Reviewerly/exHarmony huggingface dataset:
It's not just a phase: Investigating text simplification in a second language from a process and product perspective

Text simplification involves making texts easier to understand, usually for lay readers. Simplifying texts is a complex task, especially when conducted in a second language. The readability of the produced texts and the way in which authors manage the different phases of the text simplification process are influenced by their writing expertise and by their language proficiency. Training on audience awareness can be beneficial for writers, but most research so far has devoted attention to first-language writers who simplify their own texts. Therefore, this study investigated the impact of text simplification training on second-language writers (university students) who simplify already existing texts. Specifically, after identifying a first and a second phase in the text simplification process (namely, two distinct series of writing dynamics), we analyzed the impact of our training on pausing and revision behavior across phases, as well as levels of readability achieved by the students. Additionally, we examined correlations between pausing behavior and readability by using keystroke logging data and automated text analysis. We found that phases of text simplification differ along multiple dimensions, even though our training did not seem to influence pausing and revision dynamics. Our training led to texts with fewer and shorter words, and with syntactically simpler sentences. The correlation analysis showed that longer and more frequent pauses at specific text locations were linked with increased readability in the same or adjacent text locations. We conclude the paper by discussing theoretical, methodological, and pedagogical implications, alongside limitations and areas for future research.
A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening

When using digital images to obtain the displacement of a structure, noise is always a factor that greatly affects the accuracy of the displacement results. To overcome this problem, a phase-correlation improvement method, based on phase filtering by phase standard deviation (PSD), is proposed. We use PSD to remove the severe noise interference area in the cross-power spectrum matrix, then the singular value decomposition (SVD) is used to convert the two-dimensional phase to one-dimensional to reduce the difficulty of phase unwrapping. Finally, straight-line fitting is used to obtain the high-precision displacement results. Experiments were carried out using both simulated data and real data. For the simulated data, the absolute accuracy of the proposed method is around 0.3 pixels, while, for the real data, the absolute accuracy of the proposed method is 0.018 mm. The experimental results show that the method can effectively resist noise interference and still obtain accurate displacement estimation results in the case of severe noise.
Secure signaling and traffic exchanges in smart cities: A critical review of the current trends

The need to enhance convenience, transparency and reduce costs has led to the adoption of smart cities in most countries. In this environment, a myriad of internet of things devices such as sensors and actuators exchange large volumes of highly sensitive and private data. There is therefore need to develop security frameworks, protocols and architectures to offer the much required protection to the exchanged data. To this end, numerous schemes have been put forward by various researchers over the recent past. In this paper, a broad review is provided of the security, privacy and performance issues of these schemes in a smart city environment. Based on the obtained results, it is evident that the attainment of ideal security and privacy for the data exchanged over open wireless channels is an uphill task. Therefore, some recommendations are given on how best the security solutions should be tailored to bridge this gap.
Efficient Privacy-preserving Non-exhaustive Nearest Neighbor Search of large-scale databases

The Nearest Neighbor Search is a process allowing a query owner to learn the nearest neighbor of his query in a database. Nearest Neighbor Search serves an essential role in a variety of applications of similarity search. A useful technique of Nearest Neighbor Search is Non-exhaustive Nearest Neighbor Search (NENS), allowing one to learn the nearest neighbor of the query in a sublinear time. However, existing research jeopardizes the privacy of the participants when performing NENS: either the query owner must communicate the query to the database owner, or the database owner must provide his customized database to the query owner. Such data disclosures are prohibited by privacy laws when it comes to scenarios with sensitive information. In this paper, we propose two protocols for Privacy-preserving Non-exhaustive Nearest Neighbor Search. Our first protocol accomplishes its purpose by carefully combining cryptography and Inverted File System, which is a basic method to achieve NENS. Building on the first protocol, our second protocol incorporates Product Quantization, which is another vital method for NENS implementation. In addition to privacy issues, we have improved the efficiency of Privacy-preserving Non-exhaustive Nearest Neighbor Search from previous work. Extensive experimental results on datasets with scales of 1 million and 10 million demonstrated that the proposed protocols perform better than the SANNS (Chen et al., 2020). Meanwhile, our protocols can scale to the dataset of 100 million entries, pushing the limit by one order of magnitude.
Assessing the Impact of Code Samples Evolution on Developers’ Questions

