Reviewerly/exHarmony huggingface dataset:
RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization

Video action detectors are usually trained using datasets with fully-supervised temporal annotations. Building such datasets is an expensive task. To alleviate this problem, recent methods have tried to leverage weak labeling, where videos are untrimmed and only a video-level label is available. In this paper, we propose RefineLoc, a novel weaklysupervised temporal action localization method. RefineLoc uses an iterative refinement approach by estimating and training on snippet-level pseudo ground truth at every iteration. We show the benefit of this iterative approach and present an extensive analysis of five different pseudo ground truth generators. We show the effectiveness of our model on two standard action datasets, ActivityNet v1.2 and THUMOS14. RefineLoc shows competitive results with the stateof-the-art in weakly-supervised temporal localization. Additionally, our iterative refinement process is able to significantly improve the performance of two state-of-the-art methods, setting a new state-of-the-art on THUMOS14.
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network

Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More-over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements.
Within-Project Defect Prediction of Infrastructure-as-Code Using Product and Process Metrics

Infrastructure-as-code (IaC) is the DevOps practice enabling management and provisioning of infrastructure through the definition of machine-readable files, hereinafter referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IaC scripts</i> . Similarly to other source code artefacts, these files may contain defects that can preclude their correct functioning. In this paper, we aim at assessing the role of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">product</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">process</i> metrics when predicting defective IaC scripts. We propose a fully integrated machine-learning framework for IaC Defect Prediction, that allows for repository crawling, metrics collection, model building, and evaluation. To evaluate it, we analyzed 104 projects and employed five machine-learning classifiers to compare their performance in flagging suspicious defective IaC scripts. The key results of the study report <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Random Forest</small> as the best-performing model, with a median AUC-PR of 0.93 and MCC of 0.80. Furthermore, at least for the collected projects, product metrics identify defective IaC scripts more accurately than process metrics. Our findings put a baseline for investigating IaC Defect Prediction and the relationship between the product and process metrics, and IaC scripts’ quality.
RGPNet: A Real-Time General Purpose Semantic Segmentation

We propose a real-time general purpose semantic segmentation architecture, RGPNet, which achieves significant performance gain in complex environments. RGPNet consists of a light-weight asymmetric encoder-decoder and an adaptor. The adaptor helps preserve and refine the abstract concepts from multiple levels of distributed representations between encoder and decoder. It also facilitates the gradient flow from deeper layers to shallower layers. Our experiments demonstrate that RGPNet can generate segmentation results in real-time with comparable accuracy to the state-of-the-art non-real-time heavy models. Moreover, towards green AI, we show that using an optimized label-relaxation technique with progressive resizing can reduce the training time by up to 60% while preserving the performance. We conclude that RGPNet obtains a better speed-accuracy trade-off across multiple datasets.
Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images

Aerial images are increasingly used for critical tasks, such as traffic monitoring, pedestrian tracking, and infrastructure inspection. However, aerial images have the following main challenges: 1) small objects with non-uniform distribution; 2) the large difference in object size. In this paper, we propose a new network architecture, Cluster Region Estimation Network (CRENet), to solve these challenges. CRENet uses a clustering algorithm to search cluster regions containing dense objects, which makes the detector focus on these regions to reduce background interference and improve detection efficiency. However, not every cluster region can bring precision gain, so each cluster region difficulty score is calculated to mine the difficult region and eliminate the simple cluster region, which can speed up the detection. Then, a Gaussian scaling function(GSF) is used to scale the difficult cluster region to reduce the difference of object size. Our experiments show that CRENet achieves better performance than previous approaches on the VisDrone dataset. Our best model achieved 4.3 $$\%$$ improvement on the VisDrone dataset.
Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes

Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by many as black boxes. For practical use, deep learning models require explanations that are intuitive to clinicians. In this study, we developed a deep neural network model to predict outcomes following major cardiovascular procedures, using temporal image representation of past medical history as input. We created a novel explanation for the prediction of the model by defining impact scores that associate clinical observations with the outcome. For comparison, a logistic regression model was fitted to the same dataset. We compared the impact scores and log odds ratios by calculating three types of correlations, which provided a partial validation of the impact scores. The deep neural network model achieved an area under the receiver operating characteristics curve (AUC) of 0.787, compared to 0.746 for the logistic regression model. Moderate correlations were found between the impact scores and the log odds ratios. Impact scores generated by the explanation algorithm has the potential to shed light on the "black box" deep neural network model and could facilitate its adoption by clinicians.
Analysis of sentiment expressions for user-centered design

Devising intelligent systems capable of identifying the idiosyncratic needs of users at scale and translating them into attribute-level design feedback and recommendations is a key prerequisite for successful user-centered design processes. Recent studies show that 49% of design firms lack systems and tools for monitoring external platforms, and only 8% have adopted digital, data-driven approaches for new product development despite acknowledging them as a high priority. The state-of-the-art attribute-level sentiment analysis approaches based on deep learning have achieved promising results; however, these methods pose strict preconditions, require manually labeled data for training and pre-defined attributes by experts, and only classify sentiments intro predefined categories which have limited implications for designers. This article develops a rule-based methodology for extracting and analyzing the sentiment expressions of users on a large scale, from myriad reviews available on social media and e-commerce platforms. The methodology further advances current unsupervised attribute-level sentiment analysis approaches by enabling efficient identification and mapping of sentiment expressions of individual users onto their respective attributes. Experiments on a large dataset scraped from a major e-commerce retail store for apparel and indicate 74.3%–93.8% precision in extracting attribute-level sentiment expressions of users and demonstrate the feasibility and potentials of the developed methodology for large-scale need finding from user reviews.
Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning

Image outpainting is a very intriguing problem as the outside of a given image can be continuously filled by considering as the context of the image. This task has two main challenges. The first is to maintain the spatial consistency in contents of generated regions and the original input. The second is to generate a high-quality large image with a small amount of adjacent information. Conventional image outpainting methods generate inconsistent, blurry, and repeated pixels. To alleviate the difficulty of an outpainting problem, we propose a novel image outpainting method using bidirectional boundary region rearrangement. We rear-range the image to benefit from the image inpainting task by reflecting more directional information. The bidirectional boundary region rearrangement enables the generation of the missing region using bidirectional information similar to that of the image inpainting task, thereby generating the higher quality than the conventional methods using unidirectional information. Moreover, we use the edge map generator that considers images as original input with structural information and hallucinates the edges of unknown regions to generate the image. Our proposed method is compared with other state-of-the-art outpainting and inpainting methods both qualitatively and quantitatively. We further compared and evaluated them using BRISQUE, one of the No-Reference image quality assessment (IQA) metrics, to evaluate the naturalness of the output. The experimental results demonstrate that our method outperforms other methods and generates new images with 360°panoramic characteristics.
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction

The study of neurodegenerative diseases relies on the reconstruction and analysis of the brain cortex from magnetic resonance imaging (MRI). Traditional frameworks for this task like FreeSurfer demand lengthy runtimes, while its accelerated variant FastSurfer still relies on a voxel-wise segmentation which is limited by its resolution to capture narrow continuous objects as cortical surfaces. Having these limitations in mind, we propose DeepCSR, a 3D deep learning framework for cortical surface reconstruction from MRI. Towards this end, we train a neural network model with hypercolumn features to predict implicit surface representations for points in a brain template space. After training, the cortical surface at a desired level of detail is obtained by evaluating surface representations at specific coordinates, and subsequently applying a topology correction algorithm and an isosurface extraction method. Thanks to the continuous nature of this approach and the efficacy of its hypercolumn features scheme, DeepCSR efficiently reconstructs cortical surfaces at high resolution capturing fine details in the cortical folding. Moreover, DeepCSR is as accurate, more precise, and faster than the widely used FreeSurfer toolbox and its deep learning powered variant FastSurfer on reconstructing cortical surfaces from MRI which should facilitate large-scale medical studies and new healthcare applications.
A Blockchain-Assisted Massive IoT Data Collection Intelligent Framework

Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this article, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework: 1) we design a large-scale heterogeneous WSNs collaborative identity verification protocol to ensure reliable data sources; 2) we build a hierarchical massive data aggregation scheme to collect massive IoT data efficiently and securely; and 3) we depict a blockchain-based massive IoT data management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.
A neurodynamic optimization approach to supervised feature selection via fractional programming

Feature selection is an important issue in machine learning and data mining. Most existing feature selection methods are greedy in nature thus are prone to sub-optimality. Though some global feature selection methods based on unsupervised redundancy minimization can potentiate clustering performance improvements, their efficacy for classification may be limited. In this paper, a neurodynamics-based holistic feature selection approach is proposed via feature redundancy minimization and relevance maximization. An information-theoretic similarity coefficient matrix is defined based on multi-information and entropy to measure feature redundancy with respect to class labels. Supervised feature selection is formulated as a fractional programming problem based on the similarity coefficients. A neurodynamic approach based on two one-layer recurrent neural networks is developed for solving the formulated feature selection problem. Experimental results with eight benchmark datasets are discussed to demonstrate the global convergence of the neural networks and superiority of the proposed neurodynamic approach to several existing feature selection methods in terms of classification accuracy, precision, recall, and F-measure.
ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications

To train deep learning models for vision-based action recognition of elders' daily activities, we need large-scale activity datasets acquired under various daily living environments and conditions.However, most public datasets used in human action recognition either differ from or have limited coverage of elders' activities in many aspects, making it challenging to recognize elders' daily activities well by only utilizing existing datasets.Recently, such limitations of available datasets have actively been compensated by generating synthetic data from realistic simulation environments and using those data to train deep learning models.In this paper, based on these ideas we develop ElderSim, an action simulation platform that can generate synthetic data on elders' daily activities.For 55 kinds of frequent daily activities of the elders, ElderSim generates realistic motions of synthetic characters with various adjustable data-generating options and provides different output modalities including RGB videos, two-and three-dimensional skeleton trajectories.We then generate KIST SynADL, a large-scale synthetic dataset of elders' activities of daily living, from ElderSim and use the data in addition to real datasets to train three state-of-the-art human action recognition models.From the experiments following several newly proposed scenarios that assume different real and synthetic dataset configurations for training, we observe a noticeable performance improvement by augmenting our synthetic data.We also offer guidance with insights for the effective utilization of synthetic data to help recognize elders' daily activities.
The effect of water immersion on vection in virtual reality

Abstract Research about vection (illusory self-motion) has investigated a wide range of sensory cues and employed various methods and equipment, including use of virtual reality (VR). However, there is currently no research in the field of vection on the impact of floating in water while experiencing VR. Aquatic immersion presents a new and interesting method to potentially enhance vection by reducing conflicting sensory information that is usually experienced when standing or sitting on a stable surface. This study compares vection, visually induced motion sickness, and presence among participants experiencing VR while standing on the ground or floating in water. Results show that vection was significantly enhanced for the participants in the Water condition, whose judgments of self-displacement were larger than those of participants in the Ground condition. No differences in visually induced motion sickness or presence were found between conditions. We discuss the implication of this new type of VR experience for the fields of VR and vection while also discussing future research questions that emerge from our findings.
A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA

