Reviewerly/exHarmony huggingface dataset:
Agile Practices and Organizational Agility in Software Ecosystems

As software products increasingly become part of larger ecosystems, research and development (R&D) units of software producers organize themselves around projects to become more responsive to the environment. Organizations participating in software ecosystems must continuously adapt and adjust their software development and maintenance processes to drive both medium-term and long-term innovation. Agile methods and practices are widely adopted to guide the collaboration within and between project teams in software development. Moreover, when successfully attained, agility can drive innovation by enabling software development organizations to cope with technological changes and exploit emerging opportunities in software ecosystems. In this article, we focus on how organizations attain agility in the maintenance and development of software products. To answer this question, we conduct a longitudinal case study of Agile Scrum implementation in the R&D unit of a major supplier of telecommunication equipment. We investigate the emerging tensions and highlight practices used to balance these tensions in the Agile Scrum implementation. We identify four capabilities and ten practices that support effective collaboration and coordination in the development and maintenance of software products. The study offers practical guidance for R&D managers to attain agility in software ecosystems.
Optimization of Homomorphic Comparison Algorithm on RNS-CKKS Scheme

The sign function can be adopted to implement the comparison operation, max function, and rectified linear unit (ReLU) function in the Cheon&#x2013;Kim&#x2013;Kim&#x2013;Song (CKKS) scheme; hence, several studies have been conducted to efficiently evaluate the sign function in the CKKS scheme. Recently, Lee <i>et al.</i> (IEEE Trans. Depend. Sec. Comp.) proposed a practically optimal approximation method for the sign function in the CKKS scheme using a composition of minimax approximate polynomials. In addition, Lee <i>et al.</i> proposed a polynomial-time algorithm that finds the degrees of component polynomials that minimize the number of non-scalar multiplications. However, homomorphic comparison/max/ReLU functions using Lee <i>et al.</i>&#x2019;s approximation method have not been successfully implemented in the residue number system variant CKKS (RNS-CKKS) scheme. In addition, the degrees of component polynomials found by Lee <i>et al.</i>&#x2019;s algorithm are not optimized for the RNS-CKKS scheme because the algorithm does not consider that the running time of non-scalar multiplication depends significantly on the ciphertext level in the RNS-CKKS scheme. In this study, we propose a fast algorithm for the inverse minimax approximation error, which is a subroutine required to find the optimal set of degrees of component polynomials. The proposed algorithm facilitates determining the optimal set of degrees of component polynomials with higher degrees than in the previous study. In addition, we propose a method to find the degrees of component polynomials optimized for the RNS-CKKS scheme using the proposed algorithm for the inverse minimax approximation error. We successfully implement the homomorphic comparison, max function, and ReLU function algorithms on the RNS-CKKS scheme with a low comparison failure rate (<inline-formula> <tex-math notation="LaTeX">$&lt; 2^{-15}$ </tex-math></inline-formula>), and provide various parameter sets according to the precision parameter <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. We reduce the depth consumption of the homomorphic comparison, max function, and ReLU function algorithms by one depth for several values of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>. In addition, the numerical analysis demonstrates that the homomorphic comparison, max function, and ReLU function algorithms using the degrees of component polynomials found by the proposed algorithm reduce the running time by 6&#x0025;, 7&#x0025;, and 6&#x0025; on average, respectively, compared with those using the degrees of component polynomials found by Lee <i>et al.</i>&#x2019;s algorithm.
Development of Bangla Spell and Grammar Checkers: Resource Creation and Evaluation

A spell and grammar checker is profoundly essential for diverse publications especially for Bangla language in particular as it is spoken by millions of native speakers around the world. Considering the lack of research efforts, we demonstrate the development of a comprehensive Bangla spell and grammar checker with necessary resources. At first, a full-fledged and generalised Bangla monolingual corpus comprising over 100 million words has been built by scraping reputed, diversified online sources and then an extensive Bangla lexicon consisting of over 1 million unique words has been extracted from that corpus. Based on these corpus and lexicon, we have developed a combined spell and grammar checker application that simultaneously detects distinct spelling and grammatical mistakes and provides appropriate suggestions for both as well. The spell checker uses the Double Metaphone algorithm and Edit distance based on the distributed lexicons and numerical suffix dataset to detect all types of Bangla spelling mistakes with an accuracy rate of 97.21% individually. The grammar checker detects errors based on language model probability i.e. combination of bigram and trigram, and generates suggestions based on the Cosine similarity measure with the accuracy rate of 94.29% individually. The datasets and codes used in this work are freely available at https://git.io/JzJ4w.
Cos-CBDC: Design and Implementation of CBDC on Cosmos Blockchain

