Reviewerly/exHarmony huggingface dataset:
Technical Debt on Agile Projects: Managers’ point of view at Stack Exchange

Context: The literature has proposed different approaches for Agile software development technical debt (ASD-TD) management, they are usually based on the point of view of software developers. Little is known about how project management practitioners actually perceive ASD-TD. Goal: To investigate how managers commonly discuss, experience, and manage ASD-TD, as well as roles, artifacts, and practices affected by ASD-TD extracted from Stack Exchange Project Management (SEPM). Method: We compiled 79 ASD-TD discussions from SEPM, and analyzed them quantitatively and qualitatively. Results: We identified 51 indicators used by managers to recognize ASD-TD items. The most commonly discussed types of TD are process and people debt. In addition, the Product Owner and Development Team are the most important roles with respect to ASD-TD. Sprint Backlog and Sprint Planning are the agile elements most affected by ASD-TD. We organized the TD indicators, roles involved, and agile elements affected by ASD-TD into a Mind Map. Conclusion: The perspective considered by project management practitioners to analyze the ASD-TD phenomenon is different from the one considered by other roles in agile software development.
Combined Fault Injection and Real-Time Side-Channel Analysis for Android Secure-Boot Bypassing

The Secure-Boot is a critical security feature in modern devices based on System-on-Chips (SoC). It ensures the authenticity and integrity of the code before its execution, avoiding the SoC to run malicious code. To the best of our knowledge, this paper presents the first bypass of an Android Secure-Boot by using an Electromagnetic Fault Injection (EMFI). Two hardware characterization methods are combined to conduct this experiment. A real-time Side-Channel Analysis (SCA) is used to synchronize an EMFI during the Linux Kernel authentication step of the Android Secure-Boot of a smartphone-grade SoC. This new synchronization method is called Synchronization by Frequency Detection (SFD). It is based on the detection of the activation of a characteristic frequency in the target electromagnetic emanations. In this work we present a proof-of-concept of this new triggering method. By triggering the attack upon the activation of this characteristic frequency, we successfully bypassed this security feature, effectively running Android OS with a compromised Linux Kernel with one success every 15 min.
Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable

Abstract One of the most common causes of incompleteness is missing data, which occurs when no data value for the variables in observation is stored. An adaptive approach model outperforming other numerical methods in the classification problem was developed using the class center-based Firefly algorithm by incorporating attribute correlations into the imputation process (C3FA). However, this model has not been tested on categorical data, which is essential in the preprocessing stage. Encoding is used to convert text or Boolean values in categorical data into numeric parameters, and the target encoding method is often utilized. This method uses target variable information to encode categorical data and it carries the risk of overfitting and inaccuracy within the infrequent categories. This study aims to use the smoothing target encoding (STE) method to perform the imputation process by combining C3FA and standard deviation (STD) and compare by several imputation methods. The results on the tic tac toe dataset showed that the proposed method (C3FA-STD) produced AUC, CA, F1-Score, precision, and recall values of 0.939, 0.882, 0.881, 0.881, and 0.882, respectively, based on the evaluation using the kNN classifier.
Scientific data management policy in China: a quantitative content analysis based on policy text

Purpose This paper aims to understand the current development situation of scientific data management policy in China, analyze the content structure of the policy and provide a theoretical basis for the improvement and optimization of the policy system. Design/methodology/approach China's scientific data management policies were obtained through various channels such as searching government websites and policy and legal database, and 209 policies were finally identified as the sample for analysis after being screened and integrated. A three-dimensional framework was constructed based on the perspective of policy tools, combining stakeholder and lifecycle theories. And the content of policy texts was coded and quantitatively analyzed according to this framework. Findings China's scientific data management policies can be divided into four stages according to the time sequence: infancy, preliminary exploration, comprehensive promotion and key implementation. The policies use a combination of three types of policy tools: supply-side, environmental-side and demand-side, involving multiple stakeholders and covering all stages of the lifecycle. But policy tools and their application to stakeholders and lifecycle stages are imbalanced. The development of future scientific data management policy should strengthen the balance of policy tools, promote the participation of multiple subjects and focus on the supervision of the whole lifecycle. Originality/value This paper constructs a three-dimensional analytical framework and uses content analysis to quantitatively analyze scientific data management policy texts, extending the research perspective and research content in the field of scientific data management. The study identifies policy focuses and proposes several strategies that will help optimize the scientific data management policy.
Real-Time Risk Assessment Detection for Weak People by Parallel Training Logical Execution of a Supervised Learning System Based on an IoT Wearable MEMS Accelerometer

