Abstract

Recent data-driven approaches to visual servoing have shown improved performances over classical methods due to precise feature matching and depth estimation. Some recent servoing approaches use a model predictive control (MPC) framework which generalise well to novel environments and are capable of incorporating dynamic constraints, but are computationally intractable in real-time, making it difficult to deploy in real-world scenarios. On the contrary, single-step methods optimise greedily and achieve high servoing rates, but lack the benefits of the MPC multi-step ahead formulation. In this paper, we make the best of both worlds and propose a lightweight visual servoing MPC framework which generates optimal control near real-time at a frequency of 10.52 Hz. This work utilises the differential cross-entropy sampling method for quick and effective control generation along with a lightweight neural network, significantly improving the servoing frequency. We also propose a flow depth normalisation layer which amelio- rates the issue of inferior predictions of two view depth from the flow network. We conduct extensive experimentation on the Habitat simulator and show a notable decrease in servoing time in comparison with other approaches that optimise over a time horizon. We achieve the right balance between time and performance for visual servoing in six degrees of freedom (6DoF), while retaining the advantageous MPC formulation. Our code and dataset are publicly available

Abstract

Welcome to virtual HiPC 2021, the 28th edition of the IEEE International Conference on High Performance Computing, Data, and Analytics. It serves as a forum to present current work by accomplished HPC researchers from around the globe, to explore developing topics in the field, and to highlight related activities in Asia.

Abstract

Skin is one of the very important and largest organs of the human body that helps in regulating the body temperature and is responsible for sensations such as feel, touch, hot and cold. These days, skin problems are very common in day-to-day life. There may be many reasons for skin diseases: unbalanced and impure diet, several types of pollutions, or maybe family heredity. However, if skin disease after a long treatment does not show any sign of improvement or the skin cells grow abnormally, this may lead to skin cancer. There are many forms of skin cancer. For early and timely diagnosis of skin cancer, an efficient technique is required at utmost importance. Many people across the globe lost their lives due to the late diagnosis. Therefore, a technique that is cost-effective, quicker, and easily accessible needs a higher demand. These days for the classification of images, machine learning, and deep learning techniques proved to be the most efficient approach. In this paper, the dataset of several images of a benign and malignant tumor was taken and pre-processed. Once all the images were pre-processed, they are ready to fed in several CNN models. These models extract the features and pass the images to several machine learning classifiers for the classification of moles as benign or malignant. The results verify by using the classification approach it becomes very much easy for the dermatologist to easily detect the lesions and provide the appropriate treatment to the patient to save the life.

Abstract

Agriculture is an innovative way of cultivating crops where the resources are optimized to reduce the harmful impacts on environment and to get better crop yields. India has immense opportunities to make use of the potential of Agri-Tech solutions as majority of farmers possess small land holdings. Ecological sustainability is another significant challenge currently needed to be addressed. The paper propose a Smart-Agri architecture based on digital systems with available technologies for incorporating the factors required for better crop yield and to maintain a sustainable ecosystem. The architecture specifically caters to the need of Small Cardamom which is grown in hilly regions and is particularly much delicate to the climate changes. Due to the rampant use of fertilizers and pesticides, lot of degradation has happened to the Cardamom hill reserve zones over past few decades. In the system, data is to be collected from different parts of a farm using mesh connected ZigBee network making a low-power framework, to monitor soil and plant health and analyze data for finding implications to sort out recommendations meeting sustainability and crop goals. The proposed architecture aims to optimize the resources for small farms by assessing the needs of farmlands. This helps to mitigate the adverse effects on nature.

