Reviewerly/exHarmony huggingface dataset:
A Multimodality-Contribution-Aware TripNet for Histologic Grading of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a type of primary liver malignant tumor with a high recurrence rate and poor prognosis even undergoing resection or transplantation. Accurate discrimination of the histologic grades of HCC plays a critical role in the management and therapy of HCC patients. In this paper, we discuss a deep learning-based diagnostic model for HCC histologic grading with multimodal Magnetic Resonance Imaging (MRI) images to overcome the problem of limited well-annotated data and extract the discriminated fusion feature referring to the clinical diagnosis experience of radiologists. Accordingly, we propose a novel Multimodality-Contribution-Aware TripNet (MCAT) based on the metric learning and the attention-aware weighted multimodal fusion. The novelty of the method lies in the multimodality small-shot learning architecture designation and the multimodality adaptive weighted computing scheme. The comprehensive experiments are done on the clinic dataset with the well-annotation of lesion location by the professional radiologist. The experimental results show that our proposed MCAT is not only able to achieve acceptable quantitative measuring of HCC histologic grading based on the MRI sequences with small cases but also outperforms previous models in HCC histologic grading, reaching an accuracy of 84 percent, a sensitivity of 87 percent and precision of 89 percent.
Revisited—The Subliminal Channel in Blockchain and Its Application to IoT Security

Although digital signature has been a fundamental technology for cryptosystems, it still draws considerable attention from both academia and industry due to the recent raising interest in blockchains. This article revisits the subliminal channel existing digital signature and reviews its abuse risk of the constructor’s private key. From a different perspective on the subliminal channel, we find the new concept named the chamber of secrets in blockchains. The found concept, whereby the secret is hidden and later recovered by the constructor from the common transactions in a blockchain, highlights a new way to encourage implementing various applications to benefit efficiency and security. Thus, the proposed scheme benefits from the following advantages: (1) avoiding the high maintenance cost of certificate chain of certificate authority, or public key infrastructure, and (2) seamlessly integrating with blockchains using the property of chamber of secrets. In order to easily understand the superiority of this new concept, a remote authentication scenario is taken as a paradigm of IoT to demonstrate that the further advantages are achieved: (1) avoiding high demand for storage space in IoT devices, and (2) avoiding maintaining a sensitive table in IoT server.
Evaluating the AdaBoost Algorithm for Biometric-Based Face Recognition

Adaboost algorithm is a machine learning for face recognition and using eigenvalues for feature extraction. AdaBoost is also called as an adaptive boost algorithm. To create a strong learner by uses multiple iterations in the AdaBoost algorithm. AdaBoost generates a strong learner by iteratively adding weak learners. To create a strong classifier using several classifiers while training the data set, a new weak learner is adding together, and a weighting vector is adjusting to focus on examples that were misclassifying in prior rounds. The analysis of face recognition has been widely used for any application. As per the literature survey, many algorithms have been developed to recognize the face. The AdaBoost algorithm is used for increasing detection accuracy and easy to develop. Therefore, this paper evaluating a preprocessing, feature extraction by using eigenvalues, classifier for classifying a face or non-face by using an AdaBoost algorithm.
Divergence-Free Constrained Phase Unwrapping and Denoising for 4D Flow MRI Using Weighted Least-Squares

