Reviewerly/exHarmony huggingface dataset:
A deep learning-based histopathology classifier for Focal Cortical Dysplasia

Abstract A light microscopy-based histopathology diagnosis of human brain specimens obtained from epilepsy surgery remains the gold standard to confirm the underlying cause of a patient’s focal epilepsy and further inform postsurgical patient management. The differential diagnosis of neocortical specimens in the realm of epilepsy surgery remains, however, challenging. Herein, we developed an open access, deep learning-based classifier to histopathologically assess whole slide microscopy images (WSI) and to automatically recognize various subtypes of Focal Cortical Dysplasia (FCD), according to the ILAE consensus classification update of 2022. We trained a convolutional neuronal network (CNN) with fully digitalized WSI of hematoxylin–eosin stainings obtained from 125 patients covering the spectrum of mild malformation of cortical development (mMCD), mMCD with oligodendroglial hyperplasia in epilepsy (MOGHE), FCD ILAE Type 1a, 2a and 2b using 414 formalin-fixed and paraffin-embedded archival tissue blocks. An additional series of 198 postmortem tissue blocks from 59 patients without neurological disorders served as control to train the CNN for homotypic frontal, temporal and occipital areas and heterotypic Brodmann areas 4 and 17, entorhinal cortex and dentate gyrus. Special stains and immunohistochemical reactions were used to comprehensively annotate the region of interest. We then programmed a novel tile extraction pipeline and graphical dashboard to visualize all areas on the WSI recognized by the CNN. Our deep learning-based classifier is able to compute 1000 × 1000 µm large tiles and recognizes 25 anatomical regions and FCD categories with an accuracy of 98.8% (F1 score = 0.82). Microscopic review of regions predicted by the network confirmed these results. This deep learning-based classifier will be made available as online web application to support the differential histopathology diagnosis in neocortical human brain specimens obtained from epilepsy surgery. It will also serve as blueprint to build a digital histopathology slide suite addressing all major brain diseases encountered in patients with surgically amenable focal epilepsy.
Recommendation System: A Survey and New Perspectives

With the development of information technology, users usually suffer from the problem of information overload. To solve this problem, recommendation systems (RSs) are widely explored to help the user find potential interest in the overloaded information. Since the recommendation scenarios become more and more abundant, distinct recommendation tasks are accordingly proposed by the researchers. In this survey, we comprehensively investigate various recommendation domains and scenarios, and provide a classification scheme for RSs, aiming to have considerable insight for further research. This survey is supposed to give a systematic understanding of the key components in RSs and summarize the diversified characteristics in each domain or scenario.
Quantum Fisher information of phase estimation in the presence of indefinite causal order

Quantum Fisher information of phase estimation is studied for a single qubit which is influenced by the two generalized amplitude damping channels (GADCs) with indefinite causal order (ICO). The phase shift occurs between two consecutive applications of the GADCs. It is shown that the quantum Fisher information can be enhanced by the ICO of the quantum channels. Furthermore, even a completely incoherent state of the qubit is found to be useful for estimating the phase shift due to the ICO.
Deep reinforcement learning based scheduling strategy for federated learning in sensor-cloud systems

Sensor-cloud systems (SCSs) aim to provide flexible configurable platforms for monitoring and controlling the IoT-enabled applications. By integrating sensors, wireless networks and cloud for managing sensors, collecting data, and automating decision-making, the collected sensing data are typically used for machine learning purposes. With increasing emphasis in privacy protection, Federated Learning (FL) is widely adopted for enhancing privacy preservation. FL enables sharing of data for machine learning while preserving the privacy of the data owners. In SCSs, FL involves a large number of edge nodes in order to ensure a sufficient amount of data for model training. However, FL inevitably incurs prohibitive overheads if it simply gathers data from all the nodes, hence making it desirable to adopt some scheduling strategy so that data are collected only from a selected subset of nodes. This paper proposes a scheduling strategy based on deep reinforcement learning (DRL) for improving the performance and efficiency of FL in SCSs. The DRL environment, such as state space, action space, and reward function, is carefully designed. Proximal policy optimization is employed to train the DRL agent. Experimental results demonstrated that the proposed method outperforms other baselines on both independent and identically distributed (IID) and non-IID datasets.
Dual similarity pre-training and domain difference encouragement learning for vehicle re-identification in the wild

