Reviewerly/exHarmony huggingface dataset:
PID Controller-Based Stochastic Optimization Acceleration for Deep Neural Networks

Deep neural networks (DNNs) are widely used and demonstrated their power in many applications, such as computer vision and pattern recognition. However, the training of these networks can be time consuming. Such a problem could be alleviated by using efficient optimizers. As one of the most commonly used optimizers, stochastic gradient descent-momentum (SGD-M) uses past and present gradients for parameter updates. However, in the process of network training, SGD-M may encounter some drawbacks, such as the overshoot phenomenon. This problem would slow the training convergence. To alleviate this problem and accelerate the convergence of DNN optimization, we propose a proportional-integral-derivative (PID) approach. Specifically, we investigate the intrinsic relationships between the PID-based controller and SGD-M first. We further propose a PID-based optimization algorithm to update the network parameters, where the past, current, and change of gradients are exploited. Consequently, our proposed PID-based optimization alleviates the overshoot problem suffered by SGD-M. When tested on popular DNN architectures, it also obtains up to 50% acceleration with competitive accuracy. Extensive experiments about computer vision and natural language processing demonstrate the effectiveness of our method on benchmark data sets, including CIFAR10, CIFAR100, Tiny-ImageNet, and PTB. We have released the code at https://github.com/tensorboy/PIDOptimizer.
FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network

High dynamic range (HDR) image generation from a single exposure low dynamic range (LDR) image has been made possible due to the recent advances in Deep Learning. Various feed-forward Convolutional Neural Networks (CNNs) have been proposed for learning LDR to HDR representations. To better utilize the power of CNNs, we exploit the idea of feedback, where the initial low level features are guided by the high level features using a hidden state of a Recurrent Neural Network. Unlike a single forward pass in a conventional feed-forward network, the reconstruction from LDR to HDR in a feedback network is learned over multiple iterations. This enables us to create a coarse-to-fine representation, leading to an improved reconstruction at every iteration. Various advantages over standard feed-forward networks include early reconstruction ability and better reconstruction quality with fewer network parameters. We design a dense feedback block and propose an end-to-end feedback network-FHDR for HDR image generation from a single exposure LDR image. Qualitative and quantitative evaluations show the superiority of our approach over the state-of-the-art methods.
Deep Ensemble Learning for Human Action Recognition in Still Images

Numerous human actions such as “Phoning,” “PlayingGuitar,” and “RidingHorse” can be inferred by static cue-based approaches even if their motions in video are available considering one single still image may already sufficiently explain a particular action. In this research, we investigate human action recognition in still images and utilize deep ensemble learning to automatically decompose the body pose and perceive its background information. Firstly, we construct an end-to-end NCNN-based model by attaching the nonsequential convolutional neural network (NCNN) module to the top of the pretrained model. The nonsequential network topology of NCNN can separately learn the spatial- and channel-wise features with parallel branches, which helps improve the model performance. Subsequently, in order to further exploit the advantage of the nonsequential topology, we propose an end-to-end deep ensemble learning based on the weight optimization (DELWO) model. It contributes to fusing the deep information derived from multiple models automatically from the data. Finally, we design the deep ensemble learning based on voting strategy (DELVS) model to pool together multiple deep models with weighted coefficients to obtain a better prediction. More importantly, the model complexity can be reduced by lessening the number of trainable parameters, thereby effectively mitigating overfitting issues of the model in small datasets to some extent. We conduct experiments in Li’s action dataset, uncropped and 1.5x cropped Willow action datasets, and the results have validated the effectiveness and robustness of our proposed models in terms of mitigating overfitting issues in small datasets. Finally, we open source our code for the model in GitHub ( https://github.com/yxchspring/deep_ensemble_learning ) in order to share our model with the community.
Fast Panoptic Segmentation Network

In this work, we present an end-to-end network for fast panoptic segmentation. This network, called Fast Panoptic Segmentation Network (FPSNet), does not require computationally costly instance mask predictions or rule-based merging operations. This is achieved by casting the panoptic task into a custom dense pixel-wise classification task, which assigns a class label or an instance id to each pixel. We evaluate FPSNet on the Cityscapes and Pascal VOC datasets, and find that FPSNet is faster than existing panoptic segmentation methods, while achieving better or similar panoptic segmentation performance. On the Cityscapes validation set, we achieve a Panoptic Quality score of 55.1%, at prediction times of 114 milliseconds for images with a resolution of 1024 × 2048 pixels. For lower resolutions of the Cityscapes dataset and for the Pascal VOC dataset, FPSNet achieves prediction times as low as 45 and 28 milliseconds, respectively.
Adaptive Local Linear Discriminant Analysis

