Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition

Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals

Nudging for Hand Hygiene: A Systematic Review and Meta-Analysis

INTRODUCTION: Despite its importance and having few significant technological barriers, hand hygiene continues to pose a serious problem in public health. In addition to the highly public impact of hand hygiene promotion accompanying the COVID-19 pandemic, inadequate compliance with recommended hand- washing and -sanitizing procedures contributes substantially to healthcare associated infections, food-borne illness and seasonal flu transmission, school- and workplace-based outbreaks, and other arenas of preventable disease. “Nudges” – simple, inexpensive cues or selection-environment features that are designed with cognitive biases in mind – are used frequently to promote compliance in a variety of health-related activities, including hand hygiene compliance. AIM: To systematically review the literature discussing the efficacy of nudging in promoting hand hygiene, and to statistically summarize the results of those studies. METHODS: Consistent with PRISMA and Cochrane guidelines for completing a systematic review and meta-analysis (SRMA), this study performs a planned and detailed review of literature through multiple appropriate databases and external resources, selects studies based upon predetermined inclusion and exclusion criteria, sequentially screens titles, abstracts, and full-texts for relevance and usability, extracts data systematically, and summarizes statistical results and effect sizes. Technological assistance is used to promote thoroughness and evaluative integrity. Specifically, Zotero is used in the selection and screening of studies, and R, R Markdown, and the R packages metafor[i] and clubSandwich[ii] are used for conducting a meta-analysis using a correlated and hierarchical effects model.[iii] RESULTS: 14 studies contributing 34 effect sizes were analyzed using a multivariate random effects model with Robust Variance Estimation. The mean log odds ratio (LOR) was 1.416, with a mean odds ratio and 95% confidence interval of 4.12 [2.295 – 7.4] for hand hygiene within nudge-conditions compared to controls. DISCUSSION: Nudges significantly increase hand hygiene compliance in a variety of populations and socio-cultural contexts, though publication bias may exaggerate their efficac

Operationalizing Transparency and Explainability in Artificial Intelligence through Standardization

