Abstract

In addition to the general neglect of women writers, Urdu literary historiography in both English and Urdu has historically privileged north Indian and Pakistani writers while overlooking the many Urdus south of Bombay and Bhopal. Next to no work exists in English on the Urdu writers of Hyderabad, and only a handful of texts have been translated into English —an astonishing neglect, considering their contribution to the study of gender, political cultures, and regional histories. Bibi’s Room studies the lives and work of three women writers from Hyderabad who wrote in Urdu: Zeenath Sajida, Najma Nikhat and Jeelani Bano. It addresses the absence of scholarship on Hyderabadi women writers in three ways: representative translations; short, nuanced biographies; and critical analyses of their oeuvres—all framed against twentieth-century Hyderabadi history, politics, culture, and society. The three writers showcased here offer rich portrayals of Hyderabadi urban culture as well as critiques of gender and patriarchy. Zeenath Sajida’s insights into Islam dramatically alter what we know of Muslim women’s engagements with fundamental theological questions. Sajida is also a skilled proponent of Urdu humour and satire, a genre notorious for its exclusion of women writers. Jeelani Bano’s oeuvre, spanning three schools of Urdu literature, makes vital contributions to our understanding of gender, class, communalism, and national identity. Najma Nikhat’s deodi stories powerfully narrate women’s lives across class in feudal aristocratic homes, and their participation in revolutionary struggles like the Telangana movement. The picture of Hyderabadi women’s lives that emerges generates new knowledge about the conditions in which women live, write, and resist, and expands our understanding of their public participation in South Asia. Bibi’s Room is also a welcome and valuable addition to studies of Urdu literature, South Asian feminism, translation, and the history and culture of Hyderabad.

Abstract

Transfer learning is a popular method for obtaining deep trained models for data-scarce face tasks such as head pose and emotion. However, current transfer learning methods are inefficient and time-consuming as they do not fully account for the relationships between related tasks. Moreover, the transferred model is large and computationally expensive. As an alternative, we propose ETL: a technique that efficiently transfers a pre-trained model to a new task by retaining only cross-task aware filters, resulting in a sparse transferred model. We demonstrate the effectiveness of ETL by transferring VGGFace, a popular face recognition model to four diverse face tasks. Our experiments show that we attain a size reduction up to 97% and an inference time reduction up to 94% while retaining 99.5% of the baseline transfer learning accuracy

Abstract

Conventional Reinforcement Learning (RL) algorithms assume the distribution of the data to be uniform or mostly uniform. However, this is not the case with most real-world applications like autonomous driving or in nature, where animals roam. Some objects are encountered frequently, and most of the remaining experiences occur rarely; the resulting distribution is called Zipfian. Taking inspiration from the theory of complementary learning systems, an architecture for learning from Zipfian distributions is proposed where long tail states are discovered in an unsupervised manner and states along with their recurrent activation are kept longer in episodic memory. The recurrent activations are then reinstated from episodic memory using a similarity search, giving weighted importance. The proposed architecture yields improved performance in a Zipfian task over conventional architectures. Our method outperforms IMPALA by a significant margin of 20.3% when maps/objects occur with a uniform distribution and by 50.2% on the rarest 20% of the distribution.

Abstract

Named Entity Recognition (NER) is a critical task in the field of Natural Language Processing (NLP) and is also a sub-task of Information Extraction. There has been a significant amount of work done in entity extraction and Named Entity Recognition for resource-rich languages. Entity extraction from code-mixed social media data like tweets from twitter complicates the problem due to its unstructured, informal, and incomplete information available in tweets. Here, we present work on NER in Kannada-English code-mixed social media corpus with corresponding named entity tags referring to Organisation (Org), Person (Pers), and Location (Loc). We experimented with machine learning classification models like Conditional Random Fields (CRF), Bi-LSTM, and Bi-LSTM-CRF models on our corpus.

