Reviewerly/exHarmony huggingface dataset:
Chaotic improved PICEA-g-based multi-objective optimization for workflow scheduling in cloud environment

Mapping of workflow tasks to computational resources in the cloud environment has engendered research interest in workflow scheduling. As workflow scheduling belongs to NP-complete problem, so building an optimum workflow scheduler with reasonable performance and computation speed is very challenging in the heterogeneous distributed environment of clouds. Many existing studies deal with cloud workflow scheduling as a single or bi-objective optimization problem without considering some important requirements of the users or the providers. Therefore, it is highly desirable to formulate scheduling of the workflow applications as a Multi-objective Optimization Problem (MOP) taking into account the requirements from the user and the service provider. For example, the cloud workflow scheduler might wish to consider user’s Quality of Service (QoS) objectives, such as makespan and cost, as well as provider’s objectives, such as energy efficiency over the Virtual Machines (VMs). In addition, early convergence in existing algorithms is a problem that increases the number of repetitions for reaching a global optimum. To overcome these drawbacks, in this paper, an enhanced multi-objective co-evolutionary algorithm, called ch-PICEA-g, is proposed as an effective heuristic algorithm, where the logistic and tent maps as two chaotic systems are applied in generating chaotic values to overcome the permute convergence in the initial population and the genetic operators. Also, an improved fitness function is applied to increase the performance of original PICEA-g. The functionality of the proposed algorithm is validated by extensive experiments. The obtained results indicate that this proposed algorithm outperforms its counterparts in terms of different performance metrics.
Statistical Power and Translationese in Machine Translation Evaluation

The term translationese has been used to describe features of translated text, and in this paper, we provide detailed analysis of potential adverse effects of translationese on machine translation evaluation.Our analysis shows differences in conclusions drawn from evaluations that include translationese in test data compared to experiments that tested only with text originally composed in that language.For this reason we recommend that reverse-created test data be omitted from future machine translation test sets.In addition, we provide a reevaluation of a past machine translation evaluation claiming human-parity of MT.One important issue not previously considered is statistical power of significance tests applied to comparison of human and machine translation.Since the very aim of past evaluations was the investigation of ties between human and MT systems, power analysis is of particular importance, to avoid, for example, claims of human parity simply corresponding to Type II error resulting from the application of a low powered test.We provide detailed analysis of tests used in such evaluations to provide an indication of a suitable minimum sample size for future studies.
Blockchain for aerospace and defense: Opportunities and open research challenges

Blockchain is a promising and emerging technology that can have immense potential to provide decentralized trust, data security and integrity, traceability, transparency, visibility, and auditability across various areas in the aerospace and defense industries. In this survey, we study how blockchain features, platforms, and architectures can be leveraged to reshape and revolutionize modern aerospace and defense ecosystems and applications. We also provide discussion into key blockchain complementary architectural technologies such as Oracles, IPFS, and Filecoin. Furthermore, we discuss key opportunities and roles for blockchain in aerospace and military applications. We investigate such opportunities by illustrating multiple use case scenarios including battlefield operations management, border protection, swarm assistance for rescue operations, logistics and supply chain management. Moreover, we compare the existing blockchain platforms to analyze their suitability and applicability for aerospace and defense applications. Finally, we identify and discuss several open issues and challenges for the widespread adoption of blockchain in the aerospace and defense industry. These include latency, privacy, regulation and law, large data traffic, smart contract security, and interoperability.
A Lightweight Faster R-CNN for Ship Detection in SAR Images

Deep learning algorithms have been widely utilized for synthetic aperture radar (SAR) target detection. Nevertheless, the traditional feature extraction methods and deep learning methods achieve improved ship detection accuracy at a cost of increased complexity and lower detection speed. As detection speed also is meaningful, especially in real-time maritime rescue and emergency military decision-making applications, we propose a new framework of faster region-based convolutional neural network (R-CNN) detection method to handle this problem. A new lightweight basic network with feature relay amplification and multiscale feature jump connection structure is designed to extract the features of each scale target in the SAR images, so as to improve its recognition and localization task network. Moreover, the K-Means method is used to obtain the distribution of the target scale, which enables to select more appropriate preset anchor boxes to reduce the difficulty of network learning. Finally, RoIAlign instead of region of interest (RoI) Pooling is used to reduce the quantization error during positioning. Experimental results show that the detection performance of the proposed method achieves 0.898 average precision (AP), which is 2.78% better than the conventional Faster R-CNN and 800% faster detection speed.
Personalized APIs Recommendation With Cognitive Knowledge Mining for Industrial Systems

