Reviewerly/exHarmony huggingface dataset:
Differential Testing of Machine Translators based on Compositional Semantics

Powered by the advances of deep neural networks, machine translation software has achieved rapid progresses recently. Machine translators are widely adopted in people’s daily lives, e.g., for information consumption, medical consumption and online shopping. However, machine translators are far from robust, and may produce wrong translations, which could potentially cause misunderstandings or even serious consequences. It is thus critical to detect errors in machine translators, and provide informative feedback for developers. In this work, we adopt the differential testing method to test machine translators. In particular, we use mature commercial translators as reference machine translation engines. Based on the principle of compositionality, which specifies that the meaning of a complex expression is determined by the meanings of its constituent expressions and the syntactic rules used to combine them, we design the oracle which conducts similarity comparison guided by syntactic structure and semantic encoding. In particular, we employ the constituency parsing to obtain the part-whole structure relation between a sentence and one of its component. Then we compute the semantic similarity of each sentence part with pre-trained language model and expert knowledge. We implement our approach into a tool named DCS, conduct experiments on three popular machine translators, i.e., Google translate, Baidu translate and Microsoft Bing translate, and compare DCS with two state-of-the-art approaches, i.e., CIT and CAT. The experiment results show that DCS achieves 8.6% and 35.4% higher precision, respectively. Moreover, the errors reported by DCS have the lowest redundancy in terms of the duplicated error locations in the source sentence. DCS can be used in complement with existing approaches and achieve higher detection precision. It also shows comparable efficiency with state-of-the-art approaches.
Trade-Off Between Robustness and Rewards Adversarial Training for Deep Reinforcement Learning Under Large Perturbations

Deep Reinforcement Learning (DRL) has become a popular approach for training robots due to its generalization promise, complex task capacity and minimal human intervention. Nevertheless, DRL-trained controllers are vulnerable to even the smallest of perturbations on its inputs which can lead to catastrophic failures in real-world human-centric environments with large and unexpected perturbations. In this work, we study the vulnerability of state-of-the-art DRL subject to large perturbations and propose a novel adversarial training framework for robust control. Our approach generates aggressive attacks on the state space and the expected state-action values to emulate real-world perturbations such as sensor noise, perception failures, physical perturbations, observations mismatch, etc. To achieve this, we reformulate the adversarial risk to yield a trade-off between rewards and robustness (TBRR). We show that TBRR-aided DRL training is robust to aggressive attacks and outperforms baselines on standard DRL benchmarks (Cartpole, Pendulum), Meta-World tasks (door manipulation) and a vision-based grasping task with a 7DoF manipulator. Finally, we show that the vision-based grasping task trained in simulation via TBRR transfers sim2real with 70% success rate subject to sensor impairment and physical perturbations without any retraining.
Distilling Local Texture Features for Colorectal Tissue Classification in Low Data Regimes

Multi-class colorectal tissue classification is a challenging problem that is typically addressed in a setting, where it is assumed that ample amounts of training data is available. However, manual annotation of fine-grained colorectal tissue samples of multiple classes, especially the rare ones like stromal tumor and anal cancer is laborious and expensive. To address this, we propose a knowledge distillation-based approach, named KD-CTCNet, that effectively captures local texture information from few tissue samples, through a distillation loss, to improve the standard CNN features. The resulting enriched feature representation achieves improved classification performance specifically in low data regimes. Extensive experiments on two public datasets of colorectal tissues reveal the merits of the proposed contributions, with a consistent gain achieved over different approaches across low data settings. The code and models are publicly available on GitHub .
Review and Application of Knowledge Graph in Crisis Management

In the contemporary social environment, social crisis events occur frequently with significant impacts. Effective management of these events requires comprehensive group intention mining, which encompasses intention detection and intention attribution. Knowledge graph inference facilitates the detection of group intention in crisis events. This is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic information. This paper provides a comprehensive overview of the research about knowledge graph in social crisis management, focusing on three key areas: knowledge graph construction and inference, knowledge graph-based interpretable crisis attribution, and risk management. Specifically, the interpretable semantics in crisis knowledge graphs enables attribution of intention. To illustrate the significance of knowledge graphs in group intention mining, the COVID-19 and China–US game events are selected as two case studies. Finally, the paper proposes future research directions to solve the limitations of existing knowledge graph-related methods in social crises.
Interpretability-Guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data

