What makes food sound tasty? ICS researchers find out.

Yelp reviews are a part of modern dining. And good reviews are good business for eateries.

But what makes good eats sound delicious on Yelp?

That’s what two University of Hawaii information science researchers – Weranuj Ariyasriwatana and Luz Marina Quiroga – wanted to find out as part of a study that could be useful for food marketers and businesses — and maybe even a few foodies.

The researchers looked at 250 Yelp reviews for 40 highly-rated Hawaii eateries, picking out “expressions of deliciousness.”

For more details, see the Hawaii News Now story.

ICS Community Lounge Grand Opening

The ICS Department is pleased to announce the Grand Opening of our Community Lounge: a gathering place for ICS faculty, staff, and students to work, socialize, innovate, and educate.

On Friday, September 11, from 4:00-5:30pm, we will formally open the room.  There will be food, prizes, music, games, a photo booth, and much more.  Please come and join us, and RSVP here.

More information about the room, including policies and the calendar of events, is available here.

Research Experiences for Undergraduates at LAVA produces a series of tutorial videos for SAGE user community

With funding from the National Science Foundation’s Research Experiences for Undergraduates (REU) program,  undergraduate interns experienced research life at the Laboratory for Advanced Visualization and Applications (LAVA)- the premiere visualization laboratory in Hawaiʻi.

As part of their internship they learned how to produce a series of tutorial videos on how to use one of LAVA’s research products- SAGE2: the Scalable Amplified Group Environment (a $5M NSF funded project)- which is used by over 170 research institutions around the world.

Follow this link to see their videos!

2015 Creativity Workshop

May 18, 2015

The Laboratory for Advanced Visualization and Applications (LAVA) hosted a Creativity workshop attended by graduate and undergraduate students from Information and Computer Sciences, Electrical Engineering, and Academy for Creative Media. The event was held on a farm in Kaneohe, Hawaiʻi.

The goal of the workshop was to supplement the technical knowledge students receive from their regular university curriculum with activities that provide an appreciation for the environment in which they live, and how it contributes to the processes which are necessary for creative ideation to flourish.

“From the outside looking in, technical fields such as Computer Science or Electrical Engineering are not often seen as creative areas of study. In fact they are, but we rarely teach creativity within their core curriculum. That is the purpose of the workshop, and we hope to run more workshops in the future and involve even more students and faculty from other disciplines.” says Professor Jason Leigh, director of LAVA.


ICS Affiliate of NCWIT recognizes regional winners

Nine female high school student from throughout the state, including five from Kalani High, were recognized as Hawaiʻi Regional Winners for their notable projects in computer science by the National Center for Women and Information Technology. The NCWIT Award for Aspirations in Computing (AspireIT) honors young women who are active in computing and technology.

The Hawaiʻi affiliate of NCWIT is supported by the University of Hawaiʻi at Mānoa Department of Information and Computer Sciences. Department Chair and Professor David Chin spoke to the students about how computer sciences and engineering are really about creativity and how each of them can “create something real that comes out of your mind.”

Two of the five Kalani students won scholarships to attend UH Mānoa and take courses in the Department of Information and Computer Sciences. Madisyn Sim received the 2015–2016 scholarship, while Camelia Lai received the 2016–2017 academic year scholarship. In addition, Riley Kishaba, Yongqi Lin and Madisyn Sim, all from Kalani High, received the 2015 Hawaiʻi Affiliate Award Runner-Up recognition in the National AspireIT competition. Other students recognized for their oustanding work are Erina Baudat of Hawaiʻi Preparatory Academy, Yu-Ann (Ashley) Chen of Hilo High, Sara Nakagaki of Kalani High and Aliya Petranik of Punahou School.

ICS Professor Kim Binsted presented her current work with the HI-SEAS project led by the UH and funded by NASA. The project isolates a group of people in a structure high atop Mauna Loa on Hawaiʻi Island to try and find solutions that will one day sustain travelers to deep space.

ICS Professor Susanne Still also spoke to the high school students about her work studying machine learning or machines that learn by getting feedback.