Abstract

Often questions provided to open-domain question answering systems are ambiguous. Traditional QA systems that provide a single answer are incapable of answering ambiguous questions since the question may be interpreted in several ways and may have multiple distinct answers. In this paper, we address multi-answer retrieval which entails retrieving passages that can capture majority of the diverse answers to the question. We propose a re-ranking based approach using Determinantal point processes utilizing BERT as kernels. Our method jointly considers query-passage relevance and passage-passage correlation to retrieve passages that are both query-relevant and diverse. Results demonstrate that our re-ranking technique outperforms state-of-the-art method on the AmbigQA dataset.

Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model perfor- mances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. How- ever, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reser- voir, Bhadra reservoir Tunga-Bhadra River, located in India.

Abstract

Rivers are a significant and low-cost supply of irrigation, industrial, and residential water across the world. Due to urbanization, population growth, and climate change, many rivers are now confronting an irreparable water crisis. According to a study carried out in 2018 by the Central Pollution Control Board (CPCB) of India, there are a total of 351 polluted river stretches in India. Accessing the pollution level in these rivers is a challenge and requires the measurement of several water quality indices including turbidity, coliform levels, total phosphate, etc. These indices are then used to calculate the water quality index which gives us a fair estimate of the level of pollution in water. However, the measurement of all these indices is challenging and requires the use of complex measuring tools. In this paper, we try to solve this problem by building machine learning models that can predict the water quality index just by using a subset of the indices that are required for the calculation of the water quality index. The models trained take turbidity, pH, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), total coliform, hardness as CaCo3, fluoride, ammonia, Chemical Oxygen Demand (COD) and total suspended solids as input and make predictions for water quality using regression techniques. The models are trained specifically for Bhadra river data, but the technique can be extended for other river stretches and water bodies. We use mean squared error, mean absolute error and R2 score to evaluate our models. An artificial neural network trained with 7 dense layers outperforms other regressors in terms of the evaluation metrics used. With the availability of better datasets, the technique of using regressors for the prediction of water quality could help in the better and faster assessment of water quality which can eventually lead to quicker remedial measures

Abstract

In this paper, we address the problem of auto- matic academic curriculum generation. A curricu- lum outlines definitive topics with their sub-topics and enables teachers and students to form an over- all idea of the course outcomes and goals, and a plan of what to teach and learn to achieve those goals. Automatic curriculum generation is rel- evant in modern times with the ever increasing, rapidly changing, digitally-available academic con- tent, that is too large for manual processing by human teams. Using Wikipedia as an external knowledge-base, along with a pipeline of standard components, we show that it is possible to generate human-interpretable 2-level topic hierarchies. We show that our approach works on publicly available textbooks, by first removing their title-structure, and then automatically regenerating a 2-level title structure that is on-par.