As artificial intelligence (AI) has developed, it has spread to almost every aspect of our society, from electric toothbrushes and telephone applications to automated transportation and military use. As AI becomes more ubiquitous, its importance and impact on our society grow continuously. With the pursuit and development of more efficient and accurate artificial intelligence applications, AI systems have evolved into so-called “black box” models, where the operation and decision-making have become immensely complex and difficult to understand, even for experts. As AI is increasingly applied in more critical and sensitive areas, such as healthcare, for instance in support of diagnoses, the lack of transparency and explainability of these complex models and their decision-making has become a problem. If there is no understandable argumentation backing up the results produced by the system, its use is questionable or even ethically impossible in such areas. Furthermore, these AI systems may be misused or behave in very unexpected and potentially harmful ways. Issues related to the governance of AI systems are thus more important than ever before. Standards provide one way to implement AI governance and promote the transparency and explainability of AI systems. This study sets out to examine how the role of standardization in promoting AI transparency and explainability is perceived from an organizational perspective and what kind of AI transparency and explainability needs are identified among different organizational actors. In addition, efforts will be made to identify possible drivers and barriers to the adoption of AI transparency and explainability standards. The research has been carried out by interviewing representatives from a total of 11 different Finnish organizations working in the field of AI. The data gathered from the interviews has been analyzed using the Gioia method. Based on this analysis, five different roles for standards were identified regarding the promotion of explainability and transparency in AI: 1. Facilitator, 2. Validator, 3. Supporter, 4. Business enhancer, and 5. Necessary evil. Furthermore, the identified AI transparency and explainability needs are composed of the needs for ensuring general acceptability of AI and risk management needs. Finally, the identified drivers for adopting AI transparency and explainability standards comprise the requirements of the operating environment, business facilitating drivers, and business improvement drivers, whereas the barriers consist of the lack of resources, lack of knowledge and know-how, downsides of standardization, and incompatibility of standardization and AI. In addition, the results showed that the implementation of possible standards for AI transparency and explainability is largely driven by binding legislation and financial incentives rather than ethical drivers. Furthermore, building trust in AI is seen as the ultimate purpose of transparency and explainability and its standardization. This dissertation provides an empirical basis for future research regarding the need for AI standardization, standards adoption, and AI transparency and explainability from an organizational perspective.Tekoäly on kehittyessään levinnyt lähes kaikille yhteiskuntamme osa-alueille aina sähköhammasharjoista ja puhelimen sovelluksista liikenteeseen ja maanpuolustukseen. Laajan leviämisen seurauksena sen merkitys ja vaikutus yhteiskunnassamme on kasvanut jatkuvasti sekä jatkaa yhä kasvamista. Tehokkaampien ja tarkempien tekoälysovellutusten tavoittelun ja kehityksen myötä AI-sovellutuksista on kehittynyt niin sanottuja ”black box” -malleja, joiden toiminta ja päätöksenteko on hyvin monimutkaista ja vaikeasti ymmärrettävää jopa alan asiantuntijoille. Kun tekoälyä aletaan kehityksen myötä yhä enenevissä määrin soveltamaan myös kriittisemmillä ja sensitiivisemmillä osa-alueilla kuten esimerkiksi terveydenhuollossa diagnoosien tukena, ongelmaksi nousee näiden monimutkaisten mallien avoimuuden puute ja saatujen tulosten läpinäkyvyys ja selitettävyys. Jos tekoälyn tuottamalle tulokselle ei löydy perusteluita, sen käyttö on hyvin hataralla pohjalla ja eettisesti jopa mahdotonta tällaisilla aloilla. Samaan aikaan tekoälyä voidaan käyttää väärin tai se voi käyttäytyä hyvinkin odottamattomilla ja mahdollisesti haitallisilla tavoilla. Tekoälyjärjestelmien hallintaan liittyvät kysymykset ovat siten tärkeämpiä kuin koskaan ennen. Standardit tarjoavat yhden keinon toteuttaa tekoälyn hallintaa ja edistää tekoälyjärjestelmien läpinäkyvyyttä ja selitettävyyttä. Tässä tutkimuksessa pyritään tutkimaan miten standardoinnin rooli tekoälyn läpinäkyvyyden ja selitettävyyden edistämisessä koetaan organisaatioiden näkökulmasta ja millaisia tekoälyn läpinäkyvyyden ja selitettävyyden tarpeita eri sidosryhmien keskuudessa tunnistetaan. Lisäksi pyritään selvittämään mitkä ovat mahdollisia ajureita ja esteitä tekoälyn läpinäkyvyys- ja selitettävyysstandardien käyttöönotolle. Tutkimus on toteutettu haastattelemalla yhteensä 11 eri tekoälyn parissa työskentelevän suomalaisen organisaation edustajia. Haastatteluista saatu aineisto on analysoitu Gioia-menetelmää hyödyntäen. Tämän analyysin perusteella tunnistettiin yhteensä viisi eri standardien roolia tekoälyn selitettävyyden ja läpinäkyvyyden edistämisessä: 1. Fasilitaattori, 2. Validaattori, 3. Tukija, 4. Liiketoiminnan edistäjä ja 5. Välttämätön paha. Lisäksi analyysin perusteella tunnistetut tekoälyn läpinäkyvyys- ja selitettävyystarpeet koostuvat tekoälyn yleisen hyväksynnän saavuttamisen tarpeista ja riskienhallintatarpeista. Tunnistetut tekoälyn läpinäkyvyys- ja selitettävyysstandardien käyttöönoton ajurit sisältävät toimintaympäristön vaatimukset, liiketoimintaa edistävät ajurit ja liiketoiminnan parantamisen ajurit, kun taas tunnistettuja esteitä ovat resurssien puute, tiedon ja taitotiedon puute sekä standardoinnissa tunnistetut huonot puolet, sekä standardoinnin ja tekoälyn yhteensopimattomuus. Lisäksi tulokset osoittivat, että mahdollisten tekoälyn läpinäkyvyys- ja selitettävyysstandardien käyttöönotto on eettisen ajureiden sijaan pitkälti pakottavan lainsäädännön ja taloudellisten kannustimien johdattelemaa. Tekoälyn läpinäkyvyyden ja selitettävyyden sekä sen standardisoinnin perimmäisenä tarkoituksena nähdään olevan luottamuksen saavuttaminen tekoälyä kohtaan. Tämä tutkielma tarjoaa empiirisen tietoperustan tulevalle tekoälyn standardoinnin, standardien käyttöönoton ja tekoälyn läpinäkyvyyden ja selitettävyyden tarpeiden tutkimukselle organisaationäkökulmasta

