Reviewerly/exHarmony huggingface dataset:
Distributed Multi-Target Tracking for Autonomous Vehicle Fleets

We present a scalable distributed target tracking algorithm based on the alternating direction method of multipliers that is well-suited for a fleet of autonomous cars communicating over a vehicle-to-vehicle network. Each sensing vehicle communicates with its neighbors to execute iterations of a Kalman filter-like update such that each agent's estimate approximates the centralized maximum a posteriori estimate without requiring the communication of measurements. We show that our method outperforms the Consensus Kalman Filter in recovering the centralized estimate given a fixed communication bandwidth. We also demonstrate the algorithm in a high fidelity urban driving simulator (CARLA), in which 50 autonomous cars connected on a time-varying communication network track the positions and velocities of 50 target vehicles using on-board cameras.
An application of compositional data analysis to multiomic time-series data

Abstract Compositional data analysis (CoDA) methods have increased in popularity as a new framework for analyzing next-generation sequencing (NGS) data. CoDA methods, such as the centered log-ratio (clr) transformation, adjust for the compositional nature of NGS counts, which is not addressed by traditional normalization methods. CoDA has only been sparsely applied to NGS data generated from microbial communities or to multiple ‘omics’ datasets. In this study, we applied CoDA methods to analyze NGS and untargeted metabolomic datasets obtained from bacterial and fungal communities. Specifically, we used clr transformation to reanalyze NGS amplicon and metabolomics data from a study investigating the effects of building material type, moisture and time on microbial and metabolomic diversity. Compared to analysis of untransformed data, analysis of clr-transformed data revealed novel relationships and stronger associations between sample conditions and microbial and metabolic community profiles.
CFSRE: Context-aware based on frame-semantics for distantly supervised relation extraction

In relation extraction with distant supervision, noise labels are a bottleneck problem that hinders the performance of training models. Existing neural models solved this problem using attention mechanisms or multi-instance/multi-label learning to select sentences that are likely to express the relations. They mainly focused on the structural information of entity pairs and ignored semantic information of relation-clue words. Furthermore, these models do not consider the semantic scenario information of an entity pair (As the same entity pair may express different relations in different semantic scenarios). To bridge the gap, this paper proposes a novel model for relation extraction based on frame-semantics, namely Context-aware based on Frame-semantics for Distantly Supervised Relation Extraction (CFSRE). The model combines a hierarchical neural network architecture with FrameNet semantic knowledge base to solve the noise labels and the semantic representation of entity pairs. More specifically, a simple and effective instance selection method is used to select informative positive instances for model training. In addition, we propose a novel sentence representation method by combining sentence context representation with frame semantic representation of entities. We find that the joint representation leads to a better performance because it can obtain more comprehensive semantic representation of text instances. Then, a hierarchical-attention mechanism has been designed to select the most informative features for relation extraction. Finally, a softmax classifier is used to test the performance of the model. Experiments were conducted on two widely used benchmark datasets. The results demonstrate that the proposed method is significantly better than state-of-the-art methods for distantly supervised relation extraction.
3D-CNN Based Heuristic Guided Task-Space Planner for Faster Motion Planning

Motion planning is important in a wide variety of applications such as robotic manipulation. However, it is still challenging to reliably find a collision-free path within a reasonable time. To address the issue, this paper proposes a novel framework which combines a sampling-based planner and deep learning for faster motion planning, focusing on heuristics. The proposed method extends Task-Space Rapidly-exploring Random Trees (TS-RRT) to guide the trees with a "heuristic map" where every voxel has a cost-to-go value toward the goal. It also utilizes fully convolutional neural networks (CNNs) for producing more appropriate heuristic maps, rather than manually-designed heuristics. To verify the effectiveness of the proposed method, experiments for motion planning using a real environment and mobile manipulator are carried out. The results indicate that it outperforms the existing planners, especially in terms of the average planning time with smaller variance.
First U-Net Layers Contain More Domain Specific Information Than the Last Ones

MRI scans appearance significantly depends on scanning protocols and, consequently, the data-collection institution. These variations between clinical sites result in dramatic drops of CNN segmentation quality on unseen domains. Many of the recently proposed MRI domain adaptation methods operate with the last CNN layers to suppress domain shift. At the same time, the core manifestation of MRI variability is a considerable diversity of image intensities. We hypothesize that these differences can be eliminated by modifying the first layers rather than the last ones. To validate this simple idea, we conducted a set of experiments with brain MRI scans from six domains. Our results demonstrate that 1) domain-shift may deteriorate the quality even for a simple brain extraction segmentation task (surface Dice Score drops from 0.85–0.89 even to 0.09); 2) fine-tuning of the first layers significantly outperforms fine-tuning of the last layers in almost all supervised domain adaptation setups. Moreover, fine-tuning of the first layers is a better strategy than fine-tuning of the whole network, if the amount of annotated data from the new domain is strictly limited.
B&amp;Anet: Combining bidirectional LSTM and self-attention for end-to-end learning of task-oriented dialogue system

