Reviewerly/exHarmony huggingface dataset:
The diversity bonus in pooling local knowledge about complex problems

Significance Groups can collectively achieve an augmented cognitive capability that enables them to effectively tackle complex problems. Importantly, researchers have hypothesized that this group property—frequently known as collective intelligence—may be improved in functionally more diverse groups. This paper illustrates the importance of diversity for representing complex interdependencies in a social-ecological system. In an experiment with local stakeholders of a fishery ecosystem, groups with higher diversity—those with well-mixed members from diverse types of stakeholders—collectively produced more complex models of human–environment interactions which were more closely matched scientific expert opinions. These findings have implications for advancing the use of local knowledge in understanding complex sustainability problems, while also promoting the inclusion of diverse stakeholders for increasing management success.
Perception consistency ultrasound image super-resolution via self-supervised CycleGAN

Due to the limitations of sensors, the transmission medium, and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial resolution. To remedy this situation, deep learning networks have been recently developed for ultrasound image super-resolution (SR) because of the powerful approximation capability. However, most current supervised SR methods are not suitable for ultrasound medical images because the medical image samples are always rare, and usually, there are no low-resolution (LR) and high-resolution (HR) training pairs in reality. In this work, based on self-supervision and cycle generative adversarial network, we propose a new perception consistency ultrasound image SR method, which only requires the LR ultrasound data and can ensure the re-degenerated image of the generated SR one to be consistent with the original LR image, and vice versa. We first generate the HR fathers and the LR sons of the test ultrasound LR image through image enhancement, and then make full use of the cycle loss of LR–SR–LR and HR–LR–SR and the adversarial characteristics of the discriminator to promote the generator to produce better perceptually consistent SR results. The evaluation of PSNR/IFC/SSIM, inference efficiency and visual effects under the benchmark CCA-US and CCA-US datasets illustrate our proposed approach is effective and superior to other state-of-the-art methods.
A survey of data center consolidation in cloud computing systems

Virtualization technology is the backbone of cloud systems, the fastest-growing energy consumers, globally. It holds an imperative position in the resource management area by providing solutions for many related critical problems and challenges. Resource consolidation remains one of the techniques most used to develop new and more efficient resource management policies. Virtual machine packing, which is mainly addressed, allows cloud data centers to move from a particular state to a more optimized one. Currently, the unprecedented advancements in container-based cloud and containerized workloads have created new consolidation opportunities. In this article, we study the problem of consolidating data centers within distributed cloud systems. We give a generic view on IT consolidation at different levels of cloud services. Firstly, we present an overview of virtualized data centers and consolidation. Next, we present a brief thematic taxonomy and an illustration of some consolidation solutions from the literature. This presentation will then be followed by a discussion of certain research questions as well as a proposal for a certain number of future orientations in this field which we assume essential in order to contribute to the resolution of the problematic dealt with in this article.
GBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis

Advances in Deep Learning (DL) have provided alternative approaches to various complex problems, including the domain of spatial image steganalysis using Convolutional Neural Networks (CNN). Several CNN architectures have been developed in recent years, which have improved the detection accuracy of steganographic images. This work presents a novel CNN architecture which involves a preprocessing stage using filter banks to enhance steganographic noise, a feature extraction stage using depthwise and separable convolutional layers, and skip connections. Performance was evaluated using the BOSSbase 1.01 and BOWS 2 datasets with different experimental setups, including adaptive steganographic algorithms, namely WOW, S-UNIWARD, MiPOD, HILL and HUGO. Our results outperformed works published in the last few years in every experimental setting. This work improves classification accuracies on all algorithms and bits per pixel (bpp), reaching 80.3% on WOW with 0.2 bpp and 89.8% on WOW with 0.4 bpp, 73.6% and 87.1% on S-UNIWARD (0.2 and 0.4 bpp respectively), 68.3% and 81.4% on MiPOD (0.2 and 0.4 bpp), 68.5% and 81.9% on HILL (0.2 and 0.4 bpp), 74.6% and 84.5% on HUGO (0.2 and 0.4 bpp), using BOSSbase 1.01 test data.
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition

