Conference Papers

First-class state change in plaid

Sunshine J., Naden K., Stork S., Aldrich J., Tanter E.

Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA

2011

Abstract:

Objects model the world, and state is fundamental to a faithful modeling. Engineers use state machines to understand and reason about state transitions, but programming languages provide little support for building software based on state abstractions. We propose Plaid, a language in which objects are modeled not just in terms of classes, but in terms of changing abstract states. Each state may have its own representation, as well as methods that may transition the object into a new state. A formal model precisely defines the semantics of core Plaid constructs such as state transition and trait-like state composition. We evaluate Plaid through a series of examples taken from the Plaid compiler and the standard libraries of Smalltalk and Java. These examples show how Plaid can more closely model state-based designs, enhancing understandability, enhancing dynamic error checking, and providing reuse benefits.

FlowBox: anomaly detection using flow analysis in cloud applications

Fu S., Kim H., Prior R.

2015 IEEE Global Communications Conference, GLOBECOM 2015

2016

Abstract:

Many complex application services are deployed in virtualized Cloud environments. Cloud applications consist of multiple components and the data flow among these components tends to be highly complex and unpredictable. The complexity and heterogeneity make anomaly detection challenging. We propose FlowBox, a distributed anomaly detection system for Cloud applications. FlowBox considers each server component as a black box and detects performance anomalies using the flow analysis. The black box model addresses the challenge of accurately describing the complex system model. The flow analysis is based on a simple relationship of data flow in any given component of Cloud applications. Between any two components, the number of requests should always be equal to the number of responses within a given time interval during normal operations. FlowBox monitors traffic flow in each component and continually builds flow signatures in order to describe the normal application behavior. Using the flow signatures, FlowBox detects performance anomalies in Cloud applications. We evaluate FlowBox with several different kinds of Cloud applications in our datacenter. Experimental results show that FlowBox achieves 96.02% detection precision, 3.98% false positive, and 3.5% false negative in detecting various kinds of anomalies.

Foot Gesture Recognition through Dual Channel Wearable EMG System

Maragliulo S., Lopes P., Osorio L.; Almeida A.T., Tavakoli M.

IEEE Sensors Journal

2019

Abstract:

Surface Electromyography (sEMG) based Hand Gesture Recognition (HGR) is finding its application as a general-purpose wearable Human Machine Interface (HMI). However, the same is rarely explored for Foot Gesture Recognition (FGR). FGR is interesting as a hands-free controller, e.g. in case of an electric guitar player who controls the musical effects by foot pedals. Yet, FGR is challenging, since general leg movements such as walking can be classified as foot gestures. In this article, the application of a minimalistic sEMG wearable “footband” is demonstrated as a hands-free HMI. With only two channels and a Support Vector Machine (SVM) classifier, the system is able to classify five foot gestures. In addition, we added a locking/unlocking mechanism, also controlled by one of the gestures, which eliminates undesired gesture classification during general leg movements. We show that the gesture-controlled locking feature is robust, and system does not unlock during waking, jumping, climbing the stairs and similar. This work covers the system design, the real-time classification algorithm, the selection of the target muscles, and experimental results. Sessions independence, accuracy and robustness of the device are tested in a total of 18 sessions with three volunteers having different usage skills. As case study, the system was interfaced with a Window 10 application developed for controlling musical effects with the foot wearable band, as a hands-free alternative for the DJ mixer equipment.

Forecasting residential air conditioning loads

Horowitz S., Mauch B., Sowell F.

Applied Energy

2014

Abstract:

A doubly censored Tobit model is used to forecast hourly air-conditioner usage for individual households. The model worked well over a wide range of temperatures, 9–38 °C, making it possible to accurately forecast the electricity load for a variety of demand response applications including operational reserves for renewable energy integration. Individual models are simulated and summed to obtain aggregate forecasts and confidence intervals. The model allows for correlation between the individual shocks that occur in a region. This approach gives substantially more accurate results than the moving average method typically used for forecasting and measuring direct load control. Applying the model to data from three U.S. utilities produced mean square error values from 0.027 to 0.041 with average load values per customer ranging from 0.49 to 0.62 kW.

FOREST: An Interactive Multi-tree Synthesizer for Regular Expressions

Ferreira M., Terra-Neves M., Ventura M., Lynce I., Martins R.

