Reviewerly/exHarmony huggingface dataset:
How Do Developers Use the Java Stream API?

Java 8 marked a shift in the Java development landscape by introducing functional-like concepts in its stream library. Java developers can now rely on stream pipelines to simplify data processing, reduce verbosity, easily enable parallel processing and increase the expressiveness of their code. While streams have seemingly positive effects in Java development, little is known to what extent Java developers have incorporated streams into their programs and the degree of adoption by the Java community of individual stream’s features.This paper presents a replication study on which we analyze the stream usage of 610 Java projects. Our findings show that the Java streams are used mostly by software libraries rather than regular applications. Developers rarely use parallel processing, and when they do so, they only superficially use parallelism features and most of the parallel streams are used on simple forEach operations. The most common used pipelines involve map, filter and collect operations. We carefully describe a number of stream idioms we identified, and detail how we addressed the challenges we faced to complete our study. Our findings will help developers at (i) making better decisions about which features to consider when improving the API and (ii) supporting stream-related IDEs features, such as refactoring.
Full-field inverse identification of elasto-plastic model parameters for ductile cast iron

A key challenge in micromechanical simulation of ductile cast iron is the identification of the constitutive properties of the microstructural constituents. In recent scientific literature, ductile cast iron is predominantly modeled isotropically. Applied methods for the determination of model parameters for ductile cast iron include characterization by nanoindentation as well as inverse modeling approaches. In the present work, effective elastic and plastic model parameters for ferrite were determined by full-field inverse calibration. For that purpose, a stochastic speckle pattern was introduced in tensile specimens by focused ion beam (FIB). In-situ experiments were carried out in a scanning electron microscope and the strain in ferrite was measured by digital image correlation (DIC). A micromechanical model was built from the microstructure of the in-situ experiment. Measured and simulated strains were used in an optimization procedure to identify reliable plasticity parameters of ferrite in ductile cast iron. The results reveal the limitations of modeling the former isotropically.
Trust Management Framework for Handling Security Issues in Multi-cloud Environment

An emerged technology that facilitates for processing and managing data that could be stored on the servers in the remote location is termed as cloud computing. It also acts as a virtual warehouse that comprises resources that could be provided to the consumers for their usage on the demand. Through this facility, the users experience tremendous computational power and huge storage volume at less cost. The extensive use of cloud computing in various domains has to overcome several issues such as privacy, access control, data management, and resource management. The objective of this chapter is to throw a limelight into the access control models that have been developed in view of accessing the data. Initially, the chapter provides a detailed analysis of access control models that has been deployed by various providers in the cloud computing. To deploy an efficacy multi-cloud environment, there is a need for a concrete trust relationship between Cloud Service Providers (CSP), Cloud Providers (CP), and Cloud Service Users (CSU). Trustworthiness of CSP must be assessed and modified and it should be disseminated. A section will explore on the trust management mechanism in the multi-cloud environment. Also, it focuses on determining the efficiency and stability in distinguishing the reliable and unreliable CSPs in the multi-cloud environment. Finally, an exploration of the models that could provide authentication and interoperability among processes that execute on several cloud providers will be done. It also gives a brief description about data integrity algorithm. Also, this chapter provides deep insights about the challenges in security during the adoption of multi-cloud. Finally, the process of prevention and detection of threats across the multi-cloud environment will be presented.
ROCT: Radius-based Class Overlap Cleaning Technique to Alleviate the Class Overlap Problem in Software Defect Prediction

The training data commonly used in software defect prediction (SDP) usually contains some instances that have similar values on features but are in different classes, which significantly degrades the performance of prediction models trained using these instances. This is referred to as the class overlap problem (COP). Previous studies have concluded that COP has a more negative impact on the performance of prediction models than the class imbalance problem (CIP). However, less research has been conducted on COP than CIP. Moreover, the performance of the existing class overlap cleaning techniques heavily relies on the settings of hyperparameters such as the value of K in the K-nearest neighbor algorithm or the K-means algorithm, but how to find those optimal hyperparameters is still a challenge. In this study, we propose a novel technique named the radius-based class overlap cleaning technique (ROCT) to better alleviate COP without tuning hyperparameters in SDP. The basic idea of ROCT is to take each instance as the center of a hypersphere and directly optimize the radius of the hypersphere. Then ROCT identifies those instances with the opposite label of the center instance as the overlapping instance and removes them. To investigate the performance of ROCT, we conduct the empirical experiment across 29 datasets collected from various software repositories on the K-nearest neighbor, random forest, logistic regression, and naive Bayes classifiers measured by AUC, balance, pd, and pf. The experimental results show that ROCT performs the best and significantly improves the performance of prediction models by as much as 15.2% and 29.9% in terms of AUC and balance compared with the existing class overlap cleaning techniques. The superior performance of ROCT indicates that ROCT should be recommended as an efficient alternative to alleviate COP in SDP.
Semi-supervised emotion recognition in textual conversation via a context-augmented auxiliary training task