With the prevalence of web techniques and Internet-of-Things networks, an increasing number of developers build software by invoking existing application programming interfaces (APIs), especially in industrial systems. As the number of existing APIs in industrial systems is large, it is critical to recommend suitable APIs from big APIs data to developers in industrial software development. There have been some approaches proposed for APIs recommendation, but the existing approaches focus on the utilization of historical invocation records but ignore the exploitation of other information in the development process. We find that this ignored information can be mined as cognitive knowledge to learn the behavior rules of developers. In this article, we propose a holistic personalized recommendation framework that contains two individual models and one ensemble model, which are based on joint matrix factorization and cognitive knowledge mining. In the two individual models, we study the hidden relationships among users, which are mined from the APIs following records. We also study the hidden relationships among APIs, which are mined from the content information. We also propose an ensemble model. We crawled a large real-word dataset and conducted sufficient experiments, and compared our framework with well-known existing methods. The experimental results demonstrate that our framework achieves the best performance.
Automatic pancreas segmentation based on lightweight DCNN modules and spatial prior propagation

Nowadays, pancreas segmentation in CT scans has gained more and more attention for computer-assisted diagnosis of inflammation (pancreatitis) or cancer. Despite the thrilling success of deep convolutional neural networks (DCNNs) in automatic pancreas segmentation, the heavy computational complexity of such networks impedes the deployment in clinical applications. To alleviate this issue, this paper establishes a novel end-to-end DCNN model for pursuing high-accurate automatic pancreas segmentation but with low computational cost. Specifically, built upon a simplified FCN architecture, we propose two novel network modules, named as the scale-transferrable feature fusion module (STFFM) and prior propagation module (PPM), respectively, for pancreas segmentation. Equipped with the scale-transferrable operation, STFFM can learn rich fusion features but with very lightweight network architecture. By dynamically adapting the spatial prior to the input slice data as well as the deep feature maps, PPM enables the network model to explore informative spatial priors for pancreas segmentation. Comprehensive experiments on the NIH dataset and the MSD dataset are conducted to evaluate the proposed approach. The obtained experimental results demonstrate that our approach can effectively reduce the computational cost and simultaneously archive the outperforming performance when compared to the state-of-the-art methods.
Hard Negative Examples are Hard, but Useful

Triplet loss is an extremely common approach to distance metric learning. Representations of images from the same class are optimized to be mapped closer together in an embedding space than representations of images from different classes. Much work on triplet losses focuses on selecting the most useful triplets of images to consider, with strategies that select dissimilar examples from the same class or similar examples from different classes. The consensus of previous research is that optimizing with the hardest negative examples leads to bad training behavior. That’s a problem – these hardest negatives are literally the cases where the distance metric fails to capture semantic similarity. In this paper, we characterize the space of triplets and derive why hard negatives make triplet loss training fail. We offer a simple fix to the loss function and show that, with this fix, optimizing with hard negative examples becomes feasible. This leads to more generalizable features, and image retrieval results that outperform state of the art for datasets with high intra-class variance. Code is available at: https://github.com/littleredxh/HardNegative.git
Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes). The proposed task requires both localizing an object and describing its properties. To illustrate the various aspects of this task, we focus on the domain of fashion and introduce Fashionpedia as a step toward mapping out the visual aspects of the fashion world. Fashionpedia consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. In order to solve this challenging task, we propose a novel Attribute-Mask R-CNN model to jointly perform instance segmentation and localized attribute recognition, and provide a novel evaluation metric for the task. Fashionpedia is available at: https://fashionpedia.github.io/home/ .
Disaster and Pandemic Management Using Machine Learning: A Survey

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.
WITHDRAWN: Computational efficient wearable sensor network health monitoring system for sports athletics using IoT

The rapid development and growth in the Internet of Things (IoT) area offer great potential in the healthcare sector. Currently, wireless technology, fitness trackers, and body sensors in the sports field have a major impact on living efficiency and health system reliability. Wearable devices have become increasingly interested in measuring physiological factors, promoting health, and improving adherence to the practice in different populations from elite athletes to patients. In this paper, fog assisted Computational efficient Wearable sensor networks (FCE-WSN) has been proposed in health monitoring systems for sports athletic using IoT. The wearable device for the continuous real-time monitoring of heart rate, respiratory frequency, and movement cadence during physical activity has been analyzed. Besides, the sensor collected data is uploaded to the IoT-connection system Ethernet module, and the Authorized Individual accesses the data via the internet to track the athletics health. Moreover, the wearable device's analytical model and its use illustrate how computing resources' costs can be minimized while preserving health requests for access to medical information stored in a fog and cloud distribution setting. Our assessment system depends on a queue and can estimate the minimum computing resources needed to achieve the Service Level Agreement (SLA) (both fog and cloud nodes). The experimental results show that the proposed method is user-friendly, reliable, and economical to use regularly.
From experiencing to expressing: A virtual reality approach to facilitating pupils’ descriptive paper writing performance and learning behavior engagement

