Technology has undoubtedly improved at vertiginous speeds in the last decades. However, there is no evidence that all this technology is helping increase the people’s general wellbeing. Calvo and Peters have attributed this to the fact that most technology professionals keep a machine-focused view of their work, avoiding to look at anything related to the user’s’ wellbeing. Nevertheless, recently there has been a growing body of efforts related to using technology to improve human wellbeing. Calvo and Peters refer to this new research field as Positive Computing.
The UIST student innovation contest (aka the “SIC”) is one of those rare moments in a student’s life: a chance to present work at the heart of one of the top venues in Human Computer Interaction (HCI). In fact, UIST (i.e., ACM’s conference for User Interface Software and Technology) is often acclaimed as the top conference for those driven by hardware / software novelty, mad inventors of the HCI kind, and the likes.
So if you are a student interested in HCI and never had a chance to visit one of the main conferences, here’s your chance; because the UIST SIC is not only a place to meet some of your favorite researchers while they try out your demo, it is also a remarkable conference to learn about the bleeding edge of the field, a financially supported opportunity for those teams that have less support by applying the UIST SIC travel grants,` a chance to get some fabulous prizes — there’s 3K USD for the winning teams but also participation awards — last but not least, it is your chance to get in touch with some novel hardware: electrical muscle stimulation:
We live in an amazing era of technology. The Internet has opened doors that have been dreamed of for years. By adding computing technology to everyday devices, like televisions, thermostats, appliances, and others, we’ve been able to automate many aspects of our daily life. The ideal experience might look something like this 50s ‘futurist’ promotional film entitled “Design For Dreaming”.
The idea of technology being embedded in every object around you is called The Internet of Things, and is one of the fastest growing areas of emerging technology. These days, manufacturers are adding Internet connection to all types of devices around you. One of the most famous examples is the Nest Thermostat [LINK]. This thermostat allows the user to adjust the temperature throughout the day, and eventually learns the user’s patterns, thereafter adjusting the temperature without intervention.
But there’s a dark side to this kind of technology, one that is becoming more visible as the technology goes through growing pains. In this article, we will discuss some of the major issues with putting a computer in every device you own (or don’t really own, as the case may be). We focus on the domestic space, rather than the industrial space, which has its own challenges and benefits. We discuss both the value and problems with adding an internet connection to a device that previously never needed an internet connection, including the reliance on a company to provide updates, security and privacy concerns, and finally judging the value that these additions provide.
In the last months, I conducted a few usability studies and upon reflecting on these I decided to share my experience as it might be helpful to anyone starting on usability. This article attemps at summarizing my experience and thoughts on usability experiments.
When trying to start a usability study or experiment, the practitioner or researcher must answer some initial questions about their future work.
Regarding your research, in general, the most important question to answer is “What is my motivation or why I am doing it?”. In a few words, as a researcher, you must not only formulate your research question but also, its answer.
Research methods are here to help you create and solve a new question on usability, user experience and also, on human-computer interaction.
Artificial Neural Networks (ANNs) are used everyday for tackling a broad spectrum of prediction and classification problems, and for scaling up applications which would otherwise require intractable amounts of data. ML has been witnessing a “Neural Revolution”1 since the mid 2000s, as ANNs found application in tools and technologies such as search engines, automatic translation, or video classification. Though structurally diverse, Convolutional Neural Networks (CNNs) stand out for their ubiquity of use, expanding the ANN domain of applicability from feature vectors to variable-length inputs.