Papers.

Using the Kano Model to Balance Delight and Frustration for an Enterprise Application

Persistent Sharing of Fitness App Status on Twitter

Large-scale analysis of Spanish /s/-lenition using audiobooks

Missed Architectural Dependencies: The Elephant in the Room

Research in code and architectural analysis has demonstrated that a clear understanding of structural dependencies among software elements helps developers comprehend the impact of change. Yet examples are abundant from industry of major issues due to missed dependencies associated with different views of the architecture. Key concerns include dependencies related to allocation of modules to implementation packages to improve safety-critical testing and allocation of implementation packages to hardware partitions to optimize performance. In this paper, we present an in-depth study of a safety-critical system that underwent major changes as a result of missed architectural dependencies. We describe the challenges that resulted in re-architecting the system, the techniques we used for intervention, our results, and the developers' perspective. While the engineering tools provided coverage of design concerns, they missed implications of end-to-end integration testing, latency, and cost of change. In our study, we observed that the tools led the engineers to focus on data and control flow and therefore to miss many data-entity relationships, resource behavior, and deployment-related dependencies. Research continues to focus on more tooling and automation to assist with dependency analysis rather than interim, easier-to-adopt solutions. Our findings demonstrate that providing developers with a lightweight, semantically well-defined description of dependencies enables them to reason about change impact and propagation implications that they might otherwise overlook.

Common Pitfalls of Benchmarking Big Data Systems

Benchmarking big data systems is nontrivial and error prone. The authors, from Cloudera, catalogue five common pitfalls encountered in real customer benchmarking engagements - comparing apples to oranges, using unrealistic data, ignoring system architecture, poor experimental design and misreporting results - and give guidance for benchmarks that predict production behaviour.

Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs

We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The proposed solution is fully graph-based, without any need for additional search structures (typically used at the coarse search stage of the most proximity graph techniques). Hierarchical NSW incrementally builds a multi-layer structure consisting of a hierarchical set of proximity graphs (layers) for nested subsets of the stored elements. The maximum layer in which an element is present is selected randomly with an exponentially decaying probability distribution. This allows producing graphs similar to the previously studied Navigable Small World (NSW) structures while additionally having the links separated by their characteristic distance scales. Starting the search from the upper layer together with utilizing the scale separation boosts the performance compared to NSW and allows a logarithmic complexity scaling. Additional employment of a heuristic for selecting proximity graph neighbors significantly increases performance at high recall and in case of highly clustered data. Performance evaluation has demonstrated that the proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches. Similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.

Kira: Processing Astronomy Imagery Using Big Data Technology

Scientific analyses commonly compose multiple single-process programs into a dataflow. An end-to-end dataflow of single-process programs is known as a many-task application. Typically, HPC tools are used to parallelize these analyses. In this work, we investigate an alternate approach that uses Apache Spark—a modern platform for data intensive computing—to parallelize many-task applications. We implement Kira, a flexible and distributed astronomy image processing toolkit, and its Source Extractor (Kira SE) application. Using Kira SE as a case study, we examine the programming flexibility, dataflow richness, scheduling capacity and performance of Apache Spark running on the Amazon EC2 cloud. By exploiting data locality, Kira SE achieves a 4.1× speedup over an equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon EC2 cloud. Furthermore, Kira SE on the Amazon EC2 cloud achieves a 1.8× speedup over the C program on the NERSC Edison supercomputer. A 128-core Amazon EC2 cloud deployment of Kira SE using Spark Streaming can achieve a second-scale latency with a sustained throughput of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="zhang-ieq1-2599926.gif"/></alternatives></inline-formula>800 MB/s. Our experience with Kira demonstrates that data intensive computing platforms like Apache Spark are a performant alternative for many-task scientific applications.

Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty

We introduce a novel optimization-based motion planner, Stochastic Extended LQR (SELQR), which computes a trajectory and associated linear control policy with the objective of minimizing the expected value of a user-defined cost function. SELQR applies to robotic systems that have stochastic non-linear dynamics with motion uncertainty modeled by Gaussian distributions that can be state- and control-dependent. In each iteration, SELQR uses a combination of forward and backward value iteration to estimate the cost-to-come and the cost-to-go for each state along a trajectory. SELQR then locally optimizes each state along the trajectory at each iteration to minimize the expected total cost, which results in smoothed states that are used for dynamics linearization and cost function quadratization. SELQR progressively improves the approximation of the expected total cost, resulting in higher quality plans. For applications with imperfect sensing, we extend SELQR to plan in the robot's belief space. We show that our iterative approach achieves fast and reliable convergence to high-quality plans in multiple simulated scenarios involving a car-like robot, a quadrotor, and a medical steerable needle performing a liver biopsy procedure.