Recognizing emotions in textual conversations (ERC) is to identify the emotion of utterances by considering conversational context. Current supervise-based ERC methods require a large number of diverse conversations to train a model that leverages context effectively. However, the scarcity of annotated training data in most ERC corpora hinders their performance improvement. In this paper, we explore to use the easily accessible unlabeled data mixed with labeled data to help ERC models to improve performance. Considering collected unlabeled data may not share the same emotion class with the labeled set, we propose a semi-supervised ERC algorithm that leverages the unlabeled conversational data through a novel Context-augmented AUXIliary training Task (CAUXIT), which trains along with the original ERC task in a multi-task fashion. Our idea is to utilize CAUXIT to learn a better utterance feature representation on top of existing ERC models. Especially, CAUXIT is designed to selectively mask the utterance based on a class-based sampling strategy and use the context, i.e., the rest utterances, to predict its emotion-related information rather than the lexical information of itself, which enhances the network’s ability in making emotion inference through context and consequently improve the utterance feature representation. In addition to applying CAUXIT to unlabeled data, we also extend it to labeled data to further enrich the supervision signal. As shown in the experiments, applying CAUXIT on various ERC models achieves a significant improvement over the same network architectures trained on labeled data, which verifies our approach as an effective semi-supervised ERC framework.
Sensor Pattern Noise Estimation using Non-textured Video Frames For Efficient Source Smartphone Identification and Verification

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for image authentication and source camera identification. The abundant information that the PRNU carries in terms of the frequency content makes it unique, and therefore suitable for identifying the source camera and detecting forgeries in digital images. However, PRNU estimation from smartphone videos is a challenging process due to the presence of frame-dependent information (very dark/very textured), as well as other non-unique noise components and distortions due to lossy compression. In this paper, we propose an approach that considers only the non-textured frames in estimating the PRNU because its estimation in highly textured images has been proven to be inaccurate in image forensics. Furthermore, lossy compression distortions tend to affect mainly the textured and high activity regions and consequently weakens the presence of the PRNU in such areas. The proposed technique uses a number of texture measures obtained from the Grey Level Cooccurrence Matrix (GLCM) prior to an unsupervised learning process that splits the feature space through training video frames into two different sub-spaces, i.e., the textured space and the non-textured space. Non-textured video frames are filtered out and used for estimating the PRNU. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art approach.
Multi-UAV Collaborative Path Planning Method Based on Attention Mechanism

Aiming at the problem that traditional heuristic algorithm is difficult to extract the empirical model in time from large sample terrain data, a multi-UAV collaborative path planning method based on attention reinforcement learning is proposed. The method draws on a combined consideration of influencing factors, such as survival probability, path length, and load balancing and endurance constraints, and works as a support system for multimachine collaborative optimizing. The attention neural network is used to generate the cooperative reconnaissance strategy of the UAV, and a large amount of simulation data is tested to optimize the attention network using the REINFORCE algorithm. Experimental results show that the proposed method is effective in solving the multi-UAV path planning issue with high real-time requirements, and the solving time is less than the traditional algorithms.
Federated Learning with Extreme Label Skew: A Data Extension Approach

The real-world data sets often leveraged by Federated Learning (FL) applications are mostly non-independent and non-identically distributed (non-IID). This usually results from the diverse nature of the participating clients and their individual data-gathering contexts. An effective FL algorithm must incorporate the capability to produce a joint model that generalizes and captures these diverse patterns. In this work, we show how using some wild external data samples as placeholders for missing classes on client devices can alleviate the learning difficulty often posed by inbalance data distributions. Our exploration showed that this strategy enhances learning and can significantly boost test accuracy, particularly in extreme label skew scenarios. We recorded over 25% reduction in test error rate for the pathological non-IID partitions of the CIFAR10 data set. Our results are similar to those obtainable through bound-expanding strategies such as direct data sharing among clients. But unlike these techniques, our approach rules out the risk of exposing client's private data.
Creative Knowledge Representation for Knowledge Management: The Dialectical Approach