Building dialogue systems plays an important role in modern life. amongst them, task-oriented dialogue for resolving problems in actual life is most worth exploring. Motivated by the development of end-to-end approaches, a task-oriented dialogue model based on bidirectional LSTM and self-attention mechanism is proposed. It not only makes good use of context and effectively solves the long-term dependency, but also identifies the relationship between sentences, optimizes feature vectors, and has good parallelism. In our method, the dialogue state tracker(DST) is firstly improved. Our dialogue state tracker can identify the multiple slot key-value pairs involved in the utterance without manual label. In addition, we apply the data augmentation of merging machine translation and bilingual dictionary to create more diversified data sets. Finally, in the experimental part, the enhanced data and the results of DST are fed into the proposed B&Anet (bidirectional long and short memory network and self-attention mechanism network) model. The evaluation results on DSTC2 (dialogue state tracking chanllenge2) show that our method achieves competitive performance.
Accurate detection and 3D localization of humans using a novel YOLO-based RGB-D fusion approach and synthetic training data

While 2D object detection has made significant progress, robustly localizing objects in 3D space under presence of occlusion is still an unresolved issue. Our focus in this work is on real-time detection of human 3D centroids in RGB-D data. We propose an image-based detection approach which extends the YOLO v3 architecture with a 3D centroid loss and mid-level feature fusion to exploit complementary information from both modalities. We employ a transfer learning scheme which can benefit from existing large-scale 2D object detection datasets, while at the same time learning end-to-end 3D localization from our highly randomized, diverse synthetic RGB-D dataset with precise 3D groundtruth. We further propose a geometrically more accurate depth-aware crop augmentation for training on RGB-D data, which helps to improve 3D localization accuracy. In experiments on our challenging intralogistics dataset, we achieve state-of-the-art performance even when learning 3D localization just from synthetic data.
A Fast Non-Redundant Feature Selection Technique for Text Data

Feature selection is critical in reducing the size of data and improving classifier accuracy by selecting an optimum subset of the overall features. Traditionally, each feature is given a score against a particular category (such as using Mutual Information) and the task of feature selection comes down to choosing the top $k$ ranked features with the best average score across all categories. However, this approach has two major drawbacks. Firstly, the maximum or average score of a feature with a class might not necessarily determine its discriminating strength among samples of other classes. Secondly, most feature selection methods only use the scores to select the discriminating features from the corpus without taking into account the redundancy of information provided by the selected features. In this paper, we propose a new feature ranking score measure called the Discriminative Mutual Information (DMI) score. This score helps to select features that distinguish samples of one category against all other categories. Moreover, Non-Redundant Feature Selection (NRFS) heuristic is also proposed that explicitly takes the problem of feature redundancy into account when selecting the features set. The performance of our approach is investigated and compared with other feature selection techniques on datasets derived from high-dimensional text corpora using multiple classification algorithms. The results show that the proposed method leads to better classification micro-F1 score as compared to other state-of-the-art methods. In particular, the proposed method shows great improvement when the number of selected features are small as well as an overall higher robustness to label noise.
Deep kNN for Medical Image Classification

Human-level diagnostic performance from intelligent systems often depends on large set of training data. However, the amount of available data for model training may be limited for part of diseases, which would cause the widely adopted deep learning models not generalizing well. One alternative simple approach to small class prediction is the traditional k-nearest neighbor (kNN). However, due to the non-parametric characteristics of kNN, it is difficult to combine the kNN classification into the learning of feature extractor. This paper proposes an end-to-end learning strategy to unify the kNN classification and the feature extraction procedure. The basic idea is to enforce that each training sample and its K nearest neighbors belong to the same class during learning the feature extractor. Experiments on multiple small-class and class-imbalanced medical image datasets showed that the proposed deep kNN outperforms both kNN and other strong classifiers.
Positive and Negative Label-Driven Nonnegative Matrix Factorization

