Reviewerly/exHarmony huggingface dataset:
Residual Spatial and Channel Attention Networks for Single Image Dehazing

Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map and atmospheric light. However, the formation of haze in the real world is much more complicated and inaccurate estimations further degrade the dehazing performance with color distortion, artifacts and insufficient haze removal. Moreover, most dehazing networks treat spatial-wise and channel-wise features equally, but haze is practically unevenly distributed across an image, thus regions with different haze concentrations require different attentions. To solve these problems, we propose an end-to-end trainable densely connected residual spatial and channel attention network based on the conditional generative adversarial framework to directly restore a haze-free image from an input hazy image, without explicitly estimation of any atmospheric scattering parameters. Specifically, a novel residual attention module is proposed by combining spatial attention and channel attention mechanism, which could adaptively recalibrate spatial-wise and channel-wise feature weights by considering interdependencies among spatial and channel information. Such a mechanism allows the network to concentrate on more useful pixels and channels. Meanwhile, the dense network can maximize the information flow along features from different levels to encourage feature reuse and strengthen feature propagation. In addition, the network is trained with a multi-loss function, in which contrastive loss and registration loss are novel refined to restore sharper structures and ensure better visual quality. Experimental results demonstrate that the proposed method achieves the state-of-the-art performance on both public synthetic datasets and real-world images with more visually pleasing dehazed results.
LILLIE: Information extraction and database integration using linguistics and learning-based algorithms

Querying both structured and unstructured data via a single common query interface such as SQL or natural language has been a long standing research goal. Moreover, as methods for extracting information from unstructured data become ever more powerful, the desire to integrate the output of such extraction processes with "clean", structured data grows. We are convinced that for successful integration into databases, such extracted information in the form of "triples" needs to be both (1) of high quality and (2) have the necessary generality to link up with varying forms of structured data. It is the combination of both these aspects, which heretofore have been usually treated in isolation, where our approach breaks new ground. The cornerstone of our work is a novel, generic method for extracting open information triples from unstructured text, using a combination of linguistics and learning-based extraction methods, thus uniquely balancing both precision and recall. Our system called LILLIE (LInked Linguistics and Learning-Based Information Extractor) uses dependency tree modification rules to refine triples from a high-recall learning-based engine, and combines them with syntactic triples from a high-precision engine to increase effectiveness. In addition, our system features several augmentations, which modify the generality and the degree of granularity of the output triples. Even though our focus is on addressing both quality and generality simultaneously, our new method substantially outperforms current state-of-the-art systems on the two widely-used CaRB and Re-OIE16 benchmark sets for information extraction. We have made our code publicly available1 to facilitate further research.
Contrast-Based Unsupervised Hashing Learning With Multi-Hashcode

Due to high storage and calculation efficiency, hash-based methods have been widely used in image retrieval systems. Unsupervised deep hashing methods can learn the binary representations of images effectively without any annotations. The strategy of constraining hash code in the previous unsupervised methods may not fully utilize the structural information in semantic similarity. To address this problem, we propose a new strategy based on contrastive learning to capture high-level semantic similarity among features and preserve it in generated hash codes. In addition, we employ a novel framework to handle hash codes with different lengths simultaneously which is more time-saving in generating hash codes than existing methods. Extensive experiments on MIRFlickr, NUS-WIDE, and COCO benchmark datasets show that our method makes great improvement on the performance of unsupervised image retrieval.
Comparison of Text Sentiment Analysis based on Bert and Word2vec

Most of the existing sentiment classification models use Word2Vec, GloVe, etc. to obtain the word vector representation of the text. But these methods ignore the context of words. In response to this problem, a neural network model based on the combination of BERT (bidirectional encoder representations from transformers) pre-trained language model and BLSTM (bidirectional long short-term memory network) and attention mechanism is proposed for text sentiment analysis in this paper. First, the word vector which including contextual semantic information is obtained through the BERT pre-training model. Then this paper uses the two-way long and short-term memory network to extract context-related features for deep learning. Finally, the attention mechanism is introduced to assign weights to the extracted information, highlight the important information, and perform text sentiment classification. The test accuracy rate can reach 89.17% on the SST (stanford sentiment treebank) data set, which shows that this method has a certain degree of improvement in this type of accuracy compared with other methods.
Cluster Analysis of Cell Nuclei in H&amp;E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques

Biomarker identification is very important to differentiate the grade groups in the histopathological sections of prostate cancer (PCa). Assessing the cluster of cell nuclei is essential for pathological investigation. In this study, we present a computer-based method for cluster analyses of cell nuclei and performed traditional (i.e., unsupervised method) and modern (i.e., supervised method) artificial intelligence (AI) techniques for distinguishing the grade groups of PCa. Two datasets on PCa were collected to carry out this research. Histopathology samples were obtained from whole slides stained with hematoxylin and eosin (H&E). In this research, state-of-the-art approaches were proposed for color normalization, cell nuclei segmentation, feature selection, and classification. A traditional minimum spanning tree (MST) algorithm was employed to identify the clusters and better capture the proliferation and community structure of cell nuclei. K-medoids clustering and stacked ensemble machine learning (ML) approaches were used to perform traditional and modern AI-based classification. The binary and multiclass classification was derived to compare the model quality and results between the grades of PCa. Furthermore, a comparative analysis was carried out between traditional and modern AI techniques using different performance metrics (i.e., statistical parameters). Cluster features of the cell nuclei can be useful information for cancer grading. However, further validation of cluster analysis is required to accomplish astounding classification results.
A 4.2mW 4K 6-8GHz CMOS LNA for Superconducting Qubit Readout

Millions of qubits need to be employed in a quantum computer to achieve a fault-tolerant quantum operation. To reduce the complexity in such a large-scale system, the control and readout circuitries have been proposed to be placed at the 4 K stage of dilution refrigerators [1]. CMOS technology is commonly used to leverage its scaling to enable large integration of control and readout circuitries with qubits. However, the high-fidelity readout operations require low noise amplifiers (LNAs) with a noise temperature of a few Kelvins. This necessitates the usage of HEMT and parametric amplifiers [2]. Recently reported CMOS LNAs are still far away from attaining such good performance [3–5]. Thus, this is one of the greatest challenges on the way to the fully integrated CMOS readout. Additionally, due to the limited cooling power of dilution refrigerators, low-power solutions are needed for achieving a very good noise performance at 4 K. This paper presents a 28 nm CMOS LNA for qubit readout, which achieves an order of magnitude power reduction compared to its CMOS counterparts while still providing a similar good noise Figure (NF) performance at 4 K.
Virtual information core optimization for collaborative filtering recommendation based on clustering and evolutionary algorithms

Collaborative filtering (CF), the most widely used recommendation algorithm, has to face the sparsity and scalability problem. Some researchers proposed to select a representative set of real users called information core (IC) from all the real users, which is used as the candidate neighbor set in the CF to alleviate the scalability problem. However, the rating vectors of these real users that compose IC are usually sparse, which will negatively affect the recommendation accuracy. In this paper, a virtual information core (VIC) optimization algorithm is proposed based on clustering and evolutionary algorithms for CF recommendation (VICO-CEA). The problem of searching for VIC is modeled as a combinatorial optimization problem, and is solved offline by the proposed evolutionary algorithm. The VIC is a set of virtual core users, each of which is constructed by averaging out multiple real users. These virtual core users in the VIC are no longer sparse and are found by the evolutionary optimization, which will improve the recommendation accuracy and reduce the online recommendation time as the VIC is used as the candidate neighbor set in the CF. Meanwhile, to make offline optimization more efficient, two strategies are proposed. One is to design a simple similarity measure based on dimensionality reduction and clustering to save time in calculating similarities by reducing the dimensionality of users’ rating vectors. The other is to use dimensionality reduction and clustering to construct a smaller training set and validation set by reducing the dimensionality of items’ rating vectors. The experimental results show that VICO-CEA can not only significantly reduce the online recommendation time further but also improve the recommendation accuracy greatly compared to traditional CF and other information-core-based methods.
Try before You Buy: Privacy-preserving Data Evaluation on Cloud-based Machine Learning Data Marketplace