Existing unsupervised domain adaptation (UDA) tasks require extensive annotated wild data in the source domain to be generalized to the target domain. Additionally, the large gap between the source and target domains hinder the clustering performance. Our work concentrates on few-shot UDA task to train a robust Re-ID model from practical vehicle re-identification (Re-ID). That is to say, this task learns discriminative representations from a few labeled source data to unlabeled target data. In this paper, a novel progressive few-shot UDA learning framework for vehicle Re-ID is proposed, which consists of two branches. In source branch, the dual prior model is used to gain the color and IDs in unlabeled source data. A dual constraint label smoothing regularization (DCLSR) loss is designed to supervise extensive unlabeled source data during pre-training phase, which considers color and ID constraints to mine the similarity between unlabeled source data and a few labeled ones. The target branch develops a progressive domain difference encouragement learning method to optimize the cross-domain capability of Re-ID model. The domain difference penalty term (DDPT) is encoded by the feature variations before and after style transfer, which improves clustering results and refines the pseudo label. Comprehensive experimental results verify that the proposed approach outperforms other ones in the practical UDA task.
Quantifying the stochasticity of policy parameters in reinforcement learning problems

The stochastic dynamics of reinforcement learning is studied using a master equation formalism. We consider two different problems-Q learning for a two-agent game and the multiarmed bandit problem with policy gradient as the learning method. The master equation is constructed by introducing a probability distribution over continuous policy parameters or over both continuous policy parameters and discrete state variables (a more advanced case). We use a version of the moment closure approximation to solve for the stochastic dynamics of the models. Our method gives accurate estimates for the mean and the (co)variance of policy variables. For the case of the two-agent game, we find that the variance terms are finite at steady state and derive a system of algebraic equations for computing them directly.
A corpus-assisted discourse analysis of the representation of Syrian refugees in Canadian newspapers

This paper examines the representation of Syrian refugees in the Canadian press, from December 2015 to December 2017, in four English-language major newspapers. Using methods of Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), this study found three prominent themes, namely intake, integration, and concern, through which Syrian refugees are depicted across the political spectrum. The results indicate that adopting a more inclusive immigration policy did not totally negate the biased and discriminatory representations entrenched in the media coverage of refugees, but it can set the stage for more empowering and sympathetic treatment of refugees in the media. This analysis speaks to the importance of media discourse in producing and maintaining particular depictions of refugees among the Canadian public, highlighting the role of ideological and political stances in the portrayals of refugees across news outlets.
The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets

Choosing job applicants to hire in online labor markets is hard. To identify the best applicant at hand, employers need to assess a heterogeneous population. Recommender systems can provide targeted job-applicant recommendations that help employers make better-informed and faster hiring choices. However, existing recommenders that rely on multiple user evaluations per recommended item (e.g., collaborative filtering) experience structural limitations in recommending job applicants: Because each job application receives only a single evaluation, these recommenders can only estimate noisy user-user and item-item similarities. On the other hand, existing recommenders that rely on classification techniques overcome this limitation. Yet, these systems ignore the hired worker’s performance—and, as a result, they uniformly reinforce prior observed behavior that includes unsuccessful hiring choices—while they overlook potential sequential dependencies between consecutive choices of the same employer. This work addresses these shortcomings by building a framework that uses job-application characteristics to provide recommendations that (1) are unlikely to yield adverse outcomes (performance-aware) and (2) capture the potentially evolving hiring preferences of employers (sequence-aware). Application of this framework on hiring decisions from an online labor market shows that it recommends job applicants who are likely to get hired and perform well. A comparison with advanced alternative recommender systems illustrates the benefits of modeling performance-aware and sequence-aware recommendations. An empirical adaptation of our approach in an alternative context (restaurant recommendations) illustrates its generalizability and highlights its potential implications for users, employers, workers, and markets. This paper was accepted by Kartik Hosanagar, information systems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4690 .
Adaptive weighted rain streaks model-driven deep network for single image deraining

In the computer vision field, single image rain removal is an extremely important pre-processing task since rain streaks can degrade the performance of subsequent high-level tasks and outdoor image quality. In recent years, massive deep learning-based deraining methods have been proposed to remove rain streaks from rainy images, and they have achieved promising performance. However, most deep learning-based methods lack interpretability and have limited performance in image details restoration and rain streaks removal. In this paper, we propose a novel rain streaks model-driven deep network, MSANet, to alleviate these issues. First of all, instead of simply stacking those available convolutions, pooling, and attention blocks, the structure of MSANet is derived from a prior rain removal model and the structure is thus fully interpretable. Compared to prior models available in the literature, we introduce a weighted convolutional dictionary model to capture the shapes, sizes, directions, regions, and multi-scale information of rain streaks. And to reduce the bias associated with the L2-norm, we utilize an adaptive approach to constrain the fidelity term of the model. Finally, we employ the alternating direction method of multipliers (ADMM) algorithm to optimize this model and unfold the optimization procedure into a new neural network architecture by modeling each operation in the algorithm using network blocks. All the parameters can be automatically learned by end-to-end training. Extensive experiments on several benchmark datasets demonstrate that the proposed network architecture is more efficient than state-of-the-art methods based on both subjective and objective evaluations.
Calista: A deep learning-based system for understanding and evaluating website aesthetics

