检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Patra, Aswini Kumar Sahoo, Lingaraj Dept. of Computer Science and Engineering North Eastern Regional Institute of Science and Technology Itanagar India Dept. of Bio-Science and Bio-Engineering Indian Institute of Technology Guwahati India

Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis aimed at identifying drought stress. While these approaches yield favorable results, real-time field applications requires algorithms specifically designed for the complexities of natural agricultural conditions. Our work proposes a novel deep learning framework for classifying drought stress in potato crops captured by UAVs in natural settings. The novelty lies in the synergistic combination of a pre-trained network with carefully designed custom layers. This architecture leverages the pre-trained network’s feature extraction capabilities while the custom layers enable targeted dimensionality reduction and enhanced regularization, ultimately leading to improved performance. A key innovation of our work involves the integration of Gradient-Class Activation Mapping (Grad-CAM), an explainability technique. Grad-CAM sheds light on the internal workings of the deep learning model, typically referred to as a"black box." By visualizing the model’s focus areas within the images, Grad-CAM fosters interpretability and builds trust in the model’s decision-making process. Our proposed framework achieves superior performance, particularly with the DenseNet121 pre-trained network, reaching a precision of 97% to identify the stressed class with an overall accuracy of 91%. Comparative analysis of existing state-of-the-art object detection algorithms reveals the superiority of our approach in significantly higher precision and accuracy. Thus, our explainable deep learning framework offers powerful approach for drought stress identification with high accuracy and actionable insights. Cop

关键词： Cams

来源：评论

学校读者我要写书评

暂无评论

BOXREC: Recommending a Box of Preferred Outfits in Online Shopping

arXiv

引用

arXiv 2024年

作者： Banerjee, Debopriyo Rao, Krothapalli Sreenivasa Sural, Shamik Ganguly, Niloy Dept. of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India

Fashionable outfits are generally created by expert fashionistas, who use their creativity and in-depth understanding of fashion to make attractive outfits. Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user's fashion taste. However, none of these consider user's preference of price-range for individual clothing types or an overall shopping budget for a set of items. In this paper, we propose a box recommendation framework - BOXREC - which at first, collects user preferences across different item types (namely, top-wear, bottom-wear and foot-wear) including price-range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price-ranges), creates all possible combinations of three preferred items (belonging to distinct item types) and verifies each combination using an outfit scoring framework - BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. We create an extensively annotated dataset of male fashion items across various types and categories (each having associated price) and a manually annotated positive and negative formal as well as casual outfit dataset. We consider a set of recently published pairwise compatibility prediction methods as competitors of BOXREC-OSF. Empirical results show superior performance of BOXREC-OSF over the baseline methods. We found encouraging results by p

关键词： Electronic commerce

来源：评论

学校读者我要写书评

暂无评论

Evaluating Large Language Models for Automatic Register Transfer Logic Generation via High-Level Synthesis

arXiv

引用

arXiv 2024年

作者： Swaroopa, Sneha Mukherjee, Rijoy Debnath, Anushka Chakraborty, Rajat Subhra The Dept. of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India The Dept. of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India

The ever-growing popularity of large language models (LLMs) has resulted in their increasing adoption for hardware design and verification. Prior research has attempted to assess the capability of LLMs to automate digital hardware design by producing superior-quality Register Transfer Logic (RTL) descriptions, particularly in Verilog. However, these tests have revealed that Verilog code production using LLMs at current state-of-the-art lack sufficient functional correctness to be practically viable, compared to automatic generation of programs in general-purpose programming languages such as C, C++, Python, etc. With this as the key insight, in this paper we assess the performance of a two-stage software pipeline for automated Verilog RTL generation: LLM based automatic generation of annotated C++ code suitable for high-level synthesis (HLS), followed by HLS to generate Verilog RTL. We have benchmarked the performance of our proposed scheme using the open-source VerilogEval dataset, for four different industry-scale LLMs, and the Vitis HLS tool. Our experimental results demonstrate that our two-step technique substantially outperforms previous proposed techniques of direct Verilog RTL generation by LLMs in terms of average functional correctness rates, reaching score of 0.86 in pass@1 metric. Copyright © 2024, The Authors. All rights reserved.

关键词： C++ (programming language)

来源：评论

学校读者我要写书评

暂无评论

Relating Muscle Activity and Mouse Sensitivity in FPS Game Players Using Surface EMG Signals

Relating Muscle Activity and Mouse Sensitivity in FPS Game P...