Multi-centre colonoscopy images from various medical centres exhibit distinct complicating factors and overlays that impact the image content, contingent on the specific acquisition centre. Existing Deep Segmentation networks struggle to achieve adequate generalizability in such data sets, and the currently available data augmentation methods do not effectively address these sources of data variability. As a solution, we introduce an innovative data augmentation approach centred on interpretability saliency maps, aimed at enhancing the generalizability of Deep Learning models within the realm of multi-centre colonoscopy image segmentation. The proposed augmentation technique demonstrates increased robustness across different segmentation models and domains. Thorough testing on a publicly available multi-centre dataset for polyp detection demonstrates the effectiveness and versatility of our approach, which is observed both in quantitative and qualitative results. The code is publicly available at: https://github.com/nki-radiology/interpretability_augmentation .
How Reputable Indonesian Writers from Cross-Disciplines Construct Their Research Articles During Established-Career Period? A Move Analysis

Expert writers are defined as someone that frequently publish their research articles (hereafter RAs) in reputable journals during their established-career period. Publication in highly reputable journals is an essential part of academic life. Therefore, many academicians are competing to produce articles that can be published in international journals. However, the path to achieving this goal is not easy. This study investigates the rhetorical moves in 16 English RAs written by four highly reputable lecturers from soft and hard science throughout their established-career period. The RAs were chosen randomly from highly reputable journals and were analyzed using a framework from Maswana et al. in 2015. The results revealed that there were moves/steps often present in both soft and hard science articles in some sections, i.e., Move a Step 2 Purpose; Move a Step 3 Methods; Move a Step 4 Results; Move a Step 5 Conclusion; Move 1, Step 1 Reference to established knowledge in the field; Move 7, Step 4 Stating specific findings; and Move 9, Step 1 Stating the main results and significance. However, both disciplines also organize their RAs in different ways, specifically in the Methods section. Move 5 Describing experimental procedure was found to be frequently used in hard science articles rather than in soft science. These results can conclude that the cross-disciplines partly project rhetorical moves in RAs. The findings of this study provide a guideline and overview for expert writers in evaluating their writing and for novice writers in increasing their discourse competence.
Multi‐level cross‐modality learning framework for text‐based person re‐identification

Abstract The target of text‐based person re‐identification (Re‐ID) is to retrieve the corresponding image of a person through the given text information. However, due to the homogeneous variety and modality heterogeneity, it is challenging to simultaneously learn both global‐level and local‐level cross‐modal features and align them in the same embedding space without additional networks. To address these problems, an effective multi‐level cross‐modality learning (MCL) framework for language and vision person Re‐ID is proposed. More specifically, a multi‐branch feature extraction (MFE) module is designed to comprehensively map both global and partial semantic information for the visual and textual embedding at the same time, capturing the intra‐class semantic relationships in multi‐granularities. Besides, a cross‐modal alignment (CA) module is devised to match the multi‐grained representations and reduce the inter‐class gap from global‐level to partial‐level. Extensive experiments conducted on the CUHK‐PEDES and ICFG‐PEDES datasets suggest that this method outperforms the state‐of‐the‐art models.
Precise diagnosis of breast phyllodes tumors using Raman spectroscopy: Biochemical fingerprint, tumor metabolism and possible mechanism

Breast fibroadenomas and phyllodes tumors are both fibroepithelial tumors with comparable histological characteristics. However, rapid and precise differential diagnosis is a tough point in clinical pathology. Given the tendency of phyllodes tumors to recur, the difficulty in differential diagnosis with fibroadenomas leads to the difficulty in optimal management for these patients. In this study, we used Raman spectroscopy to differentiate phyllodes tumors from breast fibroadenomas based on the biochemical and metabolic composition and develop a classification model. The model was validated by 5-fold cross-validation in the training set and tested in an independent test set. The potential metabolic differences between the two types of tumors observed in Raman spectroscopy were confirmed by targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 204 patients with formalin-fixed paraffin-embedded (FFPE) tissue samples, including 100 fibroadenomas and 104 phyllodes tumors were recruited from April 2014 to August 2021. All patients were randomly divided into the training cohort (n = 153) and the test cohort (n = 51). The Raman classification model could differentiate phyllodes tumor versus fibroadenoma with cross-validation accuracy, sensitivity, precision, and area under curve (AUC) of 85.58 % ± 1.77 %, 83.82 % ± 1.01 %, 87.65 % ± 4.22 %, and 93.18 % ± 1.98 %, respectively. When tested in the independent test set, it performed well with the test accuracy, sensitivity, specificity, and AUC of 83.50 %, 86.54 %, 80.39 %, and 90.71 %. Furthermore, the AUC was significantly higher for the Raman model than that for ultrasound (P = 0.0017) and frozen section diagnosis (P < 0.0001). When it came to much more difficult diagnosis between fibroadenoma and benign or small-size phyllodes tumor for pathological examination, the Raman model was capable of differentiating with AUC up to 97.45 % and 95.61 %, respectively. On the other hand, targeted metabolomic analysis, based on fresh-frozen tissue samples, confirmed the differential metabolites (including thymine, dihydrothymine, trans-4-hydroxy-l-proline, etc.) identified from Raman spectra between phyllodes tumor and fibroadenoma. In this study, we obtained the molecular information map of breast phyllodes tumors provided by Raman spectroscopy for the first time. We identified a novel Raman fingerprint signature with the potential to precisely characterize and distinguish phyllodes tumors from fibroadenoma as a quick and accurate diagnostic tool. Raman spectroscopy is expected to further guide the precise diagnosis and optimal treatment of breast fibroepithelial tumors in the future.
Evaluation Methodology of Open-Pit Mine Overall Slope Failure Risks