Heterogeneous face recognition (HFR) refers to matching cross-domain faces and plays a crucial role in public security. Nevertheless, HFR is confronted with challenges from large domain discrepancy and insufficient heterogeneous data. In this paper, we formulate HFR as a dual generation problem, and tackle it via a novel dual variational generation (DVG-Face) framework. Specifically, a dual variational generator is elaborately designed to learn the joint distribution of paired heterogeneous images. However, the small-scale paired heterogeneous training data may limit the identity diversity of sampling. In order to break through the limitation, we propose to integrate abundant identity information of large-scale visible data into the joint distribution. Furthermore, a pairwise identity preserving loss is imposed on the generated paired heterogeneous images to ensure their identity consistency. As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises. The identity consistency and identity diversity properties allow us to employ these generated images to train the HFR network via a contrastive learning mechanism, yielding both domain-invariant and discriminative embedding features. Concretely, the generated paired heterogeneous images are regarded as positive pairs, and the images obtained from different samplings are considered as negative pairs. Our method achieves superior performances over state-of-the-art methods on seven challenging databases belonging to five HFR tasks, including NIR-VIS, Sketch-Photo, Profile-Frontal Photo, Thermal-VIS, and ID-Camera.
Agent architecture of an intelligent medical system based on federated learning and blockchain technology

Multi-agent systems enable the division of complicated tasks into individual objects that can cooperate. Such architecture can be useful in building solutions in the Internet of Medical Things (IoMT). In this paper, we propose an architecture of such a system that ensures the security of private data, as well as allows the addition and/or modification of the used classification methods. The main advantages of the proposed system are based on the implementation of blockchain technology elements and threaded federated learning. The individual elements are located on the agents who exchange information. Additionally, we propose building an agent with a consortium mechanism for classification results from many machine learning solutions. This proposal offers a new model of agents that can be implemented as a system for processing medical data in real-time. Our proposition was described and tested to present advantages over other, existing state-of-the-art methods. We show, that this proposition can improve the Internet of Medical Thing solutions by presenting a new idea of a multi-agent system that can separate different tasks like security, or classification and as a result minimize operation time and increase accuracy.
Knowledge mapping of computer applications in education using CiteSpace

Abstract The education field plays a vital role in the growth and progress of society. In recent years, with the evolution of technology, a significant development has been observed in the publication of this field. Henceforth, a scientific review is required to explore computer applications in education using internet communication and technology (ICT) technologies. This paper analyzes the scientometric features of documents published from 2011 to 2020 in the education field using ICT. The research literature is retrieved from Scopus and CiteSpace is used to visualize network analysis of country collaboration, co‐citation, and keyword co‐occurrence. Here, the field of higher education is identified as an important area with increasing research trends. Higher education, such as engineering education with the application of ICT, has experienced the latest research trend, and it involves designing of frameworks for improving student performance and the teaching–learning process. The outcome of this paper is the identification of the research frontiers, hotspots, and the latest areas in the field of education, which will help to understand the overall development and future research directions.
Multi-Sentence Auxiliary Adversarial Networks for Fine-Grained Text-to-Image Synthesis

Due to the development of Generative Adversarial Networks (GANs), significant progress has been achieved in text-to-image synthesis task. However, most previous works have only focus on learning the semantic consistency between paired images and sentences, without exploring the semantic correlation between different yet related sentences that describe the same image, which leads to significant visual variation among the synthesized images. Accordingly, in this article, we propose a new method for text-to-image synthesis, dubbed Multi-sentence Auxiliary Generative Adversarial Networks (MA-GAN); this approach not only improves the generation quality but also guarantees the generation similarity of related sentences by exploring the semantic correlation between different sentences describing the same image. More specifically, we propose a Single-sentence Generation and Multi-sentence Discrimination (SGMD) module that explores the semantic correlation between multiple related sentences in order to reduce the variation between their generated images and enhance the reliability of the generated results. Moreover, a Progressive Negative Sample Selection mechanism (PNSS) is designed to mine more suitable negative samples for training, which can effectively promote detailed discrimination ability in the generative model and facilitate the generation of more fine-grained results. Extensive experiments on Oxford-102 and CUB datasets reveal that our MA-GAN significantly outperforms the state-of-the-art methods.
Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks

In many cases, machine learning and privacy are perceived to be at odds. Privacy concerns are especially relevant when the involved data are sensitive. This paper deals with the privacy-preserving inference of deep neural networks. We report on first experiments with a new library implementing a variant of the TFHE fully homomorphic encryption scheme. The underlying key technology is the programmable bootstrapping. It enables the homomorphic evaluation of any function of a ciphertext, with a controlled level of noise. Our results indicate for the first time that deep neural networks are now within the reach of fully homomorphic encryption. Importantly, in contrast to prior works, our framework does not necessitate re-training the model.
Enhanced Quadratic Video Interpolation