Abstract

In the traditional agriculture in India, the farmers are not able to make profit from their crops. We propose a price prediction model using Machine Learning approaches, which helps the farmers to make appropriate decisions well before cultivation and later on selling their farm outputs. We have chosen cardamom as a case study in which the data being collected over four decades’ monthly average prices. We propose an ensemble method of multivariate linear regression model and ARIMA model over the clustered average monthly cardamom prices across forty years. The robustness of the proposed model is evaluated against four- decade monthly price movemen

Abstract

Trees are an integral part of many outdoor scenes and are rendered in a wide variety of computer applications like computer games, movies, simulations, architectural models, AR and VR. This has led to increasing demand for realistic, intuitive, lightweight and easy to produce computer-generated trees. The current approaches at 3D tree generation using a library of trees lack variations in structure and are repetitive. This paper presents an extended grammar-based automated solution for 3D tree generation that can model a wide range of species, both Western and Indian. For the foliage, we adopt a particle system approach that models the leaf, its size, orientation and changes. The proposed solution additionally allows control

Abstract

The ability to predict the movements of the target person allows a person following robot (PFR) to coexist with the person while still complying with the social norms. In human-robot collaboration, this is an essential requisite for long-term time-dependent navigation and not losing sight of the person during momentary occlusions that may arise from a crowd due to static or dynamic obstacles, other human beings, or intersections in the local surrounding. The PFR must not only traverse to the previously unknown goal position but also relocate the target person after the miss, and resume following. In this paper, we try to solve this as a coupled motion-planning and control problem by formulating a model predictive control (MPC) controller with non-linear constraints for a wheeled differential-drive robot. And, using a human motion prediction strategy based on the recorded pose and trajectory information of both the moving target person and the PFR, add additional constraints to the same MPC, to recompute the optimal controls to the wheels. We make comparisons with RNNs like LSTM and Early Relocation for learning the best-predicted reference path. MPC is best suited for complex constrained problems because it allows the PFR to periodically update the tracking informa- tion, as well as to adapt to the moving person’s stride. We show the results using a simulated indoor environment and lay the foundation for its implementation on a real robot. Our proposed method offers a robust person following behaviour without the explicit need for policy learning or offline computation, allowing us to design a generalized framework.

Abstract

The pairing of natural language sentences with knowledge graph triples is essential for many downstream tasks like data-to-text generation, facts extraction from sentences (semantic parsing), knowledge graph completion, etc. Most existing methods solve these downstream tasks using neural-based end-to-end approaches that require a large amount of well-aligned training data, which is difficult and expensive to acquire. Recently various unsupervised techniques have been proposed to alleviate this alignment step by automatically pairing the structured data (knowledge graph triples) with textual data. However, these approaches are not well suited for low resource languages that provide two major challenges: (1) unavailability of pair of triples and native text with the same content distribution and (2) limited Natural language Processing (NLP) resources. In this paper, we address the unsupervised pairing of knowledge graph triples with sentences for low resource languages, selecting Hindi as the low resource language. We propose cross-lingual pairing of English triples with Hindi sentences to mitigate the unavailability of content overlap. We propose two novel approaches: NER-based filtering with Semantic Similarity and Key-phrase Extraction with Relevance Ranking. We use our best method to create a collection of 29224 well-aligned English triples and Hindi sentence pairs. Additionally, we have also curated 350 human-annotated golden test datasets for evaluation. We make the code and dataset publicly available.

Abstract

Shape Memory Alloy (SMA) wire actuator contracts or expands in response to heat due to its mechanical (NiTi) property. This paper is about the control perspective of light weight artificial muscle arm based on helical SMA actuator. The Arm is structured around the idea of simplicity and modularity. The post decomposition of the arm dynamics into position and force subspaces, Sliding Mode Control is utilized with the end goal that time taken to reach the equilibrium point from any initial state is guaranteed to be finite time. This setup presents a light weight (70 g) and noiseless operation with a maximum payload of 64 g which is noteworthy when compared to the weight of SMA based artificial muscle and the arm.

Abstract

This paper describes our team oneNLP’s (LTRC, IIIT-Hyderabad) participation for the SEPP-NLG 2021 tasks1, Sentence End and Punctuation Prediction in NLG Text-2021. We applied sequence to tag prediction over contextual embedding as fine-tuning for both of these tasks. We also explored the use of multilingual Bert and multitask learning for these tasks on English, German, French and Italian.