Slope failure risks and corresponding probabilities and consequences are related to a large number of causes, thus, their quantification is a complex process. The semi-qualitative method FMEA was modified into a quantitative value method V-FMEA for specific mining requirements, which enabled the risk assessment to perform a constant check of acceptable probabilities of the overall slope failure and the implementation of detection and preventive activities as functions of the present value of consequence costs. The large open-pit mine Field E slope failure served as a good example of the proposed new risk evaluation methodology.
Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes

The underground pressure disaster caused by the exploitation of deep mineral resources has become a major hidden danger restricting the safe production of mines. Microseismic monitoring technology is a universally recognized means of underground pressure monitoring and early warning. In this paper, the wavelet coefficient threshold denoising method in the time–frequency domain, STA/LTA method, AIC method, and skew and kurtosis method are studied, and the automatic P-phase-onset-time-picking model based on noise reduction and multiple detection indexes is established. Through the effect analysis of microseismic signals collected by microseismic monitoring system of coral Tungsten Mine in Guangxi, automatic P-phase onset time picking is realized, the reliability of the P-phase-onset-time-picking method proposed in this paper based on noise reduction and multiple detection indexes is verified. The picking accuracy can still be guaranteed under the severe signal interference of background noise, power frequency interference and manual activity in the underground mine, which is of great significance to the data processing and analysis of microseismic monitoring.
Clothoid-Based Path Correction for AGV Navigation

The paper focuses on improving the path quality for automated guided vehicle (AGV) navigation. In certain situations where AGVs are deployed in risky or hazardous areas, operators manually drive them to record the path. This recorded path is later used as a reference for the path tracker. However, the recorded path may contain inaccuracies, resulting in undesirable and jerky motion. To address this issue and eliminate inaccurate data, it is necessary to thoroughly check all the path data. In order to automate this process, we propose a clothoid-based approach that takes the recorded path and generates a corrected path. We validated the approach through field experiments using a commercial AGV operating in a factory environment.
The Relationship of Serial Storytelling Activities in Stimulating Early Reading Ability in Kindergarten

The purpose of the study was to find out how storytelling activities relate to using series image media in improving initial reading skills. The formulation of the problem stated in this study is the low initial reading ability in children of Al-Karomah Kindergarten PGRI 4 Pujon. The type of research used is quantitative, using a correlation approach. The subject to be studied is a child, which amounts to 15 children. The required data collected through observation sheets, documentation, and data acquisition of relationships of two or more variables will be analyzed using descriptive and inferential statistics. The coefficient results obtained from the regression test analysis showed that the constant value was 1.975. In contrast, the serialized image storytelling value received a value of 0.902, so the regression model equation above can be described as Y = a + bX or 1.957 + 0.902X. Following the results described, it can be concluded that there is a significant relationship between serial drawing storytelling activities and children's initial reading ability.
REKS: Role-Based Encrypted Keyword Search With Enhanced Access Control for Outsourced Cloud Data

Keyword-based search over encrypted data is an important technique to achieve both data confidentiality and utilization in cloud outsourcing services. While commonly used access control mechanisms, such as identity-based encryption and attribute-based encryption, do not generally scale well for hierarchical access permissions. To solve this problem, we propose a Role-based Encrypted Keyword Search (REKS) scheme by using the role-based access control and broadcast encryption. Specifically, REKS allows owners to deploy hierarchical access control by allowing users with parent roles to have access permissions from child roles. Using REKS, we further facilitate token generation preprocessing and efficient user management, thereby significantly reducing the users' final token generation and index update overheads, respectively. Formal security analysis proves that REKS is secure against chosen keyword and internal keyword guessing attacks, and findings from the empirical evaluations demonstrate that REKS is efficient and practical.
Há crianças portuguesas que só falam “brasileiro”: a ideologia linguística da norma na imprensa portuguesa

