Collaborative Intelligent Cross-Camera Video Analytics at Edge: Opportunities and Challenges

Nowadays, video cameras are deployed in large scale for spatial monitoring of physical places (e.g., surveillance systems in the context of smart cities). The massive camera deployment, however, presents new challenges for analyzing the enormous data, as the cost of high computational overhead of sophisticated deep learning techniques imposes a prohibitive overhead, in terms of energy consumption and processing throughput, on such resource-constrained edge devices. To address these limitations, this paper envisions a collaborative intelligent cross-camera video analytics paradigm at the network edge in which camera nodes adjust their pipelines (e.g., inference) to incorporate correlated observations and shared knowledge from other nodes' contents. By harassing redundant spatio-temporal to reduce the size of the inference search space in one hand, and intelligent collaboration between video nodes on the other, we discuss how such collaborative paradigm can considerably improve accuracy, reduce latency and decrease communication bandwidth compared to non-collaborative baselines. This paper also describes major opportunities and challenges in realizing such a paradigm.Comment: First International Workshop on Challenges in Artificial Intelligence and Machine Learnin

Efficient Cross Layer Designs for IEEE 802.11 Wireless Networks

Various properties of wireless networks, such as mobility, frequent disconnections and varying channel conditions, have made it a challenging task to design networking protocols for wireless communications. In this dissertation, we address several problems related to both the routing layer and medium access control (MAC) layer in wireless networks aiming to enhance the network performance. First, we study the effect of the channel noise on the network performance. We present mechanisms to compute energy-efficient paths in noisy environments for ad hoc networks by exploiting the IEEE 802.11 fragmentation mechanism. These mechanisms enhance the network performance up to orders of magnitude in terms of energy and throughput. We also enhance the IEEE 802.11 infrastructure networks with a capability to differentiate between different types of unsuccessful transmissions to enhance the network performance. Second, we study the effects of the physical layer capture phenomena on network performance. We modify the IEEE 802.11 protocol in a way to increase the concurrent transmissions by exploiting the capture phenomena. We analytically study the potential performance enhancement of our mechanism over the original IEEE 802.11. The analysis shows that up to 35% of the IEEE 802.11 blocking decisions are unnecessary. The results are verified by simulation in which we show that our enhanced mechanism can achieve up to 22% more throughput. Finally, we exploit the spatial reuse of the directional antenna in the IEEE 802.11 standards by developing two novel opportunistic enhancement mechanisms. The first mechanism augments the IEEE 802.11 protocol with additional information that gives a node the flexibility to transmit data while other transmissions are in its vicinity. The second mechanism changes the access routines of the IEEE 802.11 data queue. We show analytically how the IEEE 802.11 protocol using directional antenna is conservative in terms of assessing channel availability, with as much as 60% of unnecessary blocking assessments and up to 90% when we alter the accessing mechanism of the data queue. By simulation, we show an improvement in network throughput of 40% in the case of applying the first mechanism, and up to 60% in the case of applying the second mechanism

Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual Speech

Recognizing emotions in spoken communication is crucial for advanced human-machine interaction. Current emotion detection methodologies often display biases when applied cross-corpus. To address this, our study amalgamates 16 diverse datasets, resulting in 375 hours of data across languages like English, Chinese, and Japanese. We propose a soft labeling system to capture gradational emotional intensities. Using the Whisper encoder and data augmentation methods inspired by contrastive learning, our method emphasizes the temporal dynamics of emotions. Our validation on four multilingual datasets demonstrates notable zero-shot generalization. We publish our open source model weights and initial promising results after fine-tuning on Hume-Prosody.Comment: Accepted as talk at NeurIPS ML for Audio worksho

Demographic Prediction of Mobile User from Phone Usage

In this paper, we describe how we use the mobile phone usage of users to predict their demographic attributes. Using call log, visited GSM cells information, visited Bluetooth devices, visited Wireless LAN devices, accelerometer data, and so on, we predict the gender, age, marital status, job and number of people in household of users. The accuracy of developed classifiers for these classification problems ranges from 45-87% depending upon the particular classification problem

TrafficView: Towards a Scalable Traffic Monitoring System

Vehicles are part of people's life in modern society, into which more and more high-tech devices are integrated, and a common platform for inter-vehicle communication is necessary to realize an intelligent transportation system supporting safe driving, dynamic route scheduling, emergency message dissemination, and traffic condition monitoring. TrafficView, which is a part of the e-Road project, defines a framework to disseminate and gather information about the vehicles on the road. Using such a system will provide a vehicle driver with road traffic information, which helps driving in situations as foggy weather, or finding an optimal route in a trip several miles long. This paper describes the basic design of TrafficView and different algorithms used in the system. (UMIACS-TR-2003-98

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