A New Kind of Data Science: The Need for Ethical Analytics

Ethics can no longer be regarded as an add-on in data science and analytics. This paper argues for the necessity of formalizing a new, practically-oriented sub-discipline of AI ethics by outlining the needs, highlighting shortcomings in current approaches, and providing a framework for ethical analytics, which is concerned with the study of the ethical issues surrounding the development, deployment, and/or dissemination of ML/AI systems and data science research, as well as the development of tools and procedures to mitigate ethical harms. While data science and machine learning are primarily concerned with data from start to finish, ethical analytics is concerned primarily with people – moral agents, the groups and societies they comprise, and the world they inhabit. Ethical analytics should be seen as complementary to the more techno-abstracted analytic disciplines, interfacing with the nuanced, ethical issues that stem from ill-defined or vague, socially-relative normative concepts. It studies the issues that arise in this holistic sociotechnical environment, and it seeks to develop concrete solutions or interventions where possible – from mathematics and algorithms to procedures and protocols

The Epidemiology of Community-Acquired Clostridium Difficile in the Niagara Region, Ontario, Canada, Between September 2011 and December 2013

Clostridium difficile infections (CDIs) have historically been associated with exposure to healthcare settings. In recent years, however, the incidence of community-acquired Clostridium difficile infections (CA-CDI), along with the number of patients requiring hospitalization for it, has been increasing. This research uses a framework grounded in Complex Adaptive Systems (CAS) to reveal new and different epidemiological findings on CA-CDI to indicate novel health equity leverage points. It explores the epidemiology and established risk factors associated with CA-CDI in the Niagara Region, Ontario, and compares them with those of healthcare-associated CDI (HA-CDI) in the same area. The first manuscript evaluates the literature on existing evidence of risk factors for CA-CDI by applying The Joanna Briggs Institute (JBI) Reviewers Manual 2015, Methodology for JBI Scoping Reviews. The review identifies that CA-CDI is seen more often than HA-CDI in younger and female populations. Exposure to antimicrobials is common but not as common as in HA-CDI cases. The scoping review establishes the need for further epidemiological studies on CA-CDI. The second manuscript provides a nonparametric descriptive analysis, comparing CA-CDI and HA-CDI cases in Niagara Health System (NHS) hospitals, based on a retrospective case series design. Hospitalized CA-CDI patients have a lower median age and less exposure to antimicrobials and other medications. Gender proportions are similarly distributed between the two groups. The emerging recommendation is that CA-CDI must be considered as a potential diagnosis in patients admitted to hospital with diarrhea, even in the absence of conventional CDI risk factors. The third and final manuscript evaluates the spatial and genotype features of CA-CDI and HA-CDI. It finds that geographical clustering, temporal patterns, and genotypic features are unique in each category. These studies point to the need for a better understanding of transmission routes between communities and healthcare settings; further research is required to establish community CA-CDI risk factors. Together, these evaluations establish that we must develop a systems approach to explore health problems and respond effectively at a population level. The research and policy environment must be strengthened by modifying current practices, setting priorities, and providing funding for empirical studies and equitable health policies

Strategies for Mitigating Cyberattacks Against Small Retail Businesses

Abstract Small retail businesses are increasingly becoming targets for social media cyberattacks, often losing profitability when forced to close operations after a cyberattack. Small retail business leaders are concerned with the negative impact of cyberattacks on firms’ viability and competitiveness. Grounded in general systems theory, the purpose of this qualitative multiple-case study was to explore strategies retail leaders use to deter social media cyberattacks. The participants were 11 small retail business leaders. Data were collected using semistructured interviews and analyzed using thematic analysis. Three themes emerged: using multiple strategies to deter social media cyberattacks, importance of training regarding cybersecurity best practices, and the need for a contingency plan. A key recommendation is for small retail business leaders to provide employees and customers with training regarding proper cybersecurity protocols. The implications for positive social change include the potential to improve cybersecurity measures and enhance a small business’ viability and employment opportunities, positively impacting local communities and tax revenues

