39 research outputs found
Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA
Indoor localization systems have become increasingly important in a wide
range of applications, including industry, security, logistics, and emergency
services. However, the growing demand for accurate localization has heightened
concerns over privacy, as many localization systems rely on active signals that
can be misused by an adversary to track users' movements or manipulate their
measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based
indoor localization system that effectively balances accuracy and privacy.
PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that
ensures users' privacy and safeguards the integrity of their measurement data
while still achieving high accuracy. The system adopts a fingerprinting
approach to address multi-path and non-line-of-sight problems and utilizes deep
neural networks to learn the complex relationship between TDoA and location.
Evaluation in a real-world testbed demonstrates PassiFi's exceptional
performance, surpassing traditional multilateration by 128%, achieving
sub-meter accuracy on par with state-of-the-art active measurement systems, all
while preserving privacy
One Model Fits All: Cross-Region Taxi-Demand Forecasting
The growing demand for ride-hailing services has led to an increasing need
for accurate taxi demand prediction. Existing systems are limited to specific
regions, lacking generalizability to unseen areas. This paper presents a novel
taxi demand forecasting system that leverages a graph neural network to capture
spatial dependencies and patterns in urban environments. Additionally, the
proposed system employs a region-neutral approach, enabling it to train a model
that can be applied to any region, including unseen regions. To achieve this,
the framework incorporates the power of Variational Autoencoder to disentangle
the input features into region-specific and region-neutral components. The
region-neutral features facilitate cross-region taxi demand predictions,
allowing the model to generalize well across different urban areas.
Experimental results demonstrate the effectiveness of the proposed system in
accurately forecasting taxi demand, even in previously unobserved regions, thus
showcasing its potential for optimizing taxi services and improving
transportation efficiency on a broader scale.Comment: Accepted to The 31st ACM International Conference on Advances in
Geographic Information Systems(SIGSPATIAL '23) as a short paper in the
Research, Systems and Industrial Experience Papers trac
Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey
Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020
A Robust and Accurate Indoor Localization Using Learning-Based Fusion of Wi-Fi RTT and RSSI
Great attention has been paid to indoor localization due to its wide range of associated applications and services. Fingerprinting and time-based localization techniques are among the most popular approaches in the field due to their promising performance. However, fingerprinting techniques usually suffer from signal fluctuations and interference, which yields unstable localization performance. On the other hand, the accuracy of time-based techniques is highly affected by multipath propagation errors and non-line-of-sight transmissions. To combat these challenges, this paper presents a hybrid deep-learning-based indoor localization system called RRLoc which fuses fingerprinting and time-based techniques with a view of combining their advantages. RRLoc leverages a novel approach for fusing received signal strength indication (RSSI) and round-trip time (RTT) measurements and extracting high-level features using deep canonical correlation analysis. The extracted features are then used in training a localization model for facilitating the location estimation process. Different modules are incorporated to improve the deep model’s generalization against overtraining and noise. The experimental results obtained at two different indoor environments show that RRLoc improves localization accuracy by at least 267% and 496% compared to the state-of-the-art fingerprinting and ranging-based-multilateration techniques, respectively
OFCOD: On the Fly Clustering Based Outlier Detection Framework
In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics
Multi-Task Learning for Concurrent Prediction of Thermal Comfort, Sensation and Preference in Winters
Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impetus toward energy efficiency, the current focus is on data-driven TC prediction solutions that leverage state-of-the-art machine learning (ML) algorithms. However, an occupant’s perception of indoor thermal comfort (TC) is subjective and multi-dimensional. Different aspects of TC are represented by various standard metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and thermal preference (TPV). The current ML-based TC prediction solutions adopt the Single-task Learning approach, i.e., one prediction model per metric. Consequently, solutions often focus on only one TC metric. Moreover, when several metrics are considered, multiple ML models for a single indoor space lead to conflicting predictions, rendering real-world deployment infeasible. This work addresses these problems by leveraging Multi-task Learning for TC prediction in naturally ventilated buildings. First, a survey-and-measurement study is conducted in the composite climatic region of north India, in 14 naturally ventilated classrooms of 5 schools, involving 512 primary school students. Next, the dataset is analyzed for important environmental, physiological, and psycho-social factors that influence thermal comfort of children. Further, “DeepComfort”, a deep neural network based Multi-task Learning model is proposed. DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV, simultaneously through a single model. It is validated on ASHRAE-II database and the primary student dataset created in this study. It demonstrates high F1-scores, Accuracy (≈90%), and generalization capability, despite the challenges of illogical responses and data imbalance. DeepComfort is also shown to outperform 6 popular metric-specific single-task machine learning algorithms
Moving-Target Defense in Depth: Pervasive Self- and Situation-Aware VM Mobilization across Federated Clouds in Presence of Active Attacks
Federated clouds are interconnected cooperative cloud infrastructures offering vast hosting capabilities, smooth workload migration and enhanced reliability. However, recent devastating attacks on such clouds have shown that such features come with serious security challenges. The oblivious heterogeneous construction, management, and policies employed in federated clouds open the door for attackers to induce conflicts to facilitate pervasive coordinated attacks. In this paper, we present a novel proactive defense that aims to increase attacker uncertainty and complicate target tracking, a critical step for successful coordinated attacks. The presented systemic approach acts as a VM management platform with an intrinsic multidimensional hierarchical attack representation model (HARM) guiding a dynamic, self and situation-aware VM live-migration for moving-target defense (MtD). The proposed system managed to achieve the proposed goals in a resource-, energy-, and cost-efficient manner