A cloud-based data marketplace provides a service to match data shoppers with appropriate data sellers, so that data shoppers can augment their internal data sets with external data to improve their machine learning (ML) models. Since data may contain diverse values, it is critical for a shopper to evaluate the most valuable data before making the final trade. However, evaluating ML data typically requires the cloud to access a shopper’s ML model and sellers’ data, which are both sensitive. None of the existing cloud-based data marketplaces enable ML data evaluation while preserving both model privacy and data privacy. In this paper, we develop a privacy-preserving ML data evaluation framework on a cloud-based data marketplace to protect shoppers’ ML models and sellers’ data. First, we provide a privacy-preserving framework that allows shoppers and sellers to encrypt their models and data, respectively, while preserving data functionality and model functionality in the cloud. We then develop a privacy-preserving data selection protocol that enables the cloud to help shoppers select the most valuable ML data. Also, we develop a privacy-preserving data validation protocol that allows shoppers to further check the quality of the selected data. Compared to random data selection, the experimental results show that our solution can reduce 60% prediction errors.
Nudging through Friction: An Approach for Calibrating Trust in Explainable AI

Explainability has become an essential requirement for safe and effective collaborative Human-AI environments., especially when generating recommendations through black-box modality. One goal of eXplainable AI (XAI) is to help humans calibrate their trust while working with intelligent systems., i.e., avoid situations where human decision-makers over-trust the AI when it is incorrect., or under-trust the AI when it is correct. XAI., in this context., aims to help humans understand AI reasoning and decide whether to follow or reject its recommendations. However., recent studies showed that users., on average., continue to overtrust (or under-trust) AI recommendations which is an indication of XAI's failure to support trust calibration. Such a failure to aid trust calibration was due to the assumption that XAI users would cognitively engage with explanations and interpret them without bias. In this work., we hypothesize that XAI interaction design can play a role in helping users' cognitive engagement with XAI and consequently enhance trust calibration. To this end., we propose friction as a Nudge-based approach to help XAI users to calibrate their trust in AI and present the results of a preliminary study of its potential in fulfilling that role.
Monocular Tracking of Human Hand on a Smart Phone Camera using MediaPipe and its Application in Robotics

With Industry 4.0, robots are finding their way into the production lines of multiple industries. The conventional methods to program these robots require special training, help from a robot technician, or have a basic understanding of robot kinematics. Integration of machine learning into robotics can make the programming process shorter, cost-effective and user friendly. In this paper, we propose to use a customizable machine learning solution of &#x2018;Google MediaPipe Hands&#x0027; to track human hands from a monocular camera of a smartphone and use it for robotic applications. MediaPipe Hands provides only a 2.5D pose estimation; we propose to use a simple calibration and the concept of perspective projection to get the 3D position of the hands relative to the smartphone. By conducting multiple experiments, we found that the hand tracking solution has satisfactory accuracy rates. We developed robot simulations in Python to check the viability of the hand tracker for robotic applications. We could accurately control the end-effector movement using the hand tracker.
Single image dehazing based on pixel-wise transmission estimation with estimated radiance patches

Images acquired outdoors can be affected by atmospheric conditions, such as fog and haze, and image dehazing is used to restore scene radiance in hazy images. In image dehazing, atmospheric light and transmission estimation are essential; transmission estimation is an essential step. In particular, the dark channel prior (DCP) is widely used for the transmission estimation. When using DCP-based methods, an initial transmission map is obtained through a morphological operation based on the assumption that the scene transmission in a local area is constant. However, the depth discontinuity problem cannot be avoided, and outliers are produced in the process of refining the initial transmission map. In addition, the estimation accuracy varies depending on the scene configuration due to the limitations of DCP-based methods, which simply estimate the transmission based on pixel intensity. To overcome these problems, we propose the pixel-wise transmission estimation method with estimated radiance patches (PTERP) for image dehazing. We first approximate the transmission range in the pixel location using the transmission map obtained using DCP. A patch is then set around each pixel, and several estimated radiance patches are obtained using each value belonging to the transmission range. The transmission value in the corresponding pixel location is determined using the information from the estimated radiance patches. The transmission map is then obtained by estimating the transmission value for each pixel in the entire image. With this approach, scene radiance can be restored using the determined transmission map. We performed experiments using various images, and the results demonstrated that proposed PTERP outperformed the conventional methods both quantitatively and qualitatively.
A dynamic time warping approach for handling class imbalanced medical datasets with missing values: A case study of protein localization site prediction