With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (e.g. acceleration) and successfully models the estimation of interpolated flow. However, its produced intermediate frames still contain some unsatisfactory ghosting, artifacts and inaccurate motion, especially when large and complex motion occurs. In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model. In particular, we adopt a rectified quadratic flow prediction (RQFP) formulation with least squares method to estimate the motion more accurately. Complementary with image pixel-level blending, we introduce a residual contextual synthesis network (RCSN) to employ contextual information in high-dimensional feature space, which could help the model handle more complicated scenes and motion patterns. Moreover, to further boost the performance, we devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process. The proposed EQVI model won the first place in the AIM2020 Video Temporal Super-Resolution Challenge. Codes are available at https://github.com/lyh-18/EQVI .
MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoder-decoder architecture, has been used frequently for image segmentation with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolution-based U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign semantic features contained in both low-level and high-level feature maps; (2) the asymmetric convolution block strengthens the feature representation and feature extraction capability of a standard convolution layer. Experiments conducted on two remotely sensed datasets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-NetPPL, U-Net 3+, amongst other benchmark approaches. Code is available at https://github.com/lironui/MACU-Net.
Industrial Blockchain: A state-of-the-art Survey

As an underlying and backbone technology of Bitcoin, Blockchain attracted extensive attention worldwide in recent years due to its unique characteristics of decentralization, openness, immutability, anonymity, etc., which enables it to build a trust basis through recording the point-to-point decentralized transactions in an immutable way via the attached timestamp, thereby improving system efficiency and reducing the cost without relying on the central agent. As it is considered to be a potentially revolutionary technology, Blockchain has been introduced into various industrial fields including finance, supply chain, manufacturing, healthcare, energy, and smart city. In this paper, we conduct a state-of-the-art survey of industrial Blockchain in terms of published articles between 2017 and 2020, and worldwide Blockchain movement including North America, Europe, and the Asia Pacific region so far. We conduct a statistic analysis of the collected articles in terms of three dimensions, which are year of publication, leading research institutes and researchers, and article classification to present a multi-dimensional trend or conclusion. Besides, we analyse articles that are cited over a certain number of times in detail to investigate the hot research directions. Finally, the challenges, opportunities, and future perspectives are discussed to summarize the main obstacles of industrial Blockchain and identify the open research questions in the near future.
Dual attention deep fusion semantic segmentation networks of large-scale satellite remote-sensing images

Since DCNNs (deep convolutional neural networks) have been successfully applied to various academic and industrial fields, semantic segmentation methods, based on DCNNs, are increasingly explored for remote-sensing image interpreting and information extracting. It is still highly challenging due to the presence of irregular target shapes, and similarities of inter – and intra-class objects in large-scale high-resolution satellite images. A majority of existing methods fuse the multi-scale features that always fail to provide satisfactory results. In this paper, a dual attention deep fusion semantic segmentation network of large-scale satellite remote-sensing images is proposed (DASSN_RSI). The framework consists of novel encoder-decoder architecture, and a weight-adaptive loss function based on focal loss. To refine high-level semantic and low-level spatial feature maps, the deep layer channel attention module (DLCAM) and shallow layer spatial attention module (SLSAM) are designed and appended with specific blocks. Then the DUpsampling is incorporated to fuse feature maps in a lossless way. Peculiarly, the weight-adaptive focal loss (W-AFL) is inferred and embedded successfully, alleviating the class-imbalanced issue as much as possible. The extensive experiments are conducted on Gaofen image dataset (GID) datasets (Gaofen-2 satellite images, coarse set with five categories and refined set with fifteen categories). And the results show that our approach achieves state-of-the-art performance compared to other typical variants of encoder-decoder networks in the numerical evaluation and visual inspection. Besides, the necessary ablation studies are carried out for a comprehensive evaluation.
AS-NAS: Adaptive Scalable Neural Architecture Search With Reinforced Evolutionary Algorithm for Deep Learning

