Reviewerly/exHarmony huggingface dataset:
Joining Forces: Applying Design Thinking Techniques in Scrum Meetings

The most prominent Agile framework Scrum is often criticized for its amount of meetings. These regular events are essential to the empirical inspect and adapt cycle proposed by Agile methods. Scrum meetings face several challenges, such as being perceived as boring, repetitive, or irrelevant, leading to decreased cooperation in teams and less successful projects. In an attempt to address these challenges, Agile practitioners have adopted teamwork, innovation, and design techniques geared toward improving collaboration. Additionally, they have developed their own activities to be used in Scrum meetings, most notably for conducting retrospective and planning events. Design thinking incorporates non-designers and designers in design and conceptualization activities, including user research, ideation, or testing. Accordingly, the design thinking approach provides a process with different phases and accompanying techniques for each step. While these techniques are often not new, they are revised and customized for teams with little design experience. These design thinking techniques can support shared understanding in teams and can improve collaboration, creativity, and product understanding. For these reasons, design thinking techniques represent a worthwhile addition to the Scrum meeting toolkit and can support Agile meetings in preventing or countering common meeting challenges and achieving meeting goals. This chapter explores how techniques from the design thinking toolkit can support Scrum meetings from a theoretical and practical viewpoint. We analyze Scrum meetings’ requirements, goals, and challenges and link them to groups of techniques from the design thinking toolkit. In addition, we review interview and observational data from two previous studies with software development practitioners and derive concrete examples. As a result, we present initial guidelines on integrating design thinking techniques into Scrum meetings to make them more engaging, collaborative, and interactive.
Digital media zero watermark copyright protection algorithm based on embedded intelligent edge computing detection

&lt;abstract&gt; &lt;p&gt;With the rapid development of computer technology and network communication technology, copyright protection caused by widely spread digital media has become the focus of attention in various fields. For digital media watermarking technology research emerge in endlessly, but the results are not ideal. In order to better realize the copyright identification and protection, based on the embedded intelligent edge computing detection technology, this paper studies the zero watermark copyright protection algorithm of digital media. Firstly, this paper designs an embedded intelligent edge detection module based on Sobel operator, including image line buffer module, convolution calculation module and threshold processing module. Then, based on the embedded intelligent edge detection module, the Arnold transform of image scrambling technology is used to preprocess the watermark, and finally a zero watermark copyright protection algorithm is constructed. At the same time, the robustness of the proposed algorithm is tested. The image is subjected to different proportion of clipping and scaling attacks, different types of noise, sharpening and blur attacks, and the detection rate and signal-to-noise ratio of each algorithm are calculated respectively. The performance of the watermark image processed by this algorithm is evaluated subjectively and objectively. Experimental data show that the detection rate of our algorithm is the highest, which is 0.89. In scaling attack, the performance of our algorithm is slightly lower than that of Fourier transform domain algorithm, but it is better than the other two algorithms. The Signal to Noise Ratio of the algorithm is 36.854% in P6 multiplicative noise attack, 39.638% in P8 sharpening edge attack and 41.285% in fuzzy attack. This shows that the algorithm is robust to conventional attacks. The subjective evaluation of 33% and 39% of the images is 5 and 4. The mean values of signal to noise ratio, peak signal to noise ratio, mean square error and mean absolute difference are 20.56, 25.13, 37.03 and 27.64, respectively. This shows that the watermark image processed by this algorithm has high quality. Therefore, the digital media zero watermark copyright protection algorithm based on embedded intelligent edge computing detection is more robust, and its watermark invisibility is also very superior, which is worth promoting.&lt;/p&gt; &lt;/abstract&gt;
A Formal Analysis of EnOcean’s Teach-in and Authentication

The security of protocols and the absence of design-related weaknesses and vulnerabilities is crucial for the prevention of cyber attacks. This paper provides the first formal model for EnOcean, an IoT protocol widely used in home automation systems. Based on EnOcean’s security specification a formal model of its teach-in and high security authentication is created in the applied pi calculus. In an automated security analysis with the security protocol model checker ProVerif several security requirements are checked. While the analysis shows that all the secrecy statements can be verified, it identifies some weaknesses for the authentication. Based on an analysis of the potential attacks, we suggest a provable fix for the detected attacks.
Revertible Guidance Image Based Image Detail Enhancement

