Reviewerly/exHarmony huggingface dataset:
Quality culture boosts agile transformation—Action research in a business‐to‐business software business

Abstract Agile methodologies are sometimes adopted, with the assumption that benefits can be attained by only using a set of best practices, which can sometimes work to a degree. In this paper, a case is discussed where a software‐producing organization of seven teams achieved significant improvements. The goal of the research was to answer two questions: how an already agile organization could improve its performance further and what is the impact of promoting quality aspects? The questions were answered by implementing interventions based on prior literature and data emerging from semi‐structured interviews. The context was an established business with a complex revenue stream structure, meaning the mix of various project/service/product based work rendered the adoption of agile methods a challenge. Action research comprising three rounds of interventions was conducted to improve the organization and its quality culture while enforcing code review practices. Interventions resulted in a significant improvement in quality, as measured by reported defects. Therefore, it is suggested that agile methods are not sufficient on their own to take software business forward unless a quality‐focused culture is simultaneously achieved through a mindset change and organizational structures to enforce quality practices. The paper contributes to research on the managerial practices of software business and agile transformation by providing empirical support to introducing formal quality improvement to the agile mix as a method for practitioners to improve organizations with complex business models and multiple teams.
Short-Term Interval Prediction of Wind Power Based on KELM and a Universal Tabu Search Algorithm

Installed wind power has significantly grown in recent years to synchronize with the ever-increasing demand for environment-friendly and renewable energy. However, wind energy has significant uncertainty or random futures, and will give rise to destructive effects on the safety operations of the power system. In this respect, an accurate and reliable wind power prediction is of great significance for improving the power system stability and optimizing the dispatch plan. Compared with traditionally deterministic point forecast techniques, probabilistic forecasting approaches can provide more stochastic information to quantify the random characteristics of wind power and to estimate its impacts on the power system. Moreover, the interval of the output power is a key stochastic information on wind power. In general, an interval prediction needs to compromise the calibration and the average width of the predicted interval. To find the best combination of these two metrics, a methodology based on a kernel extreme learning machine (KELM) and an improved universal tabu search algorithm is proposed. In the proposed methodology, to eliminate the inherent randomness on the weights between the input and hidden lays in the commonly used extreme learning machine, a radial-basis-function-based kernel extreme learning machine is proposed, and an improved tabu search method is introduced to optically compromise the calibration and the average width of the predicted interval to overcome the deficiency of existing algorithms, such as the insufficient global search ability of a particle swarm optimization. A prototype wind farm is utilized as a case study to verify the efficiency and advantage of the proposed methodology.
MFVP: Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming

Viewport prediction is the crucial task for viewport-adaptive 360-degree video streaming. Various viewport prediction methods are studied and adopted from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to introduce minimal transmission and computation overhead for mobile terminals. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into the adaptive 360-degree video live streaming by formulating and solving the bitrate adaptation problem. Extensive experiment results show that our prediction approach can work in real-time for live streaming and can achieve higher accuracies compared to other existing prediction methods on mobile clients, which, together with our proposed bitrate adaptation algorithm, significantly improves the streaming Quality-of-Experience (QoE) from various aspects.
Aspect-Based Sentiment Analysis of Indonesian-Language Hotel Reviews Using Long Short-Term Memory with an Attention Mechanism

The development of tourism and technology has given rise to many online hotel booking services that allow users to leave reviews on hotels. Therefore, an analytical model that can comprehensively present the aspects and sentiments in user reviews is required. This study proposes the use of a long short-term memory (LSTM) model with an attention mechanism to perform aspect-based sentiment analysis. The architecture used also implements double fully-connected layers to improve performance. The architecture is used simultaneously for aspect extraction and sentiment polarity detection. Using 5200 Indonesian-language hotel-review data points with labels of five aspects and three sentiments, the model is trained with the configuration of hidden units, dropout, and recurrent dropout parameters in the LSTM layer. The best model performance resulted in a micro-averaged F1-measure value of 0.7628 using a hidden units parameter of 128, dropout parameter of 0.3, and recurrent dropout parameter of 0.3. Results show that the attention mechanism can improve the performance of the LSTM model in performing aspect-based sentiment analysis.
Effective algorithms to mine skyline frequent-utility itemsets

