Reviewerly/exHarmony huggingface dataset:
A risk management framework for distributed scrum using PRINCE2 methodology

The distributed Agile development approach has been accepted by software companies due to its promised benefits. However, due to the controversial nature of distributed and Agile development, significant challenges arise from spatial, temporal, social, and cultural differences between distributed teams. Scrum, as the most popular Agile methodology, assumes that team members work together in the same room. But this principle does not apply in a realistic scenario where Scrum teams are distributed in different locations. Hence, proposing a risk management framework is necessary in order to succeed such teams. The purpose of this research was to propose a risk management framework in Scrum using the PRINCE2 methodology, which includes the perceived risks in distributed Scrum projects and their causes and roots for managing these risks. By embedding distributed Scrum in delivery layer of PRINCE2 and considering perceived risk factors, along with a hybrid model, a risk management framework was suggested. This framework has been used in a case study, and the results showed its proper functionality in detecting and eliminating potential risks in the case under study. Also, using this framework led to higher team efficiency in terms of increasing the number of completed user stories in each sprint.
Single Underwater Image Restoration Based on Adaptive Transmission Fusion

Underwater images are often deteriorated with blurring, darkness, poor visual quality of low contrast, and color diminishing. This is mainly due to the fact that the light is exponentially attenuated while traveling through water and the strength of attenuation is color dependent. After constructing a simplified image formation model, this paper proposes a new strategy for single underwater image restoration. In light of different perspectives, two distinct transmission coefficient estimation approaches have been developed. One is based on the optical characteristics while the other relies on the essence of image processing knowledge. Subsequently, these two transmission maps are fused to produce the final outcome, which is adaptively weighted by their respective saliency maps. The obtained signal radiance is dissolved through point spread function deconvolution and color compensation to produce the final scene radiance. A variety of underwater images with various scenarios were exploited to evaluate the restoration performance. Experimental results demonstrated the superiority of the proposed algorithm over other competitive methods for underwater image restoration.
Examining influential factors for acknowledgements classification using supervised learning

Acknowledgements have been examined as important elements in measuring the contributions to and intellectual debts of a scientific publication. Unlike previous studies that were limited in the scope of analysis and manual examination. The present study aimed to conduct the automatic classification of acknowledgements on a large scale of data. To this end, we first created a training dataset for acknowledgements classification by sampling the acknowledgements sections from the entire PubMed Central database. Second, we adopted various supervised learning algorithms to examine which algorithm performed best in what condition. In addition, we observed the factors affecting classification performance. We investigated the effects of the following three main aspects: classification algorithms, categories, and text representations. The CNN+Doc2Vec algorithm achieved the highest performance of 93.58% accuracy in the original dataset and 87.93% in the converted dataset. The experimental results indicated that the characteristics of categories and sentence patterns influenced the performance of classification. Most of the classifiers performed better on the categories of financial, peer interactive communication, and technical support compared to other classes.
On the Relationship between Software Complexity and Security

This work aims at discussing the complexity aspect of software while demonstrating its relationship with security.Complexity is an essential part of software; however, numerous studies indicate that they increase the vulnerability of the software systems and introduce bugs in the program.Many developers face difficulty when trying to understand the complex components of software.Complexity in software increases when objects in the software are used to design a more complex object while creating a hierarchical complexity in the system.However, it is necessary for the developers to strive for minimum complexity, as increased complexity introduces security risks in the software, which can cause severe monetary and reputational damage to a government or a private organization.It even causes bodily harm to human beings with various examples found in previous years where security breaches led to severe consequences.Hence it is vital to maintain low complexity and simple design of structure.Various developers tend to introduce deliberate complexities in the system so that they do not have to write the same program twice; however, it is getting problematic for the software organizations as the demands of security are continually increasing.
Secret Image Sharing with Dealer-Participatory and Non-Dealer-Participatory Mutual Shadow Authentication Capabilities