Positive label is often used as the supervisory information in the learning scenario, which refers to the category that a sample is assigned to. However, another side information lying in the labels, which describes the categories that a sample is exclusive of, have been largely ignored. In this paper, we propose a nonnegative matrix factorization (NMF) based classification method leveraging both positive and negative label information, which is termed as positive and negative label-driven NMF (PNLD-NMF). The proposed scheme concurrently accomplishes data representation and classification in a joint manner. Owing to the complementary characteristics between positive and negative labels, we further design a new regularization framework to take advantage of these two label types. Extensive experiments on six image classification benchmark datasets show that the proposed scheme is able to consistently deliver better classification accuracy.
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks

Deep Neural Networks are an important class of machine learning algorithms that have demonstrated state-of-the-art accuracy for different cognitive tasks like image and speech recognition. Modem deep networks have millions to billions of parameters, which leads to high memory and energy requirements during training as well as during inference on resource-constrained edge devices. Consequently, pruning techniques have been proposed that remove less significant weights in deep networks, thereby reducing their memory and computational requirements. Pruning is usually performed after training the original network, and is followed by further retraining to compensate for the accuracy loss incurred during pruning. The prune-and-retrain procedure is repeated iteratively until an optimum tradeoff between accuracy and efficiency is reached. However, such iterative retraining adds to the overall training complexity of the network. In this work, we propose a dynamic pruning-while-training procedure, wherein we prune filters of the convolutional layers of a deep neural network during training itself, thereby precluding the need for separate retraining. We evaluate our dynamic pruning-while-training approach with three different pre-existing pruning strategies, viz. mean activation-based pruning, random pruning, and L1 normalization-based pruning. Our results for VGG-16 trained on CIFAR10 shows that L1 normalization provides the best performance among all the techniques explored in this work with less than 1% drop in accuracy after pruning 80% of the filters compared to the original network. We further evaluated the L1 normalization based pruning mechanism on CIFAR100. Results indicate that pruning while training yields a compressed network with almost no accuracy loss after pruning 50% of the filters compared to the original network and ~5% loss for high pruning rates (> 80%). The proposed pruning methodology yields 41% reduction in the number of computations and memory accesses during training for CIFAR10, CIFAR100 and ImageNet compared to training with retraining for 10 epochs.
IoT Based Low-Cost Robotic Agent Design for Disabled and Covid-19 Virus Affected People

Disabled people and Virus affected patients can be helped through Internet of Things and Robotic systems in this modern era.Recently the whole world is suffering from the Covid-19 pandemic.The virus affected and disabled people are helpless because caregivers, doctors and other people are afraid of the contagious virus.This work will result in an IoT basedRobotic agent which will be able to help disabled and virus affected people with low cost systems.The robotic agent will be able to recognize the patient's Gesture and follow instructions through it with 360-degree movement.Without image processing the system is made with MPU 6050 Accelerometer Gyroscope sensor for Gesture Recognition.Radio Frequency communication was used to make the system wireless.
DLEP: A Deep Learning Model for Earthquake Prediction

Earthquakes are one of the most costly natural disasters facing human beings, which happens without an explicit warning, therefore earthquake prediction becomes a very important and challenging task for humanity. Although many existing methods attempt to address this task, most of them use either seismic indicators (explicit features) designed by geologists, or feature vectors (implicit features) extracted by deep learning methods, to characterize an earthquake for earthquake prediction. The problem of combining these two kind of features to improve final earthquake prediction performance remains pretty much open. To this end, we propose a deep learning model named DLEP to effectively fuse the explicit and implicit features for accurate earthquake prediction. In DLEP, we adopt eight precursory pattern-based indicators as the explicit features, and use a convolutional neural network (CNN) to extract implicit features. Then, an attention-based strategy is suggested to fuse these two kinds of features well. In addition, a dynamic loss function is designed to deal with the category imbalance of seismic data. Finally, experimental results on eight datasets from different regions demonstrate the effectiveness of the proposed DLEP for earthquake prediction comparing to several state-of-the-art baselines.
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes

We introduce a novel technique for distribution learning based on a notion of sample compression . Any class of distributions that allows such a compression scheme can be learned with few samples. Moreover, if a class of distributions has such a compression scheme, then so do the classes of products and mixtures of those distributions. As an application of this technique, we prove that ˜Θ( kd 2 /ε 2 ) samples are necessary and sufficient for learning a mixture of k Gaussians in R d , up to error ε in total variation distance. This improves both the known upper bounds and lower bounds for this problem. For mixtures of axis-aligned Gaussians, we show that Õ( kd /ε 2 ) samples suffice, matching a known lower bound. Moreover, these results hold in an agnostic learning (or robust estimation) setting, in which the target distribution is only approximately a mixture of Gaussians. Our main upper bound is proven by showing that the class of Gaussians in R d admits a small compression scheme.
Sentiment Analysis on Youtube Social Media Using Decision Tree and Random Forest Algorithm: A Case Study