Skyline frequent-utility itemset mining is used to discover itemsets that are non-dominated by considering both support and utility factors. It is an extension of high-utility itemset mining. Most existing algorithms are based on the utility-list structure to mine skyline frequent-utility itemsets. A major limitation of utility-list based algorithms is that numerous join operations consume a huge amount of time and memory. To address this issue, two algorithms named EMSFUI-D and EMSFUI-B are proposed to mine skyline frequent-utility itemsets. EMSFUI-D performs the depth-first search to explore the search space of all itemsets. EMSFUI-B discovers itemsets based on the breadth-first search. Both algorithms utilize two pruning strategies to limit the search space. Moreover, in order to further facilitate the mining performance, the ISU-1 and ISU-2 structures are presented in EMSFUI-D to provide tighter utility upper bounds. These structures maintain the support and utility information of all 1-itemsets and 2-itemsets, respectively. Thus, there is no need to use these structures to prune search space in the breadth-first search algorithm. An extensive experimental study on real and synthetic datasets shows that our proposed algorithms outperform the state-of-the-art SKYFUP-D and SKYFUP-B algorithms in terms of execution time, memory consumption and pruning performance. Moreover, our designed algorithms are scalable for handling a large number of distinct items and transactions.
FAIR Versus Open Data: A Comparison of Objectives and Principles

Abstract This article assesses the difference between the concepts of ‘open data’ and ‘FAIR data’ in data management. FAIR data is understood as data that complies with the FAIR Guidelines—data that is Findable, Accessible, Interoperable and Reusable—while open data was born out of awareness of the need to democratise data by improving its accessibility, based on the idea that data should not have limitations that prevent people from using it. This study compared FAIR data with open data by analysing relevant documents using a coding analysis with conceptual labels based on Kingdon's theory of agenda setting. The study found that in relation to FAIR data the problem stream focuses on the complexity of data collected for research, while open data primarily emphasises giving the public access to non-confidential data. In the policy stream, the two concepts share common standpoints in terms of making data available and reusable, although different approaches are adopted in practice to accomplish these goals. In the politics stream, stakeholders with different objectives support FAIR data and from those who support open data.
FAIR Machine Learning Model Pipeline Implementation of COVID-19 Data

Abstract Research and development are gradually becoming data-driven and the implementation of the FAIR Guidelines (that data should be Findable, Accessible, Interoperable, and Reusable) for scientific data administration and stewardship has the potential to remarkably enhance the framework for the reuse of research data. In this way, FAIR is aiding digital transformation. The ‘FAIRification’ of data increases the interoperability and (re)usability of data, so that new and robust analytical tools, such as machine learning (ML) models, can access the data to deduce meaningful insights, extract actionable information, and identify hidden patterns. This article aims to build a FAIR ML model pipeline using the generic FAIRification workflow to make the whole ML analytics process FAIR. Accordingly, FAIR input data was modelled using a FAIR ML model. The output data from the FAIR ML model was also made FAIR. For this, a hybrid hierarchical k-means (HHK) clustering ML algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset that contains data on economic factors, healthcare facilities, and coronavirus occurrences in all the 36 states of Nigeria. The model showed that research data and the ML pipeline can be FAIRified, shared, and reused by following the proposed FAIRification workflow and implementing technical architecture.
MFIALane: Multiscale Feature Information Aggregator Network for Lane Detection

Lane detection differs from general object detection in that lane lines are usually long and narrow in the road image, and more attention to image features at different scales is required to reason about lane lines under occlusion, degradation, and bad weather. However, most existing semantic segmentation-based lane detection methods focus on solving the convolutional receptive field through aggregating information vertically and horizontally in the same feature map, which may ignore important information contained in multi-scale features. Besides, the high-level semantic information of whether the lane exists is not fully utilized, as they often add a module at the final stage of the network output to determine whether the lane exists, which is a dispensable for their network. Based on the above analysis, we design a novel lane detection network based on semantic segmentation which consists of a Multi-scale Feature Information Aggregator (MFIA) module and a Channel Attention (CA) module. Many experiments on the TRLane dataset, the generated Lane dataset, BDD100K dataset, TuSimple dataset, VIL-100 dataset and CULane dataset show that our approach can achieve the state-of-the-art performance (our code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Cuibaby/MFIALane</uri> ). In addition, considering that different perceptual tasks in autonomous driving are able to share the feature extraction network, we also conduct the experiment for drivable area segmentation on BDD100K dataset. Our approach also achieves good results compared to many existing methods, showing that our proposed model is capable of simultaneously handling multiple perceptual tasks in autonomous driving scenarios.
A Flexible and Efficient Privacy-Preserving Range Query Scheme for Blockchain-Enhanced IoT