Dimensionality reduction plays a significant role in high-dimensional data processing, and Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction approach. However, a major drawback of LDA is that it is incapable of extracting the local structure information, which is crucial for handling multimodal data. In this article, we propose a novel supervised dimensionality reduction method named Adaptive Local Linear Discriminant Analysis (ALLDA), which adaptively learns a k -nearest neighbors graph from data themselves to extract the local connectivity of data. Furthermore, the original high-dimensional data usually contains noisy and redundant features, which has a negative impact on the evaluation of neighborships and degrades the subsequent classification performance. To address this issue, our method learns the similarity matrix and updates the subspace simultaneously so that the neighborships can be evaluated in the optimal subspaces where the noises have been removed. Through the optimal graph embedding, the underlying sub-manifolds of data in intra-class can be extracted precisely. Meanwhile, an efficient iterative optimization algorithm is proposed to solve the minimization problem. Promising experimental results on synthetic and real-world datasets are provided to evaluate the effectiveness of proposed method.
Privacy preserving distributed data mining based on secure multi-party computation

Data mining is an important task to understand the valuable information for making correct decisions. Technologies for mining self-owned data of a party are rather mature. However, how to perform distributed data mining to obtain information from data owned by multiple parties without privacy leakage remains a big challenge. While secure multi-party computation (MPC) may potentially address this challenge, several issues have to be overcome for practical realizations. In this paper, we point out two unsupported tasks of MPC that are common in the real-world. Towards this end, we design algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these requirements in the MPC context. In addition, we implement them based on a SPDZ protocol, a computation framework of MPC. The experimental evaluation results show that our design and implementation are feasible and effective for privacy preserving distributed data mining.
An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

Abstract The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets.
Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling

Due to its simplicity and intuitive interpretability, spherical k-means is often used for clustering a large number of documents. However, there exist a number of drawbacks that need to be addressed for much effective document clustering. Without well-dispersed initial points, spherical k-means fails to converge quickly, which is critical for clustering a large number of documents. Furthermore, its dense centroid vectors needlessly incorporate the impact of infrequent and less-informative words, thereby distorting the distance calculation between the document vectors. In this paper, we propose practical improvements on spherical k-means to overcome these issues during document clustering. Our proposed initialization method not only guarantees dispersed initial points, but is also up to 1000 times faster than previously well-known initialization method such as k-means++. Furthermore, we enforce sparsity on the centroid vectors by using a data-driven threshold that is capable of dynamically adjusting its value depending on the clusters. Additionally, we propose an unsupervised cluster labeling method that effectively extracts meaningful keywords to describe each cluster. We have tested our improvements on seven different text datasets that include both new and publicly available datasets. Based on our experiments on these datasets, we have found that our proposed improvements successfully overcome the drawbacks of spherical k-means in significantly reduced computation time. Furthermore, we have qualitatively verified the performance of the proposed cluster labeling method by extracting descriptive keywords of the clusters from these datasets.
Hierarchical Context Features Embedding for Object Detection

Pixel-level segmentation has been widely used to improve object detection. Most of the existing methods refine detection features by adding the constraint of the segmentation branch or by simply embedding high-level segmentation features into detection features within the local receptive field. However, noisy segmentation features are unavoidable in real-word applications and can easily cause false positives. To address this problem, we propose a novel hierarchical context embedding module to effectively embed segmentation features into detection features. The idea of this module is to capture hierarchical context information that includes local objects or parts and nonlocal context features by learning multiple attention maps, and subsequently utilize interdependencies between features to recalibrate noisy segmentation features. Furthermore, we use this module in the proposed gated encoder-decoder network that adaptively aggregates feature maps of different resolutions based on the gate mechanism so that we can embed multiscale segmentation feature maps into detection features for more accurate detection of objects of all sizes. Experimental results demonstrate the effectiveness of the proposed method on the Pascal VOC 2012Seg dataset, the Pascal VOC dataset and the MS COCO dataset.
YASA

