Reviewerly/exHarmony huggingface dataset:
Histopathological Imaging Classification of Breast Tissue for Cancer Diagnosis Support Using Deep Learning Models

According to some medical imaging techniques, breast histopathology images called Hematoxylin and Eosin are considered as the gold standard for cancer diagnoses. Based on the idea of dividing the pathologic image (WSI) into multiple patches, we used the window [512, 512] sliding from left to right and sliding from top to bottom, each sliding step overlapping by 50% to augmented data on a dataset of 400 images which were gathered from the ICIAR 2018 Grand Challenge. Then use the EficientNet model to classify and identify the histopathological images of breast cancer into 4 types: Normal, Benign, Carcinoma, Invasive Carcinoma. The EffficientNet model is a recently developed model that uniformly scales the width, depth, and resolution of the network with a set of fixed scaling factors that are well suited for training images with high resolution. And the results of this model give a rather competitive classification efficiency, achieving 98% accuracy on the training set and 93% on the evaluation set.
Analysis and Prediction of Breast Cancer using Multi-model Classification Approach

Nowadays, cancer is the most recurrent illness among living kind. Breast cancer among women between the age group of 30–50 has increased significantly. Early diagnosis can improve the chance of endurance and gives a better quality of life. Lot of advanced research is happening for early detection, and preventive measures are been improving simultaneously. The paper aims for early detection and classification of the breast cancer cells into malignant or benign. The proposed approach used various machine learning techniques such as K-nearest neighbor (KNN), support vector machine (SVM), logistic regression, and random forest classifiers. The proposed system includes input data, pre-processing, feature extraction, and model building. The proposed approach used UCI breast cancer Wisconsin (diagnostic) dataset (WDBC). The dataset contains 569 records with 30 features. The approach used various machine learning models for analyzing, predicting, and classifying the breast cancer cells into benign and malignant cells. The paper compares the performance of these models with respect to their accuracy.
CMU’s IWSLT 2022 Dialect Speech Translation System

This paper describes CMU's submissions to the IWSLT 2022 dialect speech translation (ST) shared task for translating Tunisian-Arabic speech to English text.We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.We also augment the paired ASR data with pseudo translations via sequence-level knowledge distillation from an MT model and use these artificial triplet ST data to improve our end-to-end (E2E) systems.Our E2E models are based on the Multi-Decoder architecture with searchable hidden intermediates.We extend the Multi-Decoder by orienting the speech encoder towards the target language by applying ST supervision as hierarchical connectionist temporal classification (CTC) multi-task.During inference, we apply joint decoding of the ST CTC and ST autoregressive decoder branches of our modified Multi-Decoder.Finally, we apply ROVER voting, posterior combination, and minimum bayes-risk decoding with combined N-best lists to ensemble our various cascaded and E2E systems.Our best systems reached 20.8 and 19.5 BLEU on test2 (blind) and test1 respectively.Without any additional MSA data, we reached 20.4 and 19.2 on the same test sets.
Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network

In this paper, we study how to continually pretrain language models for improving the understanding of math problems.Specifically, we focus on solving a fundamental challenge in modeling math problems, i.e., how to fuse the semantics of textual description and formulas, which are highly different in essence.To address this issue, we propose a new approach called COMUS to continually pre-train language models for math problem understanding with syntax-aware memory network.In this approach, we first construct the math syntax graph to model the structural semantic information, by combining the parsing trees of the text and formulas, and then design the syntax-aware memory networks to deeply fuse the features from the graph and text.With the help of syntax relations, we can model the interaction between the token from the text and its semantic-related nodes within the formulas, which is helpful to capture fine-grained semantic correlations between texts and formulas.Besides, we devise three continual pre-training tasks to further align and fuse the representations of the text and math syntax graph.Experimental results on four tasks in the math domain demonstrate the effectiveness of our approach.Our code and data are publicly available at the link: https: //github.com/RUCAIBox/COMUS. IntroductionUnderstanding math problems via automated methods is a desired machine capacity for artificial intelligence assisted learning.Such a capacity is the key to the success of a variety of educational applications, including math problem retrieval (Reusch et al., 2021), problem recommendation (Liu et al., 2018), and problem solving (Huang et al., 2020).To automatically understand math problems, it is feasible to learn computational representations
Knowledge-Augmented Language Models for Cause-Effect Relation Classification

Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models.However, these methods behave differently across domains and downstream tasks.In this work, we investigate the augmentation of pretrained language models with knowledge graph data in the causeeffect relation classification and commonsense causal reasoning tasks.After automatically verbalizing triples in ATOMIC 20 20 , a wide coverage commonsense reasoning knowledge graph, we continually pretrain BERT and evaluate the resulting model on cause-effect pair classification and answering commonsense causal reasoning questions.Our results show that a continually pretrained language model augmented with commonsense reasoning knowledge outperforms our baselines on two commonsense causal reasoning benchmarks, COPA and BCOPA-CE, and a Temporal and Causal Reasoning (TCR) dataset, without additional improvement in model architecture or using quality-enhanced data for fine-tuning.
What’s in the Black Box? The False Negative Mechanisms Inside Object Detectors

In object detection, false negatives arise when a detector fails to detect a target object. To understand why object detectors produce false negatives, we identify five 'false negative mechanisms', where each mechanism describes how a specific component inside the detector architecture failed. Focusing on two-stage and one-stage anchor-box object detector architectures, we introduce a framework for quantifying these false negative mechanisms. Using this framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects in benchmark vision datasets and robotics datasets. We show that a detector's false negative mechanisms differ significantly between computer vision benchmark datasets and robotics deployment scenarios. This has implications for the translation of object detectors developed for benchmark datasets to robotics applications. Code is publicly available at https://github.com/csiro-robotics/fn_mechanisms
HDWR_SmartNet: A Smart Handwritten Devanagari Word Recognition System Using Deep ResNet-Based on Scan Profile Method

Handwritten script recognition is an important application in the machine learning domain and gaining more importance due to its numerous applications like automatic postal card sorting, digital signature verification, and processing of historical documents and also helps to develop applications for helping visually impaired people. English is the most widely spoken language that has witnessed much research work to read a script using the machine. Devanagari is also such a script that is used by a great number of people in the Indian Subcontinent. This chapter proposes Handwritten Devanagari Word Recognition (HDWR) using a method that is more analogous to human reading strategy. This chapter, contradictory to the traditional method, encourages a no-segmentation approach. Two novel approaches, Scan Profile and Sliding Window, have been introduced in this chapter to eliminate segmentation. First, the input word is passed to propose a no-segmentation approach where the window is defined using different methods. Secondly, each window is passed to the classifier for recognition purposes and the result is saved. Here no need to wait to segment the complete image. Immediately the window is recognized without knowing what is there in the window automatically by our designed state-of-the-art ResNet classifier like human reading. After one window recognition, the window moves next by the calculated stride to read the complete word. The proposed HDWR model successfully recognized Devanagari words with an accuracy of 86%.
Examining the Driving Factors of SOM Using a Multi-Scale GWR Model Augmented by Geo-Detector and GWPCA Analysis

A model incorporating geo-detector analysis and geographically weighted principal component analysis into Multi-scale Geographically Weighted regression (GWPCA-MGWR) was developed to reveal the factors driving spatial variation in soil organic matter (SOM). The regression accuracy and residuals from GWPCA-MGWR were compared to those of the classical Geographically Weighted regression (GWR), Multi-scale Geographically Weighted regression (MGWR), and GWPCA-GWR. Our results revealed that local multi-collinearity on model fitting negatively affects the results to different degrees. Additionally, compared to other models, GWPCA-MGWR provided the lowest MAE (0.001) and little-to-no residual spatial autocorrelation and is the best model for regression for SOM spatial distribution and identification of dominant driving factors. GWPCA-MGWR produced spatial non-stationary SOM that was variably affected by soil nutrient content, soil type, and human activity, and was geomorphic in the second place. In conclusion, the spatial information obtained from GWPCA-MGWR provides a valuable reference for understanding the factors that influence SOM variation.
Display of 3D Illuminations using Flying Light Specks