Neural architecture search (NAS) is a challenging problem in the design of deep learning due to its nonconvexity. To address this problem, an adaptive scalable NAS method (AS-NAS) is proposed based on the reinforced I-Ching divination evolutionary algorithm (IDEA) and variable-architecture encoding strategy. First, unlike the typical reinforcement learning (RL)-based and evolutionary algorithm (EA)-based NAS methods, a simplified RL algorithm is developed and used as the reinforced operator controller to adaptively select the efficient operators of IDEA. Without the complex actor–critic parts, the reinforced IDEA based on simplified RL can enhance the search efficiency of the original EA with lower computational cost. Second, a variable-architecture encoding strategy is proposed to encode neural architecture as a fixed-length binary string. By simultaneously considering variable layers, channels, and connections between different convolution layers, the deep neural architecture can be scalable. Through the integration with the reinforced IDEA and variable-architecture encoding strategy, the design of the deep neural architecture can be adaptively scalable. Finally, the proposed AS-NAS are integrated with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${L}_{1/2}$ </tex-math></inline-formula> regularization to increase the sparsity of the optimized neural architecture. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method.
Evoking Empathy: A Framework for Describing Empathy Tools

Empathy tools are experiences designed to evoke empathetic responses by placing the user in another’s lived and felt experience. The problem is that designers do not have a common vocabulary to describe empathy tool experiences; consequently, it is difficult to compare/contrast empathy tool designs or to think about their efficacy. To address this problem, we analyzed 26 publications on empathy tools to develop a descriptive framework for designers of empathy tools. Based on our analysis, we found that empathy tools can be described along three dimensions: (i) the amount of agency the tool allows, (ii) the user’s perspective while using the tool, and (iii) the type of sensations that are experienced. We show that this framework can be used to describe a wide variety of empathy tools and provide recommendations for empathy tool designers, as well as techniques for measuring the efficacy of an empathy tool experience.
TEQUILA: a platform for rapid development of quantum algorithms

Variational quantum algorithms are currently the most promising class of algorithms for deployment on near-term quantum computers. In contrast to classical algorithms, there are almost no standardized methods in quantum algorithmic development yet, and the field continues to evolve rapidly. As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in high demand for flexible and reliable ways to implement, test, and share new ideas. Inspired by this demand, we introduce tequila, a development package for quantum algorithms in python, designed for fast and flexible implementation, prototyping, and deployment of novel quantum algorithms in electronic structure and other fields. Tequila operates with abstract expectation values which can be combined, transformed, differentiated, and optimized. On evaluation, the abstract data structures are compiled to run on state-of-the-art quantum simulators or interfaces.
Sustainable MLOps: Trends and Challenges

Even simply through a GoogleTrends search it becomes clear that Machine-Learning Operations-or MLOps, for short-are climbing in interest from both a scientific and practical perspective. On the one hand, software components and middleware are proliferating to support all manners of MLOps, from AutoML (i.e., software which enables developers with limited machine-learning expertise to train high-quality models specific to their domain or data) to feature-specific ML engineering, e.g., Explainability and Interpretability. On the other hand, the more these platforms penetrate the day-to-day activities of software operations, the more the risk for AI Software becoming unsustainable from a social, technical, or organisational perspective. This paper offers a concise definition of MLOps and AI Software Sustainability and outlines key challenges in its pursuit.
Block-Wise Image Transformation With Secret Key for Adversarially Robust Defense

In this paper, we propose a novel defensive transformation that enables us to maintain a high classification accuracy under the use of both clean images and adversarial examples for adversarially robust defense. The proposed transformation is a block-wise preprocessing technique with a secret key to input images. The proposed defense obfuscates gradients in the absence of the secret key unlike previously defeated obfuscating defenses. We developed three algorithms to realize the proposed transformation: Pixel Shuffling, Bit Flipping, and FFX Encryption. Experiments were carried out on the CIFAR-10 and ImageNet datasets by using both black-box and white-box attacks with various metrics including adaptive ones. The results show that the proposed defense achieves high accuracy close to that of using clean images even under adaptive attacks for the first time. In the best-case scenario, a model trained by using images transformed by FFX Encryption (block size of 4) yielded an accuracy of 92.30% on clean images and 91.48% under PGD attack with a noise distance of 8/255, which is close to the non-robust accuracy (95.45%) for the CIFAR-10 dataset, and it yielded an accuracy of 72.18% on clean images and 71.43% under the same attack, which is also close to the standard accuracy (73.70%) for the ImageNet dataset. Overall, all three proposed algorithms are demonstrated to outperform state-of-the-art defenses including adversarial training whether or not a model is under attack.