Reviewerly/exHarmony huggingface dataset:
Revealing code change propagation channels by evolution history mining

Changes on source code may propagate to distant code entities through various kinds of relationships, which may form up change propagation channels. It is however difficult for developers to reveal code change propagate channels due to sophisticated interrelationships among code entities. In this work, we propose a novel graph representation for the changed code entities and related code entities changed within a range of space and time so that the types of relationships along which the changes are propagated can be explicitly presented. Then a subgraph mining technique is used to find the frequent change propagation channels. We finally reveal 40 types of frequent change propagation channels that cover over 98% cases of code change propagation in five well-known open-source Java projects. We find evidence that the code changes propagated through an unchanged intermediate code entity consume more time than those through a changed one, indicating the difficulties in maintaining code entities that related through indirect relationships. We find that a small proportion of code entities frequently appear in the FCPCs, and confirm the semantic relationships between code entities covered by 50 instances of FCPCs, indicating potential usefulness for developers to explain the range of change impact from given source code changes.
Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism

Bounding box regression (BBR) is one of the core tasks in object detection, and the BBR loss function significantly impacts its performance. However, we have observed that existing IoU-based loss functions suffer from unreasonable penalty factors, leading to anchor boxes expanding during regression and significantly slowing down convergence. To address this issue, we intensively analyzed the reasons for anchor box enlargement. In response, we propose a Powerful-IoU (PIoU) loss function, which combines a target size-adaptive penalty factor and a gradient-adjusting function based on anchor box quality. The PIoU loss guides anchor boxes to regress along efficient paths, resulting in faster convergence than existing IoU-based losses. Additionally, we investigate the focusing mechanism and introduce a non-monotonic attention layer that was combined with PIoU to obtain a new loss function PIoU v2. PIoU v2 loss enhances the capability to focus on anchor boxes of medium quality. By incorporating PIoU v2 into popular object detectors such as YOLOv8 and DINO, we achieved an increase in average precision (AP) and improved performance compared to their original loss functions on the MS COCO and PASCAL VOC datasets, thus validating the effectiveness of our proposed improvement strategies.
AMAD: Active learning-based multivariate time series anomaly detection for large-scale IT systems

Multivariate time series anomaly detection on key performance indicators helps mitigate the impact of large-scale IT system anomalies. Due to the large volume and the abstract nature of multivariate time series, previous works have tended to make overly strict or optimistic hypotheses on labeling costs and resulted in unsatisfactory results. Thus, it remains a challenge to make an appropriate trade-off between labeling costs and model performance. This research proposes AMAD, an active learning-based approach that works to address this problem. Its core idea is to provide the learner model high-value label queries via an ensemble query strategy, which dynamically adapts to the estimated anomaly ratio and the ever-changing model performance. Moreover, it is the first to discuss in detail the margin effect of query strategies on model performance, to our knowledge, this has not been investigated in previous works on time series anomaly detection. Extensive experiments on five public datasets demonstrate that AMAD works well and robustly on various real-world scenarios, which outperforms state-of-the-art baseline methods by 16% and 11% in terms of recall and F1, with only 3% of data being labeled.
Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification

Esophageal cancer is a serious disease with a high prevalence in Eastern Asia. Histopathology tissue analysis stands as the gold standard in diagnosing esophageal cancer. In recent years, there has been a shift towards digitizing histopathological images into whole slide images (WSIs), progressively integrating them into cancer diagnostics. However, the gigapixel sizes of WSIs present significant storage and processing challenges, and they often lack localized annotations. To address this issue, multi-instance learning (MIL) has been introduced for WSI classification, utilizing weakly supervised learning for diagnosis analysis. By applying the principles of MIL to WSI analysis, it is possible to reduce the workload of pathologists by facilitating the generation of localized annotations. Nevertheless, the approach's effectiveness is hindered by the traditional simple aggregation operation and the domain shift resulting from the prevalent use of convolutional feature extractors pretrained on ImageNet.We propose a MIL-based framework for WSI analysis and cancer classification. Concurrently, we introduce employing self-supervised learning, which obviates the need for manual annotation and demonstrates versatility in various tasks, to pretrain feature extractors. This method enhances the extraction of representative features from esophageal WSI for MIL, ensuring more robust and accurate performance.We build a comprehensive dataset of whole esophageal slide images and conduct extensive experiments utilizing this dataset. The performance on our dataset demonstrates the efficiency of our proposed MIL framework and the pretraining process, with our framework outperforming existing methods, achieving an accuracy of 93.07% and AUC (area under the curve) of 95.31%.This work proposes an effective MIL method to classify WSI of esophageal cancer. The promising results indicate that our cancer classification framework holds great potential in promoting the automatic whole esophageal slide image analysis.
Hedging inflation expectations in the cryptocurrency futures market

