Seven HCI Grand Challenges

This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements

Multi Document Summarization Based On Cross-Document Relation Using Voting Technique

News articles which are available through online search often provide readers with large collection of texts. Especially in the case of news story, different news sources reporting on the same event usually returns multiple articles in response to a reader’s search. In this work, we first identify cross-document relations from un-annotated texts using Genetic-CBR approach. Following that, we develop a new sentence scoring model based on voting technique over the identified cross-document relations. Our experiments show that incorporating the proposed methods in the summarization process yields substantial improvement over the mainstream methods. The performances of all methods were evaluated using ROUGE—a standard evaluation metric used in text summarization

The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications

Early warning system for coffee rust disease based on error correcting output codes: a proposal

Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Ma­chines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification perfor­mance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.Los productores de café colombianos han sufrido severas consecuencias por la Roya desde que fue reportada por primera vez en el país en el año 1983. Recientemente, investigadores de aprendizaje automático han intentado predecir la roya a través de clasificadores como: arboles de de­cisión, máquinas de vector de soporte, clasificadores no determinísticos y redes bayesianas, pero se ha demostrado teórica y empíricamente que la combinación de múltiples clasificadores puede mejorar sustancialmente el rendimiento en la clasificación. En este sentido es propuesto un sistema de alerta temprana para la roya en el café, basado en códigos de salida de corrección de error y máquinas de vector de soporte para calcular las funciones binarias de la densidad de planta, el nivel de sombra, la acidez del suelo, la intensidad de lluvia en la última noche, y en últimos días, con humedad relativa

Sistema de alerta temprana para la roya en el café basado en códigos de salida de corrección de error: una propuesta

Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Ma­chines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification perfor­mance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.Los productores de café colombianos han sufrido severas consecuencias por la Roya desde que fue reportada por primera vez en el país en el año 1983. Recientemente, investigadores de aprendizaje automático han intentado predecir la roya a través de clasificadores como: arboles de de­cisión, máquinas de vector de soporte, clasificadores no determinísticos y redes bayesianas, pero se ha demostrado teórica y empíricamente que la combinación de múltiples clasificadores puede mejorar sustancialmente el rendimiento en la clasificación. En este sentido es propuesto un sistema de alerta temprana para la roya en el café, basado en códigos de salida de corrección de error y máquinas de vector de soporte para calcular las funciones binarias de la densidad de planta, el nivel de sombra, la acidez del suelo, la intensidad de lluvia en la última noche, y en últimos días, con humedad relativa

Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

Operating networks in very dynamic environments makes network management both complex and difficult. It remains an open question how mesh or hastily formed networks with many nodes could be managed efficiently. Considering the various constraints such as limited communication channels on network management in dynamic environments, the need for semi-autonomous or autonomous networks is evident. Exploitation of machine learning techniques could be a way to solve this network management challenge. However, the need for large training datasets and the infrequency of network management events make it uncertain whether this approach is effective for highly dynamic networks and networks operating in unfriendly conditions, such as tactical military networks. This paper examines the feasibility of this approach by analyzing a recorded dataset of a mesh network experiment in a highly dynamic, austere military environment and derives conclusions for the design of future mesh networks and their network management systems

Machine Learning of Semi-Autonomous Intelligent Mesh Networks Operation Expertise

Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019The article of record at published may be found at https://hdl.handle.net/10125/59562.Operating networks in very dynamic environments makes network management both complex and difficult. It remains an open question how mesh or hastily formed networks with many nodes could be managed efficiently. Considering the various constraints such as limited communication channels on network management in dynamic environments, the need for semi-autonomous or autonomous networks is evident. Exploitation of machine learning techniques could be a way to solve this network management challenge. However, the need for large training datasets and the infrequency of network management events make it uncertain whether this approach is effective for highly dynamic networks and networks operating in unfriendly conditions, such as tactical military networks. This paper examines the feasibility of this approach by analyzing a recorded dataset of a mesh network experiment in a highly dynamic, austere military environment and derives conclusions for the design of future mesh networks and their network management systems