This paper presents techniques to display 3D illuminations using Flying Light Specks, FLSs. Each FLS is a miniature (hundreds of micrometers) sized drone with one or more light sources to generate different colors and textures with adjustable brightness. It is network enabled with a processor and local storage. Synchronized swarms of cooperating FLSs render illumination of virtual objects in a pre-specified 3D volume, an FLS display. We present techniques to display both static and motion illuminations. Our display techniques consider the limited flight time of an FLS on a fully charged battery and the duration of time to charge the FLS battery. Moreover, our techniques assume failure of FLSs is the norm rather than an exception. We present a hardware and a software architecture for an FLS-display along with a family of techniques to compute flight paths of FLSs for illuminations. With motion illuminations, one technique (ICF) minimizes the overall distance traveled by the FLSs significantly when compared with the other techniques.
An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation.
Machine-Learning Side-Channel Attacks on the GALACTICS Constant-Time Implementation of BLISS

Due to the advancing development of quantum computers, practical attacks on conventional public-key cryptography may become feasible in the next few decades. To address this risk, post-quantum schemes that are secure against quantum attacks are being developed. Lattice-based algorithms are promising replacements for conventional schemes, with BLISS being one of the earliest post-quantum signature schemes in this family. However, required subroutines such as Gaussian sampling have been demonstrated to be a risk for the security of BLISS, since implementing Gaussian sampling both efficient and secure with respect to physical attacks is highly challenging. This paper presents three related power side-channel attacks on GALACTICS, the latest constant-time implementation of BLISS. All attacks are based on leakages we identified in the Gaussian sampling and signing algorithm of GALACTICS. To run the attack, a profiling phase on a device identical to the device under attack is required to train machine learning classifiers. In the attack phase, the leakages of GALACTICS enable the trained classifiers to predict sensitive internal information with high accuracy, paving the road for three different key recovery attacks. We demonstrate the leakages by running GALACTICS on a Cortex-M4 and provide proof-of-concept data and implementation for all our attacks.
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions

At the foundation of scientific evaluation is the labor-intensive process of peer review.This critical task requires participants to consume vast amounts of highly technical text.Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined.We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts.DIS-APERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors' stance towards review arguments.Further, we annotate every review and rebuttal sentence.We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews.Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.
Graph-based structural knowledge-aware network for diagnosis assistant

Diagnosis assistant is an effective way to reduce the workloads of professional doctors. The rich professional knowledge plays a crucial role in diagnosis. Therefore, it is important to introduce the relevant medical knowledge into diagnosis assistant. In this paper, diagnosis assistant is treated as a classification task, and a Graph-based Structural Knowledge-aware Network (GSKN) model is proposed to fuse Electronic Medical Records (EMRs) and medical knowledge graph. Considering that different information in EMRs affects the diagnosis results differently, the information in EMRs is categorized into general information, key information and numerical information, and is introduced to GSKN by adding an enhancement layer to the Bidirectional Encoder Representation from Transformers (BERT) model. The entities in EMRs are recognized, and Graph Convolutional Neural Networks (GCN) is employed to learn deep-level graph structure information and dynamic representation of these entities in the subgraphs. An interactive attention mechanism is utilized to fuse the enhanced textual representation and the deep representation of these subgraphs. Experimental results on Chinese Obstetric Electronic Medical Records (COEMRs) and open dataset C-EMRs demonstrate the effectiveness of our model.
Formal definitions of conservative probability distribution functions (PDFs)

Under ideal conditions, the probability density function (PDF) of a random variable, such as a sensor measurement, would be well known and amenable to computation and communication tasks. However, this is often not the case, so the user looks for some other PDF that approximates the true but intractable PDF. Conservativeness is a commonly sought property of this approximating PDF, especially in distributed or unstructured data systems where the data being fused may contain un-known correlations. Roughly, a conservative approximation is one that overestimates the uncertainty of a system. While prior work has introduced some definitions of conservativeness, these definitions either apply only to normal distributions or violate some of the intuitive appeal of (Gaussian) conservative definitions. This work provides a general and intuitive definition of conservativeness that is applicable to any probability distribution that is a measure over $\mathbb{R}^m$ or an infinite subset thereof, including multi-modal and uniform distributions. Unfortunately, we show that this strong definition of conservative cannot be used to evaluate data fusion techniques. Therefore, we also describe a weaker definition of conservative and show it is preserved through common data fusion methods such as the linear and log-linear opinion pool, and homogeneous functionals, assuming the input distributions can be factored into independent and common distributions. In addition, we show that after fusion, weak conservativeness is preserved by Bayesian updates. These strong and weak definitions of conservativeness can help design and evaluate potential correlation-agnostic data fusion techniques.
Error-Tolerant Geometric Quantum Control for Logical Qubits with Minimal Resources