Abstract

1 Introduction Recent studies show that sexually-objectified women are not only treated as sex objects but also visually processed similar to objects (local processing)[1,2]. However, Sexual objectification(SO) research has predominantly studied western society's perceptions, with few empirical studies examining other cultures. In this study, we explore SO of women in an Asian-Indian cultural/social setting using a novel design and stimuli representative of the culture. We conduct an eye tracking study that uses Navon local/global and body inversion[3] paradigms to investigate the gaze behavior and underlying visual processing when looking at non-sexualized male and female images. 2 Methods The study sample consists of Ninety-four (24 women, 70 men, age=18-27, mean=21, stdev=±2.1) undergraduate and graduate students. There are 20 trial blocks. Each trial block consists of a male/female image in upright/inverted orientation, off-centre to the screen for 7 seconds followed by a plus sign for 1 second, followed by a Navon letter identification task. The priming condition and Navon task are randomised to minimise confounding effects. The Navon image is a large letter made of small letters. The participants press 'B' if they identify either one of the target letters ('H'/‘O') either as a contour or a feature (the letter itself) otherwise press ‘N’. The classification of local/global recognition is implicit as the participants press the same key ‘B’ for both local and global images. We selected black and white male images(MI) and female images(FI) wearing casual attires like jeans/pant and top/shirt. The experiment is designed using Tobii Studio. Tobiix120 eye tracker is used to capture the gaze. We mark face, chest, hip as our Areas of Interest(AoIs) and calculate the fixation duration(FD), revisit frequency(RF), first fixation duration(FFD) for the AoIs. We define sexual body(SB) parts as sum of hip and chest AoIs. For gaze analysis, 5 participants data with poor sampling rate (below 40%) are excluded. We use Tukey Fence outlier detection method to remove outliers and removed 10 participants data for first fixation duration analysis. We measure Response Time(RT) as the time taken to press the key from the onset of the Navon image presentation. For RT analysis, we consider only the data points with at least one correct response in each priming condition, reducing our data to 59 participants. The alpha level for p-value is set to 0.05 in all tests. 3 Results There is a main effect of target (priming stimulus) gender and orientation on the FD and RF. • FD: Face FD of upright > inverted and SB parts FD of upright < inverted for both MI and FI. Face FD of FI < MI and SB parts FD of FI > MI in both upright and inverted orientation. Face FD > SB parts FD for all target images. • RF: Face RF of upright > inverted for both MI and FI. SB parts RF of inverted > upright for FI. Importantly, SB parts RF of FI > MI in upright and inverted orien-tation. Face RF < SB parts RF for all target images. • FFD: There is significant effect of orientation for FI but not for MI. Face FFD of upright FI > inverted FI and SB parts FFD of inverted FI > upright FI. • RT: Global recognition is faster in female priming for both orientations. Local recognition is faster in male priming, but the significance is found only for inverted image. The participant gender effect on RT is not significant. There is no significant correlation between local/global RT and FD. 4 Discussion Objectification theory states that women have a greater propensity to be sexually objectified than men do. This is evident from our data, female sexual body parts received greater attention than male sexual body parts and vice versa for face. However the visual attention is still greatest on the face for both male and female images, in line with [4,5,6,7,8]. As for global/local perception, non-sexualized males are processed similar to objects more than females. This is in contrast to[3,1,6] but find support in a study on Chinese population [9]. We can attribute our findings to the choice of the stimuli being personalised as against sexualized. There is a strong inversion effect on gaze, both gender images are more objectified in inverted than upright position but this doesn’t affect the underlying cognitive processing implying that the difference in the visual processing between objectified and non-objectified is more quantitative than qualitative. References 3) Bernard, P., Gervais, S. J., & Klein, O. (2018). Objectifying objectification: When and why people are cognitively reduced to their parts akin to objects. European Review of So-cial Psychology, 29(1), 82–121. 4) Bernard, P., Rizzo, T., Hoonhorst, I., Deliens, G., Gervais, S., B, Bayard, C., Deltenre, P., Colin, C., & Klein, O. (2018). The Neural Correlates of Cognitive Objectification. So-cial Psychological and Personality Science, 9, 550–559. 2) Bernard, P., Gervais, S. J., Allen, J., Campomizzi, S., & Klein, O. (2012). Integrating Sexual Objectification with Object Versus Person Recognition: The Sexualized-Body-Inversion Hypothesis. Psychological Science, 23(5), 469–471. 1) Bareket, O., Shnabel, N., Abeles, D., Gervais, S., & Yuval-Greenberg, S. (2019). Evi-dence for an Association between Men’s Spontaneous Objectifying Gazing Behavior and their Endorsement of Objectifying Attitudes toward Women. Sex Roles, 81(3–4), 245–256. 5) Cogoni, C., Carnaghi, A., Mitrovic, A., Leder, H., Fantoni, C., & Silani, G. (2018). Understanding the mechanisms behind the sexualized-body inversion hypothesis: The role of asymmetry and attention biases. PLOS ONE

Abstract

Material science enables us to develop a wide array of materials capable enough to address worldly problems. Velostat is a carbon-impregnated polythene material used to address many problems including gait-related problems. This paper presents a novel approach to analyze a gait-cycle parameter called ‘Stance-phase.’ We ideated, designed, and implemented a low-cost customizable Velostat based sensing mat to calculate the stance phase. We validated our VelGmat using sensor density, sensitivity, process time, and hardware performance metrics. Our validation results helped us improve the effectiveness of gait analysis. We conducted a comparative study between our VelGmat and commercially available mat to understand the contrast in stance phase values. We observed 95% accuracy in stance phase values compared with commercially available mat.