With the advent of e-commerce and electronic payment systems, the use of paper currency is decreasing. Therefore, it is sufficiently predictable that most paper currencies will disappear and digital currencies will become the mainstream. This phenomenon is further accelerated by advances in blockchain technology and COVID-19. This is why the Central Bank Digital Currency (CBDC) has recently begun to attract attention. Currently, there are studies on CBDC with a blockchain-based distributed ledger. In this paper, we propose Cosmos blockchain based CBDC (Cos-CBDC) that enables communication between blockchains using Inter-Blockchain Communication (IBC) protocol to ensure interoperability. We not only analyze the requirements of Cos-CBDC but also design and implement it using Cosmos-SDK. Furthermore, we propose a Group Key Management system in Cos-CBDC. It can give different user privileges, and privacy-preserving is possible in the key generation process.
Multimodal Dialogue Response Generation

Responsing with image has been recognized as an important capability for an intelligent conversational agent.Yet existing works only focus on exploring the multimodal dialogue models which depend on retrieval-based methods, but neglecting generation methods.To fill in the gaps, we first present a new task: multimodal dialogue response generation (MDRG)given the dialogue context, one model needs to generate a text or an image as response.Learning such a MDRG model often requires multimodal dialogues containing both texts and images which are difficult to obtain.Motivated by the challenge in practice, we consider MDRG under a natural assumption that only limited training examples are available.Under such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.By this means, the major part of the model can be learned from a large number of text-only dialogues and textimage pairs respectively, then the whole parameters can be well fitted using just a few training examples.Extensive experiments demonstrate our method achieves state-of-the-art results in both automatic and human evaluation, and can generate informative text and high-resolution image responses.
A Hybrid Robust Image Watermarking Method Based on DWT-DCT and SIFT for Copyright Protection

In this paper, a robust hybrid watermarking method based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and scale-invariant feature transformation (SIFT) is proposed. Indeed, it is of prime interest to develop robust feature-based image watermarking schemes to withstand both image processing attacks and geometric distortions while preserving good imperceptibility. To this end, a robust watermark is embedded in the DWT-DCT domain to withstand image processing manipulations, while SIFT is used to protect the watermark from geometric attacks. First, the watermark is embedded in the middle band of the discrete cosine transform (DCT) coefficients of the HL1 band of the discrete wavelet transform (DWT). Then, the SIFT feature points are registered to be used in the extraction process to correct the geometric transformations. Extensive experiments have been conducted to assess the effectiveness of the proposed scheme. The results demonstrate its high robustness against standard image processing attacks and geometric manipulations while preserving a high imperceptibility. Furthermore, it compares favorably with alternative methods.
Development of a service parts recommendation system using clustering and classification of machine learning

After receiving a service request for malfunctioning gas boilers, service engineers visit the site and provide the appropriate service. However, the same failure phenomena can continually recur when the engineer has a lack of experience or when the cause of the failure is unclear. In response to these situations, this study proposes a machine learning (ML)-based service part recommendation system that can predict the analytic result of a problematic part that has been collected in advance using field service report data registered by service engineers, then recommend the optimal service parts based on the prediction result. First, this method starts with a clustering stage, where engineers are divided into groups according to their skill level by using the K-means clustering algorithm. In the classification stage, this system predicts the intensive analysis result of the problematic part collected in advance, which is generally time consuming. Further, cross validation is performed based on the training data, and the random forest (RF) classifier that shows the best performance is selected. Subsequently, the optimal levels for hyperparameters are derived to increase the performance of the evaluation indices. The optimal conditions for the classification decision are also presented to evaluate various evaluation indices in balance and ultimately increase the recall performance. Finally, in the recommendation stage, the set of parts with the highest service values is recommended for engineers based on data from a group of skilled engineers to achieve a quality enhancement through increasing the life span of the parts and decreasing the cost.
Dynamic Domain Adaptation for Efficient Inference

Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. However, they may have a lack of applicability to real-world situations such as real-time interaction, where low target inference latency is an essential requirement under limited computational budget. In this paper, we tackle the problem by proposing a dynamic domain adaptation (DDA) framework, which can simultaneously achieve efficient target inference in low-resource scenarios and inherit the favorable cross-domain generalization brought by DA. In contrast to static models, as a simple yet generic method, DDA can integrate various domain confusion constraints into any typical adaptive network, where multiple intermediate classifiers can be equipped to infer “easier” and “harder” target data dynamically. Moreover, we present two novel strategies to further boost the adaptation performance of multiple prediction exits: 1) a confidence score learning strategy to derive accurate target pseudo labels by fully exploring the prediction consistency of different classifiers; 2) a class-balanced self-training strategy to explicitly adapt multi-stage classifiers from source to target without losing prediction diversity. Extensive experiments on multiple benchmarks are conducted to verify that DDA can consistently improve the adaptation performance and accelerate target inference under domain shift and limited resources scenarios.
Attribute Selecting in Tree-Augmented Naive Bayes by Cross Validation Risk Minimization

As an important improvement to naive Bayes, Tree-Augmented Naive Bayes (TAN) exhibits excellent classification performance and efficiency since it allows that every attribute depends on at most one other attribute in addition to the class variable. However, its performance might be lowered as some attributes might be redundant. In this paper, we propose an attribute Selective Tree-Augmented Naive Bayes (STAN) algorithm which builds a sequence of approximate models each involving only the top certain attributes and searches the model to minimize the cross validation risk. Five different approaches to ranking the attributes have been explored. As the models can be evaluated simultaneously in one pass learning through the data, it is efficient and can avoid local optima in the model space. The extensive experiments on 70 UCI data sets demonstrated that STAN achieves superior performance while maintaining the efficiency and simplicity.
BCHisto-Net: Breast histopathological image classification by global and local feature aggregation

Breast cancer among women is the second most common cancer worldwide. Non-invasive techniques such as mammograms and ultrasound imaging are used to detect the tumor. However, breast histopathological image analysis is inevitable for the detection of malignancy of the tumor. Manual analysis of breast histopathological images is subjective, tedious, laborious and is prone to human errors. Recent developments in computational power and memory have made automation a popular choice for the analysis of these images. One of the key challenges of breast histopathological image classification at 100× magnification is to extract the features of the potential regions of interest to decide on the malignancy of the tumor. The current state-of-the-art CNN based methods for breast histopathological image classification extract features from the entire image (global features) and thus may overlook the features of the potential regions of interest. This can lead to inaccurate diagnosis of breast histopathological images. This research gap has motivated us to propose BCHisto-Net to classify breast histopathological images at 100× magnification. The proposed BCHisto-Net extracts both global and local features required for the accurate classification of breast histopathological images. The global features extract abstract image features while local features focus on potential regions of interest. Furthermore, a feature aggregation branch is proposed to combine these features for the classification of 100× images. The proposed method is quantitatively evaluated on red a private dataset and publicly available BreakHis dataset. An extensive evaluation of the proposed model showed the effectiveness of the local and global features for the classification of these images. The proposed method achieved an accuracy of 95% and 89% on KMC and BreakHis datasets respectively, outperforming state-of-the-art classifiers.
The Impact of Organizational Structure and Technology Use on Collaborative Practices in Computer Emergency Response Teams: An Empirical Study

Besides the merits of increasing digitization and interconnectedness in private and professional spaces, critical infrastructures and societies are more and more exposed to cyberattacks. In order to enhance the preventative and reactive capabilities against cyberattacks, Computer Emergency Response Teams (CERTs) are deployed in many countries and organizations. In Germany, CERTs in the public sector operate on federal and state level to provide information security services for authorities, citizens, and enterprises. Their tasks of monitoring, analyzing, and communicating threats and incidents is getting more complex due to the increasing amount of information disseminated into public channels. By adopting the perspectives of Computer-Supported Cooperative Work (CSCW) and Crisis Informatics, we contribute to the study of organizational structures, technology use, and the impact on collaborative practices in and between state CERTs with empirical research based on expert interviews with representatives of German state CERTs (N=15) and supplementary document analyses (N=25). We derive design and policy implications from our findings, including the need for interoperable and modular architecture, a shift towards service level agreements, cross-platform monitoring and analysis of incident data, use of deduplication techniques and standardized threat exchange formats, a reduction of resource costs through process automation, and transparent reporting and tool structures for information exchange.
Continual Learning at the Edge: Real-Time Training on Smartphone Devices