To accelerate the learning process of technical resources by developers, organizations such as Amazon, Google, and Microsoft have been provided and maintained the so-called code samples. Code samples are special software projects made available in public repositories with the main goal of teaching and clarifying developers’ doubts related to the use of frameworks, libraries, or other technical resources. Since code samples are software projects themselves, they change over time. If these changes do not preserve essential characteristics of code samples such as size and code simplicity throughout evolution, they may lose their usefulness and can be another source of questions. We investigated the evolution of code samples as well as whether and how it may raise questions from the developers about the code samples themselves. In this way, we analyzed the evolution of 186 code samples provided by Android, AWS, Azure, Spring Boot, and Spring Cloud, assessing metrics related to the size and complexity of over 15,184 commits. We also explored more than 1,500 developers’ questions in Stack Overflow to understand the relation between these questions and code samples’ evolution. We observed that organizations may not be preserving key characteristics of code samples during evolution. Our analysis also indicates that some untamed metrics are related to a rise of questions about code samples.
Semantic conflict detection with overriding assignment analysis

Developers typically work collaboratively and often need to embed their code into a major version of the system. This process can cause merge conflicts, affecting team productivity. Some of these conflicts require understanding software behavior (semantic conflicts) and current version control tools are not able to detect that. So here we explore how such conflicts could be automatically detected using static analysis of the integrated code. We propose and implement an assignment overriding analysis, which aims to detect interference between changes introduced by two different developers, where write paths, without intermediate assignments, to a common target indicate interference. To evaluate the implementations of the proposed analysis, a set of 78 code integration scenarios was used. The results show that the proposed analysis is able to detect scenarios with assignment overriding and with locally observable interference between the contributions.
The Private Life of Merge Conflicts

Collaborative development is an essential practice for the success of most nontrivial software projects. However, merge conflicts might occur when a developer integrates, through a remote shared repository, their changes with the changes from other developers. Such conflicts may impair developers’ productivity and introduce unexpected defects. Previous empirical studies have analyzed such conflict characteristics and proposed different approaches to avoid or resolve them. However, these studies are limited to the analysis of code shared in public repositories. This way they ignore local (developer private) repository actions and, consequently, code integration scenarios that are often omitted from the history of remote shared repositories due to the use of commands such as git rebase, which rewrite Git commit history. These studies might then be examining only part of the actual code integration scenarios and conflicts. To assess that, in this paper we aim to shed light on this issue by bringing evidence from an empirical study that analyzes git command history data extracted from the local repositories of a number of developers. This way we can access hidden integration scenarios that cannot be accessed by analyzing public repository data as in GitHub based studies. We analyze 95 git reflog files from 61 different developers. Our results indicate that hidden code integration scenarios are more frequent than the visible ones. We also find higher conflict rates than previous studies. Our evidence suggests that studies that consider only remote shared repositories might loose integration conflict data by not considering the developer’s local repository actions.
Towards an Understanding of Technical Debt in Reference Architectures

Reference architectures refer to a special type of software architecture that has supported the development, evolution, and standardization of software systems in several critical domains. However, important decisions made while designing a reference architecture can lead to technical debt (TD). Even though the academy is investing efforts in the last decade to understand the problems implied by TD in the architectural level (known as architectural technical debt - ATD), it has not been addressed in the level above, i.e., in the reference architecture level. This paper introduces an understanding of TD in reference architectures and, through a systematized research method, proposes a definition for reference architecture technical debt (RATD) and its corresponding conceptual model. RATD could draw the attention from the community about the need of managing architectural debts found in most reference architectures.
BEATS: Search Engine for Architectural Strategies for Security

The software system’s architecture is an important artifact for addressing, at design time, quality attributes requirements, e.g., performance, reliability, scalability, and security, i.e., focus of this work. Some design strategies, such the application of architectural patterns or tactics, are available in literature and industry. Their reuse is considered a good practice, since they are proven solutions in real industry scenarios, in a diversity of problems, for instance, software vulnerabilities mitigation. Specifically, architectural strategies for security are widespread through different information sources, mostly closed to community contributions, impeding the consolidation of sustainable body of knowledge for security-by-design strategies. We propose BEATS, a web browser of architectural strategies for security. This tool aims to assist the unification and centralization of architectural knowledge for security reported in diverse information sources. BEATS is a more complete repository, with a friendly user interface. It also allows the security-by-design community contributions, adding new strategies and maintaining all knowledge updated over time.
Surveying the Audience Effect in Open Peer Review of a Software Engineering Workshop

