Learning to Relate from Captions and Bounding Boxes

In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision. Our proposed approach uses a top-down attention mechanism to align entities in captions to objects in the image, and then leverage the syntactic structure of the captions to align the relations. We use these alignments to train a relation classification network, thereby obtaining both grounded captions and dense relationships. We demonstrate the effectiveness of our model on the Visual Genome dataset by achieving a recall@50 of 15% and recall@100 of 25% on the relationships present in the image. We also show that the model successfully predicts relations that are not present in the corresponding captions.Comment: ACL 201

Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer

Cloud services are omnipresent and critical cloud service failure is a fact of life. In order to retain customers and prevent revenue loss, it is important to provide high reliability guarantees for these services. One way to do this is by predicting outages in advance, which can help in reducing the severity as well as time to recovery. It is difficult to forecast critical failures due to the rarity of these events. Moreover, critical failures are ill-defined in terms of observable data. Our proposed method, Outage-Watch, defines critical service outages as deteriorations in the Quality of Service (QoS) captured by a set of metrics. Outage-Watch detects such outages in advance by using current system state to predict whether the QoS metrics will cross a threshold and initiate an extreme event. A mixture of Gaussian is used to model the distribution of the QoS metrics for flexibility and an extreme event regularizer helps in improving learning in tail of the distribution. An outage is predicted if the probability of any one of the QoS metrics crossing threshold changes significantly. Our evaluation on a real-world SaaS company dataset shows that Outage-Watch significantly outperforms traditional methods with an average AUC of 0.98. Additionally, Outage-Watch detects all the outages exhibiting a change in service metrics and reduces the Mean Time To Detection (MTTD) of outages by up to 88% when deployed in an enterprise cloud-service system, demonstrating efficacy of our proposed method.Comment: Accepted to ESEC/FSE 202

ESRO: Experience Assisted Service Reliability against Outages

Modern cloud services are prone to failures due to their complex architecture, making diagnosis a critical process. Site Reliability Engineers (SREs) spend hours leveraging multiple sources of data, including the alerts, error logs, and domain expertise through past experiences to locate the root cause(s). These experiences are documented as natural language text in outage reports for previous outages. However, utilizing the raw yet rich semi-structured information in the reports systematically is time-consuming. Structured information, on the other hand, such as alerts that are often used during fault diagnosis, is voluminous and requires expert knowledge to discern. Several strategies have been proposed to use each source of data separately for root cause analysis. In this work, we build a diagnostic service called ESRO that recommends root causes and remediation for failures by utilizing structured as well as semi-structured sources of data systematically. ESRO constructs a causal graph using alerts and a knowledge graph using outage reports, and merges them in a novel way to form a unified graph during training. A retrieval-based mechanism is then used to search the unified graph and rank the likely root causes and remediation techniques based on the alerts fired during an outage at inference time. Not only the individual alerts, but their respective importance in predicting an outage group is taken into account during recommendation. We evaluated our model on several cloud service outages of a large SaaS enterprise over the course of ~2 years, and obtained an average improvement of 27% in rouge scores after comparing the likely root causes against the ground truth over state-of-the-art baselines. We further establish the effectiveness of ESRO through qualitative analysis on multiple real outage examples.Comment: Accepted to 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023

Use of electronic waste plastic in asphalt mix with marble dust as filler

E-waste is becoming burgeoning global issue, which showcases the impacts of the same on environment and on humans. Marble cutting industries is another industry producing huge amount of marble dust as a waste which is degrading the environment. E-waste and waste marble dust can be utilized in construction industry in different forms thus helps in reducing its impact on environment. In this analysis, electronic waste plastic materials including recycled PCB’s and other PVC components of e-waste has been used as a partial replacement for coarse aggregates and marble dust is used as complete replacement for filler in asphalt mix for pavement. A significant increase of Marshall Stability and Flow Value has been observed as percentage of e-waste plastic increases. The plastic content of mix varies 0 %, 4 %, 8 % and 12 % by weight of aggregate. Plots of various Marshall Parameters such as Marshall Stability, Flow value, voids filled with bitumen, voids in mineral aggregates and unit weight against the bitumen content shows an improving trend of parameters with increasing the plastic replacement. Comparison with earlier published result shows that increasing the plastic replacement beyond 12% will have a negative influence on stability value. The recycling of e-waste plastic and marble dust in asphalt mix design for pavements provide us a better way of resource utilization in a cost-effective manner

Use of electronic waste plastic in asphalt mix with marble dust as filler

36-45E-waste is becoming burgeoning global issue, which showcases the impacts of the same on environment and on humans. Marble cutting industries is another industry producing huge amount of marble dust as a waste which is degrading the environment. E-waste and waste marble dust can be utilized in construction industry in different forms thus helps in reducing its impact on environment. In this analysis, electronic waste plastic materials including recycled PCB’s and other PVC components of e-waste has been used as a partial replacement for coarse aggregates and marble dust is used as complete replacement for filler in asphalt mix for pavement. A significant increase of Marshall Stability and Flow Value has been observed as percentage of e-waste plastic increases. The plastic content of mix varies 0 %, 4 %, 8 % and 12 % by weight of aggregate. Plots of various Marshall Parameters such as Marshall Stability, Flow value, voids filled with bitumen, voids in mineral aggregates and unit weight against the bitumen content shows an improving trend of parameters with increasing the plastic replacement. Comparison with earlier published result shows that increasing the plastic replacement beyond 12% will have a negative influence on stability value. The recycling of e-waste plastic and marble dust in asphalt mix design for pavements provide us a better way of resource utilization in a cost-effective manner

Asset Allocation using Regime Switching Methods

The aim of this thesis is to develop a Markov Regime Switching framework that can be used in asset allocation in conjunction with Modern Portfolio Theory. Modern Portfolio Theory has long been a popular tool among big financial institutions. However, one of its major limitations is assumption of stationary market volatility. In this paper, we develop a single period Mean Variance Optimization model that minimizes the variance of a portfolio subject to a specified expected return by combining Modern Portfolio Theory with a Markov Regime Switching framework. Then, we extend the above developed framework to be used in conjunction with a robust optimization framework as proposed by Goldfarb Iyengar in which regards we were partially successful. The portfolios constructed by the Markov Regime-Switching framework were tested out of sample to outperform those suggested by a Simple MVO One Factor model and the Robust MVO One Factor Model.M.A.S.2017-11-29 00:00:0