Reviewerly/exHarmony huggingface dataset:
From SLA to vendor‐neutral metrics: An intelligent knowledge‐based approach for multi‐cloud SLA‐based broker

Cloud computing has been consolidated as a support for the vast majority of current and emerging technologies. However, there are some barriers that prevent the exploitation of the full potential of this technology. First, the major cloud providers currently put the onus of implementing the mechanisms that ensure compliance with the desired service levels on cloud consumers. However, consumers do not have the required expertise. Since each cloud provider exports a different set of low-level metrics, the strategies defined to ensure compliance with the established service-level agreement (SLA) are bound to a particular cloud provider. This fosters provider lock-in and prevents consumers from benefiting from the advantages of multi-cloud environments. This paper presents a solution to the problem of automatically translating SLAs into objectives expressed as metrics that can be measured across multiple cloud providers. First, we propose an intelligent knowledge-based system capable of automatically translating high-level SLAs defined by cloud consumers into a set of conditions expressed as vendor-neutral metrics, providing feedback to cloud consumers (intelligent tutoring system). Secondly, we present the set of vendor-neutral metrics and explain how they can be measured for the different cloud providers. Finally, we report a validation based on two use cases (IaaS and PaaS) in a multi-cloud environment formed by leading cloud providers. This evaluation has demonstrated that, thanks to the complementarity of the two solutions, cloud consumers can automatically and transparently exploit the multi-cloud in many application domains, as endorsed by the cloud experts consulted in the course of this study.
Graph Automorphism Shuffles from Pile-Scramble Shuffles

A pile-scramble shuffle is one of the most effective shuffles in card-based cryptography. Indeed, many card-based protocols are constructed from pile-scramble shuffles. This paper aims to study the power of pile-scramble shuffles. In particular, for any directed graph G, we introduce a new protocol called “a graph shuffle protocol for G”, and show that it can be implemented using pile-scramble shuffles only. Our proposed protocol requires $$2(n+m)$$ cards, where n and m are the numbers of vertices and edges of G, respectively. The number of pile-scramble shuffles is $$k+1$$ , where $$1 \le k \le n$$ is the number of distinct degrees of vertices of G. As an application, a random cut for n cards, which is also an important shuffle, can be implemented by 3n cards and two pile-scramble shuffles.
Towards multidomain and multilingual abusive language detection: a survey

Abstract Abusive language is an important issue in online communication across different platforms and languages. Having a robust model to detect abusive instances automatically is a prominent challenge. Several studies have been proposed to deal with this vital issue by modeling this task in the cross-domain and cross-lingual setting. This paper outlines and describes the current state of this research direction, providing an overview of previous studies, including the available datasets and approaches employed in both cross-domain and cross-lingual settings. This study also outlines several challenges and open problems of this area, providing insights and a useful roadmap for future work.
High-precision laser transverse differential confocal radius measurement method

To meet the current need for high-precision and environment-insensitive measurement of the radius of curvature (ROC), we proposed a transverse differential confocal radius measurement (TDCRM) method based on the optical system of the confocal ROC measurement. Using a D-shaped aperture and the virtual pinhole technology, two signals, analogous to the pre-focus and post-focus signals in the two-detector-based differential confocal radius measurement (DCRM), can be obtained from two segmentations of a single CCD image. The difference of these two signals can be used to precisely determine the cat’s-eye and confocal positions, thereby achieving the high-accuracy ROC measurement as DCRM with a relative repeatability of 3.4 ppm. Furthermore, compared to DCRM, no optical alignment is needed after replacing the objective lens, which significantly reduces the time cost of measurements. We believe this novel and high-precision ROC measurement method will widen its application to optical manufacturing and provide an exciting opportunity for mass production of the ROC measurement instrument.
AI-Powered Health Chatbots: Toward a general architecture

A Chatbot is a conversational agent that simulate human conversation, through text or voice messages. One of the first goals of a Chatbot is to interact with the user just like a human. When it comes to Health Chatbots, another main goal is to be able to get the correct answer to the user request. In this paper we propose a general Architecture of an AI-Powered Health Chatbot with Four components to reach the two goals, which integrates dialogue and communication part in natural language understanding (NLU) and natural language generation (NLG), and the expert part based on deep learning whose function is to give appropriate response from pre-formatted data.
Defending digital supply chains: Evidence from a decade-long research program

