Reviewerly/exHarmony huggingface dataset:
Evolutionary deep feature selection for compact representation of gigapixel images in digital pathology

Despite the recent progress in Deep Neural Networks (DNNs) to characterize histopathology images, compactly representing a gigapixel whole-slide image (WSI) via salient features to enable computational pathology is still an urgent need and a significant challenge. In this paper, we propose a novel WSI characterization approach to represent, search and classify biopsy specimens using a compact feature vector (CFV) extracted from a multitude of deep feature vectors. Since the non-optimal design and training of deep networks may result in many irrelevant and redundant features and also cause computational bottlenecks, we proposed a low-cost stochastic method to optimize the output of pre-trained deep networks using evolutionary algorithms to generate a very small set of features to accurately represent each tissue/biopsy. The performance of the proposed method has been assessed using WSIs from the publicly available TCGA image data. In addition to acquiring a very compact representation (i.e., 11,000 times smaller than the initial set of features), the optimized features achieved 93% classification accuracy resulting in 11% improvement compared to the published benchmarks. The experimental results reveal that the proposed method can reliably select salient features of the biopsy sample. Furthermore, the proposed approach holds the potential to immensely facilitate the adoption of digital pathology by enabling a new generation of WSI representation for efficient storage and more user-friendly visualization.
DocTime: A Document-level Temporal Dependency Graph Parser

We introduce DocTime -a novel temporal dependency graph (TDG) parser that takes as input a text document and produces a temporal dependency graph.It outperforms previous BERT based solutions by a relative 4-8% on three datasets from modeling the problem as a graph-network with path-prediction loss to incorporate longer range dependencies.This work also demonstrates how the TDG graph can be used to improve the downstream tasks of temporal questions answering and NLI by a relative 4-10% with a new framework that incorporates the temporal dependency graph into the self-attention layer of Transformer models (Time-transformer).Finally, we develop and evaluate on a new temporal dependency graph dataset for the domain of contractual documents, which has not been previously explored in this setting.
To Predict Customer Sentimental behavior by using Enhanced Bi-LSTM Technique

Aspect-based totally Opinion Mining is a type of sentiment evaluation that is more advanced in that it models the semantic hyperlink between thing keywords and context terms in a sentence. The lifestyles of a range of surrounding words have a widespread impact on a commentary’s sentiment polarity. Though previous systems considered the position of an aspect in a phrase, few researches have investigated the merging of Sentiment Lexicons (SL) using Deep Learning (DL) techniques. The data for this investigation originated from online item audits compiled from several information repositories. The findings of tests for sentence-level and survey-level characterization are encouraging. The passionate investigation will be used to the survey dataset to analyze the audits and enable the client in determining whether customers are satisfied with the company’s products, prices, and customer service. Unlike traditional NLP (Natural Language Processing) approaches, the proposed framework employs the Bi-LTSM (Bidirectional Long-Short Term Memory) computation for precise results. Finally, some research insights are provided to record future feelings on examination attempts. The emotional analysis was used to the Amazon product review dataset to get the required conclusion. The framework includes fundamental data processing procedures such as data preparation, keyword extraction, and so on, and eventually the Bi-LSTM LSTM (Bidirectional Long-Short Term Memory) algorithm is applied to the data and product ranking is given. In addition, the system compares several algorithms such as CNN (Convolutional Neural Network), RNN (Recurrent Neutral Network), LSTM (Long-Short Term Memory), and Bi-LSTM (Bidirectional Long-Short Term. Memory). According to the data, enhanced Bi-LSTM (Bidirectional Long-Short Term Memory) exceeds all other algorithms in terms of accuracy.
Designing a Secure Vehicular Internet of Things (IoT) Using Blockchain

Smart vehicles are interconnected and deliver a variety of sophisticated services to their owners, transit authorities, automobile manufacturers, and other service providers. Smart cars could be exposed to a number of security and privacy risks, including GPS tracking and remote vehicle hijacking. Blockchain is a game-changing technology that can be used for everything from cryptocurrencies to smart contracts. It may be a viable solution for implementing security in vehicular IoTs. We present a blockchain-based methodology to preserve users’ privacy while simultaneously boosting vehicle security in this paper. The proposed model upgrades the services of vehicular IoT.
Developing a novel stock index trend predictor model by integrating multiple criteria decision-making with an optimized online sequential extreme learning machine