A ( k , n ) threshold secret image sharing (SIS) method is proposed to divide a secret image into n shadows. The beauty of this scheme is that one can only reconstruct a secret image with k or more than k shadows, but one cannot obtain any information about the secret from fewer than k shadows. In the ( k , n ) threshold SIS, shadow authentication means the detection and location of manipulated shadows. Traditional shadow authentication schemes require additional bits for authentication; need much information to be public; or need to put each shadow into a host image, utilizing the information hiding technique, which makes the generation, recovery and authentication complexity higher. Besides, most existing schemes work when a dealer participates in recovery. Our contribution is that we propose a SIS method for a ( k , n ) threshold with dealer-participatory and non-dealer-participatory mutual shadow authentication capabilities which integrates polynomial-based SIS and visual secret sharing (VSS) through using the result of VSS to “guide” the polynomial-based SIS by a screening operation. In our scheme, when an authentication image is public, all involved actors (participants and dealer) can mutually authenticate each other by exchange the lowest level plane instead of the whole shadow. Our scheme is suitable for the case with and without a dealer participate recovery. In addition, the proposed scheme has characteristics of low generation and authentication complexity, no pixel expansion, 100% detection rate and lossless recovery.
Impact of the Economy Digitalization on Welfare in the Developed and Developing Countries

Impact of the Economy Digitalization on Welfare in the Developed and Developing CountriesDigitalization is one of the key world economic trends.Countries with the wealthiest economies have integrated the digitalization program into their economic development plans.At the same time, monitoring of the digitalization's dynamics and degree of impact on welfare is of great importance for all countries of the global community.Nowadays, there are practically no integrated studies assessing institutional, cultural, economic, educational and infrastructural consequences of the digitalization.This article assesses how the digitalization affects the mentioned drivers of social and economic development.Moreover, it examines the digitalization's impact on welfare of the developed and developing countries.The relevance of defining the effects of digitalization's rapid implementation in various countries determined the study's purpose.We based the methodology on building the balanced panel regression.The key metric for measuring the digitalization is the Digital Evolution Index of 50 countries for 2008, 2013 and 2017.The research findings have confirmed positive impact of the digitalization in the developed countries due to a high levels of digital inclusivity, public and private investment, and digital literacy and trust.At the same time, in the developing countries the digitalization's impact on welfare was not confirmed due to a low level of state involvement in digitalization development and rigid, non-flexible institutional environment.The obtained empirical results can be used for forming management tools of the digitalization strategy for regions with various levels of social, economic, institutional and digital environment development.
Snappy: Fast On-chain Payments with Practical Collaterals

Permissionless blockchains offer many advantages but also have significant limitations including high latency.This prevents their use in important scenarios such as retail payments, where merchants should approve payments fast.Prior works have attempted to mitigate this problem by moving transactions off the chain.However, such Layer-2 solutions have their own problems: payment channels require a separate deposit towards each merchant and thus significant locked-in funds from customers; payment hubs require very large operator deposits that depend on the number of customers; and side-chains require trusted validators.In this paper, we propose Snappy, a novel solution that enables recipients, like merchants, to safely accept fast payments.In Snappy, all payments are on the chain, while small customer collaterals and moderate merchant collaterals act as payment guarantees.Besides receiving payments, merchants also act as statekeepers who collectively track and approve incoming payments using majority voting.In case of a double-spending attack, the victim merchant can recover lost funds either from the collateral of the malicious customer or a colluding statekeeper (merchant).Snappy overcomes the main problems of previous solutions: a single customer collateral can be used to shop with many merchants; merchant collaterals are independent of the number of customers; and validators do not have to be trusted.Our Ethereum prototype shows that safe, fast (<2 seconds) and cheap payments are possible on existing blockchains.
End-to-End Code-Switching ASR for Low-Resourced Language Pairs

Despite the significant progress in end-to-end (E2E) automatic speech recognition (ASR), E2E ASR for low resourced code-switching (CS) speech has not been well studied. In this work, we describe an E2E ASR pipeline for the recognition of CS speech in which a low-resourced language is mixed with a high resourced language. Low-resourcedness in acoustic data hinders the performance of E2E ASR systems more severely than the conventional ASR systems. To mitigate this problem in the transcription of archives with code-switching Frisian-Dutch speech, we integrate a designated decoding scheme and perform rescoring with neural network-based language models to enable better utilization of the available textual resources. We first incorporate a multi-graph decoding approach which creates parallel search spaces for each monolingual and mixed recognition tasks to maximize the utilization of the textual resources from each language. Further, language model rescoring is performed using a recurrent neural network pre-trained with cross-lingual embedding and further adapted with the limited amount of in-domain CS text. The ASR experiments demonstrate the effectiveness of the described techniques in improving the recognition performance of an E2E CS ASR system in a low-resourced scenario.
Teaching for Student Learning: Exploration of Teaching Strategies Based on Protocol-Guided Learning