Digital Supply Chains (DSCs) are highly integrated global internet communities of customers, distributors, producers, and suppliers. DSCs have increasingly incorporated Internet of Things (IoT) innovations such as field sensors and real time condition monitoring; and have served as effective platforms for IoT technology diffusion. However, as IoT has become more pervasive, pushing the edges of networks further out, new cyber threat windows have opened everywhere. More recently, Cyber-Supply Chain Risk Management (C-SCRM) has emerged as a critical discipline combining expertise from cybersecurity, supply chain management and enterprise risk management; and designed to stem the proliferation of digital supply chain attacks seeking illicit access to corporate networks for competitive espionage, financial and intellectual property theft, and disruption of operations. Yet to date, there has been little evidence that C-SCRM practices are actually effective in containing all or even some types of breaches. Our decade-long research provides the first statistical analysis of the effects on an organization’s breach profile based on the extent of its adoption of policies and practices defined within the U.S. National Institute of Standards and Technology (NIST) ‘s Cybersecurity Framework, increasingly the de-facto global C-SCRM standard. Our analysis determined that there were specific Framework activity areas and sets of policies/practices within those activity areas that strongly correlated with more effective control of specific breach types. Our findings lay the foundation for an evidence-based approach to mastering IT network vulnerabilities and defending global digital supply chains.
Towards a credibility revolution in bilingualism research: Open data and materials as stepping stones to more reproducible and replicable research

Abstract The extent to which findings in bilingualism research are contingent on specific analytic choices, experimental designs, or operationalisations, is currently unknown. Poor availability of data, analysis code, and materials has hindered the development of cumulative lines of research. In this review, we survey current practices and advocate a credibility revolution in bilingualism research through the adoption of minimum standards of transparency. Full disclosure of data and code is necessary not only to assess the reproducibility of original findings, but also to test the robustness of these findings to different analytic specifications. Similarly, full provision of experimental materials and protocols underpins assessment of both the replicability of original findings, as well as their generalisability to different contexts and samples. We illustrate the review with examples where good practice has advanced the agenda in bilingualism research and highlight resources to help researchers get started.
ICDAR 2021 Competition on Time-Quality Document Image Binarization

The ICDAR 2021 Time-Quality Binarization Competition assessed the performance of 12 new and 49 other previously published binarization algorithms for scanned document images. Four test sets of "real-world" documents with different features were used. For each test set, the top twenty algorithms in the quality of the resulting two-tone images had their average processing time presented, yielding an account of their time complexity.
Sensitivity of deep learning applied to spatial image steganalysis

In recent years, the traditional approach to spatial image steganalysis has shifted to deep learning (DL) techniques, which have improved the detection accuracy while combining feature extraction and classification in a single model, usually a convolutional neural network (CNN). The main contribution from researchers in this area is new architectures that further improve detection accuracy. Nevertheless, the preprocessing and partition of the database influence the overall performance of the CNN. This paper presents the results achieved by novel steganalysis networks (Xu-Net, Ye-Net, Yedroudj-Net, SR-Net, Zhu-Net, and GBRAS-Net) using different combinations of image and filter normalization ranges, various database splits, different activation functions for the preprocessing stage, as well as an analysis on the activation maps and how to report accuracy. These results demonstrate how sensible steganalysis systems are to changes in any stage of the process, and how important it is for researchers in this field to register and report their work thoroughly. We also propose a set of recommendations for the design of experiments in steganalysis with DL.
HECR-Net: Height-Embedding Context Reassembly Network for Semantic Segmentation in Aerial Images

Semantic segmentation in aerial images has become an indispensable part in remote sensing image understanding for its extensive application prospects. It is crucial to jointly reason the 2D appearance along with 3D information and acquire discriminative global context to achieve better segmentation. However, previous approaches require accurate elevation data (e.g., nDSM and DSM) as additional inputs to segment semantics which sorely limits their applications. On the other hand, due to the various forms of objects in complex scenes, the global context is generally dominated by features of salient patterns (e.g., large objects) and tends to smooth inconspicuous patterns (e.g., small stuff and boundaries). In this article, a novel joint framework named Height-Embedding Context Reassembly Network (HECR-Net) is proposed. First, considering the fact that the corresponding elevation data is insufficient while we still want to exploit the serviceable height information. To alleviate the above data constraint, our method simultaneously predicts semantic labels and height maps from single aerial images by distilling height-aware embeddings implicitly. Second, we introduce a novel context-aware Reorganization (CAR) module to generate a discriminative feature with global context appropriately assigned to each local position. It benefits from both the global context aggregation module for ambiguity eliminating and local feature redistribution module for detail refinement. Third, we make full use of the learning height-aware embeddings to promote the performance of semantic segmentation via introducing a modality-affinitive propagation (MAP) block. Finally, the segmentation results on ISPRS Vaihingen and Potsdam data set illustrate that the proposed HECR-Net achieves state-of-the-art performance
Gated-Residual Block for Semantic Segmentation Using RGB-D Data