Context: Even though Open Peer Review (OPR) is one of the pillars of Open Science, informed discussions are still necessary to understand its impact on the social behavior of reviewers and the quality of reviews. Goal: The main goal of this study is to understand the audience effect on reviewers’ behavior and reviews’ quality from the perspectives of reviewers and authors of a Software Engineering (SE) workshop. Method: We performed a survey with the workshop participants, including authors and experienced reviewers in the SE field. Results: Our results show that while the OPR model did not change the reviewers’ behavior, it reinforced reviewers to avoid hostile comments and help the authors improve their manuscript. Results also show that reviewers were comfortable with revealing their identities and reviews. Finally, both reviewers and authors agreed that although the OPR model did not increase the quality of reviews, it encouraged reviewers to think more carefully about scientific issues, contributing to the writing of evidence-supported feedback. Conclusions: Open Peer Review is innovative in interacting with reviewers and authors in Brazilian SE academic venues. OPR is also inspiring, given the potential for reinforcing positive reviewers’ behaviors. However, the thematic still requires further investigation and attention from the community.
Modeling Concretizations in Software Design

This paper discusses preliminary results of an investigation of technologies that could enable rapid adaptation of software to changes in requirements and/or operating environments. Specifically, a new approach of modeling concretizations and automatic reasoning about the satisfaction of the specific concretizations is introduced and demonstrated. The main aspects addressed relate to software architecture styles and software design principles. The initial formalization of the layered architecture style and the universal base class design principle is provided. Also, the impact of specific concretizations on the development efforts in case of requirements change during development is discussed.
Adiabatic theorem independent of the quantum representations: Resolution of the Marzlin-Sanders inconsistency

We construct the adiabatic theorem independent of the quantum representations. The quantum-representation-independent geometrical phase, adiabatic condition, and fidelity of the adiabatic approximation are derived. As an example, we apply the quantum-representation-independent adiabatic theorem to provide a thorough resolution of the Marzlin-Sanders inconsistency. We show that the Marzlin-Sanders inconsistency results from the wrong identification of the time-dependent Hamiltonian as the instantaneous energy operator and the quantum representation dependence of the Marzlin-Sanders transformation.
Differential Weight Quantization for Multi-Model Compression

Low bit-width quantization can effectively reduce the storage and computational costs of deep neural networks. Existing quantization methods are commonly designed for single model compression. For multi-model compression scenarios, multiple models for the same task or similar tasks need to be compressed simultaneously in multimedia tasks, such as compressing image super-resolution models for different scales and the transferring of different models in multimedia. However, the single model quantization methods do not consider the correlations among the weights of different models, which limits the further compression on neural networks for the above multi-model compression scenarios. To sufficiently excavate the potential of compression on multi-model, we propose a novel quantization scheme for multi-model compression, namely differential weight quantization (DWQ), which focuses on the weights increment between the target model and the reference model. Specifically, DWQ is achieved by increment computation, increment quantization and fine-tuning, which utilizes the reference model to guide the subsequent quantization on the target model. Due to the correlations between the weights of different models, the distribution of weights increment is more centralized compared with original weights, which can achieve a higher compression ratio by lower bit representation on weights increment. Moreover, the progressive training method is proposed to accelerate the convergence and reduce quantization loss on the DWQ framework. Extensive experiments validate the effectiveness of DWQ based on weight-sharing and parameterized clipping activation (PACT) quantization technologies on multiple tasks. The proposed framework can achieve 2× compression improvement and reduce 30% computational complexity with comparable performance in the popular multimedia tasks.
Adversarial Attacks and Defenses for Deployed AI Models

With the surge in the adoption of AI/ML techniques in industry, adversarial challenges are also on the rise and defense strategies need to be configured accordingly. While it is crucial to formulate new attack methods (similar to Fuzz testing) and devise novel defense strategies for coverage and robustness, it is also imperative to recognize who is responsible for implementing, validating, and justifying the necessity of AI/ML defenses. In particular, which components of the learning system are vulnerable to what type of adversarial attacks, and the expertise needed to realize the severity of such adversarial attacks. Also, how to evaluate and address the adversarial challenges to recommend defense strategies for different applications. We would like to open a discussion on the skill set needed to examine and implement various defenses for emerging adversarial attacks.
Meta Clustering for Collaborative Learning

