Reviewerly/exHarmony huggingface dataset:
Learning a Robust Society of Tracking Parts Using Co-occurrence Constraints

Object tracking is an essential problem in computer vision that has been researched for several decades. One of the main challenges in tracking is to adapt to object appearance changes over time and avoiding drifting to background clutter. We address this challenge by proposing a deep neural network composed of different parts, which functions as a society of tracking parts. They work in conjunction according to a certain policy and learn from each other in a robust manner, using co-occurrence constraints that ensure robust inference and learning. From a structural point of view, our network is composed of two main pathways. One pathway is more conservative. It carefully monitors a large set of simple tracker parts learned as linear filters over deep feature activation maps. It assigns the parts different roles. It promotes the reliable ones and removes the inconsistent ones. We learn these filters simultaneously in an efficient way, with a single closed-form formulation, for which we propose novel theoretical properties. The second pathway is more progressive. It is learned completely online and thus it is able to better model object appearance changes. In order to adapt in a robust manner, it is learned only on highly confident frames, which are decided using co-occurrences with the first pathway. Thus, our system has the full benefit of two main approaches in tracking. The larger set of simpler filter parts offers robustness, while the full deep network learned online provides adaptability to change. As shown in the experimental section, our approach achieves state of the art performance on the challenging VOT17 benchmark, outperforming the published methods both on the general EAO metric and in the number of fails, by a significant margin.
Empirical Analysis of Defects in Handheld Device Applications

A lot of effort and literature has been developed for conventional software. Defect prediction models can be helpful for project managers to improve the quality of software. However, there is insufficient literature concerning the defect proneness of handheld device (mobile) applications, (henceforth HHDA) instead of conventional applications. Still, no efforts were accomplished to figure out the distinct characteristics of handheld device app bugs and their dispersion among the layered architecture of applications. This paper aims to investigate bug proneness of handheld device applications in contrast with the conventional application. In this work, the authors analyzed the bug distribution of HHDA and conventional apps in the different layer of the architecture. There are 15591 bugs of 28 distinct applications have considered. Two-way ANOVA and Bootstrapping approach have used. This empirical analysis firmly administers that mobile application is more defect prone as compared to conventional applications in the presentation layer.
Succinct Non-overlapping Indexing

Text indexing is a fundamental problem in computer science. The objective is to preprocess a text T, so that, given a pattern P, we can find all starting positions (or simply, occurrences) of P in $$T$$ efficiently. In some cases, additional restrictions are imposed. We consider two variants, namely the non-overlapping indexing problem, and the range non-overlapping indexing problem. Given a text $$T$$ having n characters, the non-overlapping indexing problem is defined as follows: pre-process $$T$$ into a data structure, such that for any pattern P, containing |P| characters, we can report a set containing the maximum number of non-overlapping occurrences of P in $$T$$. Cohen and Porat (in: Algorithms and computation, 20th international symposium, ISAAC 2009, Honolulu, Hawaii. Proceedings, 2009) showed that by maintaining a linear space index in which the suffix tree of $$T$$ is augmented with an O(n) word data structure, a query P can be answered in optimal time $$O(|P|+nocc)$$, where $$nocc$$ is the number of occurrences reported. We present the following new result. Let $$\mathsf {CSA}$$ (not necessarily a compressed suffix array) be an index of $$T$$ that can compute (i) the suffix range of P in $$\mathsf {search}(P)$$ time, and (ii) a suffix array or an inverse suffix array value in $$\mathsf {t}_\mathsf {SA}$$ time. By using $$\mathsf {CSA}$$ alone, we can answer a query P in $$\mathsf {search}(P)+\mathsf {sort}(nocc)+O(nocc\cdot \mathsf {t}_\mathsf {SA})$$ time. The function $$\mathsf {sort}(k)$$ denotes the time for sorting k numbers in $$\{1,2,\dots ,n\}$$. In the range non-overlapping indexing problem, along with the pattern P, two integers a and b, $$b \ge a$$, are provided as input. The task is to report a set containing the maximum number of non-overlapping occurrences of P that lie within the range [a, b]. For any arbitrarily small positive constant $$\epsilon $$, we present an $$O(n \log ^\epsilon n)$$ word index with $$O(|P| + nocc_{a,b})$$ query time, where $$nocc_{a,b}$$ is the number of occurrences reported. Our index improves upon the result of Cohen and Porat [6].
Multiple Policy Value Monte Carlo Tree Search