The teaching strategy based on the case study guide is based on the case study guided, with teachers as the mainstay, students as the main body, and teacher-student working together to complete the teaching tasks, which is in line with Chinese education characteristics of localized teaching strategies. This teaching strategy advocates coordinating the relationship between various elements of teaching, so that teachers, students, and teaching materials maintain a coordinated state. We herein prospect through a case of a middle school in Jiangsu, China to explain the basic content of the teaching strategy, and analyze the practical exploration of the teaching strategy based on the protocol-guided study.
Job scheduler for streaming applications in heterogeneous distributed processing systems

In this study, we investigated the problem of scheduling streaming applications on a heterogeneous cluster environment and, based on our previous work, developed the maximum throughput scheduler algorithm (MT-Scheduler) for streaming applications. The proposed algorithm uses a dynamic programming technique to efficiently map the application topology onto the heterogeneous distributed system based on computing and data transfer requirements, while also taking into account the capacity of the underlying cluster resources. The proposed approach maximizes the system throughput by identifying and minimizing the time incurred at the computing/transfer bottleneck. The MT-Scheduler supports scheduling applications structured as a directed acyclic graph. We conducted experiments using three Storm microbenchmark topologies in both simulation and real Apache Storm environments. In terms of the performance evaluation, we compared the proposed MT-Scheduler with the simulated round robin and the default Storm scheduler algorithms. The results indicated that the MT-Scheduler outperforms the default round robin approach in terms of both the average system latency and throughput.
Learning Perspective Undistortion of Portraits

Near-range portrait photographs often contain perspective distortion artifacts that bias human perception and challenge both facial recognition and reconstruction techniques. We present the first deep learning based approach to remove such artifacts from unconstrained portraits. In contrast to the previous state-of-the-art approach [23], our method handles even portraits with extreme perspective distortion, as we avoid the inaccurate and error-prone step of first fitting a 3D face model. Instead, we predict a distortion correction flow map that encodes a per-pixel displacement that removes distortion artifacts when applied to the input image. Our method also automatically infers missing facial features, i.e. occluded ears caused by strong perspective distortion, with coherent details. We demonstrate that our approach significantly outperforms the previous state-of-the-art [23] both qualitatively and quantitatively, particularly for portraits with extreme perspective distortion or facial expressions. We further show that our technique benefits a number of fundamental tasks, significantly improving the accuracy of both face recognition and 3D reconstruction and enables a novel camera calibration technique from a single portrait. Moreover, we also build the first perspective portrait database with a large diversity in identities, expression and poses.
Majority values of school biological education in the context of education for sustainable development

The idea of the study is that the value potential of the subject is a fundamental element of the knowledge system as part of the realization of the idea of sustainable development in education. The basis of the study used the classification of value categories proposed by Milton Rokeach in 1979. We found that in the main school, the content of the subject “Biology” is primarily aimed at forming ideas about terminal values – “life”, “health”, “nature”; in high school – about terminal values “life”, “health”, as well as instrumental values – “perseverance”. The value ideas of a biology teacher are one of the key factors in the formation of students’ value ideas. In the structure of value representations of biology teachers, the three leaders are orientations towards terminal values. We have put forward the assumption that there is a dissonance between the value ideas of respondents and the values that the school biology course is aimed at forming an idea about.
Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach

Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-labor-intensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a two-fold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge.
Correlation-Based Weight Adjusted Naive Bayes

Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting is known as a flexible model via assigning each attribute a different weight discriminatively to improve NB. Attribute weighting approaches can fall into two broad categories: filters and wrappers. Wrappers receive a bigger boost in terms of classification accuracy compared with filters, but the time complexity of wrappers is much higher than filters. In order to improve the time complexity of a wrapper, a filter can be used to optimize the initial weight of all attributes as a preprocessing step. So a hybrid attribute weighting approach is proposed in this paper, and the improved model is called correlation-based weight adjusted naive Bayes (CWANB). In CWANB, the correlation-based attribute weighting filter is used to initialize the attribute weights, and then each weight is optimized by the attribute weight adjustment wrapper where the objective function is designed based on dynamic adjustment of attribute weights. Extensive experimental results show that CWANB outperforms NB and some other existing state-of-the-art attribute weighting approaches in terms of the classification accuracy. Meanwhile, compared with the existing wrapper, the CWANB approach reduces the time complexity dramatically.
Kernel truncated regression representation for robust subspace clustering

