Accurate estimation of above-ground biomass is vital to improve agricultural productivity, support breeding programs, and advancing the understanding of crop physiology. Traditional measurement techniques are often labor-intensive, time-consuming, and unsuitable for large-scale applications. LiDAR offers a non-destructive and scalable alternative by capturing detailed 3D plant structures. In this work, AgriFormer is introduced, a novel deep learning framework designed to enhance AGB prediction from 3D LiDAR point clouds. AgriFormer builds upon BioNet framework and employs a multi-scale feature extraction strategy to better capture structural and contextual information from plant data. Evaluated on a public wheat and triticale dataset, AgriFormer significantly outperforms existing approaches, reducing the RMSE from 99.33 to 82.8. These results demonstrate the potential of advanced deep learning techniques for accurate and efficient digital biomass assessment in precision agriculture.

Person re-identification (Re-ID) is a computer vision task that involves recognizing and tracking individuals across multiple non-overlapping cameras or over time within the same camera view. It is particularly important in surveillance systems, where it can help in identifying potential threats or tracking suspects. Convolutional neural networks (CNNs) have been used to extract invariant person representation for this challenging task. However, CNNs do not consider global dependencies in their initial layers, causing some vital information to be lost during the convolution process. The development of vision-based transformers has opened up new research avenues for person re-identification. This work proposes a purely transformer-based solution, called TansPose Re-ID, that learns pose-invariant person representations. The proposed system uses a vision transformer baseline and enhances its architecture by introducing multiple streams to learn global and local dependencies as well as pose invariance in person images. The architecture includes a Global Self-Attention Module (GSM) and a Local Self-Attention Module (LSM) that jointly learn global and local patch-based person embeddings. The LSM is further improved by stochastically grouping local patches and aligning them. Additionally, an attention feature learning module (AFLM) is introduced in the LSM to handle pose and viewpoint variations. The proposed method is evaluated on two public Re-ID benchmarks, Market1501 and DukeMTMC-ReID, and demonstrates superior performance compared to existing transformer baselines.

Transformer-based architectures have recently shown impressive performance on various point cloud understanding tasks such as 3D object shape classification and semantic segmentation. Particularly, this can be attributed to their self-attention mechanism, which has the ability to capture long-range dependencies. However, current methods have constrained it to operate in local patches due to its quadratic memory constraints. This hinders their generalization ability and scaling capacity due to the loss of non-locality in early layers. To tackle this issue, we propose a window-based transformer architecture that captures long-range dependencies while aggregating information in the local patches. We do this by interacting each window with a set of global point cloud tokens — a representative subset of the entire scene — and augmenting the local geometry through a 3D Histogram of Oriented Gradients (HOG) descriptor. Through a series of experiments on segmentation and classification tasks, we show that our model exceeds the state-of-the-art on S3DIS semantic segmentation (+1.67% mIoU), ShapeNetPart part segmentation (+1.03% instance mIoU) and performs competitively on ScanObjectNN 3D object classification.1

Acquiring ground-truth data for crop mapping is a challenging task in developing countries. Limited resources, inconsistent agricultural practices across the country, and inadequate infrastructure at the administrative level pose a significant challenge in collecting dataset information. This study proposes an active labelling framework for automated ground truth data generation in areas with limited or no ground truth information available. Sentinel 2 images and vegetative indices are visually interpreted for the identification of Wheat and Rice fields. This hand-labelled data is incorporated into the framework as training data to generate crop maps at 10m resolution using light weight, ConvLSTM model. Testing is performed for two spatially distinct districts with different field sizes and crop distribution in Pakistan. The results are consistent with an accuracy of 77.8%, F1 score of 87.3% and IOU of 70.7% for Gujranwala and 76%, 82% and 70% for Sargodha, promising the applicability of the proposed approach to generate large-scale crop labels, thereby enhancing the efficiency of crop mapping efforts in developing countries