M.S. Defense: Zachary Tomaszewski, “Skald: An Affordance-based User Interface for Interactive Fiction”

Skald: An Affordance-based User Interface for Interactive Fiction
Zachary Tomaszewski
Friday, February 27, 2015,
POST 302, 9:30am

Abstract: Interactive fiction (IF) is a long-lived text-based computer game genre. A small community of game authors are still producing independent IF games, and IF can be used for a number of serious applications, including education and AI research. This work explores the defining features of IF as a form, as well as its shortcomings in terms of user interface (UI) affordances. It then provides Skald, an alternative menu-driven UI for IF. An empirical evaluation shows that Skald offers a number of advantages over the traditional IF interface. It eliminates user input errors, is rated as easier to use, and encourages players to explore the virtual game world to a greater degree. However, the study also reveals that Skald does not significantly increase users’ feelings of world-level user agency and that a sizable minority of players still prefer the traditional UI overall.

Scott Robertson (ICS, Chair)
Kim Binsted (ICS)
John Zuern (English)

Ph.D. Defense: Michael Gowanlock, “In-Memory Distance Threshold Searches on Moving Object Trajectories”

Wed 2/11/2015, 3PM

POST 302


Processing moving object trajectories arises in many applications. To gaininsight into target domains, historical continuous trajectory similarity searches find those trajectories that have similar attributes in common. In this work, we focus on a trajectory similarity search, the distance threshold search, which finds all trajectories within a given distance of a query trajectory over a time interval. We investigate novel approaches for the efficient processing of these searches over in-memory databases on the CPU and on the GPU.

On the CPU, we use an in-memory R-tree index to store trajectory data and evaluate its performance on a range of trajectory datasets. The R-tree is a well-studied out-of-core indexing scheme. However, in the context of in-memory searches, we find that the traditional notion of considering good trajectory splits by minimizing the volume of MBBs so as to reduce index overlap is not well-suited to improve the performance of in-memory distance threshold searches. Another finding is that computing good trajectory splits to achieve a trade-off between the time to search the index-tree and the time to process the candidate set of trajectory segments may not be beneficial when considering large datasets.

The GPU is an attractive technology for distance threshold searches because of the inherent data parallelism involved in calculating moving distances between pairs of polylines; however, challenges arise from the SIMD programming model and limited GPU memory. We study the processing of distance threshold searches using GPU implementations that avoid the use of index-trees. We focus on two scenarios for which we propose GPU-friendly indexing schemes for trajectory data. In the first scenario the database fits in GPU memory but the entire query set does not, thereby requiring back-and-forth communication between the host and the GPU. We find that our indexing scheme yields significant speedup over a multithreaded CPU implementation. We gain insight into our GPU algorithms via a performance model that proves accurate across a range of experimental scenarios. In the second scenario, we study the case where both the query set and the database fit in GPU memory. With this relaxed memory constraint, the design space for trajectory indexing schemes is larger. We investigate indexing schemes with temporal, spatial, and spatiotemporal selectivity. Through performance analyses, we determine the salient characteristics of the trajectories that are conducive to the proposed indexes. In particular, we find that there are two complementary niches for GPU and CPU distance threshold search algorithms. Namely, using the GPU is preferable when the trajectory dataset is large and/or when the threshold distance is large. The GPU is thus the better choice for large-scale scientific trajectory dataset processing, as for instance in the motivating application for this work in the domain of Astrobiology.


AT&T Mobile App Hackathon

Mobile App Hackathon is an event produced by the AT&T Developer Program  that is designed for attendees (technical & non-technical) to build apps/mobile apps, get fed, compete for prizes across different categories and most importantly: meet new people and scout for teammates to work on new or current projects. Our hackathon will introduce you to the latest cutting edge tools to help deploy your own app with a website backend, fully hosted in the cloud.

There are $5,000 in prizes!

We Supply: Quick presentations and code samples that help to bootstrap your hacking, food to keep you going, and caffeine to keep you awake. Along with technical senseis to assist you in building faster, smarter, and with new tools.

You Bring: Your laptop, skills & ideas. Come with a collaborative, team focused mindset and/or team up in advance on Twitter/Facebook/Google+ via the #atthack hashtag. Whether you are a backend person, designer, entrepreneur, student, or just interested in tech; you are invited to attend this event. Every group needs a good balance of talent and your development skills are needed!