Activity monitoring has become a necessary demand for weak people to guarantee their safety. The paper proposed a Parallel Training Logical Execution (PTLE) system using machine learning (ML) models on a microelectromechanical system (MEMS) accelerometer to detect coughs, falls, and other normal activities. When there are many categories, the ML prediction can be confused between these activities with each other. The PTLE system trains several models in parallel with more specific activity classes in each dataset. The shared tasks between parallel models relieve the complexity for a single one. There are six additional parameters for accelerometer characteristics, which were calculated from three axes accelerations as input features to improve the ML's consciousness. Once all models were trained, the system was ready to receive the input accelerations and activated the logical flow to manage link operation between these ML models for output predictions. Random Forest (RF) had the highest potential among the ML classification algorithms after the validation. In the experiment, the comparison between the PTLE model and the regular ML model were carried out with real-time data from an M5stickC wearable device on the user's chest to the trained models on PC. The result showed the advancement of the proposed method in term of precision, recall, F1-score with an overall accuracy of 98% in the real-time test. The accelerations from the wearable device were sent to ML models via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker, and the activity predictions were transferred to the cloud for the family members or doctor care based on Internet of Things (IoT) communication.
Blind Video Quality Assessment for Ultra-High-Definition Video Based on Super-Resolution and Deep Reinforcement Learning

Ultra-high-definition (UHD) video has brought new challenges to objective video quality assessment (VQA) due to its high resolution and high frame rate. Most existing VQA methods are designed for non-UHD videos-when they are employed to deal with UHD videos, the processing speed will be slow and the global spatial features cannot be fully extracted. In addition, these VQA methods usually segment the video into multiple segments, predict the quality score of each segment, and then average the quality score of each segment to obtain the quality score of the whole video. This breaks the temporal correlation of the video sequences and is inconsistent with the characteristics of human visual perception. In this paper, we present a no-reference VQA method, aiming to effectively and efficiently predict quality scores for UHD videos. First, we construct a spatial distortion feature network based on a super-resolution model (SR-SDFNet), which can quickly extract the global spatial distortion features of UHD videos. Then, to aggregate the spatial distortion features of each UHD frame, we propose a time fusion network based on a reinforcement learning model (RL-TFNet), in which the actor network continuously combines multiple frame features extracted by SR-SDFNet and outputs an action to adjust the current quality score to approximate the subjective score, and the critic network outputs action values to optimize the quality perception of the actor network. Finally, we conduct large-scale experiments on UHD VQA databases and the results reveal that, compared to other state-of-the-art VQA methods, our method achieves competitive quality prediction performance with a shorter runtime and fewer model parameters.
RDASNet: Image Denoising via a Residual Dense Attention Similarity Network

In recent years, thanks to the performance advantages of convolutional neural networks (CNNs), CNNs have been widely used in image denoising. However, most of the CNN-based image-denoising models cannot make full use of the redundancy of image data, which limits the expressiveness of the model. We propose a new image-denoising model that aims to extract the local features of the image through CNN and focus on the global information of the image through the attention similarity module (ASM), especially the global similarity details of the image. Furthermore, dilation convolution is used to enlarge the receptive field to better focus on the global features. Moreover, avg-pooling is used to smooth and suppress noise in the ASM to further improve model performance. In addition, through global residual learning, the effect is enhanced from shallow to deep layers. A large number of experiments show that our proposed model has a better image-denoising effect, including quantitative and visual results. It is more suitable for complex blind noise and real images.
Margin-Based Modal Adaptive Learning for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VIPR) has great potential for intelligent transportation systems for constructing smart cities, but it is challenging to utilize due to the huge modal discrepancy between visible and infrared images. Although visible and infrared data can appear to be two domains, VIPR is not identical to domain adaptation as it can massively eliminate modal discrepancies. Because VIPR has complete identity information on both visible and infrared modalities, once the domain adaption is overemphasized, the discriminative appearance information on the visible and infrared domains would drain. For that, we propose a novel margin-based modal adaptive learning (MMAL) method for VIPR in this paper. On each domain, we apply triplet and label smoothing cross-entropy functions to learn appearance-discriminative features. Between the two domains, we design a simple yet effective marginal maximum mean discrepancy (M3D) loss function to avoid an excessive suppression of modal discrepancies to protect the features' discriminative ability on each domain. As a result, our MMAL method could learn modal-invariant yet appearance-discriminative features for improving VIPR. The experimental results show that our MMAL method acquires state-of-the-art VIPR performance, e.g., on the RegDB dataset in the visible-to-infrared retrieval mode, the rank-1 accuracy is 93.24% and the mean average precision is 83.77%.
Detecting Drift in Healthcare AI Models Based on Data Availability