Abstract The explosive growth of textual data on the web and the problem of obtaining desired information through this enormous volume of data has led to a dramatic increase in demand for developing automatic text summarization systems. For this reason, this paper presents a novel multi-document text summarization approach, called MTSQIGA, which extracts salient sentences from source document collection to generate the summary. The proposed generic summarizer models extractive summarization as a binary optimization problem that applies a modified quantum-inspired genetic algorithm (QIGA) in its processing stage to find the best solution. Objective function of our approach plays an important role in optimizing linear combination of coverage, relevance, and redundancy factors which consists of six sentence scoring measures. To ensures the generation of a summary with predefined length limit, the presented QIGA employs a modified quantum measurement and a self-adaptive quantum rotation gate based on the quality and length of the summary. Evaluation of the proposed system was performed on DUC 2005 and 2007 benchmark datasets in terms of ROUGE standard measures. Comparison of MTSQIGA with existing state-of-the-art approaches for multi-document summarization shows superior performance of the proposed systems over other methods on both existing benchmark datasets. It also indicates promising efficiency of our proposed algorithm on applying quantum-inspired genetic algorithm to the text summarization tasks.
A clustering and ensemble based classifier for data stream classification

In the era of data mining, the research industry has great attention to data stream mining as well as it has a great impact on a wide range of applications like networking, telecommunication, education, banking, weather forecasting, a stock market, and so on. Because of these data stream mining having more attention from researchers. The handling of concept drifting data streams is one of the major issues and challenges in the data stream mining field. In the presence of the concept drift, the performance of the learning algorithm always degrades. In this paper, a hybrid method has been proposed which are the combination of an ensemble, and grid and density-based clustering methods. The proposed method is tested on both synthetic as well as real data. The proposed method works well in the presence of concept drift and performance is measured in terms of time, accuracy, and memory. As compared with the state-of-art algorithms, the proposed method performed well and gave better accuracy using synthetic datasets like 88.29%, 71.34%, and 75.39% for Hyperplane, RBF, and LED respectively and for real datasets 86.17%, 86.28%, 95.15%, and 99.83% for Adult, Census-Income, KDDCup99%–10%, and Covertype respectively.
WITHDRAWN: An energy efficient load balancing on cloud computing using adaptive cat swarm optimization

Cloud has been a dominant player in information technology in recent years. To meet the growing demands for computing and storage resources, cloud providers deploy data centers (DC) across the globe that includes hundreds of thousands of IT servers. Due to massive energy use, every DC leave a huge carbon footprint and thus, have bad effects on the environment. Because of this, efficient computing resource utilization and minimization of energy usage are critical issues to create cloud computing more booming. Efficient load balancing is the primary means of solving these problems. In order to overcome optimization problems, this study has developed a load balancing system using adaptive cat swarm optimization (ACSO) algorithm. The efficiency of the proposed technique is analyzed by using different value metrics and performance differentiated with other methods.
A QoE adaptive management system for high definition video streaming over wireless networks

The development of the smart devices had led to demanding high-quality streaming videos over wireless communications. In Multimedia technology, the Ultra-High Definition (UHD) video quality has an important role due to the smart devices that are capable of capturing and processing high-quality video content. Since delivery of the high-quality video stream over the wireless networks adds challenges to the end-users, the network behaviors ‘factors such as delay of arriving packets, delay variation between packets, and packet loss, are impacted on the Quality of Experience (QoE). Moreover, the characteristics of the video and the devices are other impacts, which influenced by the QoE. In this research work, the influence of the involved parameters is studied based on characteristics of the video, wireless channel capacity, and receivers’ aspects, which collapse the QoE. Then, the impact of the aforementioned parameters on both subjective and objective QoE is studied. A smart algorithm for video stream services is proposed to optimize assessing and managing the QoE of clients (end-users). The proposed algorithm includes two approaches: first, using the machine-learning model to predict QoE. Second, according to the QoE prediction, the algorithm manages the video quality of the end-users by offering better video quality. As a result, the proposed algorithm which based on the least absolute shrinkage and selection operator (LASSO) regression is outperformed previously proposed methods for predicting and managing QoE of streaming video over wireless networks.