Abstract

Despite existing works of Audio-Visual Saliency Prediction (AVSP) models claiming to achieve promising results by fusing audio modality over visual-only models, these models fail to leverage audio information. In this paper, we investigate the relevance of audio cues in conjunction with the visual ones and conduct extensive analysis by employing well-established audio modules and fusion techniques from diverse correlated audio-visual tasks. Our analysis on ten diverse saliency datasets suggests that none of the methods worked for incorporating audio. Furthermore, we bring to light, why AVSP models show a gain in performance over visual-only models, though the audio branch is agnostic at inference. Our work questions the role of audio in current deep AVSP models and motivates the community to a clear avenue for reconsideration of the complex architectures by demonstrating that simpler alternatives work equally well.

Abstract

We investigate the task of complex NER for the English language. The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature. Using pre-trained language models such as BERT, we obtain a competitive performance on this task. We qualitatively analyze the performance of multiple architectures for this task. All our models are able to outperform the baseline by a significant margin. Our best performing model beats the baseline F1-score by over 9%.

Abstract

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Abstract

We introduce the task of detection of competing model entities from scientific documents. We define competing models as those models that solve a particular task that is investigated in the target research document. The task is challenging due to the fact that contextual information is required from the entire target document to predict the model entities. Hence, traditional sequence labelling approaches fail in such settings. Furthermore, model entities themselves are long-tailed in nature, i.e, their prevalence in scientific literature is limited, along with a scarcity of labelled data for training supervised learning techniques. To address the above bottlenecks, we combine an Unsupervised Graph Ranking algorithm with a SciBERT-CRF based sequence labeller to predict the entities. We introduce a strong baseline using the above mentioned pipeline. Also, to address the label scarcity of long-tailed model entities, we use distant supervision leveraging an external Knowledge Base (KB) to generate synthetic training data. We address the problem of overfitting in small sized datasets for supervised NER baselines using a simple entity replacement technique. We introduce this model as part of a starting point for an end-to-end automated framework to extract relevant model names and link them with their respective cited papers from research documents. We believe this task will serve as an important starting point to map the research landscape of computer science in a scalable manner, needing minimal human intervention. The code and dataset is available in the given link : https://github.com/Swayatta/Competing-Models

Abstract

Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score

Abstract

An autonomous broker that liaises between retail customers and power-generating companies (GenCos) is essential for the smart grid ecosystem. The effciency brought in by such brokers to the smart grid setup can be studied through a well-developed simulation environment. In this paper, we describe the design of one such intelligent energy broker called VidyutVanika21 (VV21) and analyze its performance using a simulation platform called PowerTAC (Power Trading Agent Competition). Specifcally, we discuss the retail (VV21–RM) and wholesale market (VV21–WM) modules of VV21 that help the broker achieve high net profts in a competitive setup. Supported by game-theoretic analysis, the VV21–RM designs tariff contracts that a) maintain a balanced portfolio of different types of customers; b) sustain an appropriate level of market share, and c) introduce surcharges on customers to reduce energy usage during peak demand times. The VV21–WM aims to reduce the cost of procurement by following the supply curve of the GenCo to identify its lowest ask for each auction which is then used to generate suitable bids. We further demonstrate the effcacy of the retail and wholesale strategies of VV21 in PowerTAC 2021 fnals and through several controlled experiments.

Abstract

Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused primarily on English mainly due to the high availability of relevant datasets. Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed for generation across multiple languages alongwith a dataset, XALIGN for eight languages. However, there has been no rigorous work on the actual XF2T generation problem. We extend XALIGN dataset with annotated data for four more languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive study using popular Transformer-based text generation models on our extended multi-lingual dataset, which we call XALIGNV2. Further, we investigate the performance of different text generation strategies: multiple variations of pretraining, fact-aware embeddings and structure-aware input encoding. Our extensive experiments show that a multi-lingual mT5 model which uses fact-aware embeddings with structure-aware input encoding leads to best results on average across the twelve languages. We make our code, dataset and model publicly available, and hope that this will help advance further research in this critical area.

Abstract

Reinforcement Learning (RL) enables agents to learn how to perform various tasks from scratch. In domains like autonomous driving, recommendation systems, and more, optimal RL policies learned could cause a privacy breach if the policies memorize any part of the private reward. We study the set of existing differentially-private RL policies derived from various RL algorithms such as Value Iteration, Deep-Q Networks, and Vanilla Proximal Policy Optimization. We propose a new Privacy-Aware Inverse RL analysis framework (PRIL) that involves performing reward reconstruction as an adversarial attack on private policies that the agents may deploy. For this, we introduce the reward reconstruction attack, wherein we seek to reconstruct the original reward from a privacy-preserving policy using the Inverse RL algorithm. An adversary must do poorly at reconstructing the original reward function if the agent uses a tightly private policy. Using this framework, we empirically test the effectiveness of the privacy guarantee offered by the private algorithms on instances of the FrozenLake domain of varying complexities. Based on the analysis performed, we infer a gap between the current standard of privacy offered and the standard of privacy needed to protect reward functions in RL. We do so by quantifying the extent to which each private policy protects the reward function by measuring distances between the original and reconstructed rewards.

Abstract

A method for viding maximum traffic flow throughput and controlling vehicular pollution concentration is pro vided . The method includes ( i ) processing a source location and a destination location obtained from a user device associated with a user to determine at least one possible route to reach the destination location from the source location , ( ii ) determining at least one maximum flow route that includes at least one edge with an edge capacity from the at least one possible route , ( iii ) determining maximum traffic flow capacity for the at least one maximum flow route , ( iv ) determining active traffic flow routes by comparing continuously if shared or common edges of a plurality of route solutions is less than or equal to a pre - defined limit and ( v ) partitioning the maximum traffic flow capacity for the active traffic flow routes and permuting each partition of the maximum traffic flow capacity for the active traffic flow routes .

Abstract

Moving target defense (MTD) has emerged as a key technique that can be used in various security applications to reduce the threat of attackers by taking away their ability to perform reconnaissance and exploit vulnerabilities. However, most of the existing research in the field assumes unrealistic access to information about the attacker’s motivations and/or actions when developing MTD strategies. Many of the existing approaches also assume complete knowledge regarding the vulnerabilities of a particular application and how each of these vulnerabilities can be exploited by an attacker. In this work, we propose an algorithm that generates effective MTD strategies for web applications that does not rely on prior knowledge about the attackers. Our approach assumes that the only information the defender receives about its own reward function, is via interaction with the attacker in a repeated game setting. We evaluate our algorithm using data which is mined from the National Vulnerability Database to show that it matches the performance of the state of the art techniques, despite using much less information.