It has always been the goal of many researchers to gain a thorough understanding of the patterns in the stock market and forecast the trends it will follow. The use of an advanced forecasting model can assist with accurately forecasting the future price of stocks, their fluctuations in the markets, as well as make profits in trading. With this motivation, in this study, a novel stock index trend predictor model is designed by integrating Multiple Criteria Decision-Making (MCDM) with an optimized Online Sequential Extreme Learning Machine (OSELM). Forecasting the future stock index prices and analyzing the upward or downward trends of these price forecasts are the two objectives of the proposed model. As the performance of OSELM is heavily dependent on the activation functions used in it, suitable selection of the activation function for OSELM is addressed as a MCDM problem. According to this approach, the trend prediction performance of six popular activation functions is assessed based on five regression-based and five classification-based criteria. In this investigation, three MCDM approaches are used to assess the performance matrix and determine which activation function is the best for OSELM based on six alternative models and ten criteria. To further optimize OSELM's performance, a hybrid crow search algorithm (hCSA) is incorporated in its training phase. By introducing the chaotic map and mutation operator in position update scheme and catfish behavior in the search process of original CSA, the proposed hCSA is able to achieve the right balance between exploration and exploitation improving the convergence. The proposed trend predictor model is empirically evaluated over historical data of three stock indices such as BSE SENSEX, S&P 500 and DJIA collected during pre-COVID and COVID time frame. In most of the test cases, the hCSA-OSELM model outperforms the state-of-the-art baseline models in terms of all evaluation criteria. When compared to the second-best baseline model, the suggested model is able to achieve the MSE improvements of 4–6%, 25–31%, and accuracy improvements of 0.4–0.8%, 0.9–1.3% over the pre-COVID and COVID time-frames, respectively. The statistical test also reveals the better performance of the proposed model. The robust and reliable MCDM-based model selection, superior prediction and classification outcomes clearly reveal that the proposed model can be used for financial time-series forecasting amid daily volatility as well as highly volatile markets.
DFTNet: Dual-Path Feature Transfer Network for Weakly Supervised Medical Image Segmentation

Medical image segmentation has long suffered from the problem of expensive labels. Acquiring pixel-level annotations is time-consuming, labor-intensive, and relies on extensive expert knowledge. Bounding box annotations, in contrast, are relatively easy to acquire. Thus, in this paper, we explore to segment images through a novel Dual-path Feature Transfer design with only bounding box annotations. Specifically, a Target-aware Reconstructor is proposed to extract target-related features by reconstructing the pixels within the bounding box through the channel and spatial attention module. Then, a sliding Feature Fusion and Transfer Module (FFTM) fuses the extracted features from Reconstructor and transfers them to guide the Segmentor for segmentation. Finally, we present the Confidence Ranking Loss (CRLoss) which dynamically assigns weights to the loss of each pixel based on the network's confidence. CRLoss mitigates the impact of inaccurate pseudo-labels on performance. Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance on the Medical Segmentation Decathlon (MSD) Brain Tumour and PROMISE12 datasets.
Reaping what you SOW: Guidelines and strategies for writing scopes of work for statistical consulting

Statistical consulting has the potential to be remarkably rewarding or frustratingly fruitless. Attentive and careful execution of a statistical scoping process will yield top‐caliber research and collaborative partnerships. We provide guidelines and strategies for how to write effective statistical scopes of work (SOWs) and build this procedure into a statistical consulting workflow, using illustrative case studies along the way. Effective SOWs provide a solid foundation on which to build fruitful research partnerships, to avoid common pitfalls and to weather turbulent situations successfully.
X-GOAL

Graphs are powerful representations for relations among objects, which have attracted plenty of attention. A fundamental challenge for graph learning is how to train an effective Graph Neural Network (GNN) encoder without labels, which are expensive and time consuming to obtain. Contrastive Learning (CL) is one of the most popular paradigms to address this challenge, which trains GNNs by discriminating positive and negative node pairs. Despite the success of recent CL methods, there are still two under-explored problems. First, how to reduce the semantic error introduced by random topology based data augmentations. Traditional CL defines positive and negative node pairs via the node-level topological proximity, which is solely based on the graph topology regardless of the semantic information of node attributes, and thus some semantically similar nodes could be wrongly treated as negative pairs. Second, how to effectively model the multiplexity of the real-world graphs, where nodes are connected by various relations and each relation could form a homogeneous graph layer. To solve these problems, we propose a novel multiplex heterogeneous graph prototypical contrastive leaning (X-GOAL) framework to extract node embeddings. X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings. Specifically, the GOAL framework captures the node-level information by a succinct graph transformation technique, and captures the cluster-level information by pulling nodes within the same semantic cluster closer in the embedding space. The alignment regularization aligns embeddings across layers at both node and cluster levels. We evaluate X-GOAL on various real-world datasets and downstream tasks to demonstrate its effectiveness.
Developing Students’ Critical Thinking Skills and Argumentation Abilities Through Augmented Reality–Based Argumentation Activities in Science Classes

