Colombo, Sri Lanka – Badly wounded from a Ukrainian attack on a Russian bunker in the Donetsk region, Sri Lankan fighter Senaka Bandara* tried to carry his fellow countryman, Nipuna Silva*, to safety. Senaka*, 36, was bleeding from his legs and hands. Nipuna's condition was worse – he had sustained injuries to his chest, hands and legs, according to Senaka. As the two Sri Lankans retreated under fire, another wave of Ukrainian drones struck their bunker in the occupied Donetsk region where the two served with the Russian military. "While I was carrying [Nipuna], there was another huge drone attack at the last bunker and Nipuna fell to the ground," Senaka said earlier this month while being treated for his injuries in a hospital in Donetsk in eastern Ukraine.

While a lot of work has been done in understanding representations learned within deep NLP models and what knowledge they capture, little attention has been paid towards individual neurons. We present a technique called as Linguistic Correlation Analysis to extract salient neurons in the model, with respect to any extrinsic property - with the goal of understanding how such a knowledge is preserved within neurons. We carry out a fine-grained analysis to answer the following questions: (i) can we identify subsets of neurons in the network that capture specific linguistic properties? (ii) how localized or distributed neurons are across the network? iii) how redundantly is the information preserved? iv) how fine-tuning pre-trained models towards downstream NLP tasks, impacts the learned linguistic knowledge? iv) how do architectures vary in learning different linguistic properties? Our data-driven, quantitative analysis illuminates interesting findings: (i) we found small subsets of neurons that can predict different linguistic tasks, ii) with neurons capturing basic lexical information (such as suffixation) localized in lower most layers, iii) while those learning complex concepts (such as syntactic role) predominantly in middle and higher layers, iii) that salient linguistic neurons are relocated from higher to lower layers during transfer learning, as the network preserve the higher layers for task specific information, iv) we found interesting differences across pre-trained models, with respect to how linguistic information is preserved within, and v) we found that concept exhibit similar neuron distribution across different languages in the multilingual transformer models. Our code is publicly available as part of the NeuroX toolkit.

Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field.

Script diversity presents a challenge to Multilingual Language Models (MLLM) by reducing lexical overlap among closely related languages. Therefore, transliterating closely related languages that use different writing scripts to a common script may improve the downstream task performance of MLLMs. We empirically measure the effect of transliteration on MLLMs in this context. We specifically focus on the Indic languages, which have the highest script diversity in the world, and we evaluate our models on the IndicGLUE benchmark. We perform the Mann-Whitney U test to rigorously verify whether the effect of transliteration is significant or not. We find that transliteration benefits the low-resource languages without negatively affecting the comparatively high-resource languages. We also measure the cross-lingual representation similarity of the models using centered kernel alignment on parallel sentences from the FLORES-101 dataset. We find that for parallel sentences across different languages, the transliteration-based model learns sentence representations that are more similar.

Neuron analysis provides insights into how knowledge is structured in representations and discovers the role of neurons in the network. In addition to developing an understanding of our models, neuron analysis enables various applications such as debiasing, domain adaptation and architectural search. We present NeuroX, a comprehensive open-source toolkit to conduct neuron analysis of natural language processing models. It implements various interpretation methods under a unified API, and provides a framework for data processing and evaluation, thus making it easier for researchers and practitioners to perform neuron analysis. The Python toolkit is available at https://www.github.com/fdalvi/NeuroX. Demo Video available at https://youtu.be/mLhs2YMx4u8.

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper health care, education, living conditions, wages, and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First classifiers are implemented with a deep convolutional neural network(CNN) andsecond a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentratedtumor boundary is contoured for the segmentation process by using the Chan-Vesesegmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, Chan- Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score,Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean absolute error (MAE), and Peak Signal to Noise Ratio (PSNR) werecalculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with average dice score of 0.92, (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076 and MAE of 52.946), pointing to high reliability of the proposed architecture.

Architectural style is a medium for the promotion of cultural identities and cohesion. South Asian Association for Regional Cooperation nations provide a prism through which all forms of vernacular architecture can be viewed. This study is presented through the lens of the soul of the eye coupled with the power of technological probing. It showcases research results combining the above (...) stated synergy--starting from some of SAARC's sophisticated historic cultures, cultures that ebbed and flowed along its shores and valleys. This paper shall touch upon unique cultural roots stretching back to the Dravidian civilization that flourished over 3500 years ago and also look at the grouping of houses within the Indus Valley Civilization in Lothal and the Sarasvati Valley Civilization in Kalibangan.

Knowledge bases have the potential to advance artificial intelligence, but often suffer from recall problems, i.e., lack of knowledge of new entities and relations. On the contrary, social media such as Twitter provide abundance of data, in a timely manner: information spreads at an incredible pace and is posted long before it makes it into more commonly used resources for knowledge extraction. In this paper we address the question whether we can exploit social media to extract new facts, which may at first seem like finding needles in haystacks. We collect tweets about 60 entities in Freebase and compare four methods to extract binary relation candidates, based on syntactic and semantic parsing and simple mechanism for factuality scoring. The extracted facts are manually evaluated in terms of their correctness and relevance for search. We show that moving from bottom-up syntactic or semantic dependency parsing formalisms to top-down frame-semantic processing improves the robustness of knowledge extraction, producing more intelligible fact candidates of better quality. In order to evaluate the quality of frame semantic parsing on Twitter intrinsically, we make a multiply frame-annotated dataset of tweets publicly available.