Semantic segmentation is an important technique for scene understanding in the intelligent transportation system. RGB-D data shows great advantages over the unimodal data in this area, and it can be easily obtained from consumer sensors nowadays. How to design effective fusion structures to fuse RGB and depth signals in RGB-D data is a challenging problem. This paper proposes a novel gated-residual block to address this problem. The structure consists of two residual units and one gated fusion unit, the residual unit progressively aggregates modality-specific features from the modality-specific signals and the gate mechanism computes complementary features for them. Based on the gated-residual block, the paper presents the deep multimodal networks, named GRBNet, for RGB-D semantic segmentation. Experiments on ScanNet, Cityscapes and SUN RGB-D datasets verify the effectiveness of the proposed approach and demonstrate that the GRBNet achieved competitive performance.
Uncivil Reactions to Sexual Assault Online: Linguistic Features of News Reports Predict Discourse Incivility

Reports of sexual assault have been found to elicit online discourse incivility. The present study employs a computerized coding tool to examine linguistic characteristics of news media that are likely to influence discourse incivility-specifically, negative emotion, disagreement, and discussion about power relations. Additionally, machine learning was harnessed to measure the levels of comment toxicity, insult, profanity, threat, and identity attack in Reddit and Twitter posts sharing news reports of sexual assault. Findings reveal that linguistic features of news articles interact with platform community norms to predict rape culture as expressed within online responses to reports of sexual assault.
A Proof-of-Transactions Blockchain Consensus Protocol for Large-Scale IoT

Blockchain technology can benefit Internet of Things (IoT) in terms of message exchange, access control and management, however, existing blockchain consensus protocols cannot provide sufficient performance without compromising security and fairness. This paper proposes a round-based two-stage blockchain consensus protocol called Proof-of-Transactions (PoT) for a variety of requirements such as performance and security of large-scale IoT. In the first stage of each round, all nodes pre-construct new blocks and exchange information about the number of valid transactions contained in each other’s blocks. The block containing most valid transactions is broadcasted in the second stage and examined by all nodes of the cluster before being received as the new block in the global blockchain ledger. The consensus reaching of a new block requires only two rounds of message exchange process. Theoretical analysis and experimental results verify the feasibility and superiority of the proposed protocol.
Understanding domain-wall encoding theoretically and experimentally

We analyse the method of encoding pairwise interactions of higher-than-binary discrete variables (these models are sometimes referred to as discrete quadratic models) into binary variables based on domain walls on one-dimensional Ising chains. We discuss how this is relevant to quantum annealing, but also many gate model algorithms such as VQE and QAOA. We theoretically show that for problems of practical interest for quantum computing and assuming only quadratic interactions are available between the binary variables, it is not possible to have a more efficient general encoding in terms of number of binary variables per discrete variable. We furthermore use a D-Wave Advantage 1.1 flux qubit quantum annealing computer to show that the dynamics effectively freeze later for a domain-wall encoding compared with a traditional one-hot encoding. This second result could help explain the dramatic performance improvement of domain wall over one-hot, which has been seen in a recent experiment on D-Wave hardware. This is an important result because usually problem encoding and the underlying physics are considered separately, our work suggests that considering them together may be a more useful paradigm. We argue that this experimental result is also likely to carry over to a number of other settings, we discuss how this has implications for gate-model and quantum-inspired algorithms. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.
Redefining the Graph Edit Distance

Abstract Graph edit distance has been used since 1983 to compare objects in machine learning when these objects are represented by attributed graphs instead of vectors. In these cases, the graph edit distance is usually applied to deduce a distance between attributed graphs. This distance is defined as the minimum amount of edit operations (deletion, insertion and substitution of nodes and edges) needed to transform a graph into another. Since now, it has been stated that the distance properties have to be applied [(1) non-negativity (2) symmetry (3) identity and (4) triangle inequality] to the involved edit operations in the process of computing the graph edit distance to make the graph edit distance a metric. In this paper, we show that there is no need to impose the triangle inequality in each edit operation. This is an important finding since in pattern recognition applications, the classification ratio usually maximizes in the edit operation combinations (deletion, insertion and substitution of nodes and edges) that the triangle inequality is not fulfilled.
Geometry Auxiliary Salient Object Detection for Light Fields via Graph Neural Networks

