Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances

Recent approaches for weakly supervised instance segmentations depend on two components: (i) a pseudo label generation model which provides instances that are consistent with a given annotation; and (ii) an instance segmentation model, which is trained in a supervised manner using the pseudo labels as ground-truth. Unlike previous approaches, we explicitly model the uncertainty in the pseudo label generation process using a conditional distribution. The samples drawn from our conditional distribution provide accurate pseudo labels due to the use of semantic class aware unary terms, boundary aware pairwise smoothness terms, and annotation aware higher order terms. Furthermore, we represent the instance segmentation model as an annotation agnostic prediction distribution. In contrast to previous methods, our representation allows us to define a joint probabilistic learning objective that minimizes the dissimilarity between the two distributions. Our approach achieves state of the art results on the PASCAL VOC 2012 data set, outperforming the best baseline by $$4.2\%\ \text {mAP}^r_{0.5}$$ and $$4.8\%\ \text {mAP}^r_{0.75}$$ .
WhoReview: A multi-objective search-based approach for code reviewers recommendation in modern code review

Contemporary software development is distributed and characterized by high dynamics with continuous and frequent changes to fix defects, add new user requirements or adapt to other environmental changes. To manage such changes and ensure software quality, modern code review is broadly adopted as a common and effective practice. Yet several open-source as well as commercial software projects have adopted peer code review as a crucial practice to ensure the quality of their software products using modern tool-based code review. Nevertheless, the selection of peer reviewers is still merely a manual and hard task especially with the growing size of distributed development teams. Indeed, it has been proven that inappropriate peer reviewers selection can consume more time and effort from both developers and reviewers and increase the development costs and time to market. To address this problem, we introduce a multi-objective search-based approach, named WhoReview, to find the optimal set of peer reviewers for code changes. We use the Indicator-Based Evolutionary Algorithm (IBEA) to find the best set of code reviewers that are (1) most experienced with the code change to be reviewed, while (2) considering their current workload, i.e., the number of open code reviews they are working on. We conduct an empirical study on 4 long-lived open source software projects to evaluate our approach. The obtained results show that WhoReview outperforms state-of-the-art approach by an average precision of 68% and recall of 77%. Moreover, we deployed our approach in an industrial context and evaluated it qualitatively from developers perspective. Results show the effectiveness of our approach with a high acceptance ratio in identifying relevant reviewers.
An efficient approach to the map-reduce framework and genetic algorithm based whale optimization algorithm for task scheduling in cloud computing environment

In the digital era, cloud computing has emerged a significant service in the IT sector. It ensures pooling of resources and provided services on-demand over the web. The scheduling of appropriate tasks is an important aspect in cloud computing in which many researches are carried out. The users demand for resources are volatile in nature and hence when a large count of resources is requested, the computational overhead in cloud is supposed to effectively allocate resources and also to complete these tasks. The research issue includes as how a VM can schedule these tasks in an effective manner. This paper proposes an efficient approach using the MAP reducing framework and GA-WOA for efficient scheduling of tasks in the given cloud. Initially, the task features are extracted from the client’s task. Then, the features are reduced by using the MRQFLDA algorithm. After that, the large tasks are separated into sub-tasks using a map-reduce framework. Finally, the tasks are efficiently scheduled by using the GA-WOA algorithm. The experimental simulations are carried out using the cloudsim environment. The results show that the proposed method GA-WOA outperforms the other methods in terms of various metrics used for the evaluation.
OnlineAugment: Online Data Augmentation with Less Domain Knowledge

Data augmentation is one of the most important tools in training modern deep neural networks. Recently, great advances have been made in searching for optimal augmentation policies in the image classification domain. However, two key points related to data augmentation remain uncovered by the current methods. First is that most if not all modern augmentation search methods are offline and learning policies are isolated from their usage. The learned policies are mostly constant throughout the training process and are not adapted to the current training model state. Second, the policies rely on class-preserving image processing functions. Hence applying current offline methods to new tasks may require domain knowledge to specify such kind of operations. In this work, we offer an orthogonal online data augmentation scheme together with three new augmentation networks, co-trained with the target learning task. It is both more efficient, in the sense that it does not require expensive offline training when entering a new domain, and more adaptive as it adapts to the learner state. Our augmentation networks require less domain knowledge and are easily applicable to new tasks. Extensive experiments demonstrate that the proposed scheme alone performs on par with the state-of-the-art offline data augmentation methods, as well as improving upon the state-of-the-art in combination with those methods.
Image encryption using q-deformed logistic map