Configurable systems allow users to derive customized software variants with behavior and functionalities tailored to individual needs. Developers of these configurable systems need to ensure that each configured software variant works as intended. Thus, software testing becomes highly relevant, but also highly expensive due to large configuration spaces that grow exponentially in the number of features. To this end, sampling techniques, such as t-wise interaction sampling, are used to generate a small yet representative subset of configurations, which can be tested even with a limited amount of resources. However, even state-of-the-art t-wise interaction sampling techniques do not scale well for systems with large configuration spaces. In this paper, we introduce the configurable technique YASA that aims to be more efficient than other existing techniques and enables control over trading-off sampling time and sample size. The general algorithm of YASA is based on the existing technique IPOG, but introduces several improvements and options to adapt the sampling procedure to a given configurable system. We evaluate our approach in terms of sampling time and sample size by comparing it to existing t-wise interaction sampling techniques. We find that YASA performs well even for large-scale system and is also able to produce smaller samples than existing techniques.
A survey on the practical use of UML for different software architecture viewpoints

Software architecture viewpoints modularize the software architectures in terms of different viewpoints that each address a different concern. Unified Modeling Language (UML) is so popular among practitioners for modeling software architectures from different viewpoints. In this paper, we aimed at understanding the practitioners’ UML usage for the modeling of software architectures from different viewpoints. To this end, 109 practitioners with diverse profiles have been surveyed to understand practitioners’ UML usage for six different viewpoints: functional, information, concurrency, development, deployment, and operational. Each viewpoint has been considered in terms of a set of software models that can be created in that viewpoint. The survey includes 35 questions for different viewpoint models, and the results lead to interesting findings. While the top popular viewpoints for the UML-based software architecture modeling are the functional (96%) and information (99%) viewpoints, the least popular one is the operational viewpoint that is considered by 26% of the practitioners. The top popular UML modeling tool is Enterprise Architect regardless of the viewpoints considered. Concerning the software models that can be created in each viewpoint, UML’s class diagram is practitioners’ top choice for the functional structure (71%), data structure (85%), concurrency structure (75%), software code structure (34%), and system installation (39%), and system support (16%) models; UML’s sequence diagram is the top choice for the data lifecycle models (47%); UML’s deployment diagram for the physical structure (71%), mapping between the functional and physical components (53%), and system migration (21%) models; UML’s activity diagram for the data flow (65%), software build and release processes (20–22%), and system administration (36%) models; UML’s component diagram for the mapping between the functional and concurrent components (35%), software module structure (47%), and system configuration (21%) models; and UML’s package diagram for the software module structure (47%) models.
Design and Comprehensive Analysis of a Noise-Tolerant ZNN Model With Limited-Time Convergence for Time-Dependent Nonlinear Minimization

Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problems broadly arisen in the science and engineering areas. The convergence and robustness are always co-pursued in ZNN. However, there exists no related work on the ZNN for time-dependent nonlinear minimization that achieves simultaneously limited-time convergence and inherently noise suppression. In this article, for the purpose of satisfying such two requirements, a limited-time robust neural network (LTRNN) is devised and presented to solve time-dependent nonlinear minimization under various external disturbances. Different from the previous ZNN model for this problem either with limited-time convergence or with noise suppression, the proposed LTRNN model simultaneously possesses such two characteristics. Besides, rigorous theoretical analyses are given to prove the superior performance of the LTRNN model when adopted to solve time-dependent nonlinear minimization under external disturbances. Comparative results also substantiate the effectiveness and advantages of LTRNN via solving a time-dependent nonlinear minimization problem.
Online Dynamic Hand Gesture Recognition Including Efficiency Analysis

Online dynamic hand gesture recognition is challenging mainly due to three reasons: (i) There is no indication when a gesture starts and ends in the video, (ii) performed gestures should only be recognized once, and (iii) the entire architecture should be designed considering the memory and power budget. In this paper, a two-level hierarchical structure consisting of a detector and a classifier is proposed which enables offline-working convolutional neural network (CNN) architectures to operate online efficiently by using sliding window approach. For efficiency analysis, different CNN architectures are applied to compare these architectures over offline classification accuracy, number of parameters and computation complexity. In order to evaluate the single-time activations of the detected gestures, we used Levenshtein distance as an evaluation metric since it can measure misclassifications, multiple detections, and missing detections at the same time. The performance of the approach is evaluated on two public datasets - EgoGesture and NVIDIA Dynamic Hand Gesture Datasets - which require temporal detection and classification of the performed hand gestures. ResNeXt-101 model achieves the state-of-the-art offline classification accuracy of 94.03% on EgoGesture benchmark and competitive results on NVIDIA benchmarks. In online recognition, we obtain very good performances with considerable early detections.
User-centred design of a digital advisory service: enhancing public agricultural extension for sustainable intensification in Tanzania