Summary Malware is a malicious program used for unauthorized access to organizational infrastructure and systems. To overcome challenges of exponential growth of malware, notable research has been made for unsupervised clustering of Windows‐based portable executable (PE). Nevertheless, to the best of our knowledge there has been no research for robust cluster prediction of Windows based PEs using static features. To this end, we proposed an ensemble neural network architecture for unsupervised feature learning and its distribution modeling for robust clustering of PE(s). The novel architecture is a cascaded formation of a deep autoencoder (AE) network and latent distribution modeling (LDM) network. The AE performs feature learning using latent representation and LDM performs the distribution modeling of latent representation using Gaussian approximation. An objective function is also devised for model optimization. The network adjusts the Gaussian components to optimize the distribution modeling. It also performs adjustments for data representations toward related Gaussian centers to make the model behave in adaptive manner. A novel malware dataset has also been collected by employing endpoint security management solution over enterprise network to assess proposed architecture. The dataset contains 21,486 samples including 14,497 malicious and 6989 benign ones. We also performed the evaluation of proposed architecture over publicly available benchmark malware dataset including 138,047 samples comprising 96,742 malicious and 41,323 benign PEs. The experimental results demonstrated that the proposed architecture yielded more than 95% accuracy for cluster prediction. The novel architecture has achieved superior performance and outperformed progressive techniques. The dataset along with implementation are accessible at bit.ly/3J6ZF8S .

Nowadays, customer support systems are one of the key factors in maintaining any big company's reputation and success. These systems are capable of handling a large number of tickets systemically and provides a mechanism to track/logs the communication between customer and support agents. Companies invest huge amounts of money in training support agents and deploying customer care services for their products and services. Support agents are responsible for handling different customer queries and implementing required actions to solve a particular issue or problem raised by the service/product user. In a bigger picture, customer support systems could receive a large amount of ticket raised depending upon the number of users and services being offered. Customer care service gets directly affected due to the high volume of tickets and a limited number of support agents. Therefore, providing support agents with the recommendations about the possible resolution actions for a new ticket would be helpful and can save a lot of time. This study is focused on the development of an end-to-end framework for suggesting resolution actions rather than recommending free form resolution text against a newly raised ticket. To develop such a system, the pipeline is broadly divided into four components that are data preprocessing, actions extractor, resolution predictor, and evaluation. In actions extractor module, we have proposed a technique to identify and extract actionable phrases from resolution text. For resolution predictor, we have proposed two different pipelines that are referred as “Similarity Search Model” and “End-to-End Model.” The similarity search method is based on a ticket similarity search to find the most relevant historical tickets which then leads to corresponding resolution actions. On the other hand, end-to-end model make use of actions extractor module directly and implemented in a way to directly predict resolution actions. To compare and evaluate the mentioned methods on the same ground, we also proposed an actions evaluation criterion which uses BertScore and METEOR score jointly to compute the score against actual and predicted actions for a particular test ticket. The analysis and experiments are performed on the real-world IBM ticket data set. Overall, we observed that end-to-end model outperformed similarity search-based methods and achieved better performance and scores comparatively. The trained models and code are available at https://bit.ly/2GbUBVk.

Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224×224 ) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of 1792×1792 pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method.

Applications of Deep learning based methods are enormously growing in order to help the blind to see the world and/or enable the deaf to hear and speak. Re-Identification of a person among a set of cameras, is one of the latest challenges, in Computer Vision. Person Re-Identification deals with matching images of the same person over multiple non-overlapping camera views. Commonly, the task of Re-Id is broken down into three sub-modules, which are detection, tracking, and matching. Most of the techniques use manually annotated bounding boxes and only focus on matching between probes and cropped candidate images. This is not desirable in a real-time environment where the localization of object boundaries is not available. The target person needs to be identified from the complete image which may contain many distractors. To address the issue we investigated how the localization and matching of the target person can be done without using any prior annotation of bounding boxes. Our proposed method is based on an end to end deep learning technique, which not only targets matching but localization of objects as well. It handles detection and Re-Identification together. The research provides an end-to-end implementation of person tracking across multiple cameras in Surveillance context. The model is tested under diverse situations and resulted in a higher retrieval accuracy. The whole network is jointly optimized, using CIFM loss and fine-tuned to get better accuracy. The proposed approach outperforms state of the art methods on the PRW datasets, which demonstrates the effectiveness and generalization ability of our proposed approach.