In collaborative learning, learners coordinate to enhance each of their learning performances. From the perspective of any learner, a critical challenge is to filter out unqualified collaborators. We propose a framework named meta clustering to address the challenge. Unlike the classical problem of clustering data points, meta clustering categorizes learners. Assuming each learner performs a supervised regression on a standalone local dataset, we propose a Select-Exchange-Cluster (SEC) method to classify the learners by their underlying supervised functions. We theoretically show that the SEC can cluster learners into accurate collaboration sets. Empirical studies corroborate the theoretical analysis and demonstrate that SEC can be computationally efficient, robust against learner heterogeneity, and effective in enhancing single-learner performance. Also, we show how the proposed approach may be used to enhance data fairness. Supplementary materials for this article are available online.
Forecasting Severe Thunderstorm by Applying SVM Technique on Cloud Imageries

Severe thunderstorm prediction by analysis of cloud imageries is becoming an interesting area in research field. It causes destruction to daily life. Therefore correct prediction of severe thunderstorm has important significance. Here in this study Support Vector Machine (SVM) has been applied on cloud imageries for the prediction purpose. Colour is one of the most important features of image. Here colour has been considered as only feature for the classification purpose. Two sets of cloud imageries have been considered here, one for ‘squall days’ and another for ‘no squall days’. The imageries for squall days have been indicated by ‘1’ and ‘no squall days’ by ‘0’. The linear Support Vector Classifier (SVC) has been applied here for classification. Principal Component Analysis (PCA) has been applied here for feature reduction purpose, which yields better result. This prediction has a lead time of 5–6 h which is enough to save society from devastation created by severe thunderstorm.
Histogram Based Initial Centroids Selection for K-Means Clustering

K-Means clustering algorithm is one of the most popular unsupervised clustering algorithms which can be used for segmentation to analyze the data. It is an algorithm based on centroids, where the distances are calculated to assign a point to a cluster. Each cluster is associated with a centroid. The selection of initial centroids and the number of clusters play a major role to decide the performance of the algorithm. In this context, many researchers worked on, but they may not reach a goal to cluster the images in minimum runtime. Existing histogram based initial centroid selection methods are used on grayscale images only. Two methods, i.e., Histogram based initial centroids selection and Equalized Histogram based initial centroids selection to cluster colour images have been proposed in this paper. The colour image has been divided into R, G, B, three channels and calculated histogram to select initial centroids for clustering algorithm. This method has been validated on three benchmark images and compared to the existing K-Means algorithm and K-Means++ algorithms. The proposed methods give an efficient result compared to the existing algorithms in terms of run time.
Effective Sentiment Analysis of Bengali Corpus by Using the Machine Learning Approach

With the evolution of the social media-based application, users share their views, opinions and emotions liberally in language English and any native language like Bengali. The posts or comments collected from the social networking sites are needed to be analysed for identifying the positive or negative public sentiments regarding the products or services of any organization in order to improve their services. Over the last few years, research on sentiment analysis using Machine Learning (ML) tools is very popular. However, the sentiment analysis of Bengali data corpus using a Machine Learning tool is limited. It has been observed that the ML models for sentiment analysis are suffering from misclassification and researchers are still trying to come up with better solutions. In this paper, a Machine Learning approach is proposed for sentiment analysis of the Bengali data set collected from Facebook. Since the posts or comments that exhibit negative polarity are more actionable than the positive ones, the prime aim of this paper is to identify the negative sentiments without being misclassified and introduces a new technique to achieve almost 100% specificity by tuning the class weight of the ML classification model. We have also tried to improve the accuracy of ML classifiers during the sentiment analysis by combining the Part-of-Speech (POS) tagging with Term Frequency—Inverse Document Frequency (TF-IDF) vectorization. The overall accuracy of each ML classifier for combining the POS tagging with TF-IDF shows an increment of 2.74–4.17% from the TF-IDF vectorization without combining with POS tagging.
RETRACTED CHAPTER: Literature Review on Sign Language Generation

The deaf and dumb community uses sign language to communicate. Sign language is a language of signs including facial expressions and not of spoken words. It is a visual mode of communication. The position of hands, the movement of fingers and the expressions on the face play a vital role in sign language. Sign languages have a very limited set of words. The grammar is difficult to understand. On the contrary, spoken languages across the globe have a rich vocabulary. It is difficult for signers to understand a spoken language. There is a need to develop a system that establishes a link between spoken and sign languages. Translating from spoken languages to sign languages is a challenging task. This paper presents the state-of-the work that has been done in the field of translating English (a spoken language) to Indian Sign Language.