Many of the strongest game playing programs use a combination of Monte Carlo tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as policy or value evaluators. Given a limited budget, such as online playing or during the self-play phase of AlphaZero (AZ) training, a balance needs to be reached between accurate state estimation and more MCTS simulations, both of which are critical for a strong game playing agent. Typically, larger DNNs are better at generalization and accurate evaluation, while smaller DNNs are less costly, and therefore can lead to more MCTS simulations and bigger search trees with the same budget. This paper introduces a new method called the multiple policy value MCTS (MPV-MCTS), which combines multiple policy value neural networks (PV-NNs) of various sizes to retain advantages of each network, where two PV-NNs f_S and f_L are used in this paper. We show through experiments on the game NoGo that a combined f_S and f_L MPV-MCTS outperforms single PV-NN with policy value MCTS, called PV-MCTS. Additionally, MPV-MCTS also outperforms PV-MCTS for AZ training.
Application of Image Recognition Technology in Garbage Classification Education Platform

In view of the current situation of increasing garbage production and insufficient awareness of garbage classification in China. Based on the cultivation of children's awareness of garbage classification, this paper designs a garbage classification education platform for children using image recognition technology. The platform takes raspberry pi as the main control module, uses machine vision to perform image acquisition, and cooperates with a rotary table for multi-angle recognition. At the same time, the label database and education information database are established, and the relevant education information is displayed through the display, LED lamp, and other ways. Through the combination of pictures, sound, and animation, children are educated about garbage classification and knowledge.
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection

Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for object detection, mainly because the costly ImageNet pre-training is always required for detectors. Training from scratch, as a substitute, demands more epochs to converge and brings no computation saving. To overcome this obstacle, we introduce a practical neural architecture transformation search(NATS)algorithm for object detection in this paper. Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights. We propose a novel neural architecture search strategy in channel-level instead of path-level and devise a search space specially targeting at object detection. With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection. Since our method is gradient-based and only searches for a transformation scheme, the weights of models pretrained inImageNet could be utilized in both searching and retraining stage, which makes the whole process very efficient. The transformed network requires no extra parameters and FLOPs, and is friendly to hardware optimization, which is practical to use in real-time application. In experiments, we demonstrate the effectiveness of NATSon networks like ResNet and ResNeXt. Our transformed networks, combined with various detection frameworks, achieve significant improvements on the COCO dataset while keeping fast.
A Novel Method for Improving the Training Efficiency of Deep Multi-Agent Reinforcement Learning

Deep reinforcement learning (RL) holds considerable promise to help address a variety of multi-agent problems in a dynamic and complex environment. In multi-agent scenarios, most tasks require multiple agents to cooperate and the number of agents has a negative impact on the training efficiency of reinforcement learning. To this end, we propose a novel method, which uses the framework of centralized training and distributed execution and uses parameter sharing among homogeneous agents to replace partial calculation of network parameters in policy evolution. The parameter asynchronous sharing mechanism and the soft sharing mechanism are used to balance the exploratory of agents and the consistency of homogenous agents’ policy. We experimentally validated our approach in different types of multi-agent scenarios. The empirical results show that our method can significantly promote training efficiency in collaborative tasks, competitive tasks, and mixed tasks without affecting the performance.
Collision-Free Path Planning for Cooperative Aerial Manipulators Under Velocity and Curvature Constraints

This paper presents a path planning problem with velocity and curvature constraints for cooperative aerial manipulators in obstacle environments. While potential-flow-based path planning approaches can help generate smooth paths in which cooperative aerial manipulators can avoid obstacles, they do not facilitate the consideration of velocity and curvature constraints. By contrast, while such constraints can be considered in the optimization approach, the approach often requires a heavy computational load. To overcome these drawbacks, we propose a simple approach based on curvature-constrained harmonic potential flow and a streamline-changing algorithm. We also handle the initial and final velocity problem by a smooth activation function, which is crucial for the tracking performance at the initial and final position. The proposed approach can be used to generate a smooth path for cooperative robots in real time. For the validation of the approach, the simulation results obtained with the proposed algorithm were compared with those acquired with an existing approach including dynamic movement primitives(DMPs). Furthermore, we performed load-carrying experiment using custom-made aerial manipulators.
Benchmarking VM Startup Time in the Cloud