Class imbalanced medical datasets, such as cancer prediction, contain imbalanced numbers of data in different classes leading to skewed class distribution, which makes it very difficult for a classifier to distinguish between minority (i.e. cancer) and majority (i.e. non-cancer) classes. Related studies in the literature have proposed different types of solutions for the class imbalance problem including data level, algorithmic level, and cost-sensitive learning approaches. However, none of these potential solutions have considered the issue of missing attribute values residing in the class imbalanced medical datasets, especially for the minority class. Missing value imputation is commonly used for the construction of some models where statistical or machine learning techniques are used to produce estimations to replace the missing values. However, the existing imputation methods require a certain number of observed data to produce their estimations, the major challenge for them being that the amount of observed data (with no missing values) in the minority class is very limited, or that some data are not complete. In this paper, we proposed a novel approach, namely Dynamic Time Warping-based Imputation (DTWI), to handle class imbalanced datasets with missing values. Based on the similarity measurement technique of DTW, all of the data (with or without missing values) in the minority class can be used for missing value imputation. The experimental results based on 10 different class imbalanced medical datasets show that when the missing rates in the minority classes are smaller than 30%, DTWI performs similarly to the baseline K-NN imputation method and better than the mean/mode imputation and case deletion methods. When the missing rates are larger than 30%, DTWI significantly outperform the other techniques.
Research on Higher English Internationalization Education Model and Evaluation Index System Based on Multi-Source Information Fusion

With the continuous development of the global economy, the degree of internationalization and openness has gradually deepened. Higher English internationalization education also needs to keep pace with the times and keep pace with international development. At this time, it is necessary to build a multi-source information fusion algorithmic higher English international education model and evaluation index system to better adapt to the trend of higher English international education in the future. In the current higher English teaching process, it is necessary to change the traditional teaching concepts, change the previous teaching ideas, continuously expand the horizons, build an international and diversified English teaching training program, and actively absorb excellent education concepts from foreign excellent teaching models, which has better promoted the development of English teaching.
Feature Selection Based Coral Reefs Optimization for Breast Cancer Classification

Early detection of cancer cases is one of the most important things that help complete treatment and disappearance of this disease from the human body. Breast cancer is the most widespread invasive cancer in women, and after lung cancer, it is the second leading cause of cancer death in women. The first symptoms of breast cancer usually appear as an area of thickened tissue in the breast or a lump in the breast or an armpit. Consequently, many features can be found to indicate the existence of cancer or not. This chapter employs the coral reefs optimization (CRO) algorithm for feature selection; the CRO has shown to be very effective with various classification approaches. In this chapter, we used five standard classifiers: Logistic Regression (LR), K-nearest neighbor (KNN), Support Vector Machine with Radial Basis Function (SVM-RBF), Random Forest (RF), Decision Tree (DT). All These classifiers are presented with and without feature selection using the CRO algorithm. The results indicated that using the feature selection based on CRO achieved better results before the feature selection. The most common dataset called Breast Diagnostic Cancer Wisconsin (BDCW) is utilized to select the most significant and classify the cancer cases with a tested accuracy of 100%, 99.1%, 100%, 100%, and 100% using LR, KNN, SVM-RBF, RF, and DT respectively.
COVID-19 EnsembleVis: Visual Analysis of County-Level Ensemble Forecast Models

The spread of the SARS-Co V- 2 virus and its contagious disease COVID-19 has impacted countries to an extent not seen since the 1918 flu pandemic. In the absence of an effective vaccine and as cases surge worldwide, governments were forced to adopt measures to inhibit the spread of the disease. To reduce its impact and to guide policy planning and resource allocation, researchers have been developing models to forecast the infectious disease. Ensemble models, by aggregating forecasts from multiple individual models, have been shown to be a useful forecasting method. However, these models can still provide less-than-adequate forecasts at higher spatial resolutions. In this paper, we built COVID-19 Ensemble Vis, a web-based interactive visual interface that allows the assessment of the errors of ensembles and individual models by enabling users to effortlessly navigate through and compare the outputs of models considering their space and time dimensions. COVID-19 Ensemble Vis enables a more detailed understanding of uncertainty and the range of forecasts generated by individual models.