Abstract

Analysis of player movements is a crucial subset of sports analysis. Existing player movement analysis methods use recorded videos after the match is over. In this work, we propose an end-to-end framework for player movement analysis for badminton matches on live broadcast match videos. We only use the visual inputs from the match and, unlike other approaches which use multi-modal sensor data, our approach uses only visual cues. We propose a method to calculate the on-court distance covered by both the players from the video feed of a live broadcast badminton match. To perform this analysis, we focus on the gameplay by removing replays and other redundant parts of the broadcast match. We then perform player tracking to identify and track the movements of both players in each frame. Finally, we calculate the distance covered by each player and the average speed with which they move on the court. We further show a heatmap of the areas covered by the player on the court which is useful for analyzing the gameplay of the player. Our proposed framework was successfully used to analyze live broadcast matches in real-time during the Premier Badminton League 2019 (PBL 2019), with commentators and broadcasters appreciating the utility

Abstract

The rapid growth of online communication using social media platforms has led to an increase in the presence of hate speech, especially in terms of sexist language online. The proliferation of such hate speech has a significant impact on the mental health and well-being of the users and hence the need for automated systems to detect and filter such texts. In this study, we explore the effectiveness of conventional machine learning techniques for detecting sexist text. We explore five conventional classifiers, namely, Logistic Regression, Decision Tree, XGBoost, Support Vector Machines, and Random Forest. The results show that different classifiers perform differently on each task due to their different inherent architectures which may be suited to a certain problem more. These models are trained on the shared task dataset, which includes both sexist and non-sexist texts.All in all, this study explores the potential of conventional machine learning techniques in detecting online sexist content. The results of this study highlight the strengths and weaknesses of all classifiers with respect to all subtasks. The results of this study will be useful for researchers and practitioners interested in developing systems for detecting or filtering online hate speech.

Abstract

The Clickbait Spoiling shared task aims at tackling two aspects of spoiling: classifying the spoiler type based on its length and generating the spoiler. This paper focuses on the task of classifying the spoiler type. Better classification of the spoiler type would eventually help in generating a better spoiler for the post. We use BERT-base (cased) to classify the clickbait posts. The model achieves a balanced accuracy of 0.63 as we give only the post content as the input to our model instead of the concatenation of the post title and post content to find out the differences that the post title might be bringing in.

Abstract

For decades, fingerprint recognition has been prevalent for security, forensics, and other biometric applications. However, the availability of good-quality fingerprints is challenging, making recognition difficult. Fingerprint images might be degraded with a poor ridge structure and noisy or less contrasting backgrounds. Hence, fingerprint enhancement plays a vital role in the early stages of the fingerprint recognition/verification pipeline. In this paper, we investigate and improvise the encoder-decoder style architecture and suggest intuitive modifications to U-Net to enhance low-quality fingerprints effectively. We investigate the use of Discrete Wavelet Transform (DWT) for fingerprint enhancement and use a wavelet attention module instead of max pooling which proves advantageous for our task. Moreover, we replace regular convolutions with depthwise separable convolutions, which significantly reduces the memory footprint of the model without degrading the performance. We also demonstrate that incorporating domain knowledge with fingerprint minutiae prediction task can improve fingerprint reconstruction through multi-task learning. Furthermore, we also integrate the orientation estimation task to propagate the knowledge of ridge orientations to enhance the performance further. We present the experimental results and evaluate our model on FVC 2002 and NIST SD302 databases to show the effectiveness of our approach compared to previous works.

Abstract

Remote labs allow students from anywhere in the world to access and conduct experiments without the need to physically be present in a lab at anytime. This is extremely important for students with little to no access to proper science labs because of a lack of infrastructure or a pandemic. There is a potential for scaling up the process so that queue delay can be removed and people are able to access dedicated experiment setups. This paper proposes a way for cost-effective scaling of Remote Labs by miniaturization of setups, image processing techniques, and partial streaming (using single camera to stream multiple experiments). For this, a use-case of the Vanishing Glass Rods experiment is considered. An end-to-end remote lab is created, including hardware setups and a dashboard to demonstrate the efficacy of the proposed approach in comparison to the existing approach of using lab-scale setups and one camera per experiment.

