Australian Privacy Foundation, and until recently was the Vice-Chair. Michael researches on the socio-ethical implications of emerging technologies with an emphasis on an all-hazards approach to national security. She has written and edited six books, guest edited numerous special issue journals on themes related to radio-frequency identification (RFID) tags, supply chain management, location-based services, innovation and surveillance/ uberveillance for Proceedings of the IEEE, Computer and IEEE Potentials. Prior to academia, Katina worked for Nortel Networks as a senior network engineer in Asia, and also in information systems for OTIS and Andersen Consulting. She holds cross-disciplinary qualifications in technology and law.

Scenari applicativi per Smart Home su piattaforma Home Manager: verso il livello intelligence

Analisi degli scenari applicativi in ambiente Home Manager e progettazione, implementazione e collaudo di alcune delle funzionalità proposte.

Does Emotional Intelligence have a “Dark” Side? A Review of the Literature

Emotional intelligence (EI) was once touted as the ‘panacea’ for a satisfying and successful life. Consequently, there has been much emphasis on developing interventions to promote this personal resource in applied settings. Despite this, a growing body of research has begun to identify particular contexts when EI does not appear helpful and may even be deleterious to a person, or those they have contact with, suggesting a ‘dark’ side to the construct. This paper provides a review of emergent literature to examine when, why and how trait and ability EI may contribute to negative intrapersonal (psychological ill-health; stress reactivity) and interpersonal outcomes (emotional manipulation; antisocial behaviour). Negative effects were found to operate across multiple contexts (health, academic, occupational) however these were often indirect, suggesting that outcomes depend on pre-existing qualities of the person. Literature also points to the possibility of ‘optimal’ levels of EI – both within and across EI constructs. Uneven profiles of self-perceptions (trait facets) or actual emotional skills contribute to poorer outcomes, particularly emotional awareness and management. Moreover, individuals who possess high levels of skill but have lower self-perceptions of their abilities fare worse that those with more balanced profiles. Future research must now improve methodological and statistical practices to better capture EI in context and the negative corollary associated with high levels.

Values and Beliefs Held About Parenting and Education by School Staff and Parents of Pupils with Special Educational Needs in the Context of Home- School Collaboration

Effective collaboration between school staff and parents of children identified as having special educational needs is considered to be an essential component of the child’s successful education. Differences in beliefs and perspectives adopted by the school staff and parents play an important role in the process of collaboration. However, little is known about the precise relationship between the beliefs and the process of collaboration. The purpose of this study was to explore the values and beliefs held by the school staff and parents in the areas of parenting and education. The study also explored the link between these beliefs and the process of collaboration within four parent-teacher dyads from mainstream primary schools. Focus groups and semi-structured interviews based on repertory grid technique were used. The findings highlighted an overall similarity in the participants’ views on collaboration and in their important beliefs about parenting and education. At the same time, differences in perspectives adopted by parents and teachers were also identified. The author discusses how these differences in perspectives are manifested in the process of collaboration from the point of Cultural Capital Theory. The factors such as power differentials, trust between parents and teachers, and limited resources and constraints of educational system are highlighted. Implication for practice for teachers and educational psychologists are discussed.

Pervasive eHealth services a security and privacy risk awareness survey

The human factor is often recognised as a major aspect of cyber-security research. Risk and situational perception are identified as key factors in the decision making process, often playing a lead role in the adoption of security mechanisms. However, risk awareness and perception have been poorly investigated in the field of eHealth wearables. Whilst end-users often have limited understanding of privacy and security of wearables, assessing the perceived risks and consequences will help shape the usability of future security mechanisms. This paper present a survey of the the risks and situational awareness in eHealth services. An analysis of the lack of security and privacy measures in connected health devices is described with recommendations to circumvent critical situations.

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

Educational software in english to the awareness in a preschool education supervised context

Information and communication technologies play an increasingly important role in society, in the sense that all areas and professions make use of digital resources. The school can not be brushed off this reality, aim to create full subjects and integrated in society today. Educational software can be used very early in the education of children, but they must be carefully and monitoring. This article aims to present the results of the use of educational software in English to the awareness of context with children of pre-school education in kindergarten, nursery center Redemptorist Fathers - The smallest fox in White Castle, a 21 group children under 5 years. Early awareness of foreign language such as English can be started with digital multimedia capabilities and various software available on the market. However, the small study described the case reveals some resistance from parents and educators, in the preparation of these to choose and monitor the use of ICT by children, in addition to also highlight the self-interest of the children involved and their learning a few words in English language in different contexts of daily worked. The study opens perspectives on close monitoring needs of such uses and training of educators in the field of use of resources multilingual awareness in pre-school education.

An Assessment of Pharmacological Properties of Schinus Essential Oils: A Soft Computing Approach

Plants of genus Schinus are native South America and introduced in Mediterranean countries, a long time ago. Some Schinus species have been used in folk medicine, and Essential Oils of Schinus spp. (EOs) have been reported as having antimicrobial, anti-tumoural and anti-inflammatory properties. Such assets are related with the EOs chemical composition that depends largely on the species, the geographic and climatic region, and on the part of the plants used. Considering the difficulty to infer the pharmacological properties of EOs of Schinus species without a hard experimental setting, this work will focus on the development of an Artificial Intelligence grounded Decision Support System to predict pharmacological properties of Schinus EOs. The computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters to the handling of incomplete, unknown, or even self-contradictory information. New clustering methods centered on an analysis of attribute’s similarities were used to distinguish and aggregate historical data according to the context under which it was added to the Case Base, therefore enhancing the prediction process.

