Reviewerly/exHarmony huggingface dataset:
Transforming OpenAPI Specification 3.0 documents into RDF-based semantic web services

Abstract Web services are provided with documents that at the very least specify the endpoint, input parameters, and output or response of each operation to expose their capabilities. This should be considered through an understandable format for humans and/or machines. In the Representational State Transfer (REST) architectural style, the OpenAPI Specification (OAS) is used as a reference to create web service descriptions. However, it only supports syntactic interoperability, leading to the incapability of supporting the automated selection process. To overcome this, OAS documents must be enhanced by including semantics to each resource to provide “understandable” services. Therefore, this study aims to develop a system capable of transforming resources in OAS documents into RDF-based semantic web services. To begin, a relational database schema based on the OAS structure is created to store all objects in the OAS document. The published open-linked vocabulary was then used to create the ontology, which maps resources and their relationships on the RDF data model. To build RDF-based semantic web services, R2RML was used to generate the relational database model into triple RDF. The proposed system was also tested through prototyping and using a dataset of 106 OAS documents, which were downloaded from APIs.guru between 5–10 May 2021. The number of triple RDFs generated per document varied with resource rate. An OAS document generates 36 to 16,505 triple RDF in a dataset. The end product was a triple RDF knowledge base maintained by a graph management database. It is now possible to find service operations, input and output parameters, and service composition requirements utilizing the repository semantic web services using SPARQL. On the other hand, the use of relational databases to store OAS resources increased reuse efficiency by approximately 48%, owing to service developers designing interoperability between uniform parameter services, which were then used as input and output.
Autonomous Drone Swarm Navigation and Multitarget Tracking With Island Policy-Based Optimization Framework

Autonomous modeling of artificial swarms is necessary because manual creation is a time intensive and complicated procedure which makes it impractical. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstacles and resistive forces (like linear drag, angular drag, and gravity) are modeled to track multiple dynamic targets. Moreover, reward functions for robust swarm formation and target tracking are devised for learning complex swarm behaviors. Since the number of agents is not fixed and has only the partial observance of the environment, swarm formation and navigation become challenging. In this regard, the proposed strategy consists of three main phases to tackle the aforementioned challenges: 1) A methodology for dynamic swarm management, 2) Avoiding obstacles, Finding the shortest path towards the targets, 3) Tracking the targets and Island modeling. The dynamic swarm management phase translates basic sensory input to high level commands to enhance swarm navigation and decentralized setup while maintaining the swarms size fluctuations. While, in the island modeling, the swarm can split into individual subswarms according to the number of targets, conversely, these subswarms may join to form a single huge swarm, giving the swarm ability to track multiple targets. Customized state of the art policy based deep reinforcement learning algorithms are employed to achieve significant results. The promising results show that our proposed strategy enhances swarm navigation and can track multiple static and dynamic targets in complex dynamic environments.
Robust Watermarking via Multidomain Transform Over Wireless Channel: Design and Experimental Validation

Watermarking is a technique that provides ownership rights for shared data, and this technique can also help to transmit confidential information to realize secure communication. In this paper, we design a blind digital watermarking based on Discrete Wavelet Transform-Discrete Cosine Transform-Singular Value Decomposition (DWT-DCT-SVD) and optimize it by genetic algorithm (GA). This watermarking has both high robustness and high imperceptibility. Then we design a transmitter and receiver system based on the IEEE 802.11a and software-defined radio (SDR). We adopt several optimization methods for the system, such as ACK/HARQ mechanism, different redundant coding, frame length adaption, adaptive modulation, and so on, aiming to improve communication reliability and rate. We verify its high communication reliability and rate through actual experiments and complete the combination of digital watermarking and wireless transmission. We could build the application scenario of Image Security Wireless Communication based on Wireless Local Area Network (WLAN) protocol and SDR platform indoors or outdoors in small areas.
Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution

To achieve promising results on blind image super-resolution (SR),some attempts leveraged the low resolution (LR) images to predict the kernel and improve the SR performance. However, these Supervised Kernel Prediction (SKP) methods are impractical due to the unavailable real-world blur kernels. Although some Unsupervised Degradation Prediction (UDP) methods are proposed to bypass this problem, the inconsistency between degradation embedding and SR feature is still challenging. By exploring the correlations between degradation embedding and SR feature, we observe that jointly learning the content and degradation aware feature is optimal. Based on this observation, a Content and Degradation aware SR Network dubbed CDSR is proposed. Specifically, CDSR contains three newly-established modules: (1) a Lightweight Patch-based Encoder (LPE) is applied to jointly extract content and degradation features; (2) a Domain Query Attention based module (DQA) is employed to adaptively reduce the inconsistency; (3) a Codebook-based Space Compress module (CSC) that can suppress the redundant information. Extensive experiments on several benchmarks demonstrate that the proposed CDSR outperforms the existing UDP models and achieves competitive performance on PSNR and SSIM even compared with the state-of-the-art SKP methods.
Multi-sensory display of self-avatar's physiological state: virtual breathing and heart beating can increase sensation of effort in VR

In this paper we explore the multi-sensory display of self-avatars' physiological state in Virtual Reality (VR), as a means to enhance the connection between the users and their avatar. Our approach consists in designing and combining a coherent set of visual, auditory and haptic cues to represent the avatar's cardiac and respiratory activity. These sensory cues are modulated depending on the avatar's simulated physical exertion. We notably introduce a novel haptic technique to represent respiratory activity using a compression belt simulating abdominal movements that occur during a breathing cycle. A series of experiments was conducted to evaluate the influence of our multi-sensory rendering techniques on various aspects of the VR user experience, including the sense of virtual embodiment and the sensation of effort during a walking simulation. A first study ($\mathrm{N}=30$) that focused on displaying cardiac activity showed that combining sensory modalities significantly enhances the sensation of effort. A second study ($\mathrm{N}=20$) that focused on respiratory activity showed that combining sensory modalities significantly enhances the sensation of effort as well as two sub-components of the sense of embodiment. Interestingly, the user's actual breathing tended to synchronize with the simulated breathing, especially with the multi-sensory and haptic displays. A third study ($\mathrm{N}=18$) that focused on the combination of cardiac and respiratory activity showed that combining both rendering techniques significantly enhances the sensation of effort. Taken together, our results promote the use of our novel breathing display technique and multi-sensory rendering of physiological parameters in VR applications where effort sensations are prominent, such as for rehabilitation, sport training, or exergames.
Short Composite Quantum Gate Robust against Two Common Systematic Errors

Unavoidable systematic errors hinder precise quantum control. Pulse length error (PLE) and off-resonance one (ORE) are typical systematic errors that are encountered during one-qubit control. A composite operation, one of open-loop error cancellation techniques, can help compensate for the effects of systematic errors during quantum operation. Several composite operations that are robust against either PLE or ORE have been identified. However, few attempts have been made to construct composite operations that are robust against both errors (bi-robust) simultaneously. We develop a new bi-robust composite operation for controlling one-qubit, which exhibits a short operation time.
Occluded Person Re-Identification Method Based on Multiscale Features and Human Feature Reconstruction

Occluded Re-ID task is proposed mainly because people are often occluded by various obstacles in the real world, which greatly affects the accuracy of model matching.In view of the challenge of occluded Re-ID, the main work of this paper is as follows :(i) Aiming at the incompleteness of human body under occlusion, an occluded Re-ID method based on multi-scale features is proposed. A partial human body locator is constructed by using the target detection algorithm to automatically recognize and cut partial human body in this method. Then this method designs a horizontal pyramid pooling strategy to extract multi-scale features and enhance the robustness of the model under the occlusion problem. Experiments show that, this method has better matching accuracy in the occluded Re-ID task. (ii) Aiming at the problem that it is difficult to align the local features between different people images under occlusion, an occluded Re-ID method based on human feature reconstruction is proposed. This method is an unaligned method, which uses sparse representation to reconstruct human body features. Difficult sample triplet loss function was improved by using human feature reconstruction distance and the proportion of similar parts to matching correlation was increased. Experiments show that this method can effectively improve the occlusion resistance of the model.
Why higher-level reading is important

Societies are facing fundamental transformations as digital technologies are changing the ways we live, interact, work, study and read. The social and cultural impact of the digitization process on reading skills and practices remains under-researched. While digital technologies offer much potential for new forms of reading, recent empirical research shows that the digital environment is having a negative impact on reading, in particular on long-form reading and reading comprehension. It also remains unclear whether the transition to digital media actually lives up to its promise of improving learning outcomes. Recent studies of various kinds indicate a decline of crucial higher-level reading competencies and practices, such as critical and conscious reading, slow reading, non-strategic reading and long-form reading. Current educational policy, meanwhile, relies heavily on monocultural standardized testing of basic reading capabilities and on growing use of digital technologies. Reading education, assessment, research and policy-making should focus more on higher-level reading practices in both adults and children in order to understand the development of reading skills and practices in an age increasingly dependent on a ubiquitous digital infrastructure.
Biological Activated Sludge from Wastewater Treatment Plant before and during the COVID-19 Pandemic