A chaotic image encryption algorithm based on a deformation of the logistic map is proposed. The use of a deformed chaotic map has several advantages. In particular, we are able to not only enlarge the key space size but also describe a parametric region for the selection of a key avoiding non-chaotic regions, which is a fundamental requirement for real applications. Moreover, a security analysis of the proposed algorithm shows its efficiency and robustness in resisting attacks.
Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games

The Nash equilibrium is an important concept in game theory. It describes the least exploitability of one player from any opponents. We combine game theory, dynamic programming, and recent deep reinforcement learning (DRL) techniques to online learn the Nash equilibrium policy for two-player zero-sum Markov games (TZMGs). The problem is first formulated as a Bellman minimax equation, and generalized policy iteration (GPI) provides a double-loop iterative way to find the equilibrium. Then, neural networks are introduced to approximate Q functions for large-scale problems. An online minimax Q network learning algorithm is proposed to train the network with observations. Experience replay, dueling network, and double Q-learning are applied to improve the learning process. The contributions are twofold: 1) DRL techniques are combined with GPI to find the TZMG Nash equilibrium for the first time and 2) the convergence of the online learning algorithm with a lookup table and experience replay is proven, whose proof is not only useful for TZMGs but also instructive for single-agent Markov decision problems. Experiments on different examples validate the effectiveness of the proposed algorithm on TZMG problems.
Learning Medical Image Denoising with Deep Dynamic Residual Attention Network

Image denoising performs a prominent role in medical image analysis. In many cases, it can drastically accelerate the diagnostic process by enhancing the perceptual quality of noisy image samples. However, despite the extensive practicability of medical image denoising, the existing denoising methods illustrate deficiencies in addressing the diverse range of noise appears in the multidisciplinary medical images. This study alleviates such challenging denoising task by learning residual noise from a substantial extent of data samples. Additionally, the proposed method accelerates the learning process by introducing a novel deep network, where the network architecture exploits the feature correlation known as the attention mechanism and combines it with spatially refine residual features. The experimental results illustrate that the proposed method can outperform the existing works by a substantial margin in both quantitative and qualitative comparisons. Also, the proposed method can handle real-world image noise and can improve the performance of different medical image analysis tasks without producing any visually disturbing artefacts.
Effects of Emotion and Agency on Presence in Virtual Reality

Arguably one of the most important characteristics of virtual reality (VR) is its ability to induce higher feelings of presence. Still, research has remained inconclusive on how presence is affected by human factors such as emotion and agency. Here we adopt a novel design to investigate their effects by testing virtual environments inducing either happiness or fear, with or without user agency. Results from 121 participants showed that the dominant emotion induced by a virtual environment is positively correlated with presence. In addition, agency had a significant positive effect on presence and, furthermore, moderated the effect of emotion on presence. We show for the first time that the effects of emotion and agency on presence are not straightforward but they can be modelled by separating design factors from subjective measures. We discuss how these findings can explain seemingly conflicting results of related work and their implications for VR design.
FuzzyGCP: A deep learning architecture for automatic spoken language identification from speech signals

In this modern era, language has no geographic boundary. Therefore, for developing an automated system for search engines using audio, tele-medicine, emergency service via phone etc., the first and foremost requirement is to identify the language. The fundamental difficulty of automatic speech recognition is that the speech signals vary significantly due to different speakers, speech variation, language variation, age and sex wise voice modulation variation, contents and acoustic conditions and so on. In this paper, we have proposed a deep learning based ensemble architecture, called FuzzyGCP, for spoken language identification from speech signals. This architecture combines the classification principles of a Deep Dumb Multi Layer Perceptron (DDMLP), Deep Convolutional Neural Network (DCNN) and Semi-supervised Generative Adversarial Network (SSGAN) to increase the precision to maximum and finally applies Ensemble learning using Choquet integral to predict the final output, i.e., the language class. We have evaluated our model on four standard benchmark datasets comprising of two Indic language datasets and two foreign language datasets. Irrespective of the languages, the F1-score of the proposed language identification model is as high as 98% in MaSS dataset and worst performance is that of 67% on the VoxForge dataset which is much better compared to maximum of 44% by state-of-the-art models on multi-class classification. The link to the source code of our model is available here.
Universal Semantic Segmentation for Fisheye Urban Driving Images