Abstract

The exponential performance growth guaranteed by Moore’s law has started to taper in recent years. At the same time, emerging applications like image processing demand heavy computational performance. These factors inevitably lead to the emergence of domain-specific accelerators (DSA) to fill the performance void left by conventional architectures. FPGAs are rapidly evolving towards becoming an alternative to custom ASICs for designing DSAs because of their low power consumption and a higher degree of parallelism. DSA design on FPGAs requires careful calibration of the FPGA compute and memory resources towards achieving optimal throughput. Hardware Descriptive Languages (HDL) like Verilog have been traditionally used to design FPGA hardware. HDLs are not geared towards any domain, and the user has to put in much effort to describe the hardware at the register transfer level. Domain Specific Languages (DSLs) and compilers have been recently used to weave together handwritten HDLs templates targeting a specific domain. Recent efforts have designed DSAs with image-processing DSLs targeting FPGAs. Image computations in the DSL are lowered to pre-existing templates or lower-level languages like HLS-C. This approach requires expensive FPGA re-flashing for every new workload. In contrast to this fixed-function hardware approach, overlays are gaining traction. Overlays are DSAs resembling a processor, which is synthesized and flashed on the FPGA once but is flexible enough to process a broad class of computations through soft reconfiguration. Less work has been reported in the context of image processing overlays. Image processing algorithms vary in size and shape, ranging from simple blurring operations to complex pyramid systems. The primary challenge in designing an image-processing overlay is maintaining flexibility in mapping different algorithms

Abstract

A numerical study is done to determine the seismic performance of G+1 industrial RC building after retrofitting beams and columns. Deterioration of building has occurred due to excess chemical spillage over structural elements. Distressed beams and columns were retrofitted using jacketing techniques. The building is located in Indian Seismic Zone IV, so there is a need to understand the global seismic behaviour of the building after retrofitting. Nonlinear static pushover analysis is carried out to and the results indicate a decrease in the storey shear values. Keywords: Compressive Strength, Seismic Retrofitting, Push-over Analysis, Base Shear, Displacement, Jacketing

Abstract

The present study describes the interdependency between earthquake ground motion parameters and seismic damage to various buildings. A novel ranking procedure between energy, damage and seismic risk has been proposed for buildings subjected to ground motions. Correlation analysis was done between damage to buildings, energy and total energy to confirm the trend of parameters and their influence on total energy ranking. It was found that high peak ground acceleration (PGA) or high energy alone may not lead to extensive damage to the buildings. However, they were affected by the predominant frequency, duration of ground motion and amplitude. A correlation study was performed using over 300 ground motions datasets. It was found that the seismic damage of low-rise buildings had a moderate correlation with root mean square acceleration, characteristic intensity, sustained maximum acceleration and effective design acceleration. Also, a weak correlation was observed between seismic damage of high-rise, tall buildings and (V/H)PGA, Arms. The damage and seismic risk rankings may help change Government policies for retrofitting buildings.

Abstract

Many tall buildings are being constructed in different Indian cities to cater to the demand generated by the large number of people migrating from rural areas to urban centres. The safety of such tall buildings is ensured by designing them for dynamic loads, viz. wind and earthquake. To withstand these loads, computation of the natural period becomes essential. The current Indian seismic code IS 1893 (2016) has outlined a few empirical expressions based on different structural systems to compute the natural period. These expressions have been developed using data obtained from experiments performed on low to midrise buildings. Thus, verifying their applicability for tall structures before using them is important. To achieve this, in the present study ambient vibration testing was done on 28 reinforced concrete (RC) tall buildings in the Indian cities of Hyderabad and Mumbai, whose heights ranged from 50 to 150 m. These tests’ natural periods were compared with existing Indian and international codes. Based on the comparison, a novel empirical expression of RC tall buildings is proposed here.

Abstract

With rapid technological growth, security attacks are drastically increasing. In many crucial Internet-of-Things (IoT) applications such as healthcare and defense, the early detection of security attacks plays a significant role in protecting huge resources. An intrusion detection system is used to address this problem. The signature-based approaches fail to detect zero-day attacks. So anomaly-based detection particularly AI tools, are becoming popular. In addition, the imbalanced dataset leads to biased results. In Machine Learning (ML) models, F1 score is an important metric to measure the accuracy of class-level correct predictions. The model may fail to detect the target samples if the F1 is considerably low. It will lead to unrecoverable consequences in sensitive applications such as healthcare and defense. So, any improvement in the F1 score has significant impact on the resource protection. In this paper, we present a framework for ML-based intrusion detection system for an imbalanced dataset. In this study, the most recent dataset, namely CICIoT2023 is considered. The random forest (RF) algorithm is used in the proposed framework. The proposed approach improves 3.72%, 3.75% and 4.69% in precision, recall and F1 score, respectively, with the existing method. Additionally, for unsaturated classes (i.e., classes with F1 score < 0.99), F1 score improved significantly by 7.9%. As a result, the proposed approach is more suitable for IoT security applications for efficient detection of intrusion and is useful in further studies.