This paper finds the first evidence of time variation in the relationship between inflation expectations and the price of cryptocurrency futures. Daily data on the futures markets of Bitcoin—starting in December 2017—and Ethereum—available since February 2021—reveal responses to inflation expectations that are consistently positive across both measures in a full sample encompassing 2022. These results hold for a sample that precedes the Luna crash in May 13, 2022. However, the response turns negative in the period between the failures of the Luna and FTX crypto exchanges. We find cryptocurrency futures provide an effective hedge against inflation expectations and may provide a hedge against idiosyncratic market risk if the ensuing uncertainty is embraced by traders leading them to search-for-yield behavior. Risk that is more systemic—and not properly digested by financial markets—may lead futures contract holders to exit their positions ahead of expiration, leading to a bid down of futures prices and an erosion of their hedging ability. This may have contributed to the turbulence in cryptocurrencies experienced during the latter part of 2022.
Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged. This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure. The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.
Neighborhood preserving embedding with autoencoder

Neighborhood preserving embedding (NPE) is a classical method for dimensionality reduction (DR), and it is a linear version of the locally linear embedding method. However, NPE and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively "represent" the original samples. To address this problem, we improve NPE based on linear autoencoder. The conventional projection of NPE is considered as the encoding stage, and the decoder stage is a reconstruction from the low-dimensional space to the original high-dimensional space, which is the key to maintaining more significant information. Based on this, we propose a new NPE method called NPEAE (neighborhood preserving embedding with autoencoder) in this paper. NPEAE performs excellently in face recognition, handwritten character categorization, object classification, etc. The experiments on MNIST, COIL-20, the Extended Yale B, Olivetti Research Laboratory (ORL), and FERET show that NPEAE has a higher recognition accuracy than other comparative methods.
A comprehensive examination of email spoofing: Issues and prospects for email security

Attackers are becoming more skilled in recent years, using sophisticated technology to produce look-alike emails that make it difficult to distinguish between real and fake ones. Most false emails can be detected, but certain undiscovered ones can be dangerous and compromise security. The attacker compromises SMTP to launch an email spoofing attack. This is not difficult given that it was designed without any security safeguards. Spoofers typically exploit the various fields in email headers. By taking advantage of loopholes in email security systems, attackers can create an ideal spoofing mail. As a result, it appears as a reliable source and succeeds in phishing attempts. An in-depth analysis of the email process, its protocols, and authentication mechanisms along with the security measures and adoption rates that led to a variety of spoofing attacks has been examined in our work. Our experiments on renowned mail service suppliers observed that some of them are still vulnerable to associated flaws. Further, we analyzed how different aspects such as age and education, determine whether or not a message is spoofed, and how malware uses email as a command and control to compromise the victim's device and seize control of it. Further, it offers a multitude of mitigation strategies against spoofing attempts that aid aspirants in future research.
Fuzzy order-sorted feature logic

Order-Sorted Feature (OSF) logic is a knowledge representation and reasoning language based on function-denoting feature symbols and set-denoting sort symbols ordered in a subsumption lattice. OSF logic allows the construction of record-like terms that represent classes of entities and that are themselves ordered in a subsumption relation. The unification algorithm for such structures provides an efficient calculus of type subsumption, which has been applied in computational linguistics and implemented in constraint logic programming languages such as LOGIN and LIFE and automated reasoners such as CEDAR. This work generalizes OSF logic to a fuzzy setting. We give a flexible definition of a fuzzy subsumption relation which generalizes Zadeh's inclusion between fuzzy sets. Based on this definition we define a fuzzy semantics of OSF logic where sort symbols and OSF terms denote fuzzy sets. We extend the subsumption relation to OSF terms and prove that it constitutes a fuzzy partial order with the property that two OSF terms are subsumed by one another in the crisp sense if and only if their subsumption degree is greater than 0. We show how to find the greatest lower bound of two OSF terms by unifying them and how to compute the subsumption degree between two OSF terms, and we provide the complexity of these operations.
Diagnosis of Osteosarcoma Based on Multimodal Microscopic Imaging and Deep Learning

