Reviewerly/exHarmony huggingface dataset:
<scp>DHHoE</scp>: Deep hybrid homogenous ensemble for digital histological breast cancer classification

Abstract The progress of deep learning architectures, machine learning models and pathology slide digitization is an encouraging step toward meeting the growing demand for more precise classification and prediction diagnosis for the breast tumours. The BreakHis dataset with four magnification factors (40X, 100X, 200X and 400X), as well as seven deep learning architectures used for feature extraction (DenseNet 201, Inception ResNet V2, Inception V3, ResNet 50, MobileNet V2,VGG16 and VGG19), four machine learning models for classification (MLP, SVM, DT, and KNN), and two combination rules (hard and weighted voting) were investigated in this paper to design and evaluate a new proposed approach consisting of building deep hybrid homogenous ensemble. Additionally, the best proposed models were compared to deep stacked, deep bagging, deep boosting, and deep hybrid heterogenous ensemble to choose the best strategy in building deep ensemble learning techniques. The four performance measures accuracy, precision, recall, and F1‐score were used in the empirical evaluations, as well as 5‐fold cross‐validation, the Scott Knott statistical test, and the Borda Count voting method. The results demonstrated the new approach's potential since it outscored both singles and other deep ensemble learning strategies, achieving accuracy values of 98.3% and 97.7% for the MFs 40X, 100X and 200X, 400X, respectively. The empirical results demonstrated that the proposed ensembles are impactful for histopathological breast cancer images classification, and they provided a promising tool to assist pathologists in the diagnosis of breast cancer.
CGuard: Scalable and Precise Object Bounds Protection for C

Spatial safety violations are the root cause of many security attacks and unexpected behavior of applications. Existing techniques to enforce spatial safety work broadly at either object or pointer granularity. Object-based approaches tend to incur high CPU overheads, whereas pointer-based approaches incur both high CPU and memory overheads. SGXBounds, an object-based approach, provides precise out-of-bounds protection for objects at a lower overhead compared to other tools with similar precision. However, a major drawback of this approach is that it cannot support address space larger than 32-bit.
Oven: Safe and Live Communication Protocols in Scala, using Synthetic Behavioural Type Analysis

We present Oven: a toolset to assure safety and liveness of communication protocols among threads in concurrent programs in Scala.
Back Deduction Based Testing for Word Sense Disambiguation Ability of Machine Translation Systems

Machine translation systems have penetrated our daily lives, providing translation services from source language to target language to millions of users online daily. Word Sense Disambiguation (WSD) is one of the essential functional requirements of machine translation systems, which aims to determine the exact sense of polysemes in the given context. Commercial machine translation systems (e.g., Google Translate) have been shown to fail in identifying the proper sense and consequently cause translation errors. However, to our knowledge, no prior studies focus on testing such WSD bugs for machine translation systems.
Quantitative Symbolic Similarity Analysis

Similarity analysis plays a crucial role in various software engineering tasks, such as detecting software changes, version merging, identifying plagiarism, and analyzing binary code. Equivalence analysis, a stricter form of similarity, focuses on determining whether different programs or versions of the same program behave identically. While extensive research exists on code and binary similarity as well as equivalence analysis, there is a lack of quantitative reasoning in these areas. Non-equivalence is a spectrum that requires deeper exploration, as it can manifest in different ways across the input domain space. This paper emphasizes the importance of quantitative reasoning on non-equivalence which arises due to semantic differences. By quantitatively reasoning about non-equivalence, it becomes possible to identify specific input ranges for which programs are equivalent or non-equivalent. We aim to address the gap in quantitative reasoning in symbolic similarity analysis, enabling a more comprehensive understanding of program behavior.
Type Automata

PL researchers have a profound understanding of automata theory but fail to grasp the subtleties and nuances of the type systems used in modern programming languages. My research pursues new insights into the computational power of type systems by connecting them with well-founded classes of automata through type automata---machines that employ program types as control and storage. In addition, I demonstrate advanced type-level metaprogramming applications of type automata that coerce the compiler into performing computations at compile time.
Quantitative Robustness Analysis of Neural Networks

