Reviewerly/exHarmony huggingface dataset:
BCData: A Large-Scale Dataset and Benchmark for Cell Detection and Counting

Breast cancer is a main malignant tumor for women and the incidence is trending to ascend. Detecting positive and negative tumor cells in the immunohistochemically stained sections of breast tissue to compute the Ki-67 index is an essential means to determine the degree of malignancy of breast cancer. However, there are scarcely public datasets about cell detection of Ki-67 stained images. In this paper, we introduce a large-scale Breast tumor Cell Dataset (BCData) for cell detection and counting, which contains 1,338 images with 181,074 annotated cells belonging to two categories, i.e., positive and negative tumor cells. (We state that our dataset can only be used for non-commercial research.) Our dataset varies widely in both the distributing density of tumor cells and the Ki-67 index. We conduct several cell detection and counting methods on this dataset to set the first benchmark. We believe that our dataset will facilitate further research in cell detection and counting fields in clustering, overlapping, and variational stained conditions. Our dataset is available at https://sites.google.com/view/bcdataset
Explainable Uncertainty-Aware Convolutional Recurrent Neural Network for Irregular Medical Time Series

Influenced by the dynamic changes in the severity of illness, patients usually take examinations in hospitals irregularly, producing a large volume of irregular medical time-series data. Performing diagnosis prediction from the irregular medical time series is challenging because the intervals between consecutive records significantly vary along time. Existing methods often handle this problem by generating regular time series from the irregular medical records without considering the uncertainty in the generated data, induced by the varying intervals. Thus, a novel Uncertainty-Aware Convolutional Recurrent Neural Network (UA-CRNN) is proposed in this article, which introduces the uncertainty information in the generated data to boost the risk prediction. To tackle the complex medical time series with subseries of different frequencies, the uncertainty information is further incorporated into the subseries level rather than the whole sequence to seamlessly adjust different time intervals. Specifically, a hierarchical uncertainty-aware decomposition layer (UADL) is designed to adaptively decompose time series into different subseries and assign them proper weights in accordance with their reliabilities. Meanwhile, an Explainable UA-CRNN (eUA-CRNN) is proposed to exploit filters with different passbands to ensure the unity of components in each subseries and the diversity of components in different subseries. Furthermore, eUA-CRNN incorporates with an uncertainty-aware attention module to learn attention weights from the uncertainty information, providing the explainable prediction results. The extensive experimental results on three real-world medical data sets illustrate the superiority of the proposed method compared with the state-of-the-art methods.
An overview of privacy preserving schemes for industrial Internet of Things

The concept of Internet of Everything is like a revolutionary storm, bringing the whole society closer together. Internet of Things (IoT) has played a vital role in the process. With the rise of the concept of Industry 4.0, intelligent transformation is taking place in the industrial field. As a new concept, an industrial IoT system has also attracted the attention of industry and academia. In an actual industrial scenario, a large number of devices will generate numerous industrial datasets. The computing efficiency of an industrial IoT system is greatly improved with the help of using either cloud computing or edge computing. However, privacy issues may seriously harmed interests of users. In this article, we summarize privacy issues in a cloud- or an edge-based industrial IoT system. The privacy analysis includes data privacy, location privacy, query and identity privacy. In addition, we also review privacy solutions when applying software defined network and blockchain under the above two systems. Next, we analyze the computational complexity and privacy protection performance of these solutions. Finally, we discuss open issues to facilitate further studies.
Democratizing the development of recommender systems by means of low-code platforms

In recent years, recommender systems have gained an increasingly crucial role in software engineering. Such systems allow developers to exploit a plethora of reusable artifacts, including source code and documentation, which can support the development activities. However, recommender systems are complex tools that are difficult to personalize or fine-tune if developers want to improve them for increasing the relevance of the retrievable recommendations.
A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder

Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the relations among the multiple heterogeneous segmentation criteria and further improve the performance of each single criterion.Previous work usually regards MCCWS as different tasks, which are learned together under the multi-task learning framework.In this paper, we propose a concise but effective unified model for MC-CWS, which is fully-shared for all the criteria.By leveraging the powerful ability of the Transformer encoder, the proposed unified model can segment Chinese text according to a unique criterion-token indicating the output criterion.Besides, the proposed unified model can segment both simplified and traditional Chinese and has an excellent transfer capability.Experiments on eight datasets with different criteria show that our model outperforms our single-criterion baseline model and other multi-criteria models.Source codes of this paper are available on Github 1 .
On the importance of pre-training data volume for compact language models

