Reviewerly/exHarmony huggingface dataset:
Planning deliveries with UAV routing under weather forecast and energy consumption constraints

In this paper, a depth-first search strategy to cope with the problem of multi-trip unmanned aerial vehicle (UAV) fleet mission planning is proposed. The considered UAVs delivery problem aims at a trajectory planning issue addressed for UAVs operating in a hostile environment while considering battery and payload weight as well as vehicles reuse. Employed UAVs fly on a 3D plane matching a distribution network while servicing customers and ensuring collision avoidance among team members. The objective is to get a sequence of submissions that ensures delivery to customers satisfying the requested amount and demands within a given time horizon. The method proposed in this paper offers solutions to several questions related to the multistage mission planning that could be applied to solve problems such as minimizing energy consumption, conducting the mission in the shortest possible time, just-in-time replenishing of supplies, and so on. The computational experiment illustrates possibilities of the proposed method.
Fully-Textile, Wearable Chipless Tags for Identification and Tracking Applications

In this work, two fully-textile wearable devices, to be used as chipless identification tags in identification and tracking applications are presented. For the fabrication of the fully-textile tags, a layer of fleece was used as a substrate, while an adhesive non-woven conductive fabric was employed for the conductive parts. To allow radio-frequency identification of these chipless tags, two alternative techniques were used. One relies on associating a binary code with the resonance frequency of resonant devices: the presence/absence of the resonance peaks in the transmission scattering parameter, | S 21 | , of a set of resonators is used to encode a string of bits. The second technique for accomplishing radio-frequency identification of the chipless tags resorts to a frequency-shift coding technique, which is implemented by modifying the configuration of a hairpin resonator. The obtained numerical and experimental results confirm the suitability of the proposed strategies for obtaining entirely-textile, wearable chipless tags for identification and tracking purposes, which can be particularly useful, especially in the industrial sector. In this field, in fact, the proposed solutions would guarantee a seamless integration with clothes and would facilitate the user's interaction with the IoT infrastructure. In this regard, one of the envisaged application scenarios related to the tracking of hides in the leather industry is also presented.
Retrieval Topic Recurrent Memory Network for Remote Sensing Image Captioning

Remote sensing image (RSI) captioning aims to generate sentences to describe the content of RSIs.Generally, five sentences are used to describe the RSI in caption datasets.Every sentence can just focus on part of images' contents due to the different attention parts of annotation persons.One annotated sentence may be ambiguous compared with other four sentences.However, previous methods, treating five sentences separately, may generate an ambiguous sentence.In order to consider five sentences together, a collection of words, which named topic words contained common information among five sentences, is jointly incorporated into a captioning model to generate a determinate sentence that covers common contents in RSIs.Instead of employing a naive recurrent neural network, a memory network in which topic words can be naturally included as memory cells is introduced to generate sentences.A novel retrieval topic recurrent memory network is proposed to utilize the topic words.First, a topic repository is built to record the topic words in training datasets.Then, the retrieval strategy is exploited to obtain the topic words for a test image from topic repository.Finally, the retrieved topic words are incorporated into a recurrent memory network to guide the sentence generation.In addition to getting topics through retrieval, the topic words of test images can also be edited manually.The proposed method sheds light on controllability of caption generation.Experiments are conducted on two caption datasets to evaluate the proposed method.
Evaluating Aggression Identification in Social Media

In this paper, we present the report and findings of the Shared Task on Aggression and Gendered Aggression Identification organised as part of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC - 2) at LREC 2020. The task consisted of two sub-tasks - aggression identification (sub-task A) and gendered identification (sub-task B) - in three languages - Bangla, Hindi and English. For this task, the participants were provided with a dataset of approximately 5,000 instances from YouTube comments in each language. For testing, approximately 1,000 instances were provided in each language for each sub-task. A total of 70 teams registered to participate in the task and 19 teams submitted their test runs. The best system obtained a weighted F-score of approximately 0.80 in sub-task A for all the three languages. While approximately 0.87 in sub-task B for all the three languages.
Intention2Basket: A Neural Intention-driven Approach for Dynamic Next-basket Planning

User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.
HAL: Computer System for Scalable Deep Learning

We describe the design, deployment and operation of a computer system built to efficiently run deep learning frameworks. The system consists of 16 IBM POWER9 servers with 4 NVIDIA V100 GPUs each, interconnected with Mellanox EDR InfiniBand fabric, and a DDN all-flash storage array. The system is tailored towards efficient execution of the IBM Watson Machine Learning enterprise software stack that combines popular open-source deep learning frameworks. We build a custom management software stack to enable an efficient use of the system by a diverse community of users and provide guides and recipes for running deep learning workloads at scale utilizing all available GPUs. We demonstrate scaling of a PyTorch and TensorFlow based deep neural networks to produce state-of-the-art performance results.
CHIMERA: Combining Ring-LWE-based Fully Homomorphic Encryption Schemes

