Reviewerly/exHarmony huggingface dataset:
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.
FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. In this paper, we point out and solve a novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem by enabling each client to exploit multi-source data distributions under the challenging constraint of data decentralization. Our approach transmits the distribution information across clients in a privacy-protecting way through an effective continuous frequency space interpolation mechanism. With the transferred multi-source distributions, we further carefully design a boundary-oriented episodic learning paradigm to expose the local learning to domain distribution shifts and particularly meet the challenges of model generalization in medical image segmentation scenario. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks. The code is available at https://github.com/liuquande/FedDG-ELCFS.
VirTex: Learning Visual Representations from Textual Annotations

The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex – a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet – supervised or unsupervised – despite using up to ten times fewer images.
Remora optimization algorithm

In this paper, Remora Optimization Algorithm (ROA) is proposed, which is a new bionics-based, natural-inspired, and meta-heuristic algorithm. The inspiration for ROA is mainly due to the parasitic behavior of remora. Different locations are updated in different hosts: In some large hosts, remora feeds on the host's ectoparasites or wreckage and evades natural enemies, for example in the case of giant whales. In some small hosts, remora follows the host to move to the bait-rich area to prey, taking the fast-moving swordfish as an example. In the case of these two update methods, remora also makes some judges based on experience. If it takes the initiative to prey, it updates the host, makes a global update. If it eat around the host, remora does not change the host, and continues to local update. This algorithm is more inclined to provide a new idea for memetic algorithm, because the host in ROA can be reasonably replaced, such as ships, turtles, etc. The above dynamic mode and behavior are simulated mathematically and the validity of the ROA is tested with 29 benchmark questions and 5 actual engineering questions. Parallel comparisons are made with 10 other natural heuristics. The statistical results and comparisons show that ROA provides a very promising prospect and a strong competitive ability compared to other state-of-the-art heuristic techniques.
Enabling Efficient and Geometric Range Query With Access Control Over Encrypted Spatial Data

As a basic query function, range query has been exploited in many scenarios such as SQL retrieves, location-based services, and computational geometry. Meanwhile, with explosive growth of data volume, users are increasingly inclining to store data on the cloud for saving local storage and computational cost. However, a long-standing problem is that the user's data may be completely revealed to the cloud server because it has full data access right. To cope with this problem, a frequently-used method is to encrypt raw data before outsourcing them, but the availability and operability of data will be reduced significantly. In this paper, we propose an efficient and geometric range query scheme (EGRQ) supporting searching and data access control over encrypted spatial data. We employ secure KNN computation, polynomial fitting technique, and order-preserving encryption to achieve secure, efficient, and accurate geometric range query over cloud data. Then, we propose a novel spatial data access control strategy to refine user's rights in our EGRQ. To improve the efficiency, R-tree is adopted to reduce the searching space and matching times in whole search process. Finally, we theoretically prove the security of our proposed scheme in terms of confidentiality of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoors. In addition, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.
Aurora: Transparent Succinct Arguments for R1CS

We design, implement, and evaluate a zero knowledge succinct non-interactive argument (SNARG) for Rank-1 Constraint Satisfaction (R1CS), a widely-deployed NP language undergoing standardization. Our SNARG has a transparent setup, is plausibly post-quantum secure, and uses lightweight cryptography. A proof attesting to the satisfiability of n constraints has size $$O(\log ^2 n)$$ ; it can be produced with $$O(n \log n)$$ field operations and verified with O(n). At 128 bits of security, proofs are less than $${250}\,\mathrm{kB}$$ even for several million constraints, more than $$10{\times }$$ shorter than prior SNARGs with similar features. A key ingredient of our construction is a new Interactive Oracle Proof (IOP) for solving a univariate analogue of the classical sumcheck problem [LFKN92], originally studied for multivariate polynomials. Our protocol verifies the sum of entries of a Reed–Solomon codeword over any subgroup of a field. We also provide $$\texttt {libiop}$$ , a library for writing IOP-based arguments, in which a toolchain of transformations enables programmers to write new arguments by writing simple IOP sub-components. We have used this library to specify our construction and prior ones, and plan to open-source it.
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

Unintended bias in Machine Learning can manifest as systemic differences in performance for different demographic groups, potentially compounding existing challenges to fairness in society at large. In this paper, we introduce a suite of threshold-agnostic metrics that provide a nuanced view of this unintended bias, by considering the various ways that a classifier’s score distribution can vary across designated groups. We also introduce a large new test set of online comments with crowd-sourced annotations for identity references. We use this to show how our metrics can be used to find new and potentially subtle unintended bias in existing public models.
Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge

Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be learned without direct supervision from ground truth labels leveraging image synthesis on sequences or stereo pairs. Focusing on this second case, in this paper we leverage stereo matching in order to improve monocular depth estimation. To this aim we propose monoResMatch, a novel deep architecture designed to infer depth from a single input image by synthesizing features from a different point of view, horizontally aligned with the input image, performing stereo matching between the two cues. In contrast to previous works sharing this rationale, our network is the first trained end-to-end from scratch. Moreover, we show how obtaining proxy ground truth annotation through traditional stereo algorithms, such as Semi-Global Matching, enables more accurate monocular depth estimation still countering the need for expensive depth labels by keeping a self-supervised approach. Exhaustive experimental results prove how the synergy between i) the proposed monoResMatch architecture and ii) proxy-supervision attains state-of-the-art for self-supervised monocular depth estimation. The code is publicly available at https://github.com/fabiotosi92/monoResMatch-Tensorflow.
What Does the ‘Postdigital’ Mean for Education? Three Critical Perspectives on the Digital, with Implications for Educational Research and Practice

This paper examines what the term ‘postdigital’ might mean for education through the discussion of human-technology relationships. It begins with a summary of two general interpretations of the postdigital: firstly, to understand the ‘post’ as meaning simply ‘posterior to’ the digital, suggesting a different stage in the perception and use of technology; and secondly, to consider the ‘post’ as signalling a critical appraisal of the assumptions embedded in the general understanding of the digital. Subsequently, the paper outlines three critical perspectives on the digital with specific relevance for educational concerns. The first examines the economic rationales underpinning digital technology, focusing on the idea of the platform and the assumed benefits of sharing. The second discusses the role of the digital in educational policy and the compound effects of the metrification of institutional quality. The third section explores the digital as ‘material’, and the increasing attention paid to issues of labour and the exploitation of natural resources required to produce digital technologies. These perspectives suggest an understanding of the postdigital in terms of profound and far-reaching socio-technical relations, which have significant consequences for thinking about the purpose, focus, and governance of education in contemporary times.
Viewpoint: Human-in-the-loop Artificial Intelligence

Little by little, newspapers are revealing the bright future that Artificial Intelligence (AI) is building. Intelligent machines will help everywhere. However, this bright future may have a possible dark side: a dramatic job market contraction before its unpredictable transformation. Hence, in a near future, large numbers of job seekers may need financial support while catching up with these novel unpredictable jobs. This possible job market crisis has an antidote inside. In fact, the rise of AI is sustained by the biggest knowledge theft of the recent years. Many learning AI machines are extracting knowledge from unaware skilled or unskilled workers by analyzing their interactions. By passionately doing their jobs, many of these workers are shooting themselves in the feet.&#x0D; In this paper, we propose Human-in-the-loop Artificial Intelligence (HitAI) as a fairer paradigm for AI systems. Recognizing that any AI system has humans in the loop, HitAI will reward these aware and unaware knowledge producers with a different scheme: decisions of AI systems generating revenues will repay the legitimate owners of the knowledge used for taking those decisions. As modern Merry Men, HitAI researchers should fight for a fairer Robin Hood Artificial Intelligence that gives back what it steals.&#x0D; &#x0D; &#x0D; This article is part of the special track on AI and Society.&#x0D; &#x0D;
Disentangling Adversarial Robustness and Generalization

Obtaining deep networks that are robust against adversarial examples and generalize well is an open problem. A recent hypothesis even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. In an effort to clarify the relationship between robustness and generalization, we assume an underlying, low-dimensional data manifold and show that: 1. regular adversarial examples leave the manifold; 2. adversarial examples constrained to the manifold, i.e., on-manifold adversarial examples, exist; 3. on-manifold adversarial examples are generalization errors, and on-manifold adversarial training boosts generalization; 4. regular robustness and generalization are not necessarily contradicting goals. These assumptions imply that both robust and accurate models are possible. However, different models (architectures, training strategies etc.) can exhibit different robustness and generalization characteristics. To confirm our claims, we present extensive experiments on synthetic data (with known manifold) as well as on EMNIST, Fashion-MNIST and CelebA.
Cervical cancer classification using convolutional neural networks and extreme learning machines