Survey of Listeria spp. in matched clinical, food and refrigerator samples at home level in Brazil

Listeria monocytogenes is a bacterial pathogen that represents a serious threat during pregnancy and several cases of listeriosis have been linked to the consumption of contaminated foods worldwide. In Brazil, there is no report of foodborne listeriosis, despite some sporadic cases of infection by this bacterium occur. In general in our country, there is no awareness of medical personnel to instruct moms-to-be to avoid high risk foods. In the present study, a total of 141 samples were surveyed for the presence of Listeria spp., including cervicovaginal samples of patients, foods and home refrigerators. No clinical sample was positive for Listeria spp., but it was isolated from two refrigerators. L. monocytogenes was detected in two food samples out of five positive ones for Listeria spp. In conclusion, it was shown the presence of contaminated food items at home level and the lack of information on the risks of listeriosis, indicating the need of implementation of food safety education programs. (C) 2007 Elsevier Ltd. All rights reserved.

Programming internet based DAI applications in Qu-Prolog

This paper presents the unique collection of additional features of Qu-Prolog, a variant of the Al programming language Prolog, and illustrates how they can be used for implementing DAI applications. By this we mean applications comprising communicating information servers, expert systems, or agents, with sophisticated reasoning capabilities and internal concurrency. Such an application exploits the key features of Qu-Prolog: support for the programming of sound non-clausal inference systems, multi-threading, and high level inter-thread message communication between Qu-Prolog query threads anywhere on the internet. The inter-thread communication uses email style symbolic names for threads, allowing easy construction of distributed applications using public names for threads. How threads react to received messages is specified by a disjunction of reaction rules which the thread periodically executes. A communications API allows smooth integration of components written in C, which to Qu-Prolog, look like remote query threads.

Stroke awareness in Brazil - Alarming results in a community-based study

Background and Purpose-Stroke is the leading cause of death in Brazil. This community-based study assessed lay knowledge about stroke recognition and treatment and risk factors for cerebrovascular diseases and activation of emergency medical services in Brazil. Methods-The study was conducted between July 2004 and December 2005. Subjects were selected from the urban population in transit about public places of 4 major Brazilian cities: S (a) over tildeo Paulo, Salvador, Fortaleza, and Ribeir (a) over tildeo Preto. Trained medical students, residents, and neurologists interviewed subjects using a structured, open-ended questionnaire in Portuguese based on a case presentation of a typical patient with acute stroke at home. Results-Eight hundred fourteen subjects were interviewed during the study period (53.9% women; mean age, 39.2 years; age range, 18 to 80 years). There were 28 different Portuguese terms to name stroke. Twenty-two percent did not recognize any warning signs of stroke. Only 34.6% of subjects answered the correct nationwide emergency telephone number in Brazil (# 192). Only 51.4% of subjects would call emergency medical services for a relative with symptoms of stroke. In a multivariate analysis, individuals with higher education called emergency medical services (P=0.038, OR=1.5, 95%, CI: 1.02 to 2.2) and knew at least one risk factor for stroke (P<0.05, OR=2.0, 95% CI: 1.2 to 3.2) more often than those with lower education. Conclusions-Our study discloses alarming lack of knowledge about activation of emergency medical services and availability of acute stroke treatment in Brazil. These findings have implications for public health initiatives in the treatment of stroke and other cardiovascular emergencies.

Human directed aggression in Brazilian domestic cats: owner reported prevalence, contexts and risk factors

Aggression by cats towards humans is a serious behavioural, welfare and public health problem, although owners may believe it is an inevitable part of cat ownership. There has been little scientific investigation of the risk factors associated with this problem. One hundred and seven owners in the Sao Paulo region of Brazil, took part in a survey aimed at investigating the perceived prevalence of the problem, defining the most common contexts of human directed aggression and identifying associated potential risk factors. Human directed aggression occurred in 49.5%, of cats and was most commonly associated with situations involving petting and play, followed by protection of a resource, when startled, when observing an unfamiliar animal and least commonly when unfamiliar people were present. Pedigree status, neuter status, a history of early trauma, sensitivity to being stroked, the absence of other cats in the home, relationship with other animals, level of background activity at home, access to the outside and tendency to be alone (meaning tendency to staying far from the family members) were all associated with an increased risk in one or more context. However, sex, age, age when acquired, source of pet, attachment to a specific household member, type of domestic accommodation, relationship with another cat if present and contact with other animals did not appear to increase the risk. The results suggest sensitivity to being stroked and background levels of stress in the home are the most pervasive risk factors, and future research should aim to investigate these factors further. These data are of relevance when advising owners about the risk and development of this problem. (C) 2009 ESFM and AAFP. Published by Elsevier Ltd. All rights reserved.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.