Elasticity is one of the primary reasons of the popularity of cloud computing. However, a frequent problem is affecting this popularity - longer processing time for the acquired Virtual Machines (VM) to be ready for usage. This problem hinders the advantages of elasticity. This processing time, known as VM startup time, depends on various factors. VM startup time varies due to space-time trade-off. Comparing VM startup time according to distinctive factors allows users to choose their desirable VM. They have options to select among the VMs as per their preferences. In this paper, we benchmark VM startup time in Amazon EC2, Microsoft Azure and Google Cloud for factors like instance type, time of the day, instance location, cluster creation and cluster resize.
Ramen as You Like

In recent years, a large number of images are being posted on SNS. The users often synthesize or modify their photos before uploading them. However, the task of synthesizing and modifying photos requires a lot of time and skill. In this demo, we demonstrate easy and fast image synthesis and modification through "sketch-based food image generation''. The proposed system uses pix2pix to generate realistic food images based on sketched images, and DeepLab V3+ to obtain sketch masks from real photos. A user can create a realistic food image easily and fast by sketching a mask image consisting of food elements. In addition, a user can also edit a mask image automatically generated from a real photo food photo, and generate a modified food image. For training, we have created a new ramen image dataset consisting of 555 images with 15 kinds of pixel-wise labels.
Sparse Graph Attention Networks

Graph Neural Networks (GNNs) have proved to be an effective representation learning framework for graph-structured data, and have achieved state-of-the-art performance on many practical predictive tasks, such as node classification, link prediction and graph classification. Among the variants of GNNs, Graph Attention Networks (GATs) learn to assign dense attention coefficients over all neighbors of a node for feature aggregation, and improve the performance of many graph learning tasks. However, real-world graphs are often very large and noisy, and GATs are prone to overfitting if not regularized properly. Even worse, the local aggregation mechanism of GATs may fail on disassortative graphs, where nodes within local neighborhood provide more noise than useful information for feature aggregation. In this paper, we propose Sparse Graph Attention Networks (SGATs) that learn sparse attention coefficients under an $L_0$-norm regularization, and the learned sparse attentions are then used for all GNN layers, resulting in an edge-sparsified graph. By doing so, we can identify noisy/task-irrelevant edges, and thus perform feature aggregation on most informative neighbors. Extensive experiments on synthetic and real-world graph learning benchmarks demonstrate the superior performance of SGATs. In particular, SGATs can remove about 50\%-80\% edges from large assortative graphs, while retaining similar classification accuracies. On disassortative graphs, SGATs prune majority of noisy edges and outperform GATs in classification accuracies by significant margins. Furthermore, the removed edges can be interpreted intuitively and quantitatively. To the best of our knowledge, this is the first graph learning algorithm that shows significant redundancies in graphs and edge-sparsified graphs can achieve similar or sometimes higher predictive performances than original graphs.
Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language

We address the problem of retrieving a specific moment from an untrimmed video by a query sentence. This is a challenging problem because a target moment may take place in relations to other temporal moments in the untrimmed video. Existing methods cannot tackle this challenge well since they consider temporal moments individually and neglect the temporal dependencies. In this paper, we model the temporal relations between video moments by a two-dimensional map, where one dimension indicates the starting time of a moment and the other indicates the end time. This 2D temporal map can cover diverse video moments with different lengths, while representing their adjacent relations. Based on the 2D map, we propose a Temporal Adjacent Network (2D-TAN), a single-shot framework for moment localization. It is capable of encoding the adjacent temporal relation, while learning discriminative features for matching video moments with referring expressions. We evaluate the proposed 2D-TAN on three challenging benchmarks, i.e., Charades-STA, ActivityNet Captions, and TACoS, where our 2D-TAN outperforms the state-of-the-art.
3D Reconstruction of Face Image Authentication Technology in Electronic Transaction Authentication

In order to improve the reliability of electronic transactions, it is necessary to study more stringent and effective methods of identification and authentication. Face recognition technology has been applied in electronic trading, but it has not been widely promoted on a global scale. In view of this, based on the 3D modeling technology, this study combines the actual situation to carry out the demand analysis. Moreover, on this basis, the face feature recognition process is set. In addition, based on the needs of network finance for identity recognition and authentication and the characteristics and deficiencies of face image identification and authentication methods, this paper proposes an image-based identity recognition and authentication method for systems to be more strictly authenticated. Finally, this paper sets the comparative experiment to compare the performance of the algorithm. The results show that the proposed algorithm has certain practical effects and can provide theoretical reference for related research.
Deep learning-based breast tumor detection and segmentation in 3D ultrasound image