With the increasing deployment of Internet of Things (IoT) devices in recent years, blockchain-enhanced IoT has become a new research hotspot. Due to its excellent openness, traceability and other performance, it has immensely promoted the development of this field. However, the traditional “edge-fog-cloud” three-tier architecture model for IoT still faces the threats of privacy disclosure, collusion attack, and low efficiency. To solve these problems, in this article, we creatively design a new consortium blockchain-enhanced IoT architecture, which can enable the permissioned users or participants to enjoy high-quality data services with the assistance of consortium blockchain by taking advantage of its high credibility. Based on this architecture, we propose a novel consortium blockchain-based privacy-preserving range query scheme in the decentralized IoT system, named CBCPRQ. This scheme combines consortium blockchain and inner product function encryption technology to realize flexible, safe, and batch range query of data service in edge computing. Analysis of security shows that our proposed scheme can not only protect the privacy but also perfectly resist the collusion query inference attack and the replay attack. Extensive performance evaluation is implemented by using Hyperledger Fabric platform and demonstrates that our scheme is efficient, scalable, and flexible.
Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining

Fuzzy utility (FU) pattern mining with an advantage in human reasoning has become one of the interesting topics in studies of knowledge discovery. The discovered information in FU pattern mining from real-life quantitative databases with item profits is suitable for interpreting data from a human perspective because it is not expressed using numerical values but linguistic terms which consist of natural languages. State-of-the-art approaches in this literature provide extended results by considering temporal factors, such as seasons, which can be influential in real-life situations. However, they still suffer from scalability issues because they are based on level-wise approaches which generate a number of candidates. In this article, we propose a scalable and efficient approach with a novel data structure for mining high temporal FU patterns without generating candidates. Efficient pruning techniques and algorithms are presented to improve the performance of the proposed approach. Performance experiments on both real and synthetic datasets show that the suggested algorithm has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability.
Long-Tailed Traffic Sign Detection Using Attentive Fusion and Hierarchical Group Softmax

Traffic sign detection and recognition (TSDR) has attracted extensive studies recently due to its broad application prospect in Intelligent Transport Systems. TSDR is still challenging due to the small size of traffic signs in the image. Besides, the traffic signs in the real world exhibit a long-tailed distribution (i.e., data for most categories are scarce while for others are abundant.), which will lead to a significant performance drop of the detection framework. In this paper, we propose a novel traffic sign detection framework to address these challenging problems. In order to detect small traffic signs, we propose an effective adaptive and attentive spatial feature fusion module which learns the spatial attention map to fuse different feature maps at each scale while emphasizing or suppressing the features at different regions. This module can significantly alleviate the inconsistency among features and enhance feature representations of small objects. Furthermore, to address the long-tailed data problem, a hierarchical group softmax head which constructs a label tree to divide categories into different groups is proposed, in this way, categories in each group have relatively similar frequencies, then the softmax is applied in each relatively balanced group to calculate the probability of each category. Extensive experiments conducted on the TT100K and GTSDB datasets demonstrate that the proposed method achieves notable improvement in both the small traffic signs and long-tailed detection problems in TSDR.
Breast Cancer Diagnosis from Histopathology Images using Supervised Algorithms

Breast cancer is the most common cancer type worldwide. In cancer studies, histopathological breast images are used in the process of diagnosis. In this paper, we defined three sets of features to represent the characteristics of the cell nuclei to detect malignant cases. Geometric, directional, and intensity-based features, a total of 33, are derived and evaluated using breast cancer histopathological images from the BreaKHis database. Four machine learning algorithms, including Decision Tree, Support Vector Machines, K-Nearest Neighbor, and Narrow Neural Networks (NNN), are designed to assess the efficiency of the sets. The preliminary results showed that the proposed methodology achieved high performance in classifying cancerous cells as the directional feature set was the most effective set among the three sets. The combination of the sets achieved the best performance by the NNN, which reached an accuracy, recall, precision, AUC, and F1 score of 96.9%, 97.4%, 98%, 98.8%, and 97.7%, respectively.
Leveraging Clinical BERT in Multimodal Mortality Prediction Models for COVID-19

Clinical prediction models are often based solely on the use of structured data in electronic health records, e.g. vital parameters and laboratory results, effectively ignoring potentially valuable information recorded in other modalities, such as free-text clinical notes. Here, we report on the development of a multimodal model that combines structured and unstructured data. In particular, we study how best to make use of a clinical language model in a multimodal setup for predicting 30-day all-cause mortality upon hospital admission in patients with COVID-19. We evaluate three strategies for incorporating a domain-specific clinical BERT model in multimodal prediction systems: (i) without fine-tuning, (ii) with unimodal fine-tuning, and (iii) with multimodal fine-tuning. The best-performing model leverages multimodal fine-tuning, in which the clinical BERT model is updated based also on the structured data. This multimodal mortality prediction model is shown to outperform unimodal models that are based on using either only structured data or only unstructured data. The experimental results indicate that clinical prediction models can be improved by including data in other modalities and that multimodal fine-tuning of a clinical language model is an effective strategy for incorporating information from clinical notes in multimodal prediction systems.
IterDANet: Iterative Intra-Domain Adaptation for Semantic Segmentation of Remote Sensing Images