The dialectical approach to the knowledge representation in general form consists in the possibility of synthesizing new knowledge based on the explication of meaningful contradictions in the antinomic poles of dichotomies. The scientific novelty of the research lies in the fact that the dialectical approach is combined with new information technologies. This is how the “bridge” of reasoning is thrown from the concept of “knowledge management” to machine learning based on large, expertly labeled data. This is the basis of the synthesis carried out in the expert-metric approach, which is a special case of the dialectical approach. Now, with the development of the Internet, big data, there is a technological opportunity to use the creative potential of dialectics more fully. The basis of the synthesis depth is the representation of the management decision-making process by a set of decision trees. In this case, the metric approach is the use of a small number of decision trees, often presented explicitly, and the expert approach is the use of a very large number of trees that exist only in the head of an expert in an implicit, non-reflected form.
Digital Twins as a Key Technology for Ensuring Effective Modernization of Complex Industrial Systems

The paper considers the problem of the operation maintenance of complex soft- and hardware systems, for example, automated control systems of industrial objects, when individual components of the system become unavailable for replacement in case of their failure or for other reasons. When the replacement components differ from those that were designed at the system creation stage, the upgrade task arises. System upgrades require a complete cycle of work on validating both the replaceable components and the changed system itself. The system itself, if it is included in the life cycle of an existing industrial object, actually cannot be used as a testbed of the new components; generally, full-size system layouts are used for the testbed, and the complexity of those layouts may be comparable to the system itself. The inclusion of the digital twin in the life cycle of the object and its control system allows to effectively solve the problem of system modification (individual components replacement) in this case. The digital twin is not only a cheaper analog of the system that can be used for testing; due to the properties that allow parallel testing for several copies and to influence the model time within certain limits, it provides a higher quality of testing.
Distribution-based Adversarial Filter Feature Selection against Evasion Attack

Feature selection plays an important role in machine learning in order to reduce model complexity and extract more meaningful information. The recent studies indicate that not only the generalization ability but also the security should be considered in selecting features in an adversarial environment in which a classifier may be misled by an adversary intentionally. However, since only the nearest legitimate sample to a malicious sample is considered, the existing adversarial filter feature selection method is sensitive to outlier samples and is not suitable for Boolean features. A distribution-based adversarial filter feature selection method is proposed against evasion attack in this study. Our method uses distribution-based measurements, such as Symmetric Uncertainty and Earth Mover's Distance, are used to quantify the generalization ability and security of a feature subset. The experiments suggest our proposed method outperforms existing methods in terms of robustness and stability in datasets with Boolean and real-valued features.
Deepfakes Detection Based on Multi Scale Fusion

Generative adversarial networks (GANs) and deep learning technologies pose a great threat to public security. The traditional forgery and tampering detection methods are difficult to use for the detection of such images or videos. In this paper, based on the deep learning method, the deep neural network is used to extract, fuse and classify the higher dimensional space-time features of the input image and sequence frame in the spatial and temporal dimensions, and a kind of automatic deepfakes detection technology based on multi-dimensional space-time information fusion is proposed. In the spatial dimension, using the spatial learning ability of convolutional neural network (CNN), the feature pyramid (FPN) is fused to the feature map extracted by the backbone feature extraction network for up sampling and weighted fusion. According to the results of higher dimensional feature fusion classification, deepfakes are detected.
Fixpoint Semantics for Recursive SHACL

SHACL is a W3C-proposed language for expressing structural constraints on RDF graphs. The recommendation only specifies semantics for non-recursive SHACL; recently, some efforts have been made to allow recursive SHACL schemas. In this paper, we argue that for defining and studying semantics of recursive SHACL, lessons can be learned from years of research in non-monotonic reasoning. We show that from a SHACL schema, a three-valued semantic operator can directly be obtained. Building on Approximation Fixpoint Theory (AFT), this operator immediately induces a wide variety of semantics, including a supported, stable, and well-founded semantics, related in the expected ways. By building on AFT, a rich body of theoretical results becomes directly available for SHACL. As such, the main contribution of this short paper is providing theoretical foundations for the study of recursive SHACL, which can later enable an informed decision for an extension of the W3C recommendation.
DQN-based gradual fisheye image rectification