The COVID-19 pandemic and the related measures brought a change in daily life that affected the characteristics of the municipal wastewater and further, of the biological activated sludge. The activated sludge process is the most widely used biological wastewater treatment process in developed areas. In this paper, we aim to show the situation of specific investigations concerning the variation of the physicochemical parameters and biological composition of the activated sludge from one conventional wastewater treatment plant from a metropolitan area. The investigations were carried out for three years: 2019, 2020 and 2021. The results showed the most representative taxa of microorganisms: Microtrix, Aspidisca cicada, Vorticella convallaria, Ciliata free of the unknown and Epistylis and Rotifers. Even if other microorganisms were found in the sludge flocs, their small presence did not influence in any way the quality of the activated sludge and of the wastewater treatment process. That is why we conclude that protozoa (especially Flagellates and Ciliates) and rotifers were the most important. Together with the values and variation of the physicochemical parameters, they indicated a good, healthy, and stable activated sludge, along with an efficient purifying treatment process, no matter the loading conditions.
Subdivisions and crossroads: Identifying hidden community structures in a data archive’s citation network

Data archives are an important source of high quality data in many fields, making them ideal sites to study data reuse. By studying data reuse through citation networks, we are able to learn how hidden research communities - those that use the same scientific datasets - are organized. This paper analyzes the community structure of an authoritative network of datasets cited in academic publications, which have been collected by a large, social science data archive: the Interuniversity Consortium for Political and Social Research (ICPSR). Through network analysis, we identified communities of social science datasets and fields of research connected through shared data use. We argue that communities of exclusive data reuse form subdivisions that contain valuable disciplinary resources, while datasets at a "crossroads" broadly connect research communities. Our research reveals the hidden structure of data reuse and demonstrates how interdisciplinary research communities organize around datasets as shared scientific inputs. These findings contribute new ways of describing scientific communities in order to understand the impacts of research data reuse.
Symbolic knowledge extraction from opaque predictors applied to cosmic-ray data gathered with LISA Pathfinder

Machine learning models are nowadays ubiquitous in space missions, performing a wide variety of tasks ranging from the prediction of multivariate time series through the detection of specific patterns in the input data. Adopted models are usually deep neural networks or other complex machine learning algorithms providing predictions that are opaque, i.e., human users are not allowed to understand the rationale behind the provided predictions. Several techniques exist in the literature to combine the impressive predictive performance of opaque machine learning models with human-intelligible prediction explanations, as for instance the application of symbolic knowledge extraction procedures. In this paper are reported the results of different knowledge extractors applied to an ensemble predictor capable of reproducing cosmic-ray data gathered on board the LISA Pathfinder space mission. A discussion about the readability/fidelity trade-off of the extracted knowledge is also presented.
Analog quantum approximate optimization algorithm

Abstract We present an analog version of the quantum approximate optimization algorithm suitable for current quantum annealers. The central idea of this algorithm is to optimize the schedule function, which defines the adiabatic evolution. It is achieved by choosing a suitable parametrization of the schedule function based on interpolation methods for a fixed time, with the potential to generate any function. This algorithm provides an approximate result of optimization problems that may be developed during the coherence time of current quantum annealers on their way toward quantum advantage.
Test-Time Image-to-Image Translation Ensembling Improves Out-of-Distribution Generalization in Histopathology

Histopathology whole slide images (WSIs) can reveal significant inter-hospital variability such as illumination, color or optical artifacts. These variations, caused by the use of different protocols across medical centers (staining, scanner), can strongly harm algorithms generalization on unseen protocols. This motivates the development of new methods to limit such loss of generalization. In this paper, to enhance robustness on unseen target protocols, we propose a new test-time data augmentation based on multi domain image-to-image translation. It allows to project images from unseen protocol into each source domain before classifying them and ensembling the predictions. This test-time augmentation method results in a significant boost of performances for domain generalization. To demonstrate its effectiveness, our method has been evaluated on two different histopathology tasks where it outperforms conventional domain generalization, standard/H &E specific color augmentation/normalization and standard test-time augmentation techniques. Our code is publicly available at https://gitlab.com/vitadx/articles/test-time-i2i-translation-ensembling .
Generation of Coherent Multi-Sentence Texts with a Coherence Mechanism