Subspace clustering aims to group data points into multiple clusters of which each corresponds to one subspace. Most existing subspace clustering approaches assume that input data lie on linear subspaces. In practice, however, this assumption usually does not hold. To achieve nonlinear subspace clustering, we propose a novel method, called kernel truncated regression representation. Our method consists of the following four steps: 1) projecting the input data into a hidden space, where each data point can be linearly represented by other data points; 2) calculating the linear representation coefficients of the data representations in the hidden space; 3) truncating the trivial coefficients to achieve robustness and block-diagonality; and 4) executing the graph cutting operation on the coefficient matrix by solving a graph Laplacian problem. Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces. The first advantage makes our method efficient in handling large-scale datasets, and the second one enables the proposed method to conquer the nonlinear subspace clustering challenge. Extensive experiments on six benchmarks demonstrate the effectiveness and the efficiency of the proposed method in comparison with current state-of-the-art approaches.
High-Resolution Remote Sensing Image Retrieval Based on Classification-Similarity Networks and Double Fusion

In high-resolution remote sensing image retrieval (HRRSIR), convolutional neural networks (CNNs) have an absolute performance advantage over the traditional hand-crafted features. However, some CNN-based HRRSIR models are classification-oriented, they pay no attention to similarity, which is critical to image retrieval; whereas others concentrate on learning similarity, failing to take full advantage of information about class labels. To address these issues, we propose a novel model called classification-similarity network (CSN), which aims for image classification and similarity prediction at the same time. In order to further improve performance, we build and train two CSNs, and two kinds of information from them, i.e., deep features and similarity scores, are consolidated to measure the final similarity between two images. Besides, the optimal fusion theorem in biometric authentication, which gives a theoretical scheme to make sure that fusion will definitely lead to a better performance, is used to conduct score fusion. Extensive experiments are carried out over publicly available datasets, demonstrating that CSNs are distinctly superior to usual CNNs and our proposed “two CSNs + feature fusion + score fusion” method outperforms the state-of-the-art models.
Meet the Sherlock Holmes’ of Side Channel Leakage: A Survey of Cache SCA Detection Techniques

Cache Side Channel Attacks (SCAs) have gained a lot of attention in the recent past. Since, these attacks exploit the caching hardware vulnerabilities, they are fast and dangerous. Detection of cache side channel attacks is an important step towards mitigating against such hostile entities. Researchers have already proposed different techniques to detect cache side channel attacks. This paper provides a detailed survey of literature related to the state-of-the-art detection techniques for cache based side channel attacks. We identify a set of important characteristics that can be used to characterize a CSCA (cache side channel attack) detection technique. We use the identified features to compare and contrast the most important detection techniques and provide the important observations. We also identify some of the challenges that the research community will have to resolve in future to improve the efficiency of cache side channel detection techniques. To the best of our knowledge, this is the first work to do such a study. We believe that this paper will prove useful to researchers in the area of systems security.
A Perceptual-Based Noise-Agnostic 3D Skeleton Motion Data Refinement Network

In this paper, we demonstrate a perceptual-based 3D skeleton motion data refinement method based on a bidirectional recurrent autoencoder, called BRA-P. Three main technical contributions are made by the proposed network. First, the proposed BRA-P can address noisy data with different noise types and amplitudes using one network, and this attribute makes the approach more suitable for raw motion data with heterogeneous mixed noise. Second, due to the usage of perceptual loss, which measures the difference in high-level features extracted by a pretrained perceptual autoencoder, BRA-P improves the perceptual similarity between refined motion data and clean motion data, especially for the case where the noisy data and target clean data have different topologies. Third, BRA-P further improves the bone-length consistency and smoothness of the refined motion using the perceptual autoencoder as a postprocessing network. Ablation experiments verify the effect of the three technical contributions of our approach. The results of the experiments on synthetic noise data and raw motion data captured by Kinect demonstrate that our method outperforms several state-of-the-art methods in the cleaning of mixed-noise data by one network.