Abstract Due to the lack of life experience and situational experience, it is difficult to arouse pupils’ inner motivation and interest in writing. Therefore, the early stage of Chinese writing may be a challenge for Chinese pupils. In order to solve this problem, a spherical video‐based virtual reality (SVVR) experience learning system was developed to help pupils improve their writing performance and interest. To evaluate the effectiveness of the proposed approach, an experiment was carried out in a primary school in Zhejiang province, China. First, a writing pretest was administered in two grade 4 classes. The researchers then randomly selected 40 pupils of about 10 years old with similar writing abilities, and assigned them to an experimental group, which used the SVVR learning method, and a control group, which used the traditional learning method. The researchers observed and recorded the pupils’ writing process and then, interviewed them. The results showed that there was a significant difference in the comprehensive writing performance of the two groups ( F = 6.272; p = .017 &lt; .05). The mean of the experimental group (Mean = 73.85) was higher than that of the control group (Mean = 70.00). Moreover, there were significant differences between the two groups in terms of the thematic coherence, structural integrity and linguistic expressiveness of their writing performance. However, there was no significant difference in the achievement of creative thinking. In the experimental group, different levels of learning behavior engagement could affect the structural integrity and language expression of writing achievements, and the degree of writing learning behavior engagement was highly correlated with reading time. Through the interviews, it was found that the pupils in the experimental group were more satisfied with the SVVR learning method than the control group pupils were with the traditional learning method. Practitioner Notes What is already known about this topic? Spherical video‐based virtual reality (SVVR) set a simulated environment enabling students to have in‐depth experience in learning contexts. Most SVVR studies focused on speaking and writing in middle schools and colleges, while its impacts on young children’s writing performance is generally ignored. What this paper adds? An SVVR system based on experiencing learning theory was developed to promote young students’ in‐depth experience and perception in descriptive paper writing. The proposed approach promoted students’ writing performances, writing experience and learning behavior engagement. Implications for practice and/or policy SVVR is a potential technology for implementing experiential learning activities, especially in improving thematic coherence, structural integrity and linguistic expressiveness. It is worth promoting such a low‐tech and low‐cost experiential learning approach to other courses.
CNN with depthwise separable convolutions and combined kernels for rating prediction

Recently, deep learning based techniques exploiting reviews are extensively studied for rating prediction and result in good performance. Some studies consider word level review information along with attention mechanism to capture the most influential content, thus making the methods even complex. Deep neural networks with Depthwise Separable Convolutions have made significant progress in the area of image and video analysis. The success of these techniques encourages adopting them towards improvements in rating prediction using review text. In this paper, we present a novel CNN based architecture with Depthwise Separable Convolutions and Combined Kernels (CNN-DSCK) for rating prediction exploiting product reviews. In the proposed method, we use two parallel CNNs with Depthwise Separable Convolutions to extract semantic features from the text reviews of users and items using different kernels in parallel and then select the important information from these features through pooling. Finally concatenate the pooling information obtained from different kernels in each network. The features obtained through each network are then fused and the most relevant higher-order features are extracted through fully connected dense layer at the top of network. Extensive experiments on real-world datasets demonstrate that CNN-DSCK significantly outperforms state of the art baseline models.
On the Security of Homomorphic Encryption on Approximate Numbers

We present passive attacks against CKKS, the homomorphic encryption scheme for arithmetic on approximate numbers presented at Asiacrypt 2017. The attack is both theoretically efficient (running in expected polynomial time) and very practical, leading to complete key recovery with high probability and very modest running times. We implemented and tested the attack against major open source homomorphic encryption libraries, including HEAAN, SEAL, HElib and PALISADE, and when computing several functions that often arise in applications of the CKKS scheme to machine learning on encrypted data, like mean and variance computations, and approximation of logistic and exponential functions using their Maclaurin series. The attack shows that the traditional formulation of $$\textsf {IND}\hbox {-}\textsf {CPA}$$ security (or indistinguishability against chosen plaintext attacks) achieved by CKKS does not adequately capture security against passive adversaries when applied to approximate encryption schemes, and that a different, stronger definition is required to evaluate the security of such schemes. We provide a solid theoretical basis for the security evaluation of homomorphic encryption on approximate numbers (against passive attacks) by proposing new definitions, that naturally extend the traditional notion of $$\textsf {IND}\hbox {-}\textsf {CPA}$$ security to the approximate computation setting. We propose both indistinguishability-based and simulation-based variants, as well as restricted versions of the definitions that limit the order and number of adversarial queries (as may be enforced by some applications). We prove implications and separations among different definitional variants, and discuss possible modifications to CKKS that may serve as a countermeasure to our attacks.
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation

Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key problem. Traditional methods solve it by discretely searching for the layer-wise pruning ratios, which lacks efficiency. In this paper, we propose Differentiable Sparsity Allocation (DSA), an efficient end-to-end budgeted pruning flow. Utilizing a novel differentiable pruning process, DSA finds the layer-wise pruning ratios with gradient-based optimization. It allocates sparsity in continuous space, which is more efficient than methods based on discrete evaluation and search. Furthermore, DSA could work in a pruning-from-scratch manner, whereas traditional budgeted pruning methods are applied to pre-trained models. Experimental results on CIFAR-10 and ImageNet show that DSA could achieve superior performance than current iterative budgeted pruning methods, and shorten the time cost of the overall pruning process by at least 1.5 $$\times $$ in the meantime.
Multi-Domain Image Completion for Random Missing Input Data

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.