Semantic segmentation is a critical method in the field of autonomous driving. When performing semantic image segmentation, a wider field of view (FoV) helps to obtain more information about the surrounding environment, making automatic driving safer and more reliable, which could be offered by fisheye cameras. However, large public fisheye datasets are not available, and the fisheye images captured by the fisheye camera with large FoV comes with large distortion, so commonly-used semantic segmentation model cannot be directly utilized. In this paper, a seven degrees of freedom (DoF) augmentation method is proposed to transform rectilinear image to fisheye image in a more comprehensive way. In the training process, rectilinear images are transformed into fisheye images in seven DoF, which simulates the fisheye images taken by cameras of different positions, orientations and focal lengths. The result shows that training with the seven-DoF augmentation can improve the model's accuracy and robustness against different distorted fisheye data. This seven-DoF augmentation provides a universal semantic segmentation solution for fisheye cameras in different autonomous driving applications. Also, we provide specific parameter settings of the augmentation for autonomous driving. At last, we tested our universal semantic segmentation model on real fisheye images and obtained satisfactory results. The code and configurations are released at https://github.com/Yaozhuwa/FisheyeSeg.
Complexity of mixed Gaussian states from Fisher information geometry

A bstract We study the circuit complexity for mixed bosonic Gaussian states in harmonic lattices in any number of dimensions. By employing the Fisher information geometry for the covariance matrices, we consider the optimal circuit connecting two states with vanishing first moments, whose length is identified with the complexity to create a target state from a reference state through the optimal circuit. Explicit proposals to quantify the spectrum complexity and the basis complexity are discussed. The purification of the mixed states is also analysed. In the special case of harmonic chains on the circle or on the infinite line, we report numerical results for thermal states and reduced density matrices.
A hybrid deep learning architecture for opinion-oriented multi-document summarization based on multi-feature fusion

Opinion summarization is a process to produce concise summaries from a large number of opinionated texts. In this paper, we present a novel deep-learning-based method for the generic opinion-oriented extractive summarization of multi-documents (also known as RDLS). The method comprises sentiment analysis embedding space (SAS), text summarization embedding spaces (TSS) and opinion summarizer module (OSM). SAS employs recurrent neural network (RNN) which is composed by long short-term memory (LSTM) to take advantage of sequential processing and overcome several flaws in traditional methods, where order and information about a word have vanished. Furthermore, it uses sentiment knowledge, sentiment shifter rules and multiple strategies to overcome the existing drawbacks. TSS exploits multiple sources of statistical and linguistic knowledge features to augment word-level embedding and extract a proper set of sentences from multiple documents. TSS also uses the Restricted Boltzmann Machine algorithm to enhance and optimize those features and improve resultant accuracy without losing any important information. OSM consists of two phases: sentence classification and sentence selection which work together to produce a useful summary. Experiment results show that RDLS outperforms other existing methods. Moreover, the ensemble of statistical and linguistic knowledge, sentiment knowledge, sentiment shifter rules and word-embedding model allows RLDS to achieve significant accuracy.
A fractal interpolation approach to improve neural network predictions for difficult time series data

Deep Learning methods, such as Long Short-Term Memory (LSTM) neural networks prove capable of predicting real-life time series data. Crucial for this technique to work is a sufficient amount of data. This can either be very long time-series data or fine-grained time-series data. If the data is insufficient, in terms of length or complexity, LSTM approaches perform poorly. We propose a fractal interpolation approach to generate a more fine-grained time series out of insufficient data sets. The interpolation is dynamically adapted to the time series using a time-dependent complexity feature so that the complexity properties of the interpolated time series are related to that of the original one. Also we perform a linear interpolation with the same number of interpolation points to compare results. This paper shows that predictions of fractal interpolated and linear interpolated time series clearly outperform the ones of the original data for a test fit on unknown data. Though predictions of linear and fractal interpolated time series data perform very similar, the fractal interpolated ones outperform the linear interpolated ones on difficult time series data. Also, though the complexities of sub-intervals are tailored to match the one from the original data, the interpolated time series shows a much higher degree of persistency and (in terms of the Hurst exponent) a much higher degree of long term memory.
An Iterative Two-Phase Optimization Method Based on Divide and Conquer Framework for Integrated Scheduling of Multiple UAVs

Task scheduling of multiple UAVs has become a highly active area of research in recent years. Previous research has generally solved the problem in a whole manner, which makes it hard to efficiently generate high-quality task scheduling schemes due to prohibitive computational complexity. By contrast, the paper constructs a novel divide and conquer framework for multi-UAV task scheduling (DCF), which partitions the original multi-UAV scheduling problem into multiple scheduling sub-problems for all the UAVs. To be specific, DCF includes two phases: one is the task allocation phase which produces multiple scheduling sub-problems and the other is the single UAV scheduling phase which generates the scheduling scheme with sequential tasks for each single UAV considering constraints involving UAV capabilities and task demands. Two phases are iteratively performed until the predefined stopping criteria are met. In the task allocation phase, we propose a tabu-list-based simulated annealing (SATL) algorithm to realize task allocation among multiple UAVs. After obtaining the task allocation scheme, a satisfactory scheduling scheme of each single UAV is generated by variable neighborhood descent (VND) algorithm. Extensive experiments and comparative studies are conducted, demonstrating the efficiency of DCF and the proposed SATL-VND algorithm.
Eye movement‐related brain potentials during assisted navigation in real‐world environments

