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Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines. We aim to automatically construct research highlights given certain segments of a research paper. We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings. We test our model on a benchmark dataset, CSPubSum, and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation. For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature. On the CSPubSum dataset, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78, 9.76 and 29.3, respectively, METEOR score of 24.00, and BERTScore F1 of 85.25.

Software developers often face challenges in terms of quality and productivity to match competitive costs. The software industry seeks options to minimize this cost during different phases of software development and maintenance with improved productivity. Software developers adopt different tools for different purposes, such as understanding program behavior, debugging memory issues, debugging concurrency issues, and testing. In this article we study different debugging tools mostly used for program design analysis, thread debugging, and resource management. Stand-alone tools do track static or dynamic control flow, thread activities, etc. But these do not specifically identify the thread work-breakdown-structure, global memory location management, thread-data interaction, etc. to allow good comprehension of the concurrency model of the program. Similarly for resource management, we observe that the Valgrind addresses a few required features but does not offer automatic garbage collection. Moreover, to address the outcomes of different tools, developers must compile and configure the application in different environments. This is very time-consuming, requires skills in different software paradigms, and is sometimes not supported by the tool itself. As a result, they cannot be used in an inter-operable manner to analyze by relating the different tool’s outcomes. In this study, we conduct a detailed survey of the available tools and techniques and their limitations in identifying gaps. We address these gaps by implementing the tools for different phases of software development and maintenance. For example, a concurrency model detector based on thread behavior, resource debugger with features of automatic garbage collection, etc. can collectively inter-operate within our designed open-source tool framework Parallelc-Assist to address the common requests of the developers in one toolset. The tool is built upon open-source dynamic instrumentation tool PIN and supports a wide variety of IDEs and OS to detect various multi-threaded memory issues and provide additional features to inject concerns dynamically at run-time to extend it further according to the user’s needs. We verify our tool with a wide variety of industry-standard benchmarks and compare its features with other similar tools.

This paper provides a method to understand the underlying semantics of Bharatnatyam dance motion and classifies it. Each dance performance is audio-driven and spans over space and time. The dance is captured and analyzed, which is helpful in cultural heritage preservation, and tutoring systems to assist the naive learner. This paper attempts to solve the fundamental problem; recognizing the motions during a dance performance based on motion-pattern. The used dataset is the video recordings of an Indian Classical Dance form known as Bharatanatyam. The different Adavu s (The basic unit of Bharatanatyam) of Bharatanatyam dance are captured using Kinect. We choose RGB from various forms of captured data (RGB, Depth, and Skeleton). Motion History Image (MHI) and Histogram of Gradient of MHI (HoGMHI) are computed for each motion and used as an input for the Machine Learning (ML) algorithms to recognize motion. The paper explores two ML techniques; Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The overall accuracy of both the classifiers is more than 90%. The novelties of the work are (a) analysing all possible involved motions based on the motion-patterns rather than the joint velocities or pose, (b)exploring the impact of training data and the different features on the classifiers’ accuracy, (c) not restricting the number of frames in a motion during recognition and formulate a method to deal with the variable number of frames in the motions.

Identifying k ey frames is the first and necessary step before solving the variety of other B haratanatyam problems. The paper aims to partition the momentarily stationary frames (key frame s) from this dance video’s motion frames. The proposed key frame s (KFs) localization is novel, simple, and effective compared to the existing dance video analysis methods. It is distinctive from standard KFs detection algorithms as used in other human motion videos. In the dance’s basic structure, the occurrence of KFs during performances is often not completely stationary and varies with the dance form and the performer. Hence, it is not easy to decide a global threshold (on the quantum of motion) to work across dancers and performances. The earlier approaches try to compute the threshold iteratively. However, the novelty of the paper is: (a) formulating an adaptive threshold, (b) adopting Machine Learning (ML) approach and, (c) generating the effective feature by combining three frame differencing and bit-plane technique for the KF detection. In ML, we use Support Vector Machine (SVM) and Convolutional Neural Network (CNN) as the classifiers. The proposed approaches are also compared and analyzed with the earlier approaches. Finally, the proposed ML techniques emerge as a winner with around 90% accuracy.