A novel divergence-free constrained phase unwrapping method was proposed and evaluated for 4D flow MRI. The unwrapped phase field was obtained by integrating the phase variations estimated from the wrapped phase data using weighted least-squares. The divergence-free constraint for incompressible blood flow was incorporated to regulate and denoise the resulting phase field. The proposed method was tested on synthetic phase data of left ventricular flow and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> 4D flow measurement of Poiseuille flow. The method was additionally applied to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> 4D flow measurements in the thoracic aorta from 30 human subjects. The performance of the proposed method was compared to the state-of-the-art 4D single-step Laplacian algorithm. The synthetic phase data were completely unwrapped by the proposed method for all the cases with velocity encoding (venc) as low as 20% of the maximum velocity and signal-to-noise ratio as low as 5. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> Poiseuille flow data were completely unwrapped with a 60% increase in the velocity-to-noise ratio. For the in-vivo aortic datasets with venc ratio less than 0.4, the proposed method significantly improved the success rate by as much as 40% and reduced the velocity error levels by a factor of 10 compared to the state-of-the-art method. The divergence-free constrained method exhibits reliability and robustness on phase unwrapping and shows improved accuracy of velocity and hemodynamic quantities by unwrapping the low-venc 4D flow MRI data.
CRL: Collaborative Representation Learning by Coordinating Topic Modeling and Network Embeddings

Network representation learning (NRL) has shown its effectiveness in many tasks, such as vertex classification, link prediction, and community detection. In many applications, vertices of social networks contain textual information, e.g., citation networks, which form a text corpus and can be applied to the typical representation learning methods. The global context in the text corpus can be utilized by topic models to discover the topic structures of vertices. Nevertheless, most existing NRL approaches focus on learning representations from the local neighbors of vertices and ignore the global structure of the associated textual information in networks. In this article, we propose a unified model based on matrix factorization (MF), named collaborative representation learning (CRL), which: 1) considers complementary global and local information simultaneously and 2) models topics and learns network embeddings collaboratively. Moreover, we incorporate the Fletcher–Reeves (FR) MF, a conjugate gradient method, to optimize the embedding matrices in an alternative mode. We call this parameter learning method as AFR in our work that can achieve convergence after a few numbers of iterations. Also, by evaluating CRL on topic coherence and vertex classification using several real-world data sets, our experimental study shows that this collaborative model not only can improve the performance of topic discovery over the baseline topic models but also can learn better network representations than the state-of-the-art context-aware NRL models.
Energy-Efficient Virtualized Scheduling and Load Balancing Algorithm in Cloud Data Centers

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).
Sample-Efficient Adaptive Calibration of Quantum Networks Using Bayesian Optimization

All physical systems employed for quantum information tasks must act as unbiased carriers of encoded quantum states. Ensuring such indistinguishability of information carriers is a major challenge in many quantum information applications, including advanced quantum communication protocols. For photons, the workhorses of quantum communication networks, it is difficult to obtain and maintain their indistinguishability because of environment-induced transformations and loss imparted by communication channels, especially in noisy scenarios. Conventional strategies to mitigate these transformations often require hardware or software overhead that is restrictive (e.g., adding noise), infeasible (e.g., on a satellite), or time-consuming for deployed networks. Here we propose and develop resource-efficient Bayesian optimization techniques to rapidly and adaptively calibrate the indistinguishability of individual photons for quantum networks using only information derived from their measurement. To experimentally validate our approach, we demonstrate the optimization of Hong-Ou-Mandel interference between two photons--a central task in quantum networking-- finding rapid, efficient, and reliable convergence towards maximal photon indistinguishability in the presence of high loss and shot noise. We expect our resource-optimized and experimentally friendly methodology will allow fast and reliable calibration of indistinguishable quanta, a necessary task in distributed quantum computing, communications, and sensing, as well as for fundamental investigations.
Entanglement structure detection via machine learning

Detecting the entanglement structure, such as intactness and depth, of an n-qubit state is important for understanding the imperfectness of the state preparation in experiments. However, identifying such structure usually requires an exponential number of local measurements. In this letter, we propose an efficient machine learning based approach for predicting the entanglement intactness and depth simultaneously. The generalization ability of this classifier has been convincingly proved, as it can precisely distinguish the whole range of pure generalized GHZ states which never exist in the training process. In particular, the learned classifier can discover the entanglement intactness and depth bounds for the noised GHZ state, for which the exact bounds are only partially known.
New entanglement-assisted quantum MDS codes