Cervical cancer is one of the main reasons of death from cancer in women. The complication of this cancer can be limited if it is diagnosed and treated at an early stage. In this paper, we propose a cervical cancer cell detection and classification system based on convolutional neural networks (CNNs). The cell images are fed into a CNNs model to extract deep-learned features. Then, an extreme learning machine (ELM)-based classifier classifies the input images. CNNs model is used via transfer learning and fine tuning. Alternatives to the ELM, multi-layer perceptron (MLP) and autoencoder (AE)-based classifiers are also investigated. Experiments are performed using the Herlev database. The proposed CNN-ELM-based system achieved 99.5% accuracy in the detection problem (2-class) and 91.2% in the classification problem (7-class).
Depth Completion From Sparse LiDAR Data With Depth-Normal Constraints

Depth completion aims to recover dense depth maps from sparse depth measurements. It is of increasing importance for autonomous driving and draws increasing attention from the vision community. Most of the current competitive methods directly train a network to learn a mapping from sparse depth inputs to dense depth maps, which has difficulties in utilizing the 3D geometric constraints and handling the practical sensor noises. In this paper, to regularize the depth completion and improve the robustness against noise, we propose a unified CNN framework that 1) models the geometric constraints between depth and surface normal in a diffusion module and 2) predicts the confidence of sparse LiDAR measurements to mitigate the impact of noise. Specifically, our encoder-decoder backbone predicts the surface normal, coarse depth and confidence of LiDAR inputs simultaneously, which are subsequently inputted into our diffusion refinement module to obtain the final completion results. Extensive experiments on KITTI depth completion dataset and NYU-Depth-V2 dataset demonstrate that our method achieves state-of-the-art performance. Further ablation study and analysis give more insights into the proposed components and demonstrate the generalization capability and stability of our model.
Significance-Aware Information Bottleneck for Domain Adaptive Semantic Segmentation

For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex. In this work, we equip the adversarial network with a “significance-aware information bottleneck (SIB)”, to address the above problem. The new network structure, called SIBAN, enables a significance-aware feature purification before the adversarial adaptation, which eases the feature alignment and stabilizes the adversarial training course. In two domain adaptation tasks, i.e., GTA5 → Cityscapes and SYNTHIA → Cityscapes, we validate that the proposed method can yield leading results compared with other feature-space alternatives. Moreover, SIBAN can even match the state-of-the-art output-space methods in segmentation accuracy, while the latter are often considered to be better choices for domain adaptive segmentation task.
FAIR Principles: Interpretations and Implementation Considerations

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.
A Secure Data Sharing Platform Using Blockchain and Interplanetary File System

In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to the involvement of TTP, such systems lack trust, transparency, security, and immutability. To overcome these issues, this paper proposed a blockchain-based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, the user is encouraged to register the reviews about data. These reviews are validated through Watson analyzer to filter out the fake reviews. The customers registering valid reviews are given incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption, and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner, and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real set-up. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows the least computational time as compared to advanced encryption standard (AES) 128 and 256.
Secure Data Encryption Based on Quantum Walks for 5G Internet of Things Scenario

Fifth generation (5G) networks are the base communication technology for connecting objects in the Internet of Things (IoT) environment. 5G is being developed to provide extremely large capacity, robust integrity, high bandwidth, and low latency. With the development and innovating new techniques for 5G-IoT, it surely will drive to new enormous security and privacy challenges. Consequently, secure techniques for data transmissions will be needed as the basis for 5G-IoT technology to address these arising challenges. Therefore, various traditional security mechanisms are provided for 5G-IoT technologies and most of them are built on mathematical foundations. With the growth of quantum technologies, traditional cryptographic techniques may be compromised due to their mathematical computation based construction. Quantum walks (QWs) is a universal quantum computational model, which possesses inherent cryptographic features that can be utilized to build efficient cryptographic mechanisms. In this paper, we use the features of quantum walk to construct a new S-box method which plays a significant role in block cipher techniques for 5G-IoT technologies. As an application of the presented S-box mechanism and controlled alternate quantum walks (CAQWs) for 5G-IoT technologies a new robust video encryption mechanism is proposed. As well as to fulfill needs of encryption for varied files in 5G-IoT, we utilize the features of quantum walk to propose a novel encryption strategy for secure transmission of sensitive files in 5G-IoT paradigm. The analyses and results of the proposed cryptosystems show that it has better security properties and efficacy in terms of cryptographic performance.