Automatic generation of long texts containing multiple sentences has many applications in the field of Natural Language Processing (NLP) including question answering, machine translation, and paraphrase generation, etc. However, in terms of readability, the long texts generated by machines are not comparable to those organized by human beings. Through statistics, we observed that human-organized texts generally have a special property: one or more of the words (particularly nouns and pronouns) appeared in one sentence will reappear in the next one in the same or a different form. This repetition of words in consecutive sentences can greatly improve the readability. Based on this observation, we propose CMST, a deep neural network model for generating Coherent Multi-Sentence Texts. CMST explicitly incorporates a training strategy of coherence mechanism to evaluate the repetition of words in consecutive sentences. We evaluate the performance of the CMST on the CNN/Daily Mail dataset. The experimental results show that, compared with the baseline models, CMST not only improves the readability of the generated texts, but achieves higher METEOR and ROUGE values.
Exploring Spatial Correlation for Light Field Saliency Detection: Expansion From a Single View

Previous 2D saliency detection methods extract salient cues from a single view and directly predict the expected results. Both traditional and deep-learning-based 2D methods do not consider geometric information of 3D scenes. Therefore the relationship between scene understanding and salient objects cannot be effectively established. This limits the performance of 2D saliency detection in challenging scenes. In this paper, we show for the first time that saliency detection problem can be reformulated as two sub-problems: light field synthesis from a single view and light-field-driven saliency detection. This paper first introduces a high-quality light field synthesis network to produce reliable 4D light field information. Then a novel light-field-driven saliency detection network is proposed, in which a Direction-specific Screening Unit (DSU) is tailored to exploit the spatial correlation among multiple viewpoints. The whole pipeline can be trained in an end-to-end fashion. Experimental results demonstrate that the proposed method outperforms the state-of-the-art 2D, 3D and 4D saliency detection methods.
Audio Anti-spoofing Using Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning

Automatic speaker verification systems are vulnerable to a variety of access threats, prompting research into the formulation of effective spoofing detection systems to act as a gate to filter out such spoofing attacks. This study introduces a simple attention module to infer 3-dim attention weights for the feature map in a convolutional layer, which then optimizes an energy function to determine each neuron's importance. With the advancement of both voice conversion and speech synthesis technologies, unseen spoofing attacks are constantly emerging to limit spoofing detection system performance. Here, we propose a joint optimization approach based on the weighted additive angular margin loss for binary classification, with a meta-learning training framework to develop an efficient system that is robust to a wide range of spoofing attacks for model generalization enhancement. As a result, when compared to current state-of-the-art systems, our proposed approach delivers a competitive result with a pooled EER of 0.99% and min t-DCF of 0.0289.
Inverse-coefficient black-box quantum state preparation

Abstract Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms. The basic task of black-box state preparation is to transduce the data encoded as computational basis of quantum state into the amplitude. In the present work, we address the problem of transducing the reciprocal of the data, not the data itself into the amplitude, which is called the inverse-coefficient problem. This algorithm can be used directly as a subroutine in the matrix inversion algorithms. Furthermore, we extend this approach to address the more general nonlinear-coefficient problem in black-box state preparation. Our algorithm is based on the technique of inequality test. It can greatly relieve the need to do quantum arithmetic and the error is only resulted from the truncated error of binary string. The present algorithms enrich the algorithm library of black-box quantum state preparation and will be useful ingredients of quantum algorithm to implement non-linear quantum state transformations.
Typology of Cybercrime Victimization in Europe: A Multilevel Latent Class Analysis

The present study reveals hidden patterns of group membership across cybercrime victims in European countries. We used a multilevel latent class analysis of data from the 2019 Eurobarometer, a regionally representative sample of 21,908 individuals from 28 countries, to identify such subgroups and patterns of cybercrime victimization. The findings suggest there are two overarching cybercrime victim profiles in Europe based on individuals’ levels of “online activity” and “cybersecurity guardianship”: the “at-risk class” (19%, higher risk) and the “cautious class” (81%, lower risk). Ten different types of cybercrime victimization were compared, and while individual-level predictors were primarily used to produce different groups of cybercrime victims, our findings suggest that researchers consider both individual- and country-level predictors to understand cybercrime victimization patterns in greater depth.