Reviewerly/exHarmony huggingface dataset:
Practitioners’ Attitudes and Perceptions Toward the Use of Augmented and Virtual Reality Technologies in the Education of Students With Disabilities

This study surveyed school professionals about their perceptions and attitudes toward using AR and VR in their work with children with disabilities. Survey data were collected from 257 participants in 45 states across the United States and from the Philippines. The findings indicated that very few practitioners used AR ( n = 26) or VR ( n = 19) in their practices, with nine participants using both AR and VR. Most of the participants who used AR or VR received training or had knowledge of how to use the technology and had technical support available. Similarly, the majority of the participants who did not use AR or VR reported a lack of training or knowledge of how to use the technology and a lack of financial resources. Participants who reported using AR were significantly younger than those who reported that they did not use AR, but there were no other significant differences between the groups.
Understanding the process of data reuse: An extensive review

Abstract Data reuse has recently become significant in academia and is providing new impetus for academic research. This prompts two questions: What precisely is the data reuse process? What is the connection between each participating element? To address these issues, 42 studies were reviewed to identify the stages and primary data reuse elements. A meta‐synthesis was used to locate and analyze the studies, and inductive coding was used to organize the analytical process. We identified three stages of data reuse—initiation, exploration and collection, and repurposing—and explored how they interact and form iterative characteristics. The results illuminated the data reuse at each stage, including issues of data trust, data sources, scaffolds, and barriers. The results indicated that multisource data and human scaffolds promote reuse behavior effectively. Further, two data and information search patterns were extracted: reticular centripetal patterns and decentralized centripetal patterns. Three paths with elements cooperating through flexible functions and motivated by different action items were identified: data centers, human scaffolds, and publications. This study supports improvements for data infrastructure construction, data reuse, and data reuse research by providing a new perspective on the effect of information behavior and clarifying the stages and contextual relationships between various elements.
Composition pattern-aware web service recommendation based on depth factorisation machine

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
High-accuracy projector calibration method for fringe projection profilometry considering perspective transformation

Camera and projector are the key components of structured light three-dimensional (3-D) measurements, and Digital Light Processing (DLP) projector has been widely used for projecting digital structured light patterns for the measurement. The light projecting of projectors can be modeled as the inverse procedures of camera imaging, and its high-accuracy calibration is still a remaining challenge. Therefore, this paper proposes a novel projector calibration method to improve the calibration accuracy of DLP projector. By fixing the position of the camera and calibration board, this method essentially eliminates the perspective transformation error and effectively avoids the distortion of the extracted marker points. The proposed projector calibration procedures are given as follows: Firstly, the optical axis of the camera is adjusted parallel to the normal of the hollow ring calibration board, and a texture image is captured by the camera; Secondly, the horizontal and vertical fringe patterns with nine different positions and directions are projected onto the calibration board, and nine sets of projected images are taken; Finally, a one-to-one correspondence between the camera and the projector is established, and the projector is accurately calibrated using the phase equivalence. The experimental results show that the proposed projector calibration method is feasible and easy to operate, which can essentially eliminate the perspective transformation error and ensure the competitive accuracy.
Characteristics, potentials, and limitations of open-source Simulink projects for empirical research

Abstract Simulink is an example of a successful application of the paradigm of model-based development into industrial practice. Numerous companies create and maintain Simulink projects for modeling software-intensive embedded systems, aiming at early validation and automated code generation. However, Simulink projects are not as easily available as code-based ones, which profit from large publicly accessible open-source repositories, thus curbing empirical research. In this paper, we investigate a set of 1734 freely available Simulink models from 194 projects and analyze their suitability for empirical research. We analyze the projects considering (1) their development context, (2) their complexity in terms of size and organization within projects, and (3) their evolution over time. Our results show that there are both limitations and potentials for empirical research. On the one hand, some application domains dominate the development context, and there is a large number of models that can be considered toy examples of limited practical relevance. These often stem from an academic context, consist of only a few Simulink blocks, and are no longer (or have never been) under active development or maintenance. On the other hand, we found that a subset of the analyzed models is of considerable size and complexity. There are models comprising several thousands of blocks, some of them highly modularized by hierarchically organized Simulink subsystems. Likewise, some of the models expose an active maintenance span of several years, which indicates that they are used as primary development artifacts throughout a project’s lifecycle. According to a discussion of our results with a domain expert, many models can be considered mature enough for quality analysis purposes, and they expose characteristics that can be considered representative for industry-scale models. Thus, we are confident that a subset of the models is suitable for empirical research. More generally, using a publicly available model corpus or a dedicated subset enables researchers to replicate findings, publish subsequent studies, and use them for validation purposes. We publish our dataset for the sake of replicating our results and fostering future empirical research.
Identification of Fuzzy Rule-Based Models With Collaborative Fuzzy Clustering

Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either the input or output data of that system (the other part could be owned by another user); and due to the consideration of data privacy, he/she could not obtain all the needed data to build the FRBMs. Since this type of situation has not been fully realized (noticed) and studied before, our objective is to come up with some strategy to address this challenge to meet the specific privacy consideration during the modeling process. In this study, the concept and algorithm of the collaborative fuzzy clustering (CFC) are applied to the identification of FRBMs, describing either multiple-input-single-output (MISO) or multiple-input-multiple-output (MIMO) systems. The collaboration between input and output spaces based on their structural information (conveyed in terms of the corresponding partition matrices) makes it possible to build FRBMs when input and output data could not be collected and used in unison. Surprisingly, on top of this primary pursuit, with the collaboration mechanism the input and output spaces of a system are endowed with an innovative way to comprehensively share, exchange, and utilize the structural information between each other, which results in their more relevant structures that guarantee better model performance compared with performance produced by some state-of-the-art modeling strategies. The effectiveness of the proposed approach is demonstrated by experiments on a series of synthetic and publicly available datasets.
Aspects About Science in the Context of Production and Communication of Knowledge of COVID-19

In the context of the COVID-19 pandemic, and given the huge volume of information available for the general population (being part of them fake news), there is a clear need to foster people's understanding of the meaning of science, of how scientific knowledge is produced, communicated, and used. As one of the main aims of science education is the promotion of students' scientific literacy, one of the issues focused on teaching should be aspects of nature of science (NOS) - which can be introduced from discussions in socioscientific contexts. In this paper, we analyse the context of the COVID-19 pandemic in order to identify and discuss aspects of NOS involved in the production and communication of knowledge about it. We analyse selected scientific publications and reports from the general media, mainly focused on three broad topics about the production and validation of knowledge: (i) the characterisation of the virus, and (ii) the treatments and vaccines for COVID-19, and (iii) the communication of knowledge produced from the characterisation of scientific literature itself in the pandemic period. The analysis was carried out from a model that presents a broad and complex view of science, as it addresses several areas of knowledge and specific aspects of each of them and proposes the generation of one's view of science from an integration of some of its distinct areas and/or aspects. The results show the current pandemic is a rich socioscientific context whose discussion of social, political, economic, and ethical aspects may support students' learning of nature of science, thus fostering scientific literacy.
Efficient Attribute-Based Signatures for Unbounded Arithmetic Branching Programs

This paper presents the first attribute-based signature (ABS) scheme in which the correspondence between signers and signatures is captured in an arithmetic model of computation. Specifically, we design a fully secure, i.e., adaptively unforgeable and perfectly signer-private ABS scheme for signing policies realizable by arithmetic branching programs (ABP), which are a quite expressive model of arithmetic computations. On a more positive note, the proposed scheme places no bound on the size and input length of the supported signing policy ABP's, and at the same time, supports the use of an input attribute for an arbitrary number of times inside a signing policy ABP, i.e., the so called unbounded multi-use of attributes. The size of our public parameters is constant with respect to the sizes of the signing attribute vectors and signing policies available in the system. The construction is built in (asymmetric) bilinear groups of prime order, and its unforgeability is derived in the standard model under (asymmetric version of) the well-studied decisional linear (DLIN) assumption coupled with the existence of standard collision resistant hash functions. Due to the use of the arithmetic model as opposed to the boolean one, our ABS scheme not only excels significantly over the existing state-of-the-art constructions in terms of concrete efficiency, but also achieves improved applicability in various practical scenarios. Our principal technical contributions are (a) extending and refining the techniques of Okamoto and Takashima [PKC 2011, PKC 2013], which were originally developed in the context of boolean span programs, to the arithmetic setting; and (b) innovating new ideas to allow unbounded multi-use of attributes inside ABP's, which themselves are of unbounded size and input length.
Bridging the Gap Between Clone-and-Own and Software Product Lines