Image detail enhancement is widely used in image processing tasks to give better look and higher contrast to images and photos. However, the reverse process that converts an enhanced image back to its original image usually does not have an explicit representation given only the enhanced image is available. In this work, we propose a generic framework of revertible image detail enhancement so that we can estimate the original image without extra information. We define a family of revertible detail enhancement operators that convert each pixel from original image to enhanced image and vice versa. A guidance image is used to decide which operator to use for each pixel. In the enhancement process, the guidance image is generated from the original image. In the reverse process, the guidance image is estimated from iterative optimization, making the process revertible. Experimental results show that the proposed enhancement framework can convert the enhanced image back to its original image without noticeable difference.
CNN-Based Real-time Hand and Fingertip Recognition for the Design of a Virtual Keyboard

As AR/VR technology develops, the demand for interface technologies such as hand gesture recognition is increasing. For control of human-computer interface, accurate hand gesture classification and accurate finger position estimations are required. The accuracy of skin-color-based hand gesture detection methods can be affected by the presence of complex backgrounds, the intensity of light, and by the skin color backgrounds. In this paper, we propose a control method of real-time virtual keyboard that functions properly regardless of background changes through CNN-based hand gesture detection and fingertip detection. As a result of applying the proposed algorithm, the RMSE (Root mean square error) was reduced by approximately 82%. Additionally, the speed of the proposed algorithm is close to 38 fps, which is sufficient for real-time virtual keyboard applications.
Cardiac Magnetic Resonance Images Superresolution via Multichannel Residual Attention Networks

The deep neural network has achieved good results in medical image superresolution. However, due to the medical equipment limitations and the complexity of the human body structure, it is difficult to reconstruct clear cardiac magnetic resonance (CMR) superresolution images. To reconstruct clearer CMR images, we propose a CMR image superresolution (SR) algorithm based on multichannel residual attention networks (MCRN), which uses the idea of residual learning to alleviate the difficulty of training and fully explore the feature information of the image and uses the back-projection learning mechanism to learn the interdependence between high-resolution images and low-resolution images. Furthermore, the MCRN model introduces an attention mechanism to dynamically allocate each feature map with different attention resources to discover more high-frequency information and learn the dependency between each channel of the feature map. Extensive benchmark evaluation shows that compared with state-of-the-art image SR methods, our MCRN algorithm not only improves the objective index significantly but also provides richer texture information for the reconstructed CMR images, and our MCRN algorithm is better than the Bicubic algorithm in evaluating the information entropy and average gradient of the reconstructed image quality.
Robust Image Outpainting With Learnable Image Margins

Given a partial image input, image outpainting is to produce the desirable output by recovering or extending the surrounding image regions. While existing image outpainting methods achieve impressive results based on the recent advances of deep learning, they either lack the ability to extend image regions in arbitrary directions or require the filling image margins to be given in advance. To address this challenging task, we propose a unique deep learning framework for robust image outpainting, which consists of a margin prediction network and a teacher-student-based network for producing outpainted images. Our proposed model does not require image filling margins to be known beforehand, while both image appearance and perceptual feature consistencies can be jointly enforced. Our experiments quantitatively and qualitatively verify the effectiveness of our method, which is shown to perform favorably against baseline and state-of-the-art image outpainting works.
LOBD: Linked Data Dashboard for Marine Biodiversity

In this paper we describe new features and data visualization components of Linked Open Biodiversity Data (LOBD), an application that uses Linked Data to link information extracted from Global Biodiversity Information Facility (GBIF) with different linked open datasets such as Wikidata, NCBI Taxonomy and OpenCitation, to visually present the information as a scientific dashboard. The application allows to complement information about marine Biodiversity with information not initially available. To demonstrate this, a use case is presented.
Part Uncertainty Estimation Convolutional Neural Network For Person Re-Identification

Due to the large amount of noisy data in person re-identification (ReID) task, the ReID models are usually affected by the data uncertainty. Therefore, the deep uncertainty estimation method is important for improving the model robustness and matching accuracy. To this end, we propose a part-based uncertainty convolutional neural network (PUCNN), which introduces the part-based uncertainty estimation into the baseline model. On the one hand, PUCNN improves the model robustness to noisy data by distributilizing the feature embedding and constraining the part-based uncertainty. On the other hand, PUCNN improves the cumulative matching characteristics (CMC) performance of the model by filtering out low-quality training samples according to the estimated uncertainty score. The experiments on both non-video datasets, the noised Market-1501 and DukeMTMC, and video datasets, PRID2011, iLiDS-VID and MARS, demonstrate that our proposed method achieves encouraging and promising performance.
A Ranked Similarity Loss Function with pair Weighting for Deep Metric Learning