Osteosarcoma is the most common primary bone tumor with high malignancy. It is particularly necessary to achieve rapid and accurate diagnosis in its intraoperative examination and early diagnosis. Accordingly, the multimodal microscopic imaging diagnosis system constructed by bright field, spontaneous fluorescence and polarized light microscopic imaging was used to study the pathological mechanism of osteosarcoma from the tissue microenvironment level and achieve rapid and accurate diagnosis. First, the multimodal microscopic images of normal and osteosarcoma tissue slices were collected to characterize the overall morphology of the tissue microenvironment of the samples, the arrangement structure of collagen fibers and the content and distribution of endogenous fluorescent substances. Second, based on the correlation and complementarity of the feature information contained in the three single-mode images, combined with convolutional neural network (CNN) and image fusion methods, a multimodal intelligent diagnosis model was constructed to effectively improve the information utilization and diagnosis accuracy. The accuracy and true positivity of the multimodal diagnostic model were significantly improved to 0.8495 and 0.9412, respectively, compared to those of the single-modal models. Besides, the difference of tissue microenvironments before and after cancerization can be used as a basis for cancer diagnosis, and the information extraction and intelligent diagnosis of osteosarcoma tissue can be achieved by using multimodal microscopic imaging technology combined with deep learning, which significantly promoted the application of tissue microenvironment in pathological examination. This diagnostic system relies on its advantages of simple operation, high efficiency and accuracy and high cost-effectiveness, and has enormous clinical application potential and research significance.
Graph embedding-based heterogeneous domain adaptation with domain-invariant feature learning and distributional order preserving

Heterogeneous domain adaptation (HDA) methods leverage prior knowledge from the source domain to train models for the target domain and address the differences in their feature spaces. However, incorrect alignment of categories and distribution structure disruption may be caused by unlabeled target samples during the domain alignment process for most existing methods, resulting in negative transfer. Additionally, the previous works rarely focus on the robustness and interpretability of the model. To address these issues, we propose a novel Graph embedding-based Heterogeneous domain-Invariant feature learning and Distributional order preserving framework (GHID). Specifically, a bidirectional robust cross-domain alignment graph embedding structure is proposed to globally align two domains, which learns the domain-invariant and discriminative features simultaneously. In addition, the interpretability of the proposed graph structures is demonstrated through two theoretical analyses, which can elucidate the correlation between important samples from a global perspective in heterogeneous domain alignment scenarios. Then, a heterogeneous discriminative distributional order preserving graph embedding structure is designed to preserve the original distribution relationship of each domain to prevent negative transfer. Moreover, the dynamic centroid strategy is incorporated into the graph structures to improve the robustness of the model. Comprehensive experimental results on four benchmarks demonstrate that the proposed method outperforms other state-of-the-art approaches in effectiveness.
Exploring the limitations in how ChatGPT introduces environmental justice issues in the United States: A case study of 3,108 counties

The potential of Generative AI, such as ChatGPT, has sparked discussions among researchers and the public. This study empirically explores the capabilities and limitations of ChatGPT, specifically its portrayal of environmental justice issues. Using OpenAI's ChatGPT API, we asked ChatGPT (GPT-4) to answer questions about environmental justice issues in 3,108 counties in the contiguous United States. Our findings suggest that ChatGPT provides a general overview of environmental justice issues. Consistent with research, ChatGPT appears to acknowledge the disproportionate distribution of environmental pollutants and toxic materials in low-income communities and those inhabited by people of color. However, our results also highlighted ChatGPT's shortcomings in detailing specific local environmental justice issues, particularly in disadvantaged (e.g., rural and low-income) counties. For instance, ChatGPT could not provide information on local-specific environmental justice issues for 2,593 of 3,108 counties (83%). The results of the binary logistic regression model revealed that counties with lower population densities, higher percentages of white population, and lower incomes are less likely to receive local-specific responses from the ChatGPT. This could indicate a potential regional disparity in the volume and quality of training data, hinting at geographical biases. Our findings offer insights and implications for educators, researchers, and AI developers.
Multi-branch-feature fusion super-resolution network