引用

International Conference on Biomedical engineering (IBIOMED)

作者： Vaibhav Prajapati Anish C. Turlapaty Himangshu Sarma Mrinmoy Ghorai Dept. of Electronics and Communication Engineering Indian Institute of Information Technology Sri City India Dept. of Computer Science and Engineering Indian Institute of Information Technology Sri City India

ISBN: (数字)9798350389265

ISBN: (纸本)9798350389272

In the domain of first-person shooter (FPS) gaming, mouse sensitivity is crucial for performance. This paper presents an analysis showing the correlation between specific muscle activity and mouse movement. We analyzed mouse movements from twelve subjects with varying sensitivity levels. A new dataset electromyography analysis of human activity database 8 (EMAHA-DB8), is introduced which involved four mouse designs and two game modes: Medium (normal movement) and Hard (aggressive movement). Sensitivity settings were standardized using effective dots per inch (eDPI), calculated as Mouse DPI multiplied by in-game sensitivity. Hand muscle activity was measured using five Noraxon Ultium wireless surface electromyography (sEMG) sensors with Ag/AgCl electrodes. Various regression models, including Random Forest, Support Vector Regressor, K-Nearest Neighbors Regressor, MLP Regressor, and Gradient Boost Regressor, were used to predict eDPI from muscle movement patterns. Sequential feature selection identified the top ten features, highlighting the most significant muscles for predicting sensitivity. The results demonstrate that the Extensor Carpi Radialis (ECR) is the most active muscle, followed by the Extensor Digitorum (ED), while playing the chosen FPS game. These findings give insights for ergonomic and personalized equipment design to enhance gaming experience and performance.

关键词： Sensitivity Biological system modeling Games Muscles Predictive models Nearest neighbor methods Electromyography Data models Vectors Random forests

来源：评论

学校读者我要写书评

暂无评论

Drishyam: An Image is Worth a Data Prefetcher 23

Drishyam: An Image is Worth a Data Prefetcher

引用

Proceedings of the 32nd International Conference on Parallel Architectures and Compilation Techniques

作者： Shubdeep Mohapatra Biswabandan Panda Dept. of Computer Science and Engineering BITS Pilani Goa India Dept. name of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9798350342543

Hardware prefetching is a latency-hiding technique that hides the costly off-chip DRAM accesses. Although hardware prefetching is an extensively researched topic with many state-of-the-art data prefetchers pushing the performance limits, prefetching for irregular applications with hard-to-predict access patterns is still a challenging problem to solve. The usage of neural networks for hardware prefetching is a promising direction, especially for predicting irregular memory access patterns. This paper presents Drishyam, a novel hardware prefetcher based on computer vision algorithms that use images to learn memory access patterns and predict future memory accesses with high accuracy and coverage. For hardware prefetching, an image is a graphical representation of memory accesses observed over time. For a sequence of memory addresses, Drishyam creates images that predict the future addresses by predicting the future OS page and a cache line offset within the OS page. Drishyam outperforms Voyager, the state-of-the-art machine learning (ML) based prefetcher, for a set of irregular benchmarks by an average of 4.7% with an average prefetch accuracy and prefetch coverage of 89.5% and 66.6%, respectively. In terms of training time, Drishyam outperforms Voyager by 225.5%.

关键词： Cache

来源：评论

学校读者我要写书评

暂无评论

EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals

arXiv

引用

arXiv 2024年

作者： Kareemulla, Ashraf Ali Sanodiya, Rakesh Kumar Turlapaty, Anish Chand Naidu, Surya Dept. of Computer Science and Engineering Indian Institute of Information Technology Sri City India Dept. of Electronics and Communication Engineering Indian Institute of Information Technology Sri City India

Surface Electromyography (sEMG) is widely studied for its applications in rehabilitation, prosthetics, robotic arm control, and human-machine interaction. However, classifying Activities of Daily Living (ADL) using sEMG signals often requires extensive feature extraction, which can be time-consuming and energy-intensive. The objective of this study is stated as follows. Given sEMG datasets, such as electromyography analysis of human activity databases (DB1 and DB4), with multi-channel signals corresponding to ADL, is it possible to determine the ADL categories without explicit feature extraction from sEMG signals. Further is it possible to learn across the datasets to improve the classification performances. A classification framework, named EMGTTL, is developed that uses transformers for classification of ADL and the performance is enhanced by cross-data transfer learning. The methodology is implemented on EMAHA-DB1 and EMAHA-DB4. Experiments have shown that the transformer architecture achieved 64.47% accuracy for DB1 and 68.82% for DB4. Further, using transfer learning, the accuracy improved to 66.75% for DB1 (pre-trained on DB4) and 71.04% for DB4 (pre-trained on DB1). © 2024, CC BY.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Towards Energy Efficient Functional Split and Baseband Function Placement for 5G RAN

Towards Energy Efficient Functional Split and Baseband Funct...