Metric learning is a widely-used method for image retrieval. The object of metric learning is to limit the distance between similar samples and increase the distance between samples of different classes through learning. Many studies tend to pay more attention to keep the distance between positive and negative samples, but ignore the distance between different classes of negative samples. In fact, query samples should be separated from negative samples of different classes by different distances. To address these problems, we propose to build a ranked similarity loss function with pair weighting (dubbed RMS loss). The proposed RMS loss can keep a distance between samples of different classes by weighting the negative samples according to the sorting order. Meanwhile, it further widens the distance between positive and negative samples by different processing of similarity of positive pairs and negative pairs. The effectiveness of our method is evaluated by extensive experiments on four public datasets and compared with state-of-the-art methods. The results show the proposed method obtains new performance on four public datasets, e.g., reaching 67.4% on CUB200 at Recall@1.
Normal forms of conditional knowledge bases respecting system P-entailments and signature renamings

Abstract Conditionals are defeasible rules of the form If A then usually B , and they play a central role in many approaches to nonmonotonic reasoning. Normal forms of conditional knowledge bases consisting of a set of such conditionals are useful to create, process, and compare the knowledge represented by them. In this article, we propose several new normal forms for conditional knowledge bases. Compared to the previously introduced antecedent normal form, the reduced antecedent normal form (RANF) represents conditional knowledge with significantly fewer conditionals by taking nonmonotonic entailments licenced by system P into account. The renaming normal form (ρNF) addresses equivalences among conditional knowledge bases induced by renamings of the underlying signature. Combining the concept of renaming normal form with other normal forms yields the renaming antecedent normal form ( ρ ANF) and the renaming reduced antecedent normal form ( ρ RANF). For all newly introduced normal forms, we show their key properties regarding, existence, uniqueness, model equivalence, and inferential equivalence, and we develop algorithms transforming every conditional knowledge base into an equivalent knowledge base being in the respective normal form. For the most succinct normal form, the ρ RANF, we present an algorithm K B ρ r a systematically generating knowledge bases over a given signature in ρ RANF. We show that the generated knowledge bases are consistent, pairwise not antecedentwise equivalent, and pairwise not equivalent under signature renaming. Furthermore, the algorithm is complete in the sense that, when taking signature renamings and model equivalence into account, every consistent knowledge base is generated. Observing that normalizing the set of all knowledge bases over a signature Σ to ρ RANF yields exactly the same result as K B ρ r a ( Σ ), highlights the interrelationship between normal form transformations on the one hand and systematically generating knowledge bases in normal form on the other hand.
From rationale to lessons learned in the cloud information security risk assessment: a study of organizations in Sweden

Purpose This study aims to address the issue of practicing information security risk assessment (ISRA) on cloud solutions by studying municipalities and large organizations in Sweden. Design/methodology/approach Four large organizations and five municipalities that use cloud services and conduct ISRA to adhere to their information security risk management practices were studied. Data were gathered qualitatively to answer the study’s research question: How is ISRA practiced on the cloud? The Coat Hanger model was used as a theoretical lens to study and theorize the practices. Findings The results showed that the organizations aimed to follow the guidelines, in the form of frameworks or their own experience, to conduct ISRA; furthermore, the frameworks were altered to fit the organizations’ needs. The results further indicated that one of the main concerns with the cloud ISRA was the absence of a culture that integrates risk management. Finally, the findings also stressed the importance of a good understanding and a well-written legal contract between the cloud providers and the organizations using the cloud services. Originality/value As opposed to the previous research, which was more inclined to try out and evaluate various cloud ISRA, the study provides insights into the practice of cloud ISRA experienced by the organizations. This study represents the first attempt to investigate cloud ISRA that organizations practice in managing their information security.
Cloud e-mail security: An accurate e-mail spam classification based on enhanced binary differential evolution (BDE) algorithm

The evolution of technology has brought new challenges and opportunities for the different dimensions of feature space. The higher dimension of the feature space is one of the most critical issues in e-mail classification problems due to accuracy considerations. The problem of finding the subset features that significantly influence the performance of e-mail spam classification has become one of the important challenges. This paper proposes to overcome such a problem, an intelligent approach to Binary Differential Evolution Support Vector Machine (BDE-SVM). The proposed approach enhances the Binary Differential Evolution (BDE) algorithm based on the correlation coefficient as a fitness function to select the significant subset feature evaluated by an SVM classifier. To our best of knowledge, the correlation coefficient as the fitness function has not been used in the differential evolution algorithm before. The selected subset feature is used to assess the most features that contribute to the reliability of the email spam classification. The finding of the enhanced BDE is to present a powerful accuracy. The tests were conducted using “Spambase” and “SpamAssassin.” Identified benchmark datasets are to assess the feasibility of the proposed solution. The result with full-feature accuracy was 93.55 percent compared to the proposed BDE-SVM approach, which is 93.99 percent. Empirical findings also show that our method is capable of effectively increasing the number of features required to enhance the reliability of the email spam classification.
Signal Processing with a Distribution of Graph Operators

Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. However, in many applications, the graph topology maybe unknown a priori, its structure uncertain, or generated randomly from a predefined set for each observation. Each graph topology gives rise to a different shift operator. In this paper, we develop a GSP framework over a probability space of shift operators. We develop the corresponding notions of Fourier transform, convolution, and band-pass filters in this framework, which subsumes traditional GSP theory as the special case where the probability space consists of a single shift operator. Numerical results on real datasets verify the superiority of performing GSP over a probability space of shift operators versus restricting to a single shift operator.
A Proof Method for Local Sufficient Completeness of Term Rewriting Systems

A term rewriting system (TRS) is said to be sufficiently complete when each function yields some value for any input. In this paper, we present a proof method for local sufficient completeness of TRSs, which is a generalised notion of sufficient completeness and is useful for proving inductive theorems of non-terminating TRSs. The proof method is based on a sufficient condition for local sufficient completeness of TRSs that consist of functions on natural numbers and (possibly infinite) lists of natural numbers. We also make a comparison between the proof abilities of the methods by the sufficient condition and by a derivation system introduced in previous work.
VerifyB - Students’ record management and verification system

Blockchain Technology has grabbed a lot of attention in the past few decades as an emerging technology majorly for crypto-exchanges. However, in 2-3 years, blockchain technology has gone through a path of diffusion across domains which are way on the far side, from its initial fintech applications. Media, Telecommunication, life sciences, defense, healthcare, government, etc., are industries where rapid adoption of this technology is seen. The education sector can also benefit from this technology's pertinent features, such as immutability, distributed ledger, and transparency. Record management, course evaluation, assistive learning environment, transcripts protection, and verification are the potential areas where institutional organizations can utilize blockchain. VerifyB is a blockchain-based system that promotes interoperability between institutions/ organizations to manage and assure students' records without any third-party entity intervention. The system is deployed on Ethereum Blockchain. It defines how blockchain technology can be adopted and leveraged to store students' educational records using IPFS (Interplanetary File System). It proposes how students can share their transcripts with institutions and employers using QR codes. Also, institutions can sign those records with access control using web3, and the authenticated employers can verify those transcripts and the signatures on the same platform. This system will help students as well as institutions to maintain security and transparency at the same time. Based on the analysis, VerifyB is a reliable, completely decentralized, secure, and tamper-proof system that implements storage, maintenance, and verification of educational records.
Private Set Operations Over Encrypted Cloud Dataset and Applications

Abstract We introduce the notion of private set operations (PSO) as a symmetric-key primitive in the cloud scenario, where a client securely outsources his dataset to a cloud service provider and later privately issues queries in the form of common set operations. We define a syntax and security notion for PSO and propose a general construction that satisfies it. There are two main ingredients to our PSO scheme: an adjustable join (Adjoin) scheme (MIT-CSAIL-TR-2012-006 (2012) Cryptographic treatment of CryptDB’s adjustable join. http://people.csail.mit.edu/nickolai/papers/popa-join-tr.pdf) and a tuple set (TSet) scheme (Cash, D., Jarecki, S., Jutla, C. S., Krawczyk, H., Rosu, M.-C., and Steiner, M. (2013) Highly-Scalable Searchable Symmetric Encryption With Support for Boolean Queries. 33rd Annual Cryptology Conf., Santa Barbara, CA, August 18–22, pp. 353–373. Springer, Berlin, Heidelberg). We also propose an Adjoin construction that is substantially more efficient (in computation and storage) than the previous ones (Mironov, I., Segev, G., and Shahaf, I. (2017) Strengthening the Security of Encrypted Databases: Non-Transitive Joins. 15th Int. Conf., TCC 2017, Baltimore, MD, USA, November 12–15, pp. 631–661. Springer, Cham) due to the hardness assumption that we rely on, while retaining the same security notion. The proposed PSO scheme can be used to perform join queries on encrypted databases without revealing the duplicate patterns in the unqueried columns, which is inherent to an Adjoin scheme. In addition, we also show that the PSO scheme can be used to perform Boolean search queries on a collection of encrypted documents. We also provide standard security proofs for our constructions and present detailed efficiency evaluation and compare them with well-known previous ones.