Abstract This paper proposes a practical hybrid solution for combining and switching between three popular Ring-LWE-based FHE schemes: TFHE, B/FV and HEAAN. This is achieved by first mapping the different plaintext spaces to a common algebraic structure and then by applying efficient switching algorithms. This approach has many practical applications. First and foremost, it becomes an integral tool for the recent standardization initiatives of homomorphic schemes and common APIs. Then, it can be used in many real-life scenarios where operations of different nature and not achievable within a single FHE scheme have to be performed and where it is important to efficiently switch from one scheme to another. Finally, as a byproduct of our analysis we introduce the notion of a FHE module structure, that generalizes the notion of the external product, but can certainly be of independent interest in future research in FHE.
Towards Better Bus Networks: A Visual Analytics Approach

Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex bus network and the large solution space comprising alternative routes. Most of the automated approaches cannot produce satisfactory results in real-world settings without laborious inspection and evaluation of the candidates. The limitations observed in these approaches motivate us to collaborate with domain experts and propose a visual analytics solution for the performance analysis and incremental planning of bus routes based on an existing bus network. Developing such a solution involves three major challenges, namely, a) the in-depth analysis of complex bus route networks, b) the interactive generation of improved route candidates, and c) the effective evaluation of alternative bus routes. For challenge a, we employ an overview-to-detail approach by dividing the analysis of a complex bus network into three levels to facilitate the efficient identification of deficient routes. For challenge b, we improve a route generation model and interpret the performance of the generation with tailored visualizations. For challenge c, we incorporate a conflict resolution strategy in the progressive decision-making process to assist users in evaluating the alternative routes and finding the most optimal one. The proposed system is evaluated with two usage scenarios based on real-world data and received positive feedback from the experts.
Energy-Based Models for Deep Probabilistic Regression

While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x, y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences lack a natural probabilistic meaning. We address these issues by proposing a general and conceptually simple regression method with a clear probabilistic interpretation. In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x, y). This model of p(y|x) is trained by directly minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our model achieves a $$2.2\%$$ AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-of-the-art on visual tracking when applied for bounding box estimation. In contrast to confidence-based methods, our approach is also shown to be directly applicable to more general tasks such as age and head-pose estimation. Code is available at https://github.com/fregu856/ebms_regression .
Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.
Poseur: Direct Human Pose Regression with Transformers

We propose a direct, regression-based approach to 2D human pose estimation from single images. We formulate the problem as a sequence prediction task, which we solve using a Transformer network. This network directly learns a regression mapping from images to the keypoint coordinates, without resorting to intermediate representations such as heatmaps. This approach avoids much of the complexity associated with heatmap-based approaches. To overcome the feature misalignment issues of previous regression-based methods, we propose an attention mechanism that adaptively attends to the features that are most relevant to the target keypoints, considerably improving the accuracy. Importantly, our framework is end-to-end differentiable, and naturally learns to exploit the dependencies between keypoints. Experiments on MS-COCO and MPII, two predominant pose-estimation datasets, demonstrate that our method significantly improves upon the state-of-the-art in regression-based pose estimation. More notably, ours is the first regression-based approach to perform favorably compared to the best heatmap-based pose estimation methods. Code is available at: https://github.com/aim-uofa/Poseur .
On Detection, Data Association and Segmentation for Multi-Target Tracking

In this work, we propose a tracker that differs from most existing multi-target trackers in two major ways. First, our tracker does not rely on a pre-trained object detector to get the initial object hypotheses. Second, our tracker's final output is the fine contours of the targets rather than traditional bounding boxes. Therefore, our tracker simultaneously solves three main problems: detection, data association and segmentation. This is especially important because the output of each of those three problems are highly correlated and the solution of one can greatly help improve the others. The proposed algorithm consists of two main components: structured learning and Lagrange dual decomposition. Our structured learning based tracker learns a model for each target and infers the best locations of all targets simultaneously in a video clip. The inference of our structured learning is achieved through a new Target Identity-aware Network Flow (TINF), where each node in the network encodes the probability of each target identity belonging to that node. The probabilities are obtained by training target specific models using a global structured learning technique. This is followed by proposed Lagrangian relaxation optimization to find the high quality solution to the network. This forms the first component of our tracker. The second component is Lagrange dual decomposition, which combines the structured learning tracker with a segmentation algorithm. For segmentation, multi-label Conditional Random Field (CRF) is applied to a superpixel based spatio-temporal graph in a segment of video, in order to assign background or target labels to every superpixel. We show how the multi-label CRF is combined with the structured learning tracker through our dual decomposition formulation. This leads to more accurate segmentation results and also helps better resolve typical difficulties in multiple target tracking, such as occlusion handling, ID-switch and track drifting. The experiments on diverse and challenging sequences show that our method achieves superior results compared to competitive approaches for detection, multiple target tracking as well as segmentation.
A fast reconstruction method for three-dimensional shape measurement using dual-frequency grating projection and phase-to-height lookup table