TACAS 20201

2021

Abstract:

Form validators based on regular expressions are often used on digital forms to prevent users from inserting data in the wrong format. However, writing these validators can pose a challenge to some users. We present Forest, a regular expression synthesizer for digital form validations. Forest produces a regular expression that matches the desired pattern for the input values and a set of conditions over capturing groups that ensure the validity of integer values in the input. Our synthesis procedure is based on enumerative search and uses a Satisfiability Modulo Theories (SMT) solver to explore and prune the search space. We propose a novel representation for regular expressions synthesis, multi-tree, which induces patterns in the examples and uses them to split the problem through a divide-and-conquer approach. We also present a new SMT encoding to synthesize capture conditions for a given regular expression. To increase confidence in the synthesized regular expression, we implement user interaction based on distinguishing inputs. We evaluated Forest on real-world form-validation instances using regular expressions. Experimental results show that Forest successfully returns the desired regular expression in 70% of the instances and outperforms Regel, a state-of-the-art regular expression synthesizer.

Frame-Semantic Parsing

Das D., Chen D., Martins A.F.T., Schneider N., Smith N.A.

Computational Linguistics

2014

Abstract:

Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon. We solve the problem of frame-semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content words and phrases) in their sentential contexts and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. This model uses latent variables and semi-supervised learning to improve frame disambiguation for targets unseen at training time. The second stage finds the target’s locally expressed semantic arguments. At inference time, a fast exact dual decomposition algorithm collectively predicts all the arguments of a frame at once in order to respect declaratively stated linguistic constraints, resulting in qualitatively better structures than naïve local predictors. Both components are feature-based and discriminatively trained on a small set of annotated frame-semantic parses. On the SemEval 2007 benchmark data set, the approach, along with a heuristic identifier of frame-evoking targets, outperforms the prior state of the art by significant margins. Additionally, we present experiments on the much larger FrameNet 1.5 data set. We have released our frame-semantic parser as open-source software.

FSAD: Flow similarity analysis for anomaly detection in cloud applications

Fu S., Kim H., Prior R.

Proceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015

2016

Abstract:

Fast detection of performance anomalies is critical in Cloud applications, but challenging to implement in a general and effective tool with low operational overload. We propose FSAD, a performance anomaly detection system based on the concept of flow similarity. It stems from the observation that, in general, the number of responses generated by a component closely follows the number of received requests, but this relation stops holding in presence of performance anomalies. In FSAD, components are regarded as black boxes, and time series of incoming and outgoing packets are fed to the flow similarity analysis for anomaly detection. The effectiveness of FSAD is demonstrated in experimental results.

Fully Untethered Battery-free Biomonitoring Electronic Tattoo with Wireless Energy Harvesting

Alberto J., Leal C., Fernandes C., Lopes P.A., Paisana H., Almeida A.T., Tavakoli M.

Scientific Reports

2020

Abstract:

Bioelectronics stickers that interface the human epidermis and collect electrophysiological data will constitute important tools in the future of healthcare. Rapid progress is enabled by novel fabrication methods for adhesive electronics patches that are soft, stretchable and conform to the human skin. Yet, the ultimate functionality of such systems still depends on rigid components such as silicon chips and the largest rigid component on these systems is usually the battery. In this work, we demonstrate a quickly deployable, untethered, battery-free, ultrathin (~5 μm) passive “electronic tattoo” that interfaces with the human skin for acquisition and transmission of physiological data. We show that the ultrathin film adapts well with the human skin, and allows an excellent signal to noise ratio, better than the gold-standard Ag/AgCl electrodes. To supply the required energy, we rely on a wireless power transfer (WPT) system, using a printed stretchable Ag-In-Ga coil, as well as printed biopotential acquisition electrodes. The tag is interfaced with data acquisition and communication electronics. This constitutes a “data-by-request” system. By approaching the scanning device to the applied tattoo, the patient’s electrophysiological data is read and stored to the caregiver device. The WPT device can provide more than 300 mW of measured power if it is transferred over the skin or 100 mW if it is implanted under the skin. As a case study, we transferred this temporary tattoo to the human skin and interfaced it with an electrocardiogram (ECG) device, which could send the volunteer’s heartbeat rate in real-time via Bluetooth.

Functions as session-typed processes

Toninho B., Caires L., Pfenning F.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2012

Abstract:

We study type-directed encodings of the simply-typed λ-calculus in a session-typed π-calculus. The translations proceed in two steps: standard embeddings of simply-typed λ-calculus in a linear λ-calculus, followed by a standard translation of linear natural deduction to linear sequent calculus. We have shown in prior work how to give a Curry-Howard interpretation of the proofs in the linear sequent calculus as π-calculus processes subject to a session type discipline. We show that the resulting translations induce sharing and copying parallel evaluation strategies for the original λ-terms, thereby providing a new logically motivated explanation for these strategies.