Website aesthetics play an important role in attracting users and customers, as well as in enhancing user experience. In this work, we propose a tool that performs automatic evaluation of website aesthetics using deep learning models that display high correlation to human perception. These models were developed using two different datasets. The first dataset was created by employing a rating-based ranking approach and contains user judgments on websites in the form of an explicit numerical value on a scale. Using the first dataset, we developed models following three different approaches and managed to outperform previous works. In addition, we created a new dataset by employing a comparison-based ranking approach, which is a more reliable dataset in the sense that it follows a more "natural" data collection method. In this case, users were asked to compare two websites at a time and choose which is more attractive. Data collection was performed via a web application especially designed and developed for this purpose. In the experiments conducted, we evaluated each model and compared the two data collection methods. This work aims to illustrate the effectiveness of deep learning as a solution to the problem as well as to highlight the importance of comparison-based ranking in order to achieve reliable results. In order to further promote our work, we also developed a tool that scores the aesthetics of a website, simply by providing the website URL. We argue that such a tool will serve as a reliable guide in the hands of designers and developers during the design process.
DCTable: A Dilated CNN with Optimizing Anchors for Accurate Table Detection

With the widespread use of deep learning in leading systems, it has become the mainstream in the table detection field. Some tables are difficult to detect because of the likely figure layout or the small size. As a solution to the underlined problem, we propose a novel method, called DCTable, to improve Faster R-CNN for table detection. DCTable came up to extract more discriminative features using a backbone with dilated convolutions in order to improve the quality of region proposals. Another main contribution of this paper is the anchors optimization using the Intersection over Union (IoU)-balanced loss to train the RPN and reduce the false positive rate. This is followed by a RoI Align layer, instead of the ROI pooling, to improve the accuracy during mapping table proposal candidates by eliminating the coarse misalignment and introducing the bilinear interpolation in mapping region proposal candidates. Training and testing on a public dataset showed the effectiveness of the algorithm and a considerable improvement of the F1-score on ICDAR 2017-Pod, ICDAR-2019, Marmot and RVL CDIP datasets.
Collaborative Learning Supported by Blockchain Technology as a Model for Improving the Educational Process

After COVID-19, new accreditation standards include the need for developing better learning and teaching environments. This will be supported and connected with digitization, entrepreneurship, social inclusion, and a circular economy. The orientation towards equity and quality in education clearly imposes the need for an individual approach to each student separately. This situation is especially pronounced in higher education institutions in the field of technology, whose primary goal is very often individual training for use of highly specialized software and hardware tools. In such a situation, it is necessary to move away from the classical ex-cathedra methodology and develop student-centered learning environments. Global accreditation systems for teaching, learning, practice, and business communication can be simplified using blockchain. On the basis of blockchain technology (BCTs), this paper proposes a Collaborative Learning and Student Work Evaluation (CLSW) model that includes a multi-frontal teaching method (VFN) and combines scientific peer-review standards. BCTs are used to protect student project and assessment data storage and transmission. Assisting higher education institutions in finding “employable capabilities” of proactive students is the idea of CLSW. Before implementing the CLSW paradigm, a poll of lecturers’ views on BCTs was conducted. The poll results show a desire and willingness to teach with BCTs. The model’s fundamental capabilities and the key participants’ duties were described in a project framework. Additionally, this research and proposed model can improve educational process sustainability in general, as it is an open platform easily accessible by all the interested parties, thus contributing to life-long learning.
Decoupled spatiotemporal adaptive fusion network for self-supervised motion estimation