Phase-to-height mapping is indispensable part of three-dimensional (3D) shape measurement system based on phase analysis, which guarantees the accuracy of 3D reconstruction. In this paper, a fast 3D shape reconstruction method based on dual-frequency grating projection and phase-to-height lookup table is proposed. In this proposed method, the reference plane is moved with known interval along the measurement depth direction to establish the mapping lookup table between the phase of the high and low frequency projected grating and their corresponding spatial height pixel by pixel respectively. Then, two methods to establish the lookup table with the unwrapped and the wrapped phase have been respectively introduced according to the characteristics of dual-frequency grating. The actual experimental results show that the two methods can effectively extend the measurement range of the current wrapped phase-to-height lookup method, and the reconstruction accuracy is better than the traditional phase-to-height quadratic fitting method. At the same time, the consumed time for 3D shape reconstruction of the tested object by the two methods is compared and analyzed. Finally, combining the high performance parallel computation of GPU, the fast 3D shape reconstruction is realized with the speed of 60fps.
Siamese Dense Neural Network for Software Defect Prediction With Small Data

Software defect prediction (SDP) exerts a major role in software development, concerning reducing software costs and ensuring software quality. However, developing an accurate SDP model is still a severe and challenging task with the lack of training data. Fortunately, Siamese networks are powerful for learning a few samples and have been perfectly used in other fields. This paper explores the advantages of Siamese networks to propose a novel SDP model, Siamese dense neural networks (SDNNs), which integrates similarity feature learning and distance metric learning into a unified approach. It mainly includes two phases: model building and training. To be more specific, it means building the novel SDNN for capturing the highest-level similarity features and training the model to realize prediction through the designed contrast loss function with cosine proximity. Importantly, we extensively compared the SDNN approach with the state-of-the-art SDP approaches utilizing 10 software defect datasets. The experimental results show that our SDNN is a competitive approach and is able to improve the prediction performance more significantly compared with the benchmarked approaches.
An Age Estimation Method Using 3D-CNN From Brain MRI Images

A specific pattern of morphological changes in the human brain is observed during the process of brain development and healthy aging. The age of subjects can be estimated from brain images by evaluating such patterns. This paper proposes an age estimation method using 3-Dimensional Convolutional Neural Network (3D-CNN) from brain T1-weighted images so as to fully utilize the potential of volume data. Through a set of experiments using over 1,000 T1-weighted images of healthy Japanese, we demonstrate that the proposed method exhibits better performance on age estimation than the conventional methods using handcrafted local features and 2D-CNN.
A Generalized Framework for Population Based Training

Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training. Previous PBT implementations have been synchronized glass-box systems. We propose a general, black-box PBT framework that distributes many asynchronous "trials" (a small number of training steps with warm-starting) across a cluster, coordinated by the PBT controller. The black-box design does not make assumptions on model architectures, loss functions or training procedures. Our system supports dynamic hyperparameter schedules to optimize both differentiable and non-differentiable metrics. We apply our system to train a state-of-the-art WaveNet generative model for human voice synthesis. We show that our PBT system achieves better accuracy and faster convergence compared to existing methods, given the same computational resource.
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

Domain Adaptation (DA) approaches achieved significant improvements in a wide range of machine learning and computer vision tasks (i.e., classification, detection, and segmentation). However, as far as we are aware, there are few methods yet to achieve domain adaptation directly on 3D point cloud data. The unique challenge of point cloud data lies in its abundant spatial geometric information, and the semantics of the whole object is contributed by including regional geometric structures. Specifically, most general-purpose DA methods that struggle for global feature alignment and ignore local geometric information are not suitable for 3D domain alignment. In this paper, we propose a novel 3D Domain Adaptation Network for point cloud data (PointDAN). PointDAN jointly aligns the global and local features in multi-level. For local alignment, we propose Self-Adaptive (SA) node module with an adjusted receptive field to model the discriminative local structures for aligning domains. To represent hierarchically scaled features, node-attention module is further introduced to weight the relationship of SA nodes across objects and domains. For global alignment, an adversarial-training strategy is employed to learn and align global features across domains. Since there is no common evaluation benchmark for 3D point cloud DA scenario, we build a general benchmark (i.e., PointDA-10) extracted from three popular 3D object/scene datasets (i.e., ModelNet, ShapeNet and ScanNet) for cross-domain 3D objects classification fashion. Extensive experiments on PointDA-10 illustrate the superiority of our model over the state-of-the-art general-purpose DA methods.