Entanglement-assisted quantum error-correcting codes (EAQECCs) can be obtained from arbitrary classical linear codes based on the entanglement-assisted stabilizer formalism, which greatly promoted the development of quantum coding theory. In this paper, we construct several families of [Formula: see text]-ary entanglement-assisted quantum maximum-distance-separable (EAQMDS) codes of lengths [Formula: see text] with flexible parameters as to the minimum distance [Formula: see text] and the number [Formula: see text] of maximally entangled states. Most of the obtained EAQMDS codes have larger minimum distances than the codes available in the literature.
Group Fisher Pruning for Practical Network Compression

Network compression has been widely studied since it is able to reduce the memory and computation cost during inference. However, previous methods seldom deal with complicated structures like residual connections, group/depth-wise convolution and feature pyramid network, where channels of multiple layers are coupled and need to be pruned simultaneously. In this paper, we present a general channel pruning approach that can be applied to various complicated structures. Particularly, we propose a layer grouping algorithm to find coupled channels automatically. Then we derive a unified metric based on Fisher information to evaluate the importance of a single channel and coupled channels. Moreover, we find that inference speedup on GPUs is more correlated with the reduction of memory rather than FLOPs, and thus we employ the memory reduction of each channel to normalize the importance. Our method can be used to prune any structures including those with coupled channels. We conduct extensive experiments on various backbones, including the classic ResNet and ResNeXt, mobile-friendly MobileNetV2, and the NAS-based RegNet, both on image classification and object detection which is under-explored. Experimental results validate that our method can effectively prune sophisticated networks, boosting inference speed without sacrificing accuracy.
Real-world Patient Trajectory Prediction from Clinical Notes Using Artificial Neural Networks and UMLS-Based Extraction of Concepts

As more data is generated from medical attendances and as Artificial Neural Networks gain momentum in research and industry, computer-aided medical prognosis has become a promising technology. A common approach to perform automated prognoses relies on textual clinical notes extracted from Electronic Health Records (EHRs). Data from EHRs are fed to neural networks that produce a set with the most probable medical problems to which a patient is subject in her/his clinical future, including clinical conditions, mortality, and readmission. Following this research line, we introduce a methodology that takes advantage of the unstructured text found in clinical notes by applying preprocessing, concepts extraction, and fine-tuned neural networks to predict the most probable medical problems to follow in a patient's clinical trajectory. Different from former works that focus on word embeddings and raw sets of extracted concepts, we generate a refined set of Unified Medical Language System (UMLS) concepts by applying a similarity threshold filter and a list of acceptable concept types. In our prediction experiments, our method demonstrated AUC-ROC performance of 0.91 for diagnosis codes, 0.93 for mortality, and 0.72 for readmission, determining an efficacy that rivals state-of-the-art works. Our findings contribute to the development of automated prognosis systems in hospitals where text is the main source of clinical history.
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments. In this paper, we empirically show that out-of-distribution performance is strongly correlated with in-distribution performance for a wide range of models and distribution shifts. Specifically, we demonstrate strong correlations between in-distribution and out-of-distribution performance on variants of CIFAR-10 & ImageNet, a synthetic pose estimation task derived from YCB objects, satellite imagery classification in FMoW-WILDS, and wildlife classification in iWildCam-WILDS. The strong correlations hold across model architectures, hyperparameters, training set size, and training duration, and are more precise than what is expected from existing domain adaptation theory. To complete the picture, we also investigate cases where the correlation is weaker, for instance some synthetic distribution shifts from CIFAR-10-C and the tissue classification dataset Camelyon17-WILDS. Finally, we provide a candidate theory based on a Gaussian data model that shows how changes in the data covariance arising from distribution shift can affect the observed correlations.
RETRACTED: Research on the Cultivation Strategy of College Students’ Innovation and Entrepreneurship Based on Big Data Analysis from the Perspective of Economic Transformation