引用

IEEE Conference on Network Softwarization (NetSoft)

作者： Nabhasmita Sen Antony Franklin A Dept. of Computer Science and Engineering Indian Institute of Technology Hyderabad India

The energy efficiency of 5G and beyond 5G(B5G) networks is critical for reducing the high operational expenditure (OPEX) of mobile network operators. In 5G RAN, functional split enables the disaggregation of baseband functions, which significantly increases energy efficiency but induces various challenges in the placement of baseband functions. Various recent works have focused on addressing these challenges; however, most of the solutions do not consider the delay and data rate requirements of different slices as well as different functional splits. In this work, we aim to develop an energy-efficient baseband function placement strategy that jointly considers different functional splits and network slice-specific requirements. We formulate an Integer Linear Program (ILP) based optimization model to minimize the energy consumption in the network by selecting appropriate functional split and baseband function placement options for RAN slices. We show that our proposed model outperforms the baseline strategies in providing energy efficient baseband function placement solution. To tackle the computational complexity of ILP, we also design a polynomial time heuristic algorithm that can be applied in large-scale scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Vacaspati: A Diverse Corpus of Bangla Literature

arXiv

引用

arXiv 2023年

作者： Bhattacharyya, Pramit Mondal, Joydeep Maji, Subhadip Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

Bangla (or Bengali) is the fifth most spoken language globally;yet, the state-of-the-art NLP in Bangla is lagging for even simple tasks such as lemmatization, POS tagging, etc. This is partly due to lack of a varied quality corpus. To alleviate this need, we build VĀCASPATI, a diverse corpus of Bangla literature. The literary works are collected from various websites;only those works that are publicly available without copyright violations or restrictions are collected. We believe that published literature captures the features of a language much better than newspapers, blogs or social media posts which tend to follow only a certain literary pattern and, therefore, miss out on language variety. Our corpus VĀCASPATI is varied from multiple aspects, including type of composition, topic, author, time, space, etc. It contains more than 11 million sentences and 115 million words. We also built a word embedding model, VĀC-FT, using FastText from VĀCASPATI as well as trained an Electra model, VĀC-BERT, using the corpus. VĀC-BERT has far fewer parameters and requires only a fraction of resources compared to other state-of-the-art transformer models and yet performs either better or similar on various downstream tasks. On multiple downstream tasks, VĀC-FT outperforms other FastText-based models. We also demonstrate the efficacy of VĀCASPATI as a corpus by showing that similar models built from other corpora are not as effective. The models are available at https://***/. © 2023, CC BY.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Framework for Question-Answering in Sanskrit through Automated Construction of Knowledge Graphs

arXiv

引用

arXiv 2023年

作者： Terdalkar, Hrishikesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

Sanskrit (samskrta) enjoys one of the largest and most varied literature in the whole world. Extracting the knowledge from it, however, is a challenging task due to multiple reasons including complexity of the language and paucity of standard natural language processing tools. In this paper, we target the problem of building knowledge graphs for particular types of relationships from samskrta texts. We build a natural language question-answering system in samskrta that uses the knowledge graph to answer factoid questions. We design a framework for the overall system and implement two separate instances of the system on human relationships from mahābhārata and rāmāyana, and one instance on synonymous relationships from bhāvaprakāśa nighantu, a technical text from āyurveda. We show that about 50% of the factoid questions can be answered correctly by the system. More importantly, we analyse the shortcomings of the system in detail for each step, and discuss the possible ways forward. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Contention Matters: Modeling and Analyzing the Performance of Federated Learning over WiFi

Contention Matters: Modeling and Analyzing the Performance o...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Prabhleen Kukreja V. Mahendran Dept. of Computer Science and Engineering Indian Institute of Technology Tirupati India

Federated Learning (FL) is a distributed machine learning framework coordinated by a central node. The performance of the FL is characterized by the participating nodes and their underlying communication mechanism. The state-of-the-art FL over wireless predominantly considers a contention-free communication model. The proposed work argues the need for capturing random effects of the MAC layer, as this significantly affects the performance of FL in terms of the random order of node participation, and their random times of communication. To this end, a novel mathematical model is developed to characterize the performance of FL in a WiFi *** carrier-sensing and asynchronous random-backoff behavior of WiFi clients, and the synchronous coordination of the FL algorithm are modeled using renewal reward processes, and the performance metrics of FL are computed with the help of an underlying DTMC. Furthermore, a multicast-based MAC protocol for WiFi is also modeled and shown to perform marginally better than the unicast counterpart, thereby reaffirming the dominant effects of MAC level contention towards affecting the performance of FL. The proposed models are validated against extensive performance evaluation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：