Due to the COVID-19 pandemic and adapting the classes urgently to distance learning, directing students' interest in the course content became challenging. The solution to this challenge emerges through creative pedagogies that integrate the instructional methods with new technologies like augmented reality (AR). Although the use of AR in science education is increasing, the integration of AR into science classes is still naive. The lack of the ability to identify misinformation in the COVID-19 pandemic process has revealed the importance of developing students' critical thinking skills and argumentation abilities. The purpose of this study was to examine the change in critical thinking skills and argumentation abilities through augmented reality–based argumentation activities in teaching astronomy content. The participants were 79 seventh grade students from a private school. In this case study, the examination of the verbal arguments of students showed that all groups engaged in the argumentation and produced quality arguments. The critical thinking skills of the students developed until the middle of the intervention, and the frequency of using critical thinking skills varied after the middle of the intervention. The findings highlight the role of AR-based argumentation activities in students' critical thinking skills and argumentation in science education.
Qubit control noise spectroscopy with optimal suppression of dephasing

We extend quantum noise spectroscopy (QNS) of amplitude control noise to settings where dephasing noise or detuning errors make significant contributions to qubit dynamics. Previous approaches to characterize amplitude noise are limited by their vulnerability to low-frequency dephasing noise and static detuning errors, which can overwhelm the target control noise signal and introduce bias into estimates of the amplitude noise spectrum. To overcome this problem, we leverage optimal control to identify a family of amplitude control waveforms that optimally suppress low-frequency dephasing noise and detuning errors, while maintaining the spectral concentration in the amplitude filter essential for spectral estimation. The waveforms found via numerical optimization have surprisingly simple analytic forms, consisting of oscillating sine waves obeying particular amplitude and frequency constraints. In numerically simulated QNS experiments, these waveforms demonstrate superior robustness, enabling accurate estimation of the amplitude noise spectrum in regimes where existing approaches are biased by low-frequency dephasing noise and detuning errors.
Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud

Multi-cloud provides cloud services at distributed locations. As the number of cloud services from multi-cloud providers growing, how to select proper cloud services to optimize multiple potentially conflicting objectives simultaneously has become a challenging task. Multi-objective location-aware service brokering (MOLSB) aims to provide a set of trade-off solutions to minimize cost and latency. To handle dynamic resource requirements, various heuristics have been proposed to efficiently find suitable cloud services. However, these heuristics cannot achieve consistently good performance on a wide range of problem instances. Additionally, instead of replying on a single heuristic, it is desirable to design a set of effective heuristics that can balance different objectives with varied trade-offs. Genetic Programming hyper-heuristics (GPHH) have been applied to automatically design heuristics for many multi-objective dynamic optimization problems, e.g., workflow scheduling. In this pa-per, we propose a new GPHH-based approach, named GPHH-MOLSB, to automatically generate a group of Pareto-optimal heuristics that can be used to satisfy varied QoS preferences. GPHH-MOLSB can significantly outperform several existing approaches based on evaluation on real-world datasets.
Experimental study of secure quantum key distribution with source and detection imperfections

Quantum key distribution (QKD), guaranteed by the principle of quantum physics, is a promising solution for future secure information and communication technology. However, device imperfections compromise the security of real-life QKD systems, restricting the wide deployment of QKD. This study reports a decoy-state Bennett-Brassard 1984 (BB84) QKD experiment that considers both source and detection imperfections. In particular, we achieve a rigorous finite-key security bound over fiber links of up to 75 km by applying a systematic performance analysis. Furthermore, our study considers more device imperfections than most previous experiments, and the proposed theory can be extended to other discrete-variable QKD systems. These features constitute a crucial step toward securing QKD with imperfect practical devices.
Transfering Low-Frequency Features for Domain Adaptation

Previous unsupervised domain adaptation methods did not handle the cross-domain problem from the perspective of frequency for computer vision. The images or feature maps of different domains can be decomposed into the low-frequency component and high-frequency component. This paper pro-poses the assumption that low-frequency information is more domain-invariant while the high-frequency information con-tains domain-related information. Hence, we introduce an approach, named low-frequency module (LFM), to extract domain-invariant feature representations. The LFM is constructed with the digital Gaussian low-pass filter. Our method is easy to implement and introduces no extra hyperparame-ter. We design two effective ways to utilize the LFM for domain adaptation, and our method is complementary to other existing methods and formulated as a plug-and-play unit that can be combined with these methods. Experimental results demonstrate that our LFM outperforms state-of-the-art meth-ods for various computer vision tasks, including image clas-sification and object detection.
Ultrasound Volume Reconstruction From Freehand Scans Without Tracking