Fisheye image rectification is a classic and important task in the computer vision area, which is generally treated as a pre-processing step in many application scenarios. Most of the existing fisheye image rectification methods focused mainly on building a direct (one-step) projection relationship between fisheye images and corrected images. Although these methods have achieved impressive performance, they depended heavily on data distribution and cannot work well on images whose distortion parameters are out of range. To address this issue, we propose a multi-step gradual image rectification scheme. In particular, we treat the fisheye image rectification problem as one Markov Decision Process and employ a widely-used deep reinforcement learning method (i.e., Deep Q-Network) to solve the problem. In addition, we build a new large-scale fisheye image dataset to evaluate our method, which contains 50,000 training images and 5000 test images. Experimental results show the proposed method can better handle images with a wide range of distortion.
Artificial intelligent based smart system for safe mining during foggy weather

Abstract Opencast mining operations at hilly areas are usually affected during foggy weather due to the inability of drivers to operate heavy earth‐moving machinery in low visibility conditions. This article deals with an intelligent vision enhancement system for continuing opencast mining operations during foggy weather. The system integrates hardware and software to provide multistage safety features that make it unique from existing systems. The system includes hardware like thermal cameras, high definition cameras, proximity radar, wireless devices, GNSS module, graphical processing unit, display unit, and so forth, and image processing software, namely real‐time image stitching, image enhancement, and object detection using convolutional neural networks. The integrated system and algorithms display a 180° panorama field view of the vehicle's front using real‐time video stitching. The front view after image processing, rear camera view, object detection through proximity radar, and real‐time location of the vehicles on a 3D geo‐tagged mine map by GNSS modules are displayed in four splitter windows on a touch screen fitted on the dashboard in front of the driver's seat. The driver can drive the vehicle by seeing the display screen during foggy weather. The output image of the developed image‐processing algorithm has less distortion, better quality, and better depth perception than existing methods. Overall, there are significant improvements in the persistence of the color elements by 39.65%, contrast by 4.62%, and the corresponding entropy by 7.11% concerning the similar existing methods. The final system has been successfully tested in an opencast mine.
Assessing Lexical Psychological Properties in Second Language Production: A Dynamic Semantic Similarity Approach

Previous studies of the lexical psycholinguistic properties (LPPs) in second language (L2) production have assessed the degree of an LPP dimension of an L2 corpus by computing the mean ratings of unique content words in the corpus for that dimension, without considering the possibility that learners at different proficiency levels may perceive the degree of that dimension of the same words differently. This study extended a dynamic semantic similarity algorithm to estimate the degree of five different LPP dimensions of several sub-corpora of the Education First-Cambridge Open Language Database representing L2 English learners at different proficiency levels. Our findings provide initial evidence for the validity of the algorithm for assessing the LPPs in L2 production and contribute useful insights into between-proficiency relationships and cross-proficiency differences in the LPPs in L2 production as well as the relationships among different LPP dimensions.
Adversarial Attacks and Defense on Deep Learning Classification Models using YC<sub>b</sub>C<sub>r</sub> Color Images

Deep neural network models are vulnerable to adversarial perturbations that are subtle but change the model predictions. Adversarial perturbations are generally computed for RGB images and are, hence, equally distributed among the RGB channels. We show, for the first time, that adversarial perturbations prevail in the Y-channel of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{YC}_{b}\mathbf{C}_{r}$</tex> > color space and exploit this finding to propose a defense mechanism. Our defense ResUpNet, which is end-to-end trainable, removes perturbations only from the Y-channel by exploiting ResNet features in a bottleneck free up-sampling framework. The refined Y-channel is combined with the untouched <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{C}_{b}\mathbf{C}_{r}$</tex> -channels to restore the clean image. We compare ResUpNet to existing defenses in the input transformation category and show that it achieves the best balance between maintaining the original accuracies on clean images and defense against adversarial attacks. Finally, we show that for the same attack and fixed perturbation magnitude, learning perturbations only in the Y-channel results in higher fooling rates. For example, with a very small perturbation magnitude <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon=0.002$</tex> ) the fooling rates of FGSM and PGD attacks on the ResNet50 model increase by 11.1% and 15.6% respectively, when the perturbations are learned only for the Y-channel.