Software is often released in multiple variants to meet all customer requirements. While software product lines address this need by advocating the development of an integrated software platform, practitioners frequently rely on ad-hoc reuse based on a principle which is known as clone-and-own. This practice avoids high up-front investments, as new variants of a software family are created by simply copying and adapting an existing variant, but maintenance costs explode once a critical number of variants is reached. With our research project VariantSync, we aim to bridge the gap between clone-and-own and product lines by combining the minimal overhead and flexibility of clone-and-own with the systematic handling of variability in software product lines. The key idea is to transparently integrate product-line concepts with variant management facilities known from version control systems in order to automatically synchronize a set of evolving variants. We believe that VariantSync has the potential to change the way how practitioners develop multi-variant software systems for which it is hard to foresee which variants will be added in the future.
Geochemical distribution, statistical and health risk assessment of toxic elements in groundwater from a typical mining district in Nigeria

Globally, the influence of mining activities on the quality of drinking waters have been reported by several researchers. Long-term exposure to potentially toxic elements (PTEs) (e.g. As, Cd, Co, Cu, Fe, Mn, Ni, Pb, Se and Zn) can cause serious health hazards to consumers of polluted water. In the current study, twenty groundwater samples from Ameka mine area were analyzed for PTEs pollution and the associated human health risks. The concentrations of As, Cd, Ni, Pb Co, and Se were found to be above their standard limits in majority of the samples. Index methods such as heavy metal evaluation index, heavy metal pollution index and contamination degree confirmed that majority of the samples are loaded with PTEs. Similarly, synthetic pollution index further revealed that all the samples are extremely polluted and thus very unsuitable for human consumption. The health risk assessment model proposed by US-EPA was applied in assessing the associated health risks of PTEs in the groundwater resources. Non-carcinogenic health risk assessment predicted that 100% of the samples have hazard index values > 4, indicating an extremely high chronic health risk for adults and children. Similarly, carcinogenic risk assessment showed that all the samples pose high cancer risk to both populations due to As, Ni and Cd pollution. However, all the samples pose low cancer risk due to Pb enrichment. Multivariate statistical analyses (such as Pearson’s correlation matrix and principal component extractions) indicated that mining activities and rock-water interactions (weathering) are the responsible sources for the elemental pollution of the groundwater system. Before consumption, adequate treatment is recommended for the polluted waters.
Learning to recognize actionable static code warnings (is intrinsically easy)

Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the “actionable” warnings; i.e. the warnings that are usually not ignored. In this paper, we look for actionable warnings within a sample of 5,675 actionable warnings seen in 31,058 static code warnings from FindBugs. We find that data mining algorithms can find actionable warnings with remarkable ease. Specifically, a range of data mining methods (deep learners, random forests, decision tree learners, and support vector machines) all achieved very good results (recalls and AUC(TRN, TPR) measures usually over 95% and false alarms usually under 5%). Given that all these learners succeeded so easily, it is appropriate to ask if there is something about this task that is inherently easy. We report that while our data sets have up to 58 raw features, those features can be approximated by less than two underlying dimensions. For such intrinsically simple data, many different kinds of learners can generate useful models with similar performance. Based on the above, we conclude that learning to recognize actionable static code warnings is easy, using a wide range of learning algorithms, since the underlying data is intrinsically simple. If we had to pick one particular learner for this task, we would suggest linear SVMs (since, at least in our sample, that learner ran relatively quickly and achieved the best median performance) and we would not recommend deep learning (since this data is intrinsically very simple).
A Secure Image Steganography Using Improved Lsb Technique And Vigenere Cipher Algorithm

Steganography is the practise of concealing information in another type. Many different carrier file formats may be used, but digital images are most popular. This involved with the stronger and weaker image hiding, file compression, file locking, file encryption and file decryption. The applications are having the capability for establishing the characteristics like absolute confidentiality, whereas other applications are in requirement of secrecy on a large message. The aim of this analysis is to include a description which outlines the criteria of a good steganographic algorithm and briefly discusses best techniques which are in use. The knowledge is processed electronically because of development in the area of ICT. Therefore, privacy and security has been a big concern. It may also be used to encrypt messages which are embedded in a digital host before being sent to the network, thus their existence is uncertain in nature. The mechanism of hiding and encrypting sensitive data may be extended to the copyright protection of interactive media, audio, video and images. It can also be utilized to further protect the integrity of digital records. This is the practise of concealed touch. The objective towards aim is to submit the documents and to create numerous uses, then the message to be hidden is encoded with a vegenerous chip algorithm and saved in an image with the LSB steganography and finally, the virgin chip decryption algorithm can be used to obtain a real code.
A Blockchain Approach to Ensuring Provenance to Outsourced Cloud Data in a Sharing Ecosystem