Human Computer Interaction and Emerging Technologies

The INTERACT Conferences are an important platform for researchers and practitioners in the field of human-computer interaction (HCI) to showcase their work. They are organised biennially by the International Federation for Information Processing (IFIP) Technical Committee on Human–Computer Interaction (IFIP TC13), an international committee of 30 member national societies and nine Working Groups. INTERACT is truly international in its spirit and has attracted researchers from several countries and cultures. With an emphasis on inclusiveness, it works to lower the barriers that prevent people in developing countries from participating in conferences. As a multidisciplinary field, HCI requires interaction and discussion among diverse people with different interests and backgrounds. The 17th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2019) took place during 2-6 September 2019 in Paphos, Cyprus. The conference was held at the Coral Beach Hotel Resort, and was co-sponsored by the Cyprus University of Technology and Tallinn University, in cooperation with ACM and ACM SIGCHI. This volume contains the Adjunct Proceedings to the 17th INTERACT Conference, comprising a series of selected papers from workshops, the Student Design Consortium and the Doctoral Consortium. The volume follows the INTERACT conference tradition of submitting adjunct papers after the main publication deadline, to be published by a University Press with a connection to the conference itself. In this case, both the Adjunct Proceedings Chair of the conference, Dr Usashi Chatterjee, and the lead Editor of this volume, Dr Fernando Loizides, work at Cardiff University which is the home of Cardiff University Press

Co-designing an interactive artificial intelligent system with post-stroke patients and caregivers to augment the lost abilities and improve their quality of life: a human-centric approach

ObjectivesThe motor disability due to stroke compromises the autonomy of patients and caregivers. To support autonomy and other personal and social needs, trustworthy, multifunctional, adaptive, and interactive assistive devices represent optimal solutions. To fulfill this aim, an artificial intelligence system named MAIA would aim to interpret users’ intentions and translate them into actions performed by assistive devices. Analyzing their perspectives is essential to develop the MAIA system operating in harmony with patients’ and caregivers’ needs as much as possible.MethodsPost-stroke patients and caregivers were interviewed to explore the impact of motor disability on their lives, previous experiences with assistive technologies, opinions, and attitudes about MAIA and their needs. Interview transcripts were analyzed using inductive thematic analysis.ResultsSixteen interviews were conducted with 12 post-stroke patients and four caregivers. Three themes emerged: (1) Needs to be satisfied, (2) MAIA technology acceptance, and (3) Perceived trustfulness. Overall, patients are seeking rehabilitative technology, contrary to caregivers needing assistive technology to help them daily. An easy-to-use and ergonomic technology is preferable. However, a few participants trust a system based on artificial intelligence.ConclusionAn interactive artificial intelligence technology could help post-stroke patients and their caregivers to restore motor autonomy. The insights from participants to develop the system depends on their motor ability and the role of patients or caregiver. Although technology grows exponentially, more efforts are needed to strengthen people’s trust in advanced technology

Infection Control Driven Antibiotic Stewardship in a Long-term Care Facility

abstract: Antibiotics have contributed to the decline in mortality and morbidity caused by infections, but overuse may weaken effectiveness resulting in a worldwide threat. Antibiotic overuse is correlated with adverse events like Clostridium difficile infection, antimicrobial resistance, unnecessary healthcare utilization and poor health outcomes. Long term care facility (LTCF) residents are vulnerable targets for this phenomenon as antibiotics are one of the most commonly prescribed medications in this setting. Consequently, multiple organizations mandate strategies to promote antibiotic stewardship in all healthcare sites particularly LTCFs. To address this global issue, this doctoral project utilized the Outcomes-Focused Knowledge Translation intervention framework to provide sepsis education, promoted use of an established clinical algorithm, and engaged a communication tool for nurses and the certified nursing assistants (CNAs) thus, improving antibiotic stewardship. The project was conducted in a 5-star Medicare-rated LTCF in Mesa, AZ with a convenience sample of 22 participants. The participants received a knowledge questionnaire and Work Relationship Scale pre- and post- intervention to determine improvement. The results show that the education provided did not improve their knowledge with a p = 0.317 for nurses while p = 0.863 for CNAs over 8 weeks. Lastly, education provided did not improve the nurses’ Work Relationship p = 0.230 or for the CNAs p = 0.689. Though not statistically significant, the intervention tools are clinically significant. Additional research is needed to identify ways to determine barriers in implementing an antibiotic stewardship program