There is an increasing interest in the use of AI in healthcare due to its potential for diagnosis or disease prediction. However, healthcare data is not static and is likely to change over time leading a non-adaptive model to poor decision-making. The need of a drift detector in the overall learning framework is therefore essential to guarantee reliable products on the market. Most drift detection algorithms consider that ground truth labels are available immediately after prediction since these methods often work by monitoring the model performance. However, especially in real-world clinical contexts, this is not always the case as collecting labels is often more time consuming as requiring experts' input. This paper investigates methodologies to address drift detection depending on which information is available during the monitoring process. We explore the topic within a regulatory standpoint, showing challenges and approaches to monitoring algorithms in healthcare with subsequent batch updates of data. This paper explores three different aspects of drift detection: drift based on performance (when labels are available), drift based on model structure (indicating causes of drift) and drift based on change in underlying data characteristics (distribution and correlation) when labels are not available.
Parameter agnostic stacked wavelet transformer for detecting singularities

Machine learning algorithms especially deep neural networks have seen tremendous growth in their real-world deployment. While these algorithms have to yield high performances for the task at hand, it is of equal importance that they are robust against the different kinds of singularities or adversarial anomalies. Researchers have generally addressed singularity detection independently for each anomaly, however, for the algorithms to be effective in the real world, the singularity detection algorithm must be able to handle a wide variety of attacks whether trained individually on them or not seen at the time of training. With this objective, in this paper, we propose a unique end-to-end transformation domain network to detect a variety of well-known attacks. The proposed architecture utilizes a combination of two different wavelet transformations to simultaneously learn low-level and high-level image features in a deep stacked layer fashion. The proposed method is generalized to handle a broad set of singularities of several computer vision algorithms whether operating in the object recognition domain or biometrics recognition. We showcase the results on a wide range of singularity points including (i) adversarial perturbations which are learned using deep learning networks, (ii) physical presentation attacks on face recognition which aim to produce fake data for the sensor for acquisition, and identification, (iii) synthetic images, and (iv) digital retouching of the images. Even with a zero-day or open-world attack setting, the results of the proposed algorithm show improvement over state-of-the-art results. The proposed architecture is agnostic to parameters, computationally efficient to provide a sustainable detector to resource-constrained institutions, provides robustness to attacks, and supports green computing.
Finding Explanations in AI Fusion of Electro-Optical/Passive Radio-Frequency Data

In the Information Age, the widespread usage of blackbox algorithms makes it difficult to understand how data is used. The practice of sensor fusion to achieve results is widespread, as there are many tools to further improve the robustness and performance of a model. In this study, we demonstrate the utilization of a Long Short-Term Memory (LSTM-CCA) model for the fusion of Passive RF (P-RF) and Electro-Optical (EO) data in order to gain insights into how P-RF data are utilized. The P-RF data are constructed from the in-phase and quadrature component (I/Q) data processed via histograms, and are combined with enhanced EO data via dense optical flow (DOF). The preprocessed data are then used as training data with an LSTM-CCA model in order to achieve object detection and tracking. In order to determine the impact of the different data inputs, a greedy algorithm (explainX.ai) is implemented to determine the weight and impact of the canonical variates provided to the fusion model on a scenario-by-scenario basis. This research introduces an explainable LSTM-CCA framework for P-RF and EO sensor fusion, providing novel insights into the sensor fusion process that can assist in the detection and differentiation of targets and help decision-makers to determine the weights for each input.
A study on geological structure prediction based on random forest method