More details at: http://attmobileapphackathon-hawaii.splashthat.com/

Ph.D. Defense: Pavel Senin, “Software Trajectory Analysis: An empirically based method for automated software process discovery”

Software Trajectory Analysis: An empirically based method for automated software process discovery
Pavel Senin
Friday, January 16, 2014, 12:00pm
POST 302

Abstract: Recurrent behaviors are considered to be the basic building blocks of any human-driven goal-oriented process, reflecting the development of efficient ways for dealing with common tasks based on the past performance. Thus, the ability to discover recurrent behaviors is utterly important for a bottom-up systematic study, modeling, and improvement of human-driven processes. In the context of software development, whose ultimate goal is the delivery of software, the ability to recognize recurrent behaviors enables the understanding, formal description, and effective guidance of evolving software processes. While a number of approaches for recurrent behaviors discovery and software process modeling and improvement has been previously proposed, they typically built upon on-line intrusive techniques, such as observations and interviewing, therefore expensive, suffering from biases, and unwelcome by software developers.

In this exploratory study, I have developed and tested the idea of software process discovery via off-line analysis of software process artifacts. For this, I have prototyped and evaluated the Software Trajectory Analysis framework, which is built upon the definition of “software trajectory” data type, that is a temporally ordered sequence of software artifact measurements, and a novel technique for temporal data classification, that enables the software trajectory characteristic patterns discovery and ranking. By analogy with the Physics’ trajectory that describes a projectile path in metric space, a software trajectory describes the software process and product progression in a space of chosen software metrics, whereas its recurrent structural patterns are related to the recurrent behaviors.

The claim of this dissertation is that (1) it is possible to discover recurrent behaviors off- line via systematic study of software artifacts, (2) the Software Trajectory Analysis framework provides an effective off-line approach to recurrent software process-characteristic behaviors discovery. In addition to the extensive experimental evaluation of a proposed algorithm for time series characteristic pattern discovery, three empirical case studies were carried out to evaluate the claim: two using software artifacts from public software repositories and one using public dump of a Q&A web site. The results suggested that Software Trajectory Analysis is capable to discover software process-characteristic recurrent behaviors off-line, though their sensible interpretation is sometimes difficult.

Bio: Pavel Senin is a PhD candidate at the ICS department of UH Manoa, working at the Collaborative Software Development Laboratory (CSDL) under supervision of Prof. Philip M. Johnson.  He is originally from Krasnyi Luch, a city in Eastern Ukraine.  Before studying at UH, he received an MS in Applied Mathematics from SFedU, Russia.  During his time at UH Manoa he worked at ASGPB where he assembled the Transgenic Papaya Genome and annotated a representative of Verrucomicrobia phylum; he also received a practical training from JGI at LANL, where he participated in the pioneering single-cell genome sequencing research project. At CSDL Pavel was involved in the Hackystat project, where he worked on the problem of software process discovery.  He developed a novel technique for time series classification and proposed a framework for software process characteristic recurrent behaviors discovery and ranking. When not mining software repositories, Pavel tinkers with Arduino sensors — a project which led to the development of a novel approach for the discovery of spatio-temporal anomalies.

Blake Vilas wins best presentation at UROP Fall Forum for undergraduate research on 3D-printed prosthetic hands

ICS undergrad Blake Vilas won best presentation at the Undergraduate Research Opportunities Program Fall Forum on Friday, December 12th, for his research:

3D Printed Prosthetic Hands Project in Papua New Guinea

Blake Ryan Vilas
Information and Computer Sciences

The 3D-Printed Prosthetic Hands Project (3DPPHP) aims to bring state-­of-­the‐art technology to the less fortunate, starting with the Wahgi Valley people of the Western Highlands of Papua New Guinea – one of the most isolated, violent and poverty-­stricken regions of the world. In this 4,299-­square‐kilometer area live thousands of people who are missing hands due to decades of tribal wars, random acts of violence and other trauma. Leaving approximately two percent of the adult population without at least one hand; barely able to work, let alone care for themselves or their family. Presently 3D printers use biodegradable, environmentally friendly polylactic acid (PLA) and non-­biodegradable acrylonitrile butadiene styrene (ABS) filaments which can be printed at resolutions of 100, 200, and 300 microns. PLA filaments are cheaper and do not require as expensive 3D printer to print objects. Research collected is based on detailed compare and contrast analysis of PLA, ABS, and LN-­4 hand prosthetics printed at various micron resolutions. Records show dexterity, flexibility, durability, usability, stress tests, and overall cost effectiveness for each 3D printed and LN-­4 prosthetics with documented results for future projects that will lead to improvement and development of efficient, affordable, and environmentally-­friendly prosthetics.

Mentor: Scott Robertson

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