Abstract

Across the globe, stone has been extensively used as a building construction material due to its local availability and high durability. The scenario for the construction of buildings in India is no more different than compared to other parts of the world. According to the 2011 Census of India, 43.48 million houses (~ 14% of the total number of houses) have stone as the predominant wall material. Owing to the fact of non-engineered construction, wide variations in terms of stone masonry construction can be seen in the Indian subcontinent, particularly in the Himalayan belt. In this study, a field survey is conducted to identify the seismic resistant features in traditional building practices which make use of stone for construction, e.g. Thathara construction, Dry stack construction, Koti Banal construction, etc. The percentage of these types of structures is decreasing due to an increase in urbanization, increasing housing demands, non-availability of traditional construction materials and skilled artesian for building new structures as well as costly repair and retrofitting techniques for damaged and existing structures. Thus, there is a dire need to safeguard these structures as if this trend continues, these buildings will become things of the past. In this paper, a review of structural configuration, roofing systems, foundation type and potential failure modes of these construction typologies are studied, and the effect of different retrofitting techniques that exist in the construction type on their expected seismic performance is discussed.

Abstract

Recurrent earthquakes in the past two decades have taken thousands of lives (e.g., the Kashmir earthquake killed some 80,000 people) and destroyed millions of homes and other buildings. This colossal loss of lives was not because of the earthquake but because people were inside buildings (e.g., stone masonry structures) that were highly vulnerable. The death toll would have been significantly lower, if the structures had been retrofitted with a small fraction of the cost of reconstruction. The past earthquakes have brought out many weaknesses in masonry design and construction practices as these structures behaved unsatisfactorily and sustained major damage. Hence, the safety and retrofitting of existing stone masonry houses are very important. Considerable research work has been directed toward evolving suitable methods of earthquake resistance in stone masonry houses.Despite the availability of such methods, masonry buildings have been damaged in the event of earthquakes, because of the following reasons: (i) lack of awareness, formal training, and technical knowledge in earthquake-resistant construction; (ii) lack of concern about seismic safety because of the infrequent occurrence of earthquakes; (iii) people lack financial resources to meet the earthquake-resistant requirement; and (iv) despite the availability of provisions and recommendations of earthquake-resistant measures to be applied on stone masonry buildings in the form of various codes, and these are rarely being implemented in actual practice. Therefore, in the present study, an overview is presented about how the retrofitting of existing stone masonry houses using different techniques can be implemented to reduce their seismic vulnerability and loss of life.

Abstract

Stone masonry walls have inherent weaknesses against the lateral forces of an earthquake. These weaknesses result in inadequate performance during earthquakes. Mistakes are commonly committed in the construction of stone walls, especially in the random rubble-type masonry. These mistakes further erode strength. When shaken, poorly constructed walls having inadequate interlocking between the inside and the outside faces (wythes), the faces begin to separate, resulting in rapid weakening of the wall and leading to the collapse of one or both wythes. In the presence of excessive openings, the wall becomes weak against the tearing action caused by the earthquake forces that are parallel to the length of the wall. This results in diagonal cracking of varying severity in the walls. Further, due to poor connection between the walls and the floor/roof, it results in failure of the floor diaphragm and affects the overall stability of the structure. Hence, while choosing to construct a building with stone masonry, the owner must make sure that the measures required to counter these weaknesses are taken during the construction so that in the event of a potentially destructive earthquake, the structure can withstand its impact without suffering much damage. Therefore, in the present study, an overview is presented for different failure modes of stone masonry houses, seismic safety provisions for the new construction considering the regulations for the wall, floor/roof construction and the provision of horizontal and vertical reinforcement to ensure good seismic performance against the future earthquakes.

Abstract

Evaluating the risk level of adversarial images is essential for safely deploying face authentication models in the real world. Popular approaches for physical-world attacks, such as print or replay attacks, suffer from some limitations, like including physical and geometrical artifacts. Recently adversarial attacks have gained attraction, which try to digitally deceive the learning strategy of a recognition system using slight modifications to the captured image. While most previous research assumes that the adversarial image could be digitally fed into the authentication systems, this is not always the case for systems deployed in the real world. This paper demonstrates the vulnerability of face authentication systems to adversarial images in physical world scenarios. We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting. Using this attack strategy, the attack success rate goes up to 82.01%. We test AdvGen extensively on four datasets and ten state-of-the-art PADs. We also demonstrate the effectiveness of our attack by conducting experiments in a realistic, physical environment.

Abstract

We present a novel second-order cone programming frame- work to learn robust classifiers that can tolerate uncertainty in the obser- vations of partially labeled multiclass classification problems. We call it RPL-SVM. Our formulation is based on a chance-constrained framework. Experimental results show that RPL-SVM efficiently learns multiclass classifiers with missing values in a partial label setting.

Abstract

Sentiment Analysis is an important task for analysing online content across languages for tasks such as content moderation and opinion mining. Though a significant amount of resources are available for Sentiment Analysis in several Indian languages, there do not exist any large-scale, open-access corpora for Gujarati. Our paper presents and describes the Gujarati Sentiment Analysis Corpus (GSAC), which has been sourced from Twitter and manually annotated by native speakers of the language. We describe in detail our collection and annotation processes and conduct extensive experiments on our corpus to provide reliable baselines for future work using our dataset.

Abstract

This paper describes our system submission for SemEval-2023 Task 12 AfriSenti-SemEval: Sentiment Analysis for African Languages. We propose an XGBoost-based ensemble model trained on emoticon frequency-based features and the predictions of several statistical models such as SVMs, Logistic Regression, Random Forests, and BERT-based pre-trained language models such as AfriBERTa and AfroXLMR. We also report results from additional experi- ments not in the system. Our system achieves a mixed bag of results, achieving a best rank of 7th in three of the languages - Igbo, Twi, and Yoruba

Abstract

A system for generating high-resolution de-warped omni-directional stereo image from captured omni-directional stereo image by correcting optical distortions using projection patterns is provided. The system includes a projection pattern capturing arrangement, a projector or a display, and a de-warping server. The projection pattern capturing arrangement includes one or more omnidirectional cameras to capture projection patterns from the captured omni-directional stereo image from each omni-directional stereo camera. The projector or the display displays the projection patterns. The de-warping server obtain the projection patterns and processes the projection patterns to generate high resolution de-warped omni-directional stereo image by correcting optical distortions in the captured omni-directional stereo image and mapping the captured omni-directional stereo image and the high resolution de-warped omni-directional stereo image.

Abstract

Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. RoadTextVQA consists of 3,222 driving videos collected from multiple countries, annotated with 10,500 questions, all based on text or road signs present in the driving videos. We assess the performance of state-of-the-art video question answering models on our RoadTextVQA dataset, highlighting the significant potential for improvement in this domain and the usefulness of the dataset in advancing research on in-vehicle support systems and text-aware multimodal question answering.

Abstract

Movie story analysis requires understanding characters’ emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions. By leveraging annotations from the MovieGraphs dataset [72], we aim to predict classic emotions (e.g. happy, angry) and other mental states (e.g. honest, helpful). We conduct experiments on the most frequently occurring 10 and 25 labels, and a mapping that clusters 181 labels to 26. Ablation studies and comparison against adapted stateof-the-art emotion recognition approaches shows the effectiveness of EmoTx. Analyzing EmoTx’s self-attention scores reveals that expressive emotions often look at character tokens while other mental states rely on video and dialog cues.