Neural networks are an increasingly common tool for solving problems that require complex analysis and pattern matching, such as identifying stop signs or processing medical imagery. Accordingly, verification of neural networks for safety and correctness is of great importance, as mispredictions can have catastrophic results in safety critical domains. One metric for verification is robustness, which answers whether or not a misclassified input exists in a given input neighborhood. I am focusing my research at quantitative robustness---finding not only if there exist misclassified inputs within a given neighborhood but also how many exist as a proportion of the neighborhood size. My overall goal is to expand the research on quantitative neural network robustness verification and create a variety of quantitative verification tools geared towards expanding our understanding of neural network robustness.
COME: Commit Message Generation with Modification Embedding

Commit messages concisely describe code changes in natural language and are important for program comprehension and maintenance. Previous studies proposed some approaches for automatic commit message generation, but their performance is limited due to inappropriate representation of code changes and improper combination of translation-based and retrieval-based approaches. To address these problems, this paper introduces a novel framework named COME, in which modification embeddings are used to represent code changes in a fine-grained way, a self-supervised generative task is designed to learn contextualized code change representation, and retrieval-based and translation-based methods are combined through a decision algorithm. The average improvement of COME over the state-of-the-art approaches is 9.2% on automatic evaluation metrics and 8.0% on human evaluation metrics. We also analyse the effectiveness of COME's three main components and each of them results in an improvement of 8.6%, 8.7% and 5.2%.
Single-drone energy efficient coverage path planning with multiple charging stations for surveillance

Drones have started to be used for surveillance within the cities, visually scanning the predefined zones, quickly detecting abnormal states such as fires, accidents, and pollution, or assessing the disaster zones. Coverage Path Planning (CPP) is a problem that aims to determine the most suitable path or motion plan for a vehicle to cover the entire desired area in the task. So, this paper proposes a novel two-dimensional coverage path planning (CPP) mathematical model with the fact that a single drone may need to be recharged within its route based on its energy consumption, and the obstacles must be avoided while constructing the route. Our study aims to create realistic routes for drones by considering multiple charging stations and obstacles for surveillance. We tested the model for a grid example based on the scenarios obtained by changing the layout, the number of obstacles and recharging stations, and area size using the Python Gurobi Optimization library. As a contribution, we analyzed the impact of the number of existing obstacles and recharging stations, the size and layout of the area to be covered on total energy consumption, and the total solution time of CPP in our study for the first time in the literature, through a detailed Scenario Analysis. Results show that the map size and the number of covered cells affect the total energy consumption, but different layouts with shuffled cells are not effective. The area size to be covered affects the total computation time, significantly. As the number of obstacles and recharging stations increases, the computation time decreases up to a certain limit, then stabilizes.
Toward Automated Detecting Unanticipated Price Feed in Smart Contract

Decentralized finance (DeFi) based on smart contracts has reached a total value locked (TVL) of over USD 200 billion in 2022. In DeFi ecosystems, price oracles play a critical role in providing real-time price feeds for cryptocurrencies to ensure accurate asset pricing in smart contracts. However, the price oracle also faces security issues, including the possibility of unanticipated price feeds, which can lead to imbalances in debt and assets in the DeFi protocol. However, existing solutions cannot effectively combine transactions and code for real-time monitoring of price oracles.
LISTENING MATERIALS IN ENGLISH TEXTBOOK: QUALITY REVIEW

The objective of this study was to assess the quality of listening materials in the English textbook "Work in Progress: Buku SMA," published by Pusat Perbukuan in 2022. The research focused on evaluating the effectiveness of the listening materials based on a set of criteria developed by Zhang (2020). The research question formulated was, "What is the quality of the listening materials in the English textbook?" The methodology employed in this research was content analysis. The data were collected through document analysis, and the researcher organised and classified the data into eight criteria. After categorising the data according to the seven criteria, the researcher analysed the data chapter by chapter. Finally, conclusions were drawn based on the findings. According to the results, out of the eight criteria, five were fully met, one was partially met, and two were not met. Therefore, it can be concluded that the listening materials in the English textbook "Work in Progress" are deemed "effective."
Improving artificial intelligence with games

Games continue to drive progress in the development of artifi cial intelligence.
CILIATE: Towards Fairer Class-Based Incremental Learning by Dataset and Training Refinement

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models. Our code is available at https://github.com/Antimony5292/CILIATE.
DeFiTainter: Detecting Price Manipulation Vulnerabilities in DeFi Protocols