Optical flow estimation searches for correspondence between two images. In the unsupervised approach, most networks use the feature correlation volume to track the flow, and unsupervised training is achieved through a photometric loss function. However, various complex situations in the natural environment, such as object occlusion, motion blur, the camera being out-of-focus, limited perspective, and variation in lighting conditions, make it challenging to find correspondence accurately, thus complicating unsupervised optical flow estimation. This study decouples the problem into two sub-tasks: one is to search for determined correspondence within a pair of frames, and the other is to cope with mismatched regions due to occlusion, blur, light variation, etc., by introducing more spatial and temporal context information. We propose a multi-frame temporal dynamic model that recursively infers optical flow over causal sequences of arbitrary-length. Our innovative approach introduces information entropy and forward–backward consistency checks to measure the confidence regarding the matching of image pairs. To compensate for low-confidence regions, the proposed network adaptively identifies regions with correspondence confidence and utilizes temporal and spatial smoothness assumptions for motion re-prediction. Paired with well-designed simulation of dynamic occlusion pseudo-labels and scene variation, our model can learn a variety of complex scenes in a multi-frame environment to optimize low-confidence regions efficiently. Experimental results demonstrate that the proposed model is able to run at high speed in real-time tasks while maintaining high accuracy, thus achieving state-of-the-art results on Sintel Clean and Final benchmarks.
Learning to generalize aerial person re‐identification using the meta‐transfer method

Summary Person re‐identification (Re‐ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In recent years, to enable person Re‐ID technology to play out its application value in real‐world scenarios, visual surveillance through unmanned aerial vehicle (UAV) platforms has received intense attention, and aerial person datasets have been constructed. However, the pedestrian images captured by ground cameras and those captured by UAVs exhibit great differences. Person Re‐ID methods based on ground person images have difficulty performing Re‐ID on aerial person images. In this paper, we first use a meta‐transfer method to learn to generalize the aerial person Re‐ID task. Specifically, combining the ideas of meta‐learning and transfer learning, a meta‐learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilize and further improve the acquired meta‐knowledge. To prevent the catastrophic forgetting and overfitting problems caused by large‐scale model parameters, we freeze the lower‐layer neurons with powerful generalization and fine‐tune the higher‐layer neurons with strong specialization to transfer and represent the feature extractor. In addition, during the model training process, it is observed that the presence of difficult categories in the given dataset significantly affects the convergence speed and recognition accuracy of the utilized meta‐learning method, and the loss function based on the general Euclidean distance measure tends to mislead the model to optimize in a suboptimal direction. Therefore, we introduce a curriculum sampling based learning strategy that is harmonized with our meta‐transfer learning framework and a new metric formulation of sample similarity based on the Mahalanobis distance to improve the model. In the experimental part, when our method is adopted, a Rank‐1 accuracy of 63.63% and a mean average precision (mAP) of 38.02% are achieved on an aerial Re‐ID dataset, demonstrating its potential for completing person Re‐ID with aerial images. The results obtained on two commonly used ground pedestrian datasets show the generalization of the proposed method. Ablation studies also validate that each component contributes to improving the performance of the model.
Corpus-based critical discourse analysis of reporting practices in English news reports on public health event in China and United States

Introduction Reporting speech is a basic form of human language, and reporting practices play a key role in news report. As one of the important rhetorical devices to introduce the reported speech, reporting verbs can help the readers understand the source of the reported speech and the attitude of the journalist or the media toward the reported information. Method This study examines the features of reporting practice in Chinese and American news reports on public health emergency by investigating the use of reporting verbs from the perspectives of critical discourse analysis. Two English news corpora of COVID-19 pandemic are built, namely, the China Daily News Corpus and the New York Times News Corpus, with 50 news texts in each corpus. The corpus analysis tool AntConc 3.3.5 is used to conduct concordance analysis. Results and discussion It is found that Chinese and American news reports tend to use roughly the same high-frequency reporting verbs in reporting the COVID-19 pandemic. Chinese and American news corpora show difference in the distribution feature of high-frequency reporting verbs in terms of semantic category. Both Chinese and American news reports use speech reporting verbs most frequently, indicating an objective attitude toward the reported event, and use speech reporting verbs and speech act reporting verbs to introduce the reported speech with comparatively higher degree of certainty. American news reports frequently use mental reporting verbs to show the attitude of uncertainty toward the reported speech, and Chinese news reports probably need to raise the awareness of using mental reporting verbs to express the opinions and attitude of the common people or the authority. The findings of this study can provide insights into the research on reporting strategies of news reports on emergencies in China for foreign audience.
Deblurring microscopic image by integrated convolutional neural network

In microscopic vision, the sharpness of edge profile seriously affects the accuracy of measurement. However, various factors in complex environments often blur the edge profile. Herein, a CNN-related deblurring algorithm is proposed to reconstruct the real edge features from the blurred microscopic images. Based on the convolutional neural network, a network called I&R Net is built up, in which both the inception block and the residual channel attention block are integrated. Moreover, the loss functions are constructed with the additional factor of gradient by Sobel operator and Laplacian operator to enhance the inductive bias of I&R Net on real size. The results show that the I&R Net improves both the general image quality and the accuracy. Additionally, the I&R Net presents relatively strong resistance to the influence of feature-rotating.