Conducting neuroscience research in the real-world remains challenging because of movement- and environment-related artifacts as well as missing control over stimulus presentation. The present study overcame these restrictions by mobile electroencephalography (EEG) and data-driven analysis approaches during a real-world navigation task. During assisted navigation through an unfamiliar city environment, participants received either standard or landmark-based auditory navigation instructions. EEG data were recorded continuously during navigation. Saccade- and blink-events as well as gait-related EEG activity were extracted from sensor level data. Brain activity associated with the navigation task was identified by subsequent source-based cleaning of non-brain activity and unfolding of overlapping event-related potentials. When navigators received landmark-based instructions compared to those receiving standard navigation instructions, the blink-related brain potentials during navigation revealed higher amplitudes at fronto-central leads in a time window starting at 300 ms after blinks, which was accompanied by improved spatial knowledge acquisition tested in follow-up spatial tasks. Replicating improved spatial knowledge acquisition from previous experiments, the present study revealed eye movement-related brain potentials to point to the involvement of higher cognitive processes and increased processing of incoming information during periods of landmark-based instructions. The study revealed neuronal correlates underlying visuospatial information processing during assisted navigation in the real-world providing a new analysis approach for neuroscientific research in freely moving participants in uncontrollable real-world environments.
A Fair Comparison of Message Queuing Systems

The production and real-time usage of streaming data bring new challenges for data systems due to huge volume of streaming data and quick response request of applications. Message queuing systems that offer high throughput and low latency play an important role in today's big streaming data processing. There are several popular message queuing systems in production usage and also many in-lab message queuing systems in academia. These systems with different design philosopies have different characteristics. It is non-trivial for a non-expert to choose a suitable system to meet his specific requirement. With this premise, our primary contribution is to provide the community with a fair comparison among message queuing systems, using a standardized comparison metric and reproducible experimental environment. Five typical message queuing systems (including Kafka, RabbitMQ, RocketMQ, ActiveMQ and Pulsar) are evaluated qualitatively (in analysis) and quantitatively (in experimental results). This article also highlights the distinct features of each system and summarizes the best-suited use cases of each system. The fair comparison and the insight analysis provided in this article can help users choose the best-suited message queuing systems.
Control-oriented UAV highly feasible trajectory planning: A deep learning method

The highly feasible trajectory planning of unmanned aerial vehicle (UAV) is very important in some tasks but has not yet attracted sufficient study attention. Most current studies use simplified UAV model with some state constraints to plan the trajectory, but the feasibility is reduced, because the simplified model is very different from the actual UAV system, so that the tracking characteristics of UAV cannot be fully considered. In this paper, a novel control-oriented UAV highly feasible trajectory planning method is proposed. First, a UAV closed-loop model prediction method, which is the combination of a low-level controller and a UAV 6 DOF nonlinear model, is adopted in the trajectory planning phase to predict the flight trajectory. This complicated model is very similar to the actual UAV system because it comprehensively considers the controller performance and the detailed UAV model, but it also has poor efficiency. Therefore, a trajectory-mapping network (TMN) is proposed using a deep learning approach to improve the planning efficiency. Furthermore, a novel time-series convolutional neural network (TSCNN) is proposed for the TMN to further improve its computation speed and prediction accuracy. Finally, the flight trajectory predicted by the TMN is used to evaluate the planning cost. In this way, the planned trajectory will be highly feasible. The effectiveness of the proposed method is demonstrated by simulations.
Blockchain-Based Multimedia Content Protection: Review and Open Challenges

In this paper, we provide a holistic survey of multimedia content protection applications in which blockchain technology is being used. A taxonomy is developed to classify these applications with reference to the technical aspects of blockchain technology, content protection techniques, namely, encryption, digital rights management, digital watermarking and fingerprinting (or transaction tracking), and performance criteria. The study of the literature reveals that there is currently no complete and systematic taxonomy dedicated to blockchain-based copyright protection applications. Moreover, the number of successfully developed blockchain-based content protection systems is very low. This points towards a research gap. To fill this gap, we propose a taxonomy that integrates technical aspects and application knowledge and can guide the researchers towards the development of blockchain-based multimedia copyright protection systems. Furthermore, the paper discusses some technical challenges and outlines future research directions.