Transrectal ultrasound is commonly used for guiding prostate cancer biopsy, where 3D ultrasound volume reconstruction is often desired. Current methods for 3D reconstruction from freehand ultrasound scans require external tracking devices to provide spatial information of an ultrasound transducer. This paper presents a novel deep learning approach for sensorless ultrasound volume reconstruction, which efficiently exploits content correspondence between ultrasound frames to reconstruct 3D volumes without external tracking. The underlying deep learning model, deep contextual-contrastive network (DC 2-Net), utilizes self-attention to focus on the speckle-rich areas to estimate spatial movement and then minimizes a margin ranking loss for contrastive feature learning. A case-wise correlation loss over the entire input video helps further smooth the estimated trajectory. We train and validate DC 2-Net on two independent datasets, one containing 619 transrectal scans and the other having 100 transperineal scans. Our proposed approach attained superior performance compared with other methods, with a drift rate of 9.64 % and a prostate Dice of 0.89. The promising results demonstrate the capability of deep neural networks for universal ultrasound volume reconstruction from freehand 2D ultrasound scans without tracking information.
Quantum-machine-learning channel discrimination

In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measuring the output state. This work studies applications of variational quantum circuits and machine-learning techniques for discriminating such channels. In particular, we explore (i) the practical implementation of embedding this task into the framework of variational quantum computing, (ii) training a quantum classifier based on variational quantum circuits, and (iii) applying the quantum kernel estimation technique. For testing these three channel discrimination approaches, we considered a pair of entanglement-breaking channels and the depolarizing channel with two different depolarization factors. For the approach (i), we address solving the quantum channel discrimination problem using widely discussed parallel and sequential strategies. We show the advantage of the latter in terms of better convergence with less quantum resources. Quantum channel discrimination with a variational quantum classifier (ii) allows one to operate even with random and mixed input states and simple variational circuits. The kernel-based classification approach (iii) is also found effective as it allows one to discriminate depolarizing channels associated not with just fixed values of the depolarization factor, but with ranges of it. Additionally, we discovered that a simple modification of one of the commonly used kernels significantly increases the efficiency of this approach. Finally, our numerical findings reveal that the performance of variational methods of channel discrimination depends on the trace of the product of the output states. These findings demonstrate that quantum machine learning can be used to discriminate channels, such as those representing physical noise processes.
Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks

A major concern when dealing with financial time series involving a wide variety of market risk factors is the presence of anomalies. These induce a miscalibration of the models used to quantify and manage risk, resulting in potential erroneous risk measures. We propose an approach that aims to improve anomaly detection in financial time series, overcoming most of the inherent difficulties. Valuable features are extracted from the time series by compressing and reconstructing the data through principal component analysis. We then define an anomaly score using a feedforward neural network. A time series is considered to be contaminated when its anomaly score exceeds a given cutoff value. This cutoff value is not a hand-set parameter but rather is calibrated as a neural network parameter throughout the minimization of a customized loss function. The efficiency of the proposed approach compared to several well-known anomaly detection algorithms is numerically demonstrated on both synthetic and real data sets, with high and stable performance being achieved with the PCA NN approach. We show that value-at-risk estimation errors are reduced when the proposed anomaly detection model is used with a basic imputation approach to correct the anomaly.
An Anchor-Free Network With Density Map and Attention Mechanism for Multiscale Object Detection in Aerial Images

Accurate detection of the multiple classes in aerial images has become possible with the use of anchor-based object detectors. However, anchor-based object detectors place a large number of preset anchors on images and regress the target bounding box while anchor-free object detections predict the location of objects directly and avoid the carefully predefined anchor box parameters. Object detection in aerial images is faced with two main challenges: 1) the scale diversity of the geospatial objects; and 2) the cluttered background in complex scenes. In this letter, to address these challenges, we present a novel Anchor-Free Network with a Density map and attention mechanism (DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FNet). Considering the extreme density variations of the detection instances among the different categories in aerial images, the proposed DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FNet model conducts density map estimation with image-level supervision for the geospatial object counting, to acquire global knowledge about the scale information. A simple and effective image-level global counting loss function is also introduced. In addition, a compositional attention network is further introduced to enhance the saliency of the foreground objects. The proposed DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> FNet method was compared with the state-of-the-art object detection models, achieving excellent performance on the NWPU VHR-10, RSOD, and DOTA datasets.