Automated 3D breast ultrasound (ABUS) has substantial potential in breast imaging. ABUS appears to be beneficial because of its outstanding reproducibility and reliability, especially for screening women with dense breasts. However, due to the high number of slices in 3D ABUS, it requires lengthy screening time for radiologists, and they may miss small and subtle lesions. In this work, we propose to use a 3D Mask R-CNN method to automatically detect the location of the tumor and simultaneously segment the tumor contour. The performance of the proposed algorithm was evaluated using 25 patients’ data with ABUS image and ground truth contours. To further access the performance of the proposed method, we quantified the intersection over union (IoU), Dice similarity coefficient (DSC), and center of mass distance (CMD) between the ground truth and segmentation. The resultant IoU 96% &plusmn; 2%, DSC 84% &plusmn; 3%, and CMD 1.95 &plusmn; 0.89 mm respectively, which demonstrated the high accuracy of tumor detection and 3D volume segmentation of the proposed Mask R-CNN method. We have developed a novel deep learning-based method and demonstrated its capability of being used as a useful tool for computer-aided diagnosis and treatment.
Structure-Priority Image Restoration Through Genetic Algorithm Optimization

With the significant increase in the use of image information, image restoration has been gaining much attention by researchers. Restoring the structural information as well as the textural information of a damaged image to produce visually plausible restorations is a challenging task. Genetic algorithm (GA) and its variants have been applied in many fields due to their global optimization capabilities. However, the applications of GA to the image restoration domain still remain an emerging discipline. It is still challenging and difficult to restore a damaged image by leveraging GA optimization. To address this problem, this paper proposes a novel GA-based image restoration method that can successfully restore a damaged image. We name it structure-priority image restoration through GA optimization. The main idea is to convert an image restoration task into an optimization problem, and to develop a GA optimization algorithm to solve it. In this study, the structural information of a damaged image, which is represented by curves or lines (COLs), is prioritized to be repaired first. The structural information is classified into relevant and irrelevant information according to the information of their locations. The relevant information is analyzed through the proposed GA optimization algorithm to find the matched COLs. The matched COLs are used to restore the structural information of the damaged area. The textural information will then be restored according to the different partitions separated by the restored structural information. Lastly, through case studies, we evaluate the proposed method by using four typical indices to measure the differences between the original and restored image. The results of case studies demonstrate the applicability and feasibility of the proposed method.
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions

Modern deep learning models for NLP are notoriously opaque. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. While this might be useful for tasks where decisions are explicitly influenced by individual tokens in the input, we suspect that such highlighting is not suitable for tasks where model decisions should be driven by more complex reasoning. In this work, we investigate the use of influence functions for NLP, providing an alternative approach to interpreting neural text classifiers. Influence functions explain the decisions of a model by identifying influential training examples. Despite the promise of this approach, influence functions have not yet been extensively evaluated in the context of NLP, a gap addressed by this work. We conduct a comparison between influence functions and common word-saliency methods on representative tasks. As suspected, we find that influence functions are particularly useful for natural language inference, a task in which 'saliency maps' may not have clear interpretation. Furthermore, we develop a new quantitative measure based on influence functions that can reveal artifacts in training data.
Learning Navigation Subroutines from Egocentric Videos.

Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning. We propose a method to learn such hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks. We learn a self-supervised inverse model on small amounts of random interaction data to pseudo-label the expert egocentric videos with agent actions. Visuomotor subroutines are acquired from these pseudo-labeled videos by learning a latent intent-conditioned policy that predicts the inferred pseudo-actions from the corresponding image observations. We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuomotor subroutines from passive egocentric videos. We demonstrate the utility of our acquired visuomotor subroutines by using them as is for exploration, and as sub-policies in a hierarchical RL framework for reaching point goals and semantic goals. We also demonstrate behavior of our subroutines in the real world, by deploying them on a real robotic platform. Project website: this https URL.
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models

Directed graphical models (DGMs) are a class of probabilistic models that are widely used for predictive analysis in sensitive domains, such as medical diagnostics. In this paper we present an algorithm for differentially private learning of the parameters of a DGM with a publicly known graph structure over fully observed data. Our solution optimizes for the utility of inference queries over the DGM and \textit{adds noise that is customized to the properties of the private input dataset and the graph structure of the DGM}. To the best of our knowledge, this is the first explicit data-dependent privacy budget allocation algorithm for DGMs. We compare our algorithm with a standard data-independent approach over a diverse suite of DGM benchmarks and demonstrate that our solution requires a privacy budget that is $3\times$ smaller to obtain the same or higher utility.