ABSTRACTSustainable intensification (SI) is promoted as a rural development paradigm for sub-Saharan Africa. Achieving SI requires smallholder farmers to have access to information that is context-specific, increases their decision-making capacities, and adapts to changing environments. Current extension services often struggle to address these needs. New mobile phone-based services can help. In order to enhance the public extension service in Tanzania, we created a digital service that addresses smallholder farmers’ different information needs for implementing SI. Using a co-design methodology – User-Centered Design – we elicited feedback from farmers and extension agents in Tanzania to create a new digital information service, called Ushauri. This automated hotline gives farmers access to a set of pre-recorded messages. Additionally, farmers can ask questions in a mailbox. Extension agents then listen to these questions through an online platform, where they record and send replies via automated push-calls. A test with 97 farmers in Tanzania showed that farmers actively engaged with the service to access agricultural advice. Extension agents were able to answer questions with reduced workload compared to conventional communication channels. This study illustrates how User-Centered Design can be used to develop information services for complex and resource-restricted smallholder farming contexts.
Combining deep features and activity context to improve recognition of activities of workers in groups

Abstract Automatic activity recognition plays an important role in addressing the efficiency issue of site management. In recent years, there has been an increasing interest in vision‐based activity recognition, while its relatively low recognition accuracy and speed impede the practical application. This paper introduces a discriminative model to combine deep activity features and contextual information to improve the recognition of activities of workers on foot in site surveillance videos. Specifically, a conditional random field (CRF) model is designed based on deep activity features, which are extracted with a single‐stream deep activity recognition network, and spatial relevance, which are obtained with a tracking‐by‐detection multiple‐object tracking method. We have evaluated various deep activity features, including action features, activity features, and joint features. Also, we have parameterized the contextual information of activities in terms of spatial relevance and represent the context with graphs of K ‐nearest neighbors. The experimental results show that the CRF model based on deep activity features and activity context can significantly improve activity recognition performance to 98.77% average accuracy by 22.10% from the baseline 77.67%, which is obtained using the single‐stream deep activity recognition network, with a small computational overhead of 0.025 ms per segment.
Using Video-Based Instruction via Augmented Reality to Teach Mathematics to Middle School Students With Learning Disabilities

The purpose of this study was to examine the effectiveness of video-based mathematics instruction for seven middle school students with specific learning disability (SLD), using an augmented reality-based training package. The dependent variable was the percentage of steps students performed correctly to solve each type of mathematics problem. The independent variable was the augmented reality video-based intervention, which used video to model the individual steps for solving four types of multistep mathematics problems: (a) addition and subtraction of integers, (b) multiplication and division of integers, (c) using ratio reasoning to convert measurement units, and (d) using multiplication and division to calculate rate of change. Results indicated a functional relation between the video-based mathematics intervention and the percentage of steps completed correctly for each type of problem. All seven participants showed significant gains immediately after receiving the intervention and maintained improved problem-solving skills in at least three out of the four problem categories.
Makespan-Driven Workflow Scheduling in Clouds Using Immune-Based PSO Algorithm

Cloud Computing is becoming more and more popular for solving problems that need high concurrency and a lot of resources. Many traditional areas of research choose to solve their problems through the cloud, and workflow scheduling is one of them. Cloud computing brings many benefits, meanwhile, due to the almost “infinite” amount of resources for users, it also brings new challenges for scheduling and optimization, in which cost and makespan are the most concerned issues for workflow scheduling. Users want to obtain a low cost and fast makespan solution. This paper focuses on how to find an optimized solution to achieve better cost-makespan at the same time under the constraint of deadline. In order to solve this problem, an immune particle swarm optimization algorithm (IMPSO) is proposed, which effectively improves the quality and speed of the optimization. The proposed IMPSO overcomes the problem of slow convergence of PSO, which is easy to fall into local optimization. Experiments show the efficiency and effectiveness of the proposed approach.