Light field imaging, originated from the availability of light field capture technology, offers a wide range of applications in the field of computational vision. The capability of predicting salient objects of light fields remains technologically challenging due to its complicated geometry structure. In this paper, we propose a light field salient object detection approach that formulates the geometric coherence among multiple views of light fields as graphs, where the angular/central views represent the nodes and their relations compose the edges. The spatial and disparity correlations between multiple views are effectively explored through multi-scale graph neural networks, enabling the more comprehensive understanding of light field content and more representative and discriminative saliency features generation. Moreover, a multi-scale saliency feature consistency learning module is embedded to enhance the saliency features. Finally, an accurate salient object map is produced for the light field based upon the extracted features. In addition, we establish a new light field salient object detection dataset (CITYU-Lytro) that contains 817 light fields with diverse contents and their corresponding annotations, aiming to further promote the research on light field salient object detection. Quantitative and qualitative experiments demonstrate that the proposed method performs favorably compared with the state-of-the-art methods on the benchmark datasets.
Generalized Large Margin $k$NN for Partial Label Learning

To deal with noises in partial label learning (PLL), existing approaches try to perform disambiguation either by identifying the ground-truth label or by averaging the candidate labels. However, these methods can be easily misled by the false-positive noisy labels in the candidate set, and fail to generalize well in testing. When labeling information is ambiguous, learning paradigms should depend more on underlying data structure. Large margin nearest neighbour (LMNN) is a popular strategy to consider instance and class correlations in supervised learning, but can not be directly used in weakly-supervised PLL due to the ambiguity of labeling information. In this paper, we first define similarly and differently labeled pairs as well as the similarity weight to evaluate the similarties between any two instances. We then propose a novel PLL method called Generalized Large Margin <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN for Partial Label Learning (GLMNN-PLL), which adapts the framework of LMNN to PLL by modifying the constraint from ‘the same class’ to ‘similarly-labeled’. GLMNN-PLL aims to learn a new metric and perform disambiguation by reorganizing the underlying data structure, that is, making similarly labeled instances closer to each other while making differently labeled instances seperated by a large margin. As two close instances with shared labels do not necessarily belong to the same class, we put a weight on each instance pair. An efficient algorithm is designed to optimize the proposed method and the convergence is analyzed in this paper. Moreover, we present a theoretical analysis of the generalization error bound for GLMNN-PLL. Comprehensive experiments on controlled UCI datasets as well as real-world partial label datasets from various domains demonstrate the superiorities of the proposed method.
Unstructured Text Enhanced Open-Domain Dialogue System: A Systematic Survey

Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, controlling conversation topics. In this article, we study the open-domain DS that uses unstructured text as external knowledge sources ( U nstructured T ext E nhanced D ialogue S ystem ( UTEDS )). The existence of unstructured text entails distinctions between UTEDS and traditional data-driven DS and we aim at analyzing these differences. We first give the definition of the UTEDS related concepts, then summarize the recently released datasets and models. We categorize UTEDS into Retrieval and Generative models and introduce them from the perspective of model components. The retrieval models consist of Fusion, Matching, and Ranking modules, while the generative models comprise Dialogue and Knowledge Encoding, Knowledge Selection (KS), and Response Generation modules. We further summarize the evaluation methods utilized in UTEDS and analyze the current models’ performance. At last, we discuss the future development trends of UTEDS, hoping to inspire new research in this field.
A new authentication procedure for client-server applications using HMAC

AbstractAbstractHash functions are used for data integrity and authentication but unfortunately they suffer with man in the middle attack problem. An intruder who has command over the communication channel can send an altered message along with its hash and pretends to be the authentic sender. Hash based authentication methods are subject to length based attacks also. So these problems are applicable to all those procedures which involve hash based authentication and routing protocols are one such example because routing protocols use MD5 authentication which is vulnerable in various aspects. So keeping these problems in mind, in this paper, we present a new HMAC implementation method which is a solution of these problems.Subject Classification: Primary 93A30Secondary 49K15Keywords: Hash based Message Authentication CodesKey agreementMan in the Middle AttackSecured Hash AlgorithmSymmetric key