Deep neural network-based speaker embeddings for end-to-end speaker verification

Differentially-Private Mining of Representative Travel Patterns

Dependence of Voltage and Size on Write Error Rates in Spin-Transfer Torque Magnetic Random-Access Memory

Prosodic strength intrinsic to lexical items: A corpus study on tone reduction in Tone4+Tone4 words in Mandarin Chinese

Visual congruent ads for image search

Data privacy concerns involving cloud

The effect of vocal fry on pitch perception

Enabling Enterprise-Class Workloads in the Cloud

Polynomial-Time Tensor Decompositions with Sum-of-Squares.

Activating Protection and Exercising Recovery Against Large-Scale Outages on the Cloud

Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning

Attention-based neural encoder-decoder frameworks have been widely adopted for image captioning. Most methods force visual attention to be active for every generated word. However, the decoder likely requires little to no visual information from the image to predict non-visual words such as the and of. Other words that may seem visual can often be predicted reliably just from the language model e.g., sign after behind a red stop or phone following talking on a cell. In this paper, we propose a novel adaptive attention model with a visual sentinel. At each time step, our model decides whether to attend to the image (and if so, to which regions) or to the visual sentinel. The model decides whether to attend to the image and where, in order to extract meaningful information for sequential word generation. We test our method on the COCO image captioning 2015 challenge dataset and Flickr30K. Our approach sets the new state-of-the-art by a significant margin.

Disaster Recovery for Cloud-Hosted Enterprise Applications

TEFS: A flash file system for use on memory constrained devices.

Write improvement strategies for serial NOR dataflash memory.

Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment

Scalable dynamic graph summarization

ASONAM 2016 tutorials: Tutorial 1: Evolutionary computation for community detection in complex networks

Web Text Mining

Oncogenic activation of the PI3-kinase p110 beta isoform via the tumor-derived PIK3C beta(D1067V) kinase domain mutation

Gaining insights from social media language: Methodologies and challenges.

Language data available through social media provide opportunities to study people at an unprecedented scale. However, little guidance is available to psychologists who want to enter this area of research. Drawing on tools and techniques developed in natural language processing, we first introduce psychologists to social media language research, identifying descriptive and predictive analyses that language data allow. Second, we describe how raw language data can be accessed and quantified for inclusion in subsequent analyses, exploring personality as expressed on Facebook to illustrate. Third, we highlight challenges and issues to be considered, including accessing and processing the data, interpreting effects, and ethical issues. Social media has become a valuable part of social life, and there is much we can learn by bringing together the tools of computer science with the theories and insights of psychology. (PsycINFO Database Record

Mining half a billion topical experts across multiple social networks

A resource of errors written in Spanish by people with dyslexia and its linguistic, phonetic and visual analysis

Compressed linear algebra for large-scale machine learning

The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems.

Dark Matter in 3D

A Simulator to Study the Effects of Color and Color Blindness on Motion Sickness in Virtual Reality Using Head-Mounted Displays

Summary and Outlook

Results & Evaluation

TIDAIS: An Information System for Time Interval Data

TIDADISTANCE: Similarity of Time Interval Data

TIDAQL: Querying for Time Interval Data

TIDAMODEL: Modeling Time Interval Data

State of the Art

Time Interval Data Analysis

Introduction and Motivation

Analyzing Time Interval Data

Securing Data Provenance in Internet of Things (IoT) Systems

Data Provenance Model for Internet of Things (IoT) Systems

Integrated Modeling Workflow for Security Assurance

TIDAQL: A Query Language Enabling On-line Analytical Processing of Time Interval Data

Bitmap-Based On-Line Analytical Processing of Time Interval Data

LOH and Behold: Web-Scale Visual Search, Recommendation and Clustering Using Locally Optimized Hashing