Recent advances in language modeling have led to computationally intensive and resourcedemanding state-of-the-art models.In an effort towards sustainable practices, we study the impact of pre-training data volume on compact language models.Multiple BERT-based models are trained on gradually increasing amounts of French text.Through fine-tuning on the French Question Answering Dataset (FQuAD), we observe that well-performing models are obtained with as little as 100 MB of text.In addition, we show that past critically low amounts of pre-training data, an intermediate pre-training step on the task-specific corpus does not yield substantial improvements.
Facial Expression Recognition with the advent of face masks

With the worldwide spread of COVID-19, wearing face masks while interaction in public is becoming a common behavior to protect against infection. Thus, how to improve effectiveness of existing facial expression recognition (FER) technology on masked faces has become an urgent issue. However, there are no publicly available masked facial expression recognition datasets that take facial orientation into consideration. To address this issue, we propose a method that can add face masks to existing FER datasets automatically using differently shaped masks according to facial orientations. The FER models based on VGG19 and MobileNet are trained on public and private FER datasets added with mask. As part of our contribution, we collected real-world masked faces from the Internet using emotional keywords and constructed a masked FER test dataset for a fair performance evaluation. The experimental results show that training an FER model based on a simulated masked FER dataset is feasible.
A generalized average linkage criterion for Hierarchical Agglomerative Clustering

Hierarchical agglomerative clustering (HAC) is among the most widely adopted algorithms in unsupervised learning. This method employs a linkage criterion to measure the similarity between two clusters and the correct selection of this criterion highly influences the performance of HAC. This paper presents and evaluates a novel linkage criterion for HAC, which is a generalization of the Average Linkage (GAL) and it aims at improving the quality of the similarity computation of the original average linkage criterion. In order to assess the liability of the proposed criterion, an empirical analysis is conducted, which is performed on 28 datasets from the literature. In a comparative analysis, the proposed criterion is compared to seven reference methods from the literature. Our findings indicate that the results obtained by the proposed criterion are promising, surpassing all the existing reference methods.
Distance-based and RKHS-based dependence metrics in high dimension

In this paper, we study distance covariance, Hilbert–Schmidt covariance (aka Hilbert–Schmidt independence criterion [In Advances in Neural Information Processing Systems (2008) 585–592]) and related independence tests under the high dimensional scenario. We show that the sample distance/Hilbert–Schmidt covariance between two random vectors can be approximated by the sum of squared componentwise sample cross-covariances up to an asymptotically constant factor, which indicates that the standard distance/Hilbert–Schmidt covariance based test can only capture linear dependence in high dimension. Under the assumption that the components within each high dimensional vector are weakly dependent, the distance correlation based $t$ test developed by Székely and Rizzo (J. Multivariate Anal. 117 (2013) 193–213) for independence is shown to have trivial limiting power when the two random vectors are nonlinearly dependent but component-wisely uncorrelated. This new and surprising phenomenon, which seems to be discovered and carefully studied for the first time, is further confirmed in our simulation study. As a remedy, we propose tests based on an aggregation of marginal sample distance/Hilbert–Schmidt covariances and show their superior power behavior against their joint counterparts in simulations. We further extend the distance correlation based $t$ test to those based on Hilbert–Schmidt covariance and marginal distance/Hilbert–Schmidt covariance. A novel unified approach is developed to analyze the studentized sample distance/Hilbert–Schmidt covariance as well as the studentized sample marginal distance covariance under both null and alternative hypothesis. Our theoretical and simulation results shed light on the limitation of distance/Hilbert–Schmidt covariance when used jointly in the high dimensional setting and suggest the aggregation of marginal distance/Hilbert–Schmidt covariance as a useful alternative.
CNN Inference Using a Preprocessing Precision Controller and Approximate Multipliers With Various Precisions