When segmenting the continuous proliferation of unlabeled remotely sensed images, unsupervised domain adaptation (UDA) has become one of the most critical techniques and achieved significant performance. But in fact, there still exists a large performance gap between the existing UDA frameworks and supervised learning methods, for the majority of UDA frameworks don’t consider the intra-domain gap in the target domain. In this paper, to further minimize the complex intra-domain shift within the target domain in remote sensing, we propose a novel iterative intra-domain adaptation framework (IterDANet), which conducts inter-domain adaptation (InterDA), entropy-based ranking (ER) and iterative intra-domain adaptation (IntraDA). Specifically, first, to enhance the performance of InterDA built upon GAN-based image-to-image translation, we propose a new generator selection strategy to assess and choose a well-trained generator for the inter-domain classifier. Then, to produce more accurate pseudo labels for IntraDA, we propose a new pseudo label generation strategy to remove both high-entropy and low-confident pixels in predicted maps of inter-domain classifier. Finally, to better reduce the intra-domain gap, we propose to cluster all the target images into multiple subdomains using ER and iteratively align the cleanest subdomain with other noisy subdomains. The extensive experiments on the benchmark dataset, which includes cross-city aerial images, highlight the superiority and effectiveness of our IterDANet against the state-of-the-art UDA frameworks.
A secure architecture for exploring patient-level databases from distributed institutions

One of the main goals of clinical studies consists of identifying diseases' causes and improving the efficacy of medical treatments. Sometimes, the reduced number of participants is a limiting factor for these studies, leading researchers to organise multi-centre studies. However, sharing health data raises certain concerns regarding patients' privacy, namely related to the robustness of anonymisation procedures. Although these techniques remove personal identifiers from registries, some studies have shown that anonymisation procedures can sometimes be reverted using specific patients' characteristics. In this paper, we propose a secure architecture to explore distributed databases without compromising the patient's privacy. The proposed architecture is based on interoperable repositories supported by a common data model.
Knowledge graph attention mechanism for distant supervision neural relation extraction

Distant supervision neural relation extraction is used to classify the relation labels of instance bags with the same head–tail entity. Since there are usually multirelation labels in an entity pair, intensive noise occurs in the instance bag. Recent works mostly focused on developing an attention mechanism to degrade the weights of some sentences whose ground-truth relation labels are different from the instance bag. However, the general weakness is the failure to explore the semantic correlations between entity pair and its context. Additionally, the number of relation categories follows a long-tail distribution, and it is still a challenge to extract long-tail relations. Therefore, the Knowledge Graph ATTention (KGATT) mechanism is proposed to deal with the noises and long-tail problem, and it contains two modules: a fine-alignment mechanism and an inductive mechanism. In particular, the fine-alignment mechanism is used to learn the ground-truth relation of the sentence itself by aligning with all predefined relations. The inductive mechanism is employed to learn the enhanced relations from neighbors of the knowledge graph (KG) to compensate for data scarcity. With the mutual reinforcement of the two modules, our model can enrich the representation of the instance bag, which not only improves the generalization ability but also eases the long-tail phenomenon. Extensive experiments and ablation studies are conducted on the NYT-FB60K and GIDS-FB8K datasets, and the results show the KGATT is effective in improving performance. Based on Piecewise Convolution Neural Network (PCNN), our model achieves superior performances in various indicators as well as the long-tail relations.
Detection of Rogue Devices using Unintended Near and Far-field Emanations with Spectral and Temporal Signatures

In this work, using data collected from a desktop computer in an anechoic chamber, we have characterized both near and far-field emissions over a wide frequency band and demonstrated that it matches closely with the monopole antenna in Fresnel and Fraunhofer regions and can be used to detect the start-up of a rogue device in a security-critical room. Detailed spectral analysis shows that emission is strong at lower frequencies. Additionally, high-frequency leakage can also be detected from a 200 cm distance. Frequency shifting of the peaks is observed over time and linked to the program execution. Finally, detection of the onset of a rogue device in a static facility has been demonstrated for different SNRs.