Deep network attracts extensive attention in the single image super-resolution field. Most of the deep network-based super-resolution methods usually employ a feature extraction stream to generate the input for the next extraction module during extracting deep features. Whereas, many experiments show that the feature maps yielded by different feature extraction streams generally contain different but complementary detailed information. Based on this observation, we propose a multi-branch feature fusion super-resolution network termed as MBFSR to solve the single image super-resolution task. MBFSR progressively deploys multiple feature extraction modules to extract the deep feature. Specifically, the deployed feature extraction modules first generate different feature maps through three well-designed feature extraction branches, and then fuse them by the developed three-branch fusion module to generate a high-resolution feature map. Finally, the output features of all modules are directly used to generate the desired high-resolution image. Experimental results demonstrate the availability and superiority of MBFSR over other popular methods.
Commit-time defect prediction using one-class classification

Existing Just-In-Time Software Defect Prediction methods suffer from the data imbalance problem, where the majority class (normal commits) significantly outnumbers the minority class (buggy commits). This results in a higher probability of misclassification. Various data balancing techniques have been proposed to address this challenge with varying degrees of success. In this study, we propose an approach that rely on One-Class Classification (OCC) to train models using data from the majority class only. This eliminates the need for data balancing. We compare the accuracy of three OCC algorithms - One-class SVM, Isolation Forest, and One-class k-NN - to their binary counterparts - SVM, Random Forest, and k-NN - on 34 software projects. Our results show that the data imbalance ratio (the proportion of normal to buggy commits) plays a crucial role in determining the optimal classification approach. We found that for projects with medium to high imbalance ratio, OCC algorithms outperform binary classifiers with and without data balancing, using cross and time-sensitive validation approaches. Furthermore, we found that OCC methods require fewer features for projects with medium to high IR, reducing the computational overhead of training and response time while providing a better understanding of the data and algorithm behavior.
A probabilistic analysis of selected notions of iterated conditioning under coherence

It is well known that basic conditionals satisfy some desirable basic logical and probabilistic properties, such as the compound probability theorem. However checking the validity of these becomes trickier when we switch to compound and iterated conditionals. Herein we consider de Finetti's notion of conditional both in terms of a three-valued object and as a conditional random quantity in the betting framework. We begin by recalling the notions of conjunction and disjunction among conditionals in selected trivalent logics. Then we analyze the notions of iterated conditioning in the frameworks of the specific three-valued logics introduced by Cooper-Calabrese, by de Finetti, and by Farrel. By computing some probability propagation rules we show that the compound probability theorem and other important properties are not always preserved by these formulations. Then, for each trivalent logic we introduce an iterated conditional as a suitable random quantity which satisfies the compound prevision theorem as well as some other desirable properties. We also check the validity of two generalized versions of Bayes' Rule for iterated conditionals. We study the p-validity of generalized versions of Modus Ponens and two-premise centering for iterated conditionals. Finally, we observe that all the basic properties are satisfied within the framework of iterated conditioning followed in recent papers by Gilio and Sanfilippo in the setting of conditional random quantities.
Inferentialism

Abstract This chapter introduces inferentialism, focusing on its application to the logical domain. It highlights the main attraction of inferentialism, namely that it grounds meaning in our inferential practices and distinguishes between semantic and meta-semantic theses associated with inferentialism. The chapter goes on to present three problems inferentialism faces: the Problem of Defective Concepts, the Problem of Constitutive Rules, and the Problem of Limited Applicability. It is argued that while inferentialists already have in house the resources to address the first two problems, they must join forces with expressivists in order to address the third problem. Moreover, joining forces with expressivists enables inferentialists to solve the first problem whilst allowing them to hold on to a classical notion of inference.