DeFi protocols are programs that manage high-value digital assets on blockchain. The price manipulation vulnerability is one of the common vulnerabilities in DeFi protocols, which allows attackers to gain excessive profits by manipulating token prices. In this paper, we propose DeFiTainter, an inter-contract taint analysis framework for detecting price manipulation vulnerabilities. DeFiTainter features two innovative mechanisms to ensure its effectiveness. The first mechanism is to construct a call graph for inter-contract taint analysis by restoring call information, not only from code constants but also from contract storage and function parameters. The second mechanism is a high-level semantic induction tailored for detecting price manipulation vulnerabilities, which accurately identifies taint sources and sinks and tracks taint data across contracts. Extensive evaluation of real-world incidents and high-value DeFi protocols shows that DeFiTainter outperforms existing approaches and achieves state-of-the-art performance with a precision of 96% and a recall of 91.3% in detecting price manipulation vulnerabilities. Furthermore, DeFiTainter uncovers three previously undisclosed price manipulation vulnerabilities.
Understanding Breaking Changes in the Wild

Modern software applications rely heavily on the usage of libraries, which provide reusable functionality, to accelerate the development process. As libraries evolve and release new versions, the software systems that depend on those libraries (the clients) should update their dependencies to use these new versions as the new release could, for example, include critical fixes for security vulnerabilities. However, updating is not always a smooth process, as it can result in software failures in the clients if the new version includes breaking changes. Yet, there is little research on how these breaking changes impact the client projects in the wild. To identify if changes between two library versions cause breaking changes at the client end, we perform an empirical study on Java projects built using Maven. For the analysis, we used 18,415 Maven artifacts, which declared 142,355 direct dependencies, of which 71.60% were not up-to-date. We updated these dependencies and found that 11.58% of the dependency updates contain breaking changes that impact the client. We further analyzed these changes in the library which impact the client projects and examine if libraries have adhered to the semantic versioning scheme when introducing breaking changes in their releases. Our results show that changes in transitive dependencies were a major factor in introducing breaking changes during dependency updates and almost half of the detected client impacting breaking changes violate the semantic versioning scheme by introducing breaking changes in non-Major updates.
Automated Generation of Security-Centric Descriptions for Smart Contract Bytecode

Smart contract and DApp users are taking great risks, as they do not obtain necessary knowledge that can help them avoid using vulnera- ble and malicious contract code. In this paper, we develop a novel system Tx2TXT that can automatically create security-centric textual descriptions directly from smart contract bytecode. To capture the security aspect of financial applications, we formally define a funds transfer graph to model critical funds flows in smart contracts. To ensure the expressiveness and conciseness of the descriptions de- rived from these graphs, we employ a GCN-based model to identify security-related condition statements and selectively add them to our graph models. To convert low-level bytecode instructions to human- readable textual scripts, we leverage robust API signatures to recover bytecode semantics. We have evaluated Tx2TXT on 890 well-labeled vulnerable, malicious and safe contracts where developer-crafted descriptions are available. Our results have shown that Tx2TXT out- performs state-of-the-art solutions and can effectively help end users avoid risky contracts
Feature interaction network based on hierarchical decoupled convolution for 3D medical image segmentation

Manual image segmentation consumes time. An automatic and accurate method to segment multimodal brain tumors using context information rich three-dimensional medical images that can be used for clinical treatment decisions and surgical planning is required. However, it is a challenge to use deep learning to achieve accurate segmentation of medical images due to the diversity of tumors and the complex boundary interactions between sub-regions while limited computing resources hinder the construction of efficient neural networks. We propose a feature fusion module based on a hierarchical decoupling convolution network and an attention mechanism to improve the performance of network segmentation. We replaced the skip connections of U-shaped networks with a feature fusion module to solve the category imbalance problem, thus contributing to the segmentation of more complicated medical images. We introduced a global attention mechanism to further integrate the features learned by the encoder and explore the context information. The proposed method was evaluated for enhance tumor, whole tumor, and tumor core, achieving Dice similarity coefficient metrics of 0.775, 0.900, and 0.827, respectively, on the BraTS 2019 dataset and 0.800, 0.902, and 0.841, respectively on the BraTS 2018 dataset. The results show that our proposed method is inherently general and is a powerful tool for brain tumor image studies. Our code is available at: https://github.com/WSake/Feature-interaction-network-based-on-Hierarchical-Decoupled-Convolution.
Copatrec: A correlation pattern recognizer Python package for nonlinear relations