This article proposes boosting the multiplication performance for convolutional neural network (CNN) inference using a precision prediction preprocessor which controls various precision approximate multipliers. Previously, utilizing approximate multipliers for CNN inference was proposed to enhance the power, speed, and area at a cost of a tolerable drop in the accuracy. Low precision approximate multipliers can achieve massive performance gains; however, utilizing them is not feasible due to the large accuracy loss they cause. To maximize the multiplication performance gains while minimizing the accuracy loss, this article proposes using a tiny two-class precision controller to utilize low and high precision approximate multipliers hybridly. The performance benefits for the proposed concept are presented for multi-core multi-precision architectures and single-core reconfigurable architectures. Additionally, a design for a merged reconfigurable approximate multiplier with two precisions is proposed for utilization in single-core architectures. For performance comparison, several segments-based approximate multipliers with different precisions were synthesized using CMOS 15nm technology. For accuracy evaluation, the concept was simulated on VGG19, Xception, and DenseNet201 using the ImageNetV2 dataset. This article will demonstrate that the proposed concept can achieve significant performance gains with a minimal accuracy loss when compared to designs that utilize exact multipliers or single-precision approximate multipliers.
Scaling attributed network embedding to massive graphs

Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node v ∈ G to a compact vector X v , which can be used in downstream machine learning tasks. Ideally, X v should capture node v 's affinity to each attribute, which considers not only v 's own attribute associations, but also those of its connected nodes along edges in G . It is challenging to obtain high-utility embeddings that enable accurate predictions; scaling effective ANE computation to massive graphs with millions of nodes pushes the difficulty of the problem to a whole new level. Existing solutions largely fail on such graphs, leading to prohibitive costs, low-quality embeddings, or both. This paper proposes PANE, an effective and scalable approach to ANE computation for massive graphs that achieves state-of-the-art result quality on multiple benchmark datasets, measured by the accuracy of three common prediction tasks: attribute inference, link prediction, and node classification. In particular, for the large MAG data with over 59 million nodes, 0.98 billion edges, and 2000 attributes, PANE is the only known viable solution that obtains effective embeddings on a single server, within 12 hours. PANE obtains high scalability and effectiveness through three main algorithmic designs. First, it formulates the learning objective based on a novel random walk model for attributed networks. The resulting optimization task is still challenging on large graphs. Second, PANE includes a highly efficient solver for the above optimization problem, whose key module is a carefully designed initialization of the embeddings, which drastically reduces the number of iterations required to converge. Finally, PANE utilizes multi-core CPUs through non-trivial parallelization of the above solver, which achieves scalability while retaining the high quality of the resulting embeddings. Extensive experiments, comparing 10 existing approaches on 8 real datasets, demonstrate that PANE consistently outperforms all existing methods in terms of result quality, while being orders of magnitude faster.
Open Question Answering over Tables and Text

In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. Here we consider for the first time open QA over {\em both} tabular and textual data and present a new large-scale dataset \emph{Open Table-and-Text Question Answering} (OTT-QA) to evaluate performance on this task. Most questions in OTT-QA require multi-hop inference across tabular data and unstructured text, and the evidence required to answer a question can be distributed in different ways over these two types of input, making evidence retrieval challenging---our baseline model using an iterative retriever and BERT-based reader achieves an exact match score less than 10\%. We then propose two novel techniques to address the challenge of retrieving and aggregating evidence for OTT-QA. The first technique is to use ``early fusion'' to group multiple highly relevant tabular and textual units into a fused block, which provides more context for the retriever to search for. The second technique is to use a cross-block reader to model the cross-dependency between multiple retrieved evidence with global-local sparse attention. Combining these two techniques improves the score significantly, to above 27\%.
Optimization Assisted Convolutional Neural Network for Facial Emotion Recognition

Facial Expression Recognition (FER) is an important type of visual information that can be used to understand a human"s emotional situation.FER has attained a significant interest in human-computer interaction, autopilot, medical healing as well as various face expression dependent areas, and it is enormously used in most research areas.Hence, this paper intends to develop an intelligent facial emotion recognition model by following two major processes namely (a) Feature extraction and (b) Classification.Initially, the input image is subjected to extract Local Binary Pattern (LBP) based features.Further, the extracted features are classified using a Convolutional neural network (CNN).Moreover, the weights of CNN are optimally tuned by the Improvised Steering angle and Gear-based ROA (ISG-ROA) algorithm.Finally, the superiority of the ISG-ROA method is compared over existing methods and its improvement is proved effective.
Differentially Private Learning Needs Better Features (or Much More Data)

We demonstrate that differentially private machine learning has not yet reached its ''AlexNet moment'' on many canonical vision tasks: linear models trained on handcrafted features significantly outperform end-to-end deep neural networks for moderate privacy budgets. To exceed the performance of handcrafted features, we show that private learning requires either much more private data, or access to features learned on public data from a similar domain. Our work introduces simple yet strong baselines for differentially private learning that can inform the evaluation of future progress in this area.
Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers: Artificial Intelligence to Heuristics

Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum architecture. Quantum circuit compilation (QCC) aims to generate feasible mappings such that a quantum circuit can be executed in a given hardware platform with acceptable confidence in outcomes. Physical constraints of a NISQ computer change frequently, requiring QCC process to be repeated often. When a circuit cannot directly be executed on a quantum hardware due to its physical limitations, it is necessary to modify the circuit by adding new quantum gates and auxiliary qubits, increasing its space and time complexity. An inefficient QCC may significantly increase error rate and circuit latency for even the simplest algorithms. In this article, we present artificial intelligence (AI)-based and heuristic-based methods recently reported in the literature that attempt to address these QCC challenges. We group them based on underlying techniques that they implement, such as AI algorithms including genetic algorithms, genetic programming, ant colony optimization and AI planning, and heuristics methods employing greedy algorithms, satisfiability problem solvers, dynamic, and graph optimization techniques. We discuss performance of each QCC technique and evaluate its potential limitations.
Improving generalization in MR‐to‐CT synthesis in radiotherapy by using an augmented cycle generative adversarial network with unpaired data

Purpose MR‐to‐CT synthesis is one of the first steps in the establishment of an MRI‐only workflow in radiotherapy. Current MR‐to‐CT synthesis methods in deep learning use unpaired MR and CT training images with a cycle generative adversarial network (CycleGAN) to minimize the effect of misalignment between paired images. However, this approach critically assumes that the underlying interdomain mapping is approximately deterministic and one‐to‐one. In the current study, we use an Augmented CycleGAN (AugCGAN) model to create a robust model that can be applied to different scanners and sequences using unpaired data. Materials and methods This study included T2‐weighted MR and CT pelvic images of 38 patients in treatment position from five different centers. The AugCGAN was trained on 2D transverse slices of 19 patients from three different sites. The network was then used to generate synthetic CT (sCT) images of 19 patients from the two other sites. Mean absolute errors (MAEs) for each patient were evaluated between real and synthetic CT images. Original treatment plans of nine patients were recalculated using sCT images to assess the dose distribution in terms of voxel‐wise dose difference, gamma, and dose–volume histogram analysis. Results The mean MAEs were Hounsfield units ( ) and 65.8 HU for the first and second test sites, respectively. The maximum dose difference to the target was with a gamma pass rate using the 3%, 3 mm criteria above 99%. The average time required to generate a complete sCT image for a patient on our GPU was 8.5 s. Conclusion This study suggests that our unpaired approach achieves good performance in generalization with respect to sCT image generation.
Types and taxonomic structures in conceptual modeling: A novel ontological theory and engineering support

Types are fundamental for conceptual modeling and knowledge representation, being an essential construct in all major modeling languages in these fields. Despite that, from an ontological and cognitive point of view, there has been a lack of theoretical support for precisely defining a consensual view on types. As a consequence, there has been a lack of precise methodological support for users when choosing the best way to model general terms representing types that appear in a domain, and for building sound taxonomic structures involving them. For over a decade now, a community of researchers has contributed to the development of the Unified Foundational Ontology (UFO) - aimed at providing foundations for all major conceptual modeling constructs. At the core of this enterprise, there has been a theory of types specially designed to address these issues. This theory is ontologically well-founded, psychologically informed, and formally characterized. These results have led to the development of a Conceptual Modelling language dubbed OntoUML, reflecting the ontological micro-theories comprising UFO. Over the years, UFO and OntoUML have been successfully employed on conceptual model design in a variety of domains including academic, industrial, and governmental settings. These experiences exposed improvement opportunities for both the OntoUML language and its underlying theory, UFO. In this paper, we revise the theory of types in UFO in response to empirical evidence. The new version of this theory shows that many of OntoUML’s meta-types (e.g. kind, role, phase, mixin) should be considered not as restricted to substantial types but instead should be applied to model endurant types in general, including relator types, quality types, and mode types. We also contribute with a formal characterization of this fragment of the theory, which is then used to advance a new metamodel for OntoUML (termed OntoUML 2). To demonstrate that the benefits of this approach are extended beyond OntoUML, the proposed formal theory is then employed to support the definition of UFO-based lightweight Semantic Web ontologies with ontological constraint checking in OWL. Additionally, we report on empirical evidence from the literature, mainly from cognitive psychology but also from linguistics, supporting some of the key claims made by this theory. Finally, we propose a computational support for this updated metamodel.