Geometric quantum computation offers a practical strategy toward robust quantum computation due to its inherent error tolerance. However, the rigorous geometric conditions lead to complex and/or error-disturbed quantum controls, especially for logical qubits that involve more physical qubits, whose error tolerance is effective in principle, but their experimental demonstration is still demanding. Thus, how to best simplify the needed control and manifest its full advantage has become the key to widespread applications of geometric quantum computation. Here we propose a fast and robust geometric scheme, with decoherence-free subspace encoding, and present its physical implementation on superconducting quantum circuits, where we only utilize the experimentally demonstrated parametrically tunable coupling to achieve high-fidelity geometric control over logical qubits. Numerical simulation verifies that it can efficiently combine the error tolerance from both the geometric phase and logical-qubit encoding, displaying our gate-performance superiority over the conventional dynamical one without encoding, in terms of both gate fidelity and robustness. Therefore, our scheme can consolidate both error suppression methods for logical-qubit control, which sheds light on the future large-scale quantum computation.
Nonperturbative Analytical Diagonalization of Hamiltonians with Application to Circuit QED

Symbolic methods for computing effective Hamiltonian models are presented that do not rely on assumptions of perturbations and can exponentially decrease the number of terms compared with the ubiquitous Schrieffer-Wolff method.
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations

Visual Entailment with natural language explanations aims to infer the relationship between a text-image pair and generate a sentence to explain the decision-making process. Previous methods rely mainly on a pre-trained vision-language model to perform the relation inference and a language model to generate the corresponding explanation. However, the pre-trained vision-language models mainly build token-level alignment between text and image yet ignore the high-level semantic alignment between the phrases (chunks) and visual contents, which is critical for vision-language reasoning. Moreover, the explanation generator based only on the encoded joint representation does not explicitly consider the critical decision-making points of relation inference. Thus the generated explanations are less faithful to visual-language reasoning. To mitigate these problems, we propose a unified Chunk-aware Alignment and Lexical Constraint based method, dubbed as CALeC. It contains a Chunk-aware Semantic Interactor (arr. CSI), a relation inferrer, and a Lexical Constraint-aware Generator (arr. LeCG). Specifically, CSI exploits the sentence structure inherent in language and various image regions to build chunk-aware semantic alignment. Relation inferrer uses an attention-based reasoning network to incorporate the token-level and chunk-level vision-language representations. LeCG utilizes lexical constraints to expressly incorporate the words or chunks focused by the relation inferrer into explanation generation, improving the faithfulness and informativeness of the explanations. We conduct extensive experiments on three datasets, and experimental results indicate that CALeC significantly outperforms other competitor models on inference accuracy and quality of generated explanations.
Quantifying Voter Biases in Online Platforms

In content-based online platforms, use of aggregate user feedback (say, the sum of votes) is commonplace as the "gold standard" for measuring content quality. Use of vote aggregates, however, is at odds with the existing empirical literature, which suggests that voters are susceptible to different biases-reputation (e.g., of the poster), social influence (e.g., votes thus far), and position (e.g., answer position). Our goal is to quantify, in an observational setting, the degree of these biases in online platforms. Specifically, what are the causal effects of different impression signals-such as the reputation of the contributing user, aggregate vote thus far, and position of content-on a participant's vote on content? We adopt an instrumental variable (IV) framework to answer this question. We identify a set of candidate instruments, carefully analyze their validity, and then use the valid instruments to reveal the effects of the impression signals on votes. Our empirical study using log data from Stack Exchange websites shows that the bias estimates from our IV approach differ from the bias estimates from the ordinary least squares (OLS) method. In particular, OLS underestimates reputation bias (1.6-2.2x for gold badges) and position bias (up to 1.9x for the initial position) and overestimates social influence bias (1.8-2.3x for initial votes). The implications of our work include: redesigning user interface to avoid voter biases; making changes to platforms' policy to mitigate voter biases; detecting other forms of biases in online platforms.