On-device training for personalized learning is a challenging research problem.Being able to quickly adapt deep prediction models at the edge is necessary to better suit personal user needs.However, adaptation on the edge poses some questions on both the efficiency and sustainability of the learning process and on the ability to work under shifting data distributions.Indeed, naively fine-tuning a prediction model only on the newly available data results in catastrophic forgetting, a sudden erasure of previously acquired knowledge.In this paper, we detail the implementation and deployment of a hybrid continual learning strategy (AR1*) on a native Android application for real-time on-device personalization without forgetting.Our benchmark, based on an extension of the CORe50 dataset, shows the efficiency and effectiveness of our solution. IntroductionEfficiently learning at the edge is a challenging research problem for current machine learning research.In particular, the peace of advancements within the deep learning field has often been linked with a significant increase in computational and memory requirements.Deep models are often trained on remote servers and only later deployed with frozen learning capabilities on embedded devices.However, adapting prediction models at the edge is fundamental to preserve the private nature of users' data and to customize prediction models on the fly based on the specific user needs, even without an internet connection.On-device personalization could indeed provide more targeted and adaptive functionalities based on the user interaction and newly acquired data.However, on-device training is not only a matter of efficiency (i.e., memory and computation) but mostly about learning from shifting data distribution.Accumulating all the data seen over the lifetime of the deployed system and re-train the whole model from scratch on all the data becomes quickly impossible, especially with frequent real-time updates.On the other hand, just updating the prediction model using only the newly available data incurs in catastrophic forgetting [1], and in many applications retaining previous knowledge is mandatory.This paper focuses on the design and deployment of a hybrid continual learning strategy called AR1* [2] on a native Android application for real-time ondevice personalization without forgetting.AR1* has been already shown to 23
Analysis of Peculiarities of Using Digital Technologies in the University Professional Training Content

The relevance of the study is due to the increasing role of digital technologies in education. The abrupt shift of universities to distance learning during the COVID-19 pandemic has created new challenges. The purpose of this article is to analyze modern problems of traditional e-learning systems and identify the most effective digital technologies for improving the design system and implementing the content of professional educational programs at the university in the context of digitalization. Scientific articles in journals included in the Scopus database over the past five years, official websites of universities and their reports on the use of modern digital technologies were used as a statistical and empirical base for the study. Multiple comparative case studies and content analysis were ap-plied. To solve the identified problems, such modern digital technologies and methods as cloud computing, adaptive design, big data, 3D printing, wearable technologies and gamification were proposed. The paper proposes an updated digital education system model at the university consisting of such elements as Instructors &amp; Students, Master Courses, Content Delivery, Courses Collaboration, Progress Tracking, Assignments, Practices and Exams. The conclusions and rec-ommendations formulated in this study can be used by different educational systems and universities.
Multi-objective Chaotic Butterfly Optimization with Deep Neural Network based Sustainable Healthcare Management Systems

Sustainable healthcare systems are developed to priorities healthcare services involving difficult decision-making processes. Besides, wearables, internet of things (IoT), and cloud computing (CC) concepts are involved in the design of sustainable healthcare systems. In this study, a new Multi-objective Chaotic Butterfly Optimization with Deep Neural Network (MOCBOA-DNN) is presented for sustainable healthcare management systems. The goal of the MOCBOA-DNN technique aims to cluster the healthcare IoT devices and diagnose the disease using the collected healthcare data. The MOCBOA technique is derived to perform clustering process and also to tune the hyperparameters of the DNN model. Primarily, the clustering of IoT healthcare devices takes place using a fitness function to select an optimal set of cluster heads (CHs) and organize clusters. Followed by, the collected healthcare data are sent to the cloud server for further processing. Furthermore, the DNN model is used to investigate the healthcare data and thereby determine the presence of disease or not. In order to ensure the betterment of the MOCBOA-DNN technique, an extensive simulation analysis take place. The experimental results portrayed the supremacy of the MOCBOA-DNN technique over the other existing techniques interms of diverse evaluation parameters.