The differences between Brazilian Portuguese and European Portuguese have been the subject of debates, clashes and scientific research for at least two centuries. In this article, we propose to observe how the linguistic ideology of the norm materializes discursively in a contemporary journalistic text published in the Diário de Notícias, in Portugal, which portrays the presence of Brazilian Portuguese expressions in the speech of Portuguese children of school age, consequence of what parents and teachers consider an addiction, motivated by the consumption of audiovisual contente produced by Brazilian youtubers.
Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset

Digital Pathology (DP) has experienced a significant growth in recent years and has become an essential tool for diagnosing and prognosis of tumors. The availability of Whole Slide Images (WSIs) and the implementation of Deep Learning (DL) algorithms have paved the way for the appearance of Artificial Intelligence (AI) systems that support the diagnosis process. These systems require extensive and varied data for their training to be successful. However, creating labeled datasets in histopathology is laborious and time-consuming. We have developed a crowdsourcing-multiple instance labeling/learning protocol that is applied to the creation and use of the CR-AI4SkIN dataset.2 CR-AI4SkIN contains 271 WSIs of 7 Cutaneous Spindle Cell (CSC) neoplasms with expert and non-expert labels at region and WSI levels. It is the first dataset of these types of neoplasms made available. The regions selected by the experts are used to learn an automatic extractor of Regions of Interest (ROIs) from WSIs. To produce the embedding of each WSI, the representations of patches within the ROIs are obtained using a contrastive learning method, and then combined. Finally, they are fed to a Gaussian process-based crowdsourcing classifier, which utilizes the noisy non-expert WSI labels. We validate our crowdsourcing-multiple instance learning method in the CR-AI4SkIN dataset, addressing a binary classification problem (malign vs. benign). The proposed method obtains an F1 score of 0.7911 on the test set, outperforming three widely used aggregation methods for crowdsourcing tasks. Furthermore, our crowdsourcing method also outperforms the supervised model with expert labels on the test set (F1-score = 0.6035). The promising results support the proposed crowdsourcing multiple instance learning annotation protocol. It also validates the automatic extraction of interest regions and the use of contrastive embedding and Gaussian process classification to perform crowdsourcing classification tasks.
Amazon Web Service IOT and Authentication of Edge Devices

Amazon Web Services (AWS) delivers a scalable cloud computing platform designed for high availability and dependability, providing the tools that enable us to run a wide range of applications. AWS security model helps in protecting the confidentiality, integrity, and availability of systems and data. Security model of AWS successfully maintains the trust and confidence of the user. The approach for authenticating edge devices in Amazon Web Services provides resilient infrastructure that is designed for high security, without the capital outlay and operational overhead of a traditional data center. The current manuscript reviews the security mechanism used for authentication of edge devices in AWS Internet of Things (IoT).
Identification of Interval Discrete Models based on the Bee Swarm Optimization Algorithm with Adaptive Tuning of the Probability of Selecting Structural Elements

The problem of structural identification of interval discrete models of complex objects using the inductive approach based on the behavioral model of a bee colony (BMBC) is considered. A new computational scheme for the method is proposed, which involves introducing a quadratic objective function and relationships for adaptively changing the distribution law when randomly selecting structural elements of a difference equation. The first innovation allows the use of gradient optimization methods. The effectiveness of the proposed method in reducing its computational complexity is investigated. It is shown that the proposed new computational scheme makes it possible to reduce the computational complexity of identifying interval discrete models of systems, and the effectiveness of the method increases with the complexity of the model.
A Systematic Review of WebAssembly VS Javascript Performance Comparison

Web Assembly is one of brand-new programming languages on the web, it is equipped with complex binary format features so that it is more sophisticated and fast to load. In this paper, we collect material to compare quality performance of WASM and JS in various aspects (Runtime, Energy, Memory Usage). The performance hypothesis between WASM VS JS is depends on the type of web application you want to build. If the application requires very high performance and requires processing large data, then WASM may be a better choice. However, if the app is more focused on user interaction and view manipulation then JS may be a better fit. But after the authors do some systematic review of WASM and JS performance comparison, the authors conclude that WASM win in light application because it's faster and use less energy, but for heavy application, the authors conclude that JS is more convenient because it's significantly less memory needed than WASM, also it's unexpectedly more faster than WASM.