Nonlinear regression plays a significant role as a method of analysis in different fields like statistics and simulations. While many researches focus on improving technical issues in the processes of nonlinear fitting, the absence of preprocessing, model selection, and validation is causing technical problems in various fields, i.e., statistical analysis and simulation methods. This work introduces a Python package named Copatrec, which facilitates preprocessing of data (i.e., data cleaning, standardization, and outlier detection), nonlinear model selection (from a group of complex behaviors’ mathematical expressions), and validation (by returning a result object that contains validation metrics and visualization functions), as observed in diverse scientific fields, including human-related sciences (e.g., social, political, economics, psychology, and health), and engineering fields like computer science (e.g., computer vision), electrical engineering, as well as fundamental science like physics and chemistry, where a precise characterization of the nonlinear relationship is central to the analytical outcome.
Embeddings as epistemic states: Limitations on the use of pooling operators for accumulating knowledge

Various neural network architectures rely on pooling operators to aggregate information coming from different sources. It is often implicitly assumed in such contexts that vectors encode epistemic states, i.e. that vectors capture the evidence that has been obtained about some properties of interest, and that pooling these vectors yields a vector that combines this evidence. We study, for a number of standard pooling operators, under what conditions they are compatible with this idea, which we call the epistemic pooling principle. While we find that all the considered pooling operators can satisfy the epistemic pooling principle, this only holds when embeddings are sufficiently high-dimensional and, for most pooling operators, when the embeddings satisfy particular constraints (e.g. having non-negative coordinates). We furthermore show that these constraints have important implications on how the embeddings can be used in practice. In particular, we find that when the epistemic pooling principle is satisfied, in most cases it is impossible to verify the satisfaction of propositional formulas using linear scoring functions, with two exceptions: (i) max-pooling with embeddings that are upper-bounded and (ii) Hadamard pooling with non-negative embeddings. This finding helps to clarify, among others, why Graph Neural Networks sometimes under-perform in reasoning tasks. Finally, we also study an extension of the epistemic pooling principle to weighted epistemic states, which are important in the context of non-monotonic reasoning, where max-pooling emerges as the most suitable operator.
An image compression encryption based on the semi-tensor product and the DFT measurement matrix

As the volume of image data increases, optical image encryption algorithms often require more time and space to operate. To solve the problem, compression is generally used, and compressed sensing algorithms are among the most frequently used compression algorithms recently. However, most existing compressed sensing algorithms suffer from low reconstruction quality and large measurement matrix size. To address this issue, we proposed a novel compression encryption scheme using the semi-tensor product, DFT (discrete Fourier transform) measurement matrix, and cascaded diffraction system. When the amount of image data grows, the key space grows according to the factorial growth rate without an upper limit, making brute-force attacks virtually impossible. Compared with algorithms without using a semi-tensor product, the storage space shrinks to one-fourth of its original size. Experiment results proved better reconstruction quality compared with existing compressed sensing algorithms. Furthermore, our approach is robust against various attacks, which is validated through rigorous testing.
Self-supervised contrastive speaker verification with nearest neighbor positive instances

Self-supervised contrastive learning (SSCL) has achieved a great success in speaker verification (SV). All recent works treat within-utterance speaker embeddings (SE) to be positive instances, encouraging them to be as close as possible. However, positive instances from the same utterance have similar channel and related semantic information, which are difficult to distinguish from the speaker features. Moreover, these positive instances can only provide limited variances in a certain speaker. To tackle the above problems, we propose to use nearest neighbor (NN) positive instances for training, which are selected from a dynamic queue. The NN positive instances can provide different channel and semantic information, increasing the variances in a certain speaker. Our proposed method are validated through comprehensive experiments on VoxCeleb and CNCeleb1 datasets, demonstrating its effectiveness in improving both SSCL and fine-tuning results. Additionally, our SSCL model outperforms supervised training model in cross-dataset testing due to the use of massive unlabeled data.
Gone with the fire: Market reaction to cryptocurrency exchange shutdown

Disruption and shutdown of exchanges frequently happen in the cryptocurrency market, though its potential impacts are relatively under-investigated due to several empirical challenges. This study employs 20-h of service interruption on October 15th at Upbit, the dominant cryptocurrency exchange in Korea, as an exogenous shock to examine the effect of unexpected service interruption at the exchange on cryptocurrency market. Event study estimation using price data from Binance, the largest cryptocurrency exchange globally, shows the sharp and negative reactions to cryptocurrencies mostly traded at Upbit. Major currencies such as Bitcoin and Ethereum also presented limited reactions, implying that service interruption could be interpreted as vulnerability of overall cryptocurrencies.