People often gives opinions and thoughts on Social Media. Social media invites anyone who is interested to participate by giving feedback openly, giving comments, and sharing information in a fast and unlimited time. One of the social media that is still widely used in the community is Youtube. With so much video content, Youtube, one of which is also commonly used to promote a product. Case studies taken by researchers are public comments on Nokia's products. Nokia was one of the best consumer stock, and was a global-scale product, but took its downfall at year 2013. By classifying positive, negative, and neutral sentiments from various opinions on Nokia's Products, a sentiment will be conducted. From this analysis it can be determined whether the product quality is generally good or not. However, the comments are neutral labelled in dominance. The stages of sentiment analysis in this study is data preparation, data processing and evaluation. The resulting model is tested and evaluated by looking at the values of accuracy, precision, recall, and Fl-measure. The algorithm used in the conducted sentiment analysis are the Decision Tree and Random Forest Algorithm. Decision Tree algorithm has a slightly higher accuracy of 89.4% rather than Random Forest algorithm which is 88.2%.
Smoothness Regularized Multiview Subspace Clustering With Kernel Learning

Multiview subspace clustering has attracted an increasing amount of attention in recent years. However, most of the existing multiview subspace clustering methods assume linear relations between multiview data points when learning the affinity representation by means of the self-expression or fail to preserve the locality property of the original feature space in the learned affinity representation. To address the above issues, in this article, we propose a new multiview subspace clustering method termed smoothness regularized multiview subspace clustering with kernel learning (SMSCK). To capture the nonlinear relations between multiview data points, the proposed model maps the concatenated multiview observations into a high-dimensional kernel space, in which the linear relations reflect the nonlinear relations between multiview data points in the original space. In addition, to explicitly preserve the locality property of the original feature space in the learned affinity representation, the smoothness regularization is deployed in the subspace learning in the kernel space. Theoretical analysis has been provided to ensure that the optimal solution of the proposed model meets the grouping effect. The unique optimal solution of the proposed model can be obtained by an optimization strategy and the theoretical convergence analysis is also conducted. Extensive experiments are conducted on both image and document data sets, and the comparison results with state-of-the-art methods demonstrate the effectiveness of our method.
Deep-learning based descriptors in application to aging problem in face recognition

Nowadays, “aging” becomes a challenging problem for face recognition systems. The challenge is particularly due to age-related biological changes, which can lead to significant variations in facial characteristics between two images taken at different ages of the same person. As the face is the most heavily part affected by aging, there is a growing need to extracting robust face features for age-invariant face recognition, particularly, in the presence of large age differences of the same person face images. The aim of this paper is to examine effectiveness of deep-learning based methods as features extraction tool for age-invariant face recognition. In this study, we evaluate five popular pre-trained deep-convolutional neural network (CNN) models that are AlexNet, GoogleNet, Inception V3, ResNet50 and SqueezeNet, on a widely used face-aging database, namely FG-NET, using K-nearest neighbors (K-NN), discriminant analysis, and support vector machines (SVM) classifiers. Further, a statistical analysis test is performed to confirm the statistical significance of the obtained results. Experimental results on this database show the promise of using Convolutional Neural Networks(CNN) for face recognition across age progression. Also the AlexNet model appears to be most promising for age-invariant face recognition, as highest mean accuracy rate is always achieved with feature extraction using the AlexNet model. These results are more significant, according to a 95% confidence level hypothesis test.
Multimodal Vigilance Estimation Using Deep Learning

The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that consists of multichannel deep autoencoders with subnetwork neurons (MCDAE<inline-formula> <tex-math notation="LaTeX">$_{sn}$ </tex-math></inline-formula>). After we define two thresholds of &#x201C;0.35&#x201D; and &#x201C;0.70&#x201D; from the percentage of eye closure, the output values are in the continuous range of 0&#x2013;0.35, 0.36&#x2013;0.70, and 0.71&#x2013;1 representing the awake state, the tired state, and the drowsy state, respectively. To verify the efficiency of our strategy, we first applied the proposed approach to a single modality. Then, for the multimodality, since the complementary information between forehead electrooculography and electroencephalography features, we found the performance of the proposed approach using features fusion significantly improved, demonstrating the effectiveness and efficiency of our method.