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Queer in AI is an organization that aims to combat the harms faced by queer researchers within AI. Several inclusion initiatives are outlined, including those centered on policy and financial aid.

The present study was focused on optimization of microwave roasting (300–900 W, 0–15 min) of black rice to enhance the functional and quality attributes. Physico-functional properties i.e. surface area (24.89–72.86 mm2) and sphericity (0.43–0.73), water absorption (1.30–3.43 g g−1) and oil absorption capacity (1.52–2.05 g/g) were increased with roasting. Roasting enhanced the browning index (13.03–41.03), a* (3.68–4.26) and b* (4.37–8.41) values but degraded the brightness (55.42–31.28). Roasting increased the glass transition temperature, but a reduction was observed in pasting properties i.e. peak viscosity (1542–76.16cP), breakdown viscosity (748.1–9.84cP) and final viscosity (1187–88.9cP). Roasting significantly (p

Many video understanding tasks require analyzing multishot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos. To address this challenge, we collected a new dataset-YouMVaS-of 200 popular YouTube videos spanning ten genres, where each video is on average five minutes long and with 75 shots. We selected recurring actors and annotated 431K segmentation masks at a frame rate of six, exceeding previous datasets in average video duration, object variation, and narrative structure complexity. We incorporated good practices of model architecture design, memory management, and multi-shot tracking into an existing video segmentation method to build competitive baseline methods. Through error analysis, we found that these baselines still fail to cope with cross-shot appearance variation on our YouMVOS dataset. Thus, our dataset poses new challenges in multi-shot segmentation towards better video analysis. Data, code, and pre-trained models are available at https://donglaiw.github.io/proj/youMVOS

As video traffic dominates the Internet, it is important for operators to detect video quality of experience (QoE) to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection--based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for RE al-time QU ality of experience metric detection for E ncrypted T raffic— Requet —which is suitable for network middlebox deployment. Requet uses a detection algorithm that we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a machine learning algorithm to predict QoE metrics, specifically buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi and LTE network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12×, 1.53×, and 3.14×, respectively.

In this research, we have to detect the contrabands hidden in the human body’s scanned images at airport security machines using segmentation and classification. Present algorithm of security scanning machines at the airports of USA are producing high rate of false negatives which in cases lead to engage in a secondary, manual screening process that slows everything down. So to resolve this problem and to improve the detection of contrabands, new and efficient algorithm need to be made.

As video traffic dominates the Internet, it is important for operators to detect video Quality of Experience (QoE) in order to ensure adequate support for video traffic. With wide deployment of end-to-end encryption, traditional deep packet inspection based traffic monitoring approaches are becoming ineffective. This poses a challenge for network operators to monitor user QoE and improve upon their experience. To resolve this issue, we develop and present a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm we develop to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a Machine Learning (ML) algorithm to predict QoE metrics, specifically, buffer warning (low buffer, high buffer), video state (buffer increase, buffer decay, steady, stall), and video resolution. We collect a large YouTube dataset consisting of diverse video assets delivered over various WiFi network conditions to evaluate the performance. We compare Requet with a baseline system based on previous work and show that Requet outperforms the baseline system in accuracy of predicting buffer low warning, video state, and video resolution by 1.12X, 1.53X, and 3.14X, respectively.

A class of networks provides wireless services that present a promising solution to address technical issues in regions where at the time of transmission, an end-to-end path does not exist betwixt the sender and the receiver. In such networks, a node misbehaves by dropping packets or not forwarding the packets of other nodes even when it has sufficient buffer. The nodes showing this kind of behavior are termed as selfish nodes. These nodes are unwilling to use their own resources (i.e., buffer and power) when they forward the packets of other nodes. Selfish nodes reduce packet delivery ratio and waste system resources, i.e., bandwidth and power, which in turn increases the overhead. In this paper, we present a reputation-based approach that helps to remit the selfish behavior of the network nodes. According to the simulation results, mitigation of selfish nodes leads to high delivery probability and lower overhead ratio.