The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt, which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt. At present, there is a huge controversy over the closure time of the Paleo-Asian Ocean in the Xingmeng orogenic belt. One of the reasons is that the genetic tectonic setting of the Carboniferous volcanic rocks is not clear. Due to the diversity of volcanic rock geochemical characteristics and its related interpretations, there are two different views on the tectonic setting of Carboniferous volcanic rocks in the Xingmeng orogenic belt: island arc and continental rift. In recent years, it is one of the important development directions in the application of geological big data technology to analyze geochemical data based on machine learning methods and further infer the tectonic background of basalt. This paper systematically collects Carboniferous basic rock data from Dongwuqi area of Inner Mongolia, Keyouzhongqi area of Inner Mongolia and Beishan area in the southern section of the Central Asian Orogenic Belt. Random forest algorithm is used for training sets of major elements and trace elements in global island arc basalt and rift basalt, and then the trained model is used to predict the tectonic setting of the Carboniferous magmatic rock samples in the Xingmeng orogenic belt. The prediction results shows that the island arc probability of most of the research samples is between 0.65 and 1, which indicates that the island arc tectonic setting is more credible. In this paper, it is concluded that magmatism in the Beishan area of the southern part of the Central Asian Orogenic belt in the Early Carboniferous may have formed in the heyday of subduction, while the Xingmeng orogenic belt in the Late Carboniferous may have been in the late subduction stage to the collision or even the early rifting stage. This temporal and spatial evolution shows that the subduction of the Paleo-Asian Ocean is different from west to east. Therefore, the research results of this paper show that the subduction of the Xingmeng orogenic belt in the Carboniferous has not ended yet.
Email Campaign Evaluation Based on User and Mail Server Response

The goal of an email service provider company is to send out a large number of emails to help its clients realise successful email marketing activities. Thousands of emails sent every minute need to be analysed in real time to reduce spam or phishing. The paper describes a method that uses real-time tracking of key campaign metrics such as the opens count, clicks count, hard bounces count, etc., to identify campaigns that should be stopped because they can be classified as spam or phishing. The key point of this solution is that we do not analyse email content. Nevertheless, the proposed neural networks are highly effective—the F1-score is above 0.95 for any used sample. Furthermore, the approach allows us to use the same model regardless of the language of an email. The method was developed and verified in collaboration with Freshmail, a leading provider of email marketing services in Poland. Validation of the method on real data provided by the company confirmed its high effectiveness.
Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting

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Monocular 3D Object Detection Based on Pseudo Multimodal Information Extraction and Keypoint Estimation

Three-dimensional object detection is an essential and fundamental task in the field of computer vision which can be widely used in various scenarios such as autonomous driving and visual navigation. In view of the current insufficient utilization of image information in current monocular camera-based 3D object detection algorithms, we propose a monocular 3D object detection algorithm based on pseudo-multimodal information extraction and keypoint estimation. We utilize the original image to generate pseudo-lidar and a bird’s-eye view, and then feed the fused data of the original image and pseudo-lidar to the keypoint-based network for an initial 3D box estimation, finally using the bird’s-eye view to refine the initial 3D box. The experimental performance of our method exceeds state-of-the-art algorithms under the evaluation criteria of 3D object detection and localization on the KITTI dataset, achieving the best experimental performance so far.
Editorial for the Special Issue: “Multispectral Remote Sensing Satellite Data for Mineral and Hydrocarbon Exploration: Big Data Processing and Deep Fusion Learning Techniques”

Using multispectral remote sensing data in cooperation with big data processing and deep fusion learning techniques provides a new approach for mineral and hydrocarbon exploration [...]
Mental Models for Assessing Impacts of Stormwater on Urban Social–Ecological Systems

Managing social–ecological systems that benefit both humans and nature is the central challenge of natural resource management. Integrating multiple perspectives into decision-making adds flexibility to social–ecological systems by reducing rigidity and promoting adaptability. Our objective was to assess expert perceptions of the structure, content, and function of the Puget Sound stormwater social–ecological system. We interviewed Puget Sound stormwater experts to document their mental models of the system and compared the network structure of expert maps. We also assessed differences among experts in the components of the social–ecological system which were included in mental models. Our analyses revealed differences between genders, as well as between managers, in how experts characterized the system. Notably, female participants tended to characterize the social–ecological system as one in which there were many system drivers generating multiple impacts across the system. The mental models of the scientists tended to include more resource units than those of the managers, which focused more on governance compared to those of the scientists. Finally, we incorporated the diverse input from mangers and scientists to create a single mental model to represent a consensus on the Puget Sound stormwater social–ecological system. This work highlights the fact that addressing the stormwater problem requires that we embrace the varied perspectives of scientists, managers, and stakeholders. We contend that incorporating the diverse perspectives of experts within the context of social–ecological systems provides a promising path towards a lasting and durable recovery for the ecosystem.