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We present Phrase-Verified Voting, a voter-verifiable remote voting system easily assembled from commercial off-the-shelf software for small private elections. The system is transparent and enables each voter to verify that the tally includes their ballot selection without requiring any understanding of cryptography. This system is an example of making voter verification usable. The paper describes the system and an experience with it in fall 2020, to vote remotely in promotion committees in a university. Each voter fills out a form in the cloud with their selection $V$ for each race and a two-word passphrase $P$. The system generates a verification prompt of the $(V,P)$ pairs and a tally of the votes, organized to help visualize how the votes add up. After the polls close, each voter verifies that this table lists their $(V,P)$ pair and that the tally is computed correctly. The system is especially appropriate for any small group making sensitive decisions. Because the system would not prevent a coercer from demanding that their victim use a specified passphrase, it is not designed for applications where such malfeasance would be likely or go undetected. Results from 43 voters show that the system performed effectively for its intended purpose, and introduced users to the concept of voter-verified elections. Compared to the commonly-used alternatives of paper ballots or voting by email, voters found the system easier to use, and that it provided greater privacy and outcome integrity. 

This paper investigates countermeasures to side-channel attacks. A dynamic partial reconfiguration (DPR) method is proposed for field programmable gate arrays (FPGAs)s to make techniques such as differential power analysis (DPA) and correlation power analysis (CPA) difficult and ineffective. We call the technique side-channel power resistance for encryption algorithms using DPR, or SPREAD. SPREAD is designed to reduce cryptographic key related signal correlations in power supply transients by changing components of the hardware implementation on-the-fly using DPR. Replicated primitives within the advanced encryption standard (AES) algorithm, in particular, the substitution-box (SBOX)s, are synthesized to multiple and distinct gate-level implementations. The different implementations change the delay characteristics of the SBOXs, reducing correlations in the power traces, which, in turn, increases the difficulty of side-channel attacks. The effectiveness of the proposed countermeasures depends greatly on this principle; therefore, the focus of this paper is on the evaluation of implementation diversity techniques. 

This paper investigates countermeasures to side-channel attacks. A dynamic partial reconfiguration (DPR) method is proposed for field programmable gate arrays (FPGAs)s to make techniques such as differential power analysis (DPA) and correlation power analysis (CPA) difficult and ineffective. We call the technique side-channel power resistance for encryption algorithms using DPR, or SPREAD. SPREAD is designed to reduce cryptographic key related signal correlations in power supply transients by changing components of the hardware implementation on-the-fly using DPR. Replicated primitives within the advanced encryption standard (AES) algorithm, in particular, the substitution-box (SBOX)s, are synthesized to multiple and distinct gate-level implementations. The different implementations change the delay characteristics of the SBOXs, reducing correlations in the power traces, which, in turn, increases the difficulty of side-channel attacks. The effectiveness of the proposed countermeasures depends greatly on this principle; therefore, the focus of this paper is on the evaluation of implementation diversity techniques. 

Modeling buildings' heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents' behavior. Gray-box models offer an explanation of those dynamics, as expressed in a few parameters specific to built environments that can provide compelling insights into the characteristics of building artifacts. In this paper, we present a systematic study of Bayesian approaches to modeling buildings' parameters, and hence their thermal characteristics. We build a Bayesian state-space model that can adapt and incorporate buildings' thermal equations and postulate a generalized solution that can easily adapt prior knowledge regarding the parameters. We then show that a faster approximate approach using Variational Inference for parameter estimation can posit similar parameters' quantification as that of a more time-consuming Markov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on two datasets to understand the generative process and attest that the Bayesian approach is more interpretable. We further study the effects of prior selection on the model parameters and transfer learning, where we learn parameters from one season and reuse them to fit the model in other seasons. We perform extensive evaluations on controlled and real data traces to enumerate buildings' parameters within a 95% credible interval. 

Air leakages pose a major problem in both residential and commercial buildings. They increase the utility bill and result in excessive usage of Heating Ventilation and Air Conditioning (HVAC) systems, which impacts the environment and causes discomfort to residents. Repairing air leakages in a building is an expensive and time intensive task. Even detecting the leakages can require extensive professional testing. In this paper, we propose a method to identify the leaky homes from a set, provided their energy consumption data is accessible from residential smart meters. In the first phase, we employ a Non-Intrusive Load Monitoring (NILM) technique to disaggregate the HVAC data from total power consumption for several homes. We propose a recurrent neural network and a denoising autoencoder based approach to identify the 'ON' and 'OFF' cycles of the HVACs and their overall usages. We categorize the typical HVAC consumption of about 200 homes and any probable insulation and leakage problems using the Air Changes per Hour at 50 Pa (ACH50) metric in the Dataport datasets. We perform our proposed NILM analysis on different granularities of smart meter data such as 1 min, 15 mins, and 1 hour to observe its effect on classifying the leaky homes. Our results show that disaggregation can be used to identify the residential air-conditioning, at 1 min granularity which in turn helps us to identify the leaky potential homes, with an accuracy of 86%. 

Leaky windows and doors, open refrigerators, unattended appliances, left-on lights, and other sources subtly leak energy accounting for a large portion of waste. Formal energy audits are expensive and time consuming and do not capture many sources of leakage and waste. In this short paper, we present a hybrid IR/RGB imaging system for an end-user to deploy to perform longitudinal detection of energy waste. The system uses a low resolution, 16 x 4 IR camera and a low cost digital camera mounted on a steerable platform to automatically scan a room, periodically taking low resolution IR and RGB images. The system uses image stitching to create an IR/RGB hybrid panoramic image and segmentation to determine temperature extrema in the scanned room. Finally, this data is combined with thermostat set-point information to highlight hot-spots or cold-spots which likely indicate energy leakage or wastage. The system obviates the need for expensive, time-consuming waste detection methods, for professional setup, and for more intrusive instrumentation of the home. 

Activity recognition has applications in a variety of human-in-the-loop settings such as smart home health monitoring, green building energy and occupancy management, intelligent transportation, and participatory sensing. While fine-grained activity recognition systems and approaches help enable a multitude of novel applications, discovering them with non-intrusive ambient sensor systems pose challenging design, as well as data processing, mining, and activity recognition issues. In this paper, we develop a low-cost heterogeneous Radar based Activity Monitoring (RAM) system for recognizing fine-grained activities. We exploit the feasibility of using an array of heterogeneous micro-doppler radars to recognize low-level activities. We prototype a short-range and a long-range radar system and evaluate the feasibility of using the system for fine-grained activity recognition. In our evaluation, using real data traces, we show that our system can detect fine-grained user activities with 92.84% accuracy.