Cloud servers offer data owners the opportunityto upload and store encrypted data that several data users can access. However, after outsourcing data to the cloud, data owners have limited control of their data, and external systems are incorporated to manage it. Several research-based solutions use encryption techniques to prevent unauthorized access to the data but ignore the challenge of maintaining the trace of valid changes applied to the data. Provenance data can be used to ascertain the integrity of data and, therefore, should be immutable and secured from adversaries since it contains private information. This article proposes an efficient way of securing access logs by leveraging blockchain. A generic framework is designed, tested, and evaluated, with results showing our model can effectively enhance the security of provenance data. This work considers two categories of data users and account for the distinct roles and permitted actions on the outsourced data. In summary, this work ensures that the data is trustworthy, and verification and management of the outsourced data are assured. Experimental results illustrate the efficiency and scalability of our solution.
Transformer-Based Language-Person Search With Multiple Region Slicing

Language-person search is an essential technique for applications like criminal searching, where it is more feasible for a witness to provide language descriptions of a suspect than providing a photo. Most existing works treat the language-person pair as a black-box, neither considering the inner structure in a person picture, nor the correlations between image regions and referring words. In this work, we propose a transformer-based language-person search framework with matching conducted between words and image regions, where a person picture is vertically separated into multiple regions using two different ways, including the overlapped slicing and the key-point-based slicing. The co-attention between linguistic referring words and visual features are evaluated via transformer blocks. Besides the obtained outstanding searching performance, the proposed method enables to provide interpretability by visualizing the co-attention between image parts in the person picture and the corresponding referring words. Without bells and whistles, we achieve the state-of-the-art performance on the CUHK-PEDES dataset with Rank-1 score of 57.67&#x0025; and the PA100K dataset with mAP of 22.88&#x0025;, with simple yet elegant design. Code is available on <uri>https://github.com/detectiveli/T-MRS</uri>.
PP-CSA: A Privacy-Preserving Cloud Storage Auditing Scheme for Data Sharing

Data sharing is one important service provided by cloud storage. In order to share data conveniently and securely, Shen et al. proposed a cloud storage auditing scheme for data sharing, which uses the sanitizable signature to hide sensitive information. However, it may cause unauthorized access to the data, since anyone can access the data stored on the cloud server. This article proposes a privacy-preserving cloud storage auditing (PP-CSA) scheme for data sharing, where only authorized users can access the data. Furthermore, PP-CSA adopts the Diffie-Hellman protocol to avoid the secure channel between the data owner and the sanitizer. Finally, the security analysis and the experimental results prove that the security and efficiency of PP-CSA can be accepted.
A deep learning based multiscale approach to segment the areas of interest in whole slide images

This paper addresses the problem of liver cancer segmentation in Whole Slide Images (WSIs). We propose a multi-scale image processing method based on an automatic end-to-end deep neural network algorithm for the segmentation of cancerous areas. A seven-level gaussian pyramid representation of the histopathological image was built to provide the texture information at different scales. In this work, several neural architectures were compared using the original image level for the training procedure. The proposed method is based on U-Net applied to seven levels of various resolutions (pyramidal subsampling). The predictions in different levels are combined through a voting mechanism. The final segmentation result is generated at the original image level. Partial color normalization and the weighted overlapping method were applied in preprocessing and prediction separately. The results show the effectiveness of the proposed multi-scale approach which achieved better scores than state-of-the-art methods.
Variational Autoencoders for Top-K Recommendation with Implicit Feedback

Variational Autoencoders (VAEs) have shown to be effective for recommender systems with implicit feedback (e.g., browsing history, purchasing patterns, etc.). However, a little attention is given to ensembles of VAEs, that can learn user and item representations jointly. We introduce Joint Variational Autoencoder (JoVA), an ensemble of two VAEs, which jointly learns both user and item representations to predict user preferences. This design allows JoVA to capture user-user and item-item correlations simultaneously. We also introduce JoVA-Hinge, a JoVA's extension with a hinge-based pairwise loss function, to further specialize it in recommendation with implicit feedback. Our extensive experiments on four real-world datasets demonstrate that JoVA-Hinge outperforms a broad set of state-of-the-art methods under a variety of commonly-used metrics. Our empirical results also illustrate the effectiveness of JoVA-Hinge for handling users with limited training data.