Adapting robot behavior to user preferences in assistive scenarios

Robotic assistants have inspired numerous books and science fiction movies. In the real world, these kinds of devices are a growing need in amongst the elderly, who while life continue requiring more assistance. While life expectancy is increasing, life quality is not necessarily doing so. Thus, we may find ourselves and our loved ones being dependent and needing another person to perform the most basic tasks, which has a strong psychological impact. Accordingly, assistive robots may be the definitive tool to give more quality of life by empowering dependent people and extending their independent living. Assisting users to perform daily activities requires adapting to them and their needs, as they might not be able to adapt to the robot. This thesis tackles adaptation and personalization issues through user preferences. We 'focus on physical tasks that involve close contact, as these present interesting challenges, and are of great importance for he user. Therefore, three tasks are mainly used throughout the thesis: assistive feeding, shoe fitting, and jacket dressing. We first describe a framework for robot behavior adaptation that illustrates how robots should be personalized for and by end- users or their assistants. Using this framework, non-technical users determine how !he robot should behave. Then, we define the concept of preference for assistive robotics scenarios and establish a taxonomy, which includes hierarchies and groups of preferences, grounding definitions and concepts. We then show how the preferences in the taxonomy are used with Al planning systems to adapt the robot behavior to the preferences of the user obtained from simple questions. Our algorithms allow for long-term adaptations as well as to cope with misinformed user models. We further integrate the methods with low-level motion primitives that provide a more robust adaptation and behavior while lowering the number of needed actions and demonstrations. Moreover, we perform a deeper analysis in Planning and preferences with the introduction of new algorithms to provide preference suggestions in planning domains. The thesis then concludes with a user study that evaluates the use of the preferences in the three real assistive robotics scenarios. The experiments show a clear understanding of the preferences of users, who were able to assess the impact of their preferences on the behavior of the robot. In summary, we provide tools and algorithms to design the robotic assistants of the future. Assistants that should be able to adapt to the assisted user needs and preferences, just as human assistants do nowadays.Els assistents robòtics han inspirat nombrosos llibres i pel·lícules de ciència-ficció al llarg de la història. Però tornant al món real, aquest tipus de dispositius s'estan tornant una necessitat per a una societat que envelleix a un ritme ràpid i que, per tant, requerirà més i més assistència. Mentre l'esperança de vida augmenta, la qualitat de vida no necessàriament ho fa. Per tant, ens podem trobar a nosaltres mateixos i als nostres estimats en una situació de dependència, necessitant una altra persona per poder fer les tasques més bàsiques, cosa que té un gran impacte psicològic. En conseqüència, els robots assistencials poden ser l'eina definitiva per proporcionar una millor qualitat de vida empoderant els usuaris i allargant la seva capacitat de viure independentment. L'assistència a persones per realitzar tasques diàries requereix adaptar-se a elles i les seves necessitats, donat que aquests usuaris no poden adaptar-se al robot. En aquesta tesi, abordem el problema de l'adaptació i la personalització d'un robot mitjançant preferències de l'usuari. Ens centrem en tasques físiques, que involucren contacte amb la persona, per les seves dificultats i importància per a l'usuari. Per aquest motiu, la tesi utilitzarà principalment tres tasques com a exemple: donar menjar, posar una sabata i vestir una jaqueta. Comencem definint un marc (framework) per a la personalització del comportament del robot que defineix com s'han de personalitzar els robots per usuaris i pels seus assistents. Amb aquest marc, usuaris sense coneixements tècnics són capaços de definir com s'ha de comportar el robot. Posteriorment definim el concepte de preferència per a robots assistencials i establim una taxonomia que inclou jerarquies i grups de preferències, els quals fonamenten les definicions i conceptes. Després mostrem com les preferències de la taxonomia s'utilitzen amb sistemes planificadors amb IA per adaptar el comportament del robot a les preferències de l'usuari, que s'obtenen mitjançant preguntes simples. Els nostres algorismes permeten l'adaptació a llarg termini, així com fer front a models d'usuari mal inferits. Aquests mètodes són integrats amb primitives a baix nivell que proporcionen una adaptació i comportament més robusts a la mateixa vegada que disminueixen el nombre d'accions i demostracions necessàries. També fem una anàlisi més profunda de l'ús de les preferències amb planificadors amb la introducció de nous algorismes per fer suggeriments de preferències en dominis de planificació. La tesi conclou amb un estudi amb usuaris que avalua l'ús de les preferències en les tres tasques assistencials. Els experiments demostren un clar enteniment de les preferències per part dels usuaris, que van ser capaços de discernir quan les seves preferències eren utilitzades. En resum, proporcionem eines i algorismes per dissenyar els assistents robòtics del futur. Uns assistents que haurien de ser capaços d'adaptar-se a les preferències i necessitats de l'usuari que assisteixen, tal com els assistents humans fan avui en dia