Automated Road Lane Detection for Intelligent Vehicles

Automated road lane detection is the crucial part of vision-based driver assistance system of intelligent vehicles. This driver assistance system reduces the road accidents, enhances safety and improves the traffic conditions. In this paper, we present an algorithm for detecting marks of road lane and road boundary with a view to the smart navigation of intelligent vehicles. Initially, it converts the RGB road scene image into gray image and employs the flood-fill algorithm to label the connected components of that gray image. Afterwards, the largest connected component which is the road region is extracted from the labeled image using maximum width and no. of pixels. Eventually, the outside region is subtracted and the marks or road lane and road boundary are extracted from connected components. The experimental results show the effectiveness of the proposed algorithm on both straight and slightly curved road scene images under different day light conditions and the presence of shadows on the roads

Word Sense Induction with Knowledge Distillation from BERT

Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typically use one vector to encode multiple different meanings of a word, and incur errors due to polysemy. This paper proposes a two-stage method to distill multiple word senses from a pre-trained language model (BERT) by using attention over the senses of a word in a context and transferring this sense information to fit multi-sense embeddings in a skip-gram-like framework. We demonstrate an effective approach to training the sense disambiguation mechanism in our model with a distribution over word senses extracted from the output layer embeddings of BERT. Experiments on the contextual word similarity and sense induction tasks show that this method is superior to or competitive with state-of-the-art multi-sense embeddings on multiple benchmark data sets, and experiments with an embedding-based topic model (ETM) demonstrates the benefits of using this multi-sense embedding in a downstream application

Improving Neural Ranking Models with Traditional IR Methods

Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions. Nevertheless, they are computationally expensive to create, and require a great deal of labeled data for specialized corpora. In this paper, we explore a low resource alternative which is a bag-of-embedding model for document retrieval and find that it is competitive with large transformer models fine tuned on information retrieval tasks. Our results show that a simple combination of TF-IDF, a traditional keyword matching method, with a shallow embedding model provides a low cost path to compete well with the performance of complex neural ranking models on 3 datasets. Furthermore, adding TF-IDF measures improves the performance of large-scale fine tuned models on these tasks.Comment: Short paper, 4 page

A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction

Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understanding and knowledge discovery, recent works in this domain have largely focused on binary classification of a text segment as causal or non-causal. In this regard, we perform a thorough analysis of three sequence tagging models for causal knowledge extraction and compare it with a span based approach to causality extraction. Our experiments show that embeddings from pre-trained language models (e.g. BERT) provide a significant performance boost on this task compared to previous state-of-the-art models with complex architectures. We observe that span based models perform better than simple sequence tagging models based on BERT across all 4 data sets from diverse domains with different types of cause-effect phrases

Cryptic Rhinolophus pusillus Temminck, 1834 (Chiroptera, Rhinolophidae): a new distribution record from the Chittagong Hill Tracts, Bangladesh

Rhinolophus pusillus is a common species of India and Nepal in South Asia. Here, we report a new record of this bat captured in the mixed evergreen forest in Rangamati, southeastern part of Bangladesh. The identification was based on external morphology along with cranio-dental measurements. Roost counts was conducted through direct observation.&nbsp

Bi-directional determination of sparse Jacobian matrices : algorithms and lower bounds

Efficient estimation of large sparse Jacobian matrices is a requisite in many large-scale scientific and engineering problems. It is known that estimation of non-zeroes of a matrix can be viewed as a graph coloring problem. Due to the presence of dense rows or dense columns, unidirectional partitioning does not always give good results. Bi-Directional partitioning handles the problem of dense rows and dense columns quite well[16]. Lower bound to determine the non-zeroes of a sparse Jacobian matrix can be defined as the least number of groups necessary to determine the matrix. For unidirectional partitioning, a good lower bound is given by the maximum number of non-zeroes in any row[4]. For bi-directional determination, both columns and rows must be considered to obtain a lower bound. In this thesis, we provide an easily computed better lower bound. We have developed a heuristic algorithm and an iterative algorithm to determine non-zeroes of sparse Jacobian matrices using Bi-Directional partitioning. Our heuristic algorithm is inspired from graph coloring problems and recursive largest first partitioning of graphs. Our algorithm provides a better result than the existing algorithms. For the iterative method, we have introduced randomization technique to color the vertices of the graph. A part of our work was presented at "Applied Mathematics, Modelling and Computational Science" (AMMCS-2015)

Roles of technology for risk communication and community engagement in Bangladesh during COVID-19 Pandemic

The COVID-19 pandemic required handling a clear communication of risk and community engagement. A gap is noted in scholarly studies portraying strong community engagement for risk handling, particularly in resource constrained regions in HCI community. This study covers community engagement and its use of technology during COVID-19 through a qualitative study of Bangladesh. The study looks at marginalized communities who have struggled through the pandemic yet handled the difficult time through their effective problem solving, working together as a community when there was not enough support from authorities. It is a qualitative study during the pandemic consisting of 9 communities, presenting 58 participants (N=58, Female= 33, Male=23, Transgender =2) across four divisions of Bangladesh covering urban, semi urban, and rural regions. The study uncovers the challenges and close community structures. It also shows the enhanced and increased positive role of technology during the pandemic while referring to a few communities being digitally disconnected communities that could benefit from digital connectivity in the future through increased awareness and support

Risk communication during COVID-19 pandemic: impacting women in Bangladesh

Risk communication during COVID-19 is essential to have support, but it is challenging in developing countries due to a lack of communication setup. It is more difficult for the low-income, marginal communities, and specifically, women in developing countries. To understand this, particularly during the COVID-19 pandemic, we conducted a qualitative study among N = 37 women (urban 20, rural = 17) across Bangladesh that presents the risk communication factors related to social and financial challenges. It reveals that the majority of the urban communities lack communication with local authorities, where urban low-income communities are the worst sufferers. Due to that, the majority of the urban participants could not get financial support, whereas the rural participants received such support for having communications with local authorities during the pandemic. However, access to technology helped some participants share and receive pandemic-related information about risk communication, and the adoption of financial technology helped to get emergency financial support through risk communication. Moreover, this work is expected to understand the role of risk communication during the COVID-19 pandemic among women in Bangladesh