Abstract With the expansion of social economy, there is an urgent need for innovative talents, especially college students with innovative thinking and innovative consciousness, which is an important intellectual support and source of social progress and economic and industrial transformation and upgrading. However, college students’ innovation and pioneering are faced with many challenges. The integration of big data analysis and innovation and pioneering helps to realize their complementary advantages. Based on this, this paper first analyses the concept and connotation of college students’ innovation and pioneering from the perspective of economic transformation, and then gives the cultivation strategy of college students’ innovation and pioneering based on big data analysis.
Who gains from economic freedom? A panel analysis on decile income levels

This paper revisits Saccone’s (2020) analysis of the impact of economic freedom on decile income shares for a panel of developed and developing nations during 1980-2014. Saccone found that higher levels of economic freedom corresponded with lower income shares for the bottom 8 deciles and higher shares only for the top 2 deciles. Instead of decile income shares, this paper focuses on the impact of economic freedom on decile income levels. We find that the income per capita for all 10 deciles increases as economic freedom increases.
Designing a Cost Function to Assess a Neural Network to Detect Distributed Denial of Service Attacks

This paper presents a model for designing a cost function for neural networks. The proposed procedure consists of enriching a basis cost function with distinguishable features as the coefficients to create a highly sensitive cost function. Since the Internet traffic data that contains distributed denial of service is not balanced, exaggerating the anomalous part of data leads to a better classification and data class separation. To develop the proposed cost function, sensitivity analysis is used as a measure to assess and test the cost function’s parameters. The principle components of the most sensitive cost function are extracted. The most efficient principle component with the highest variance is selected as the weights for the selected cost function. Therefore, the highest separation between two normal and anomalous clusters can be gained.
BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter

This paper describes Kata.ai's submission for the Social Media Mining for Health (SMM4H) 2021 shared task.We participated in three tasks: classifying adverse drug effect, COVID-19 self-report, and COVID-19 symptoms.Our system is based on BERT model pre-trained on the domain-specific text.In addition, we perform data cleaning and augmentation, as well as hyperparameter optimization and model ensemble to further boost the BERT performance.We achieved the first rank in both classifying adverse drug effects and COVID-19 selfreport tasks.
An exploratory multi-scale framework to reservoir digital twin

In order to make full use of the information provided in the physical reservoirs, including the production history and environmental conditions, the whole life cycle of reservoir discovery and recovery should be considered when mapping in the virtual space. A new concept of reservoir digital twin and the exploratory multi-scale framework is proposed in this paper, covering a wide range of engineering processes related with the reservoirs, including the drainage, sorption and phase change in the reservoirs, as well as extended processes like injection, transportation and on-ﬁeld processing. The mathematical tool package for constructing the numerical description in the digital space for various engineering processes in the physical space is equipped with certain advanced models and algorithms developed by ourselves. For a macroscopic ﬂow problem, we can model it either in the Navier-Stokes scheme, suitable for the injection, transportation and oil-water separation processes, or in the Darcy scheme, suitable for the drainage and sorption processes. Lattice Boltzmann method can also be developed as a special discretization of the Navier-Stokes scheme, which is easy to be coupled with multiple distributions, for example, temperature ﬁeld, and a rigorous Chapman-Enskog expansion is performed to show the equivalence between the lattice Bhatnagar-Gross-Krook formulation and the corresponding Navier-Stokes equations and other macroscopic models. Based on the mathematical toolpackage, for various practical applications in petroleum engineering related with reservoirs, we can always ﬁnd the suitable numerical tools to construct a digital twin to simulate the operations, design the facilities and optimize the processes. Cited as: Zhang, T., Sun, S. An exploratory multi-scale framework to reservoir digital twin. Advances in Geo-Energy Research, 2021, 5(3): 239-251, doi: 10.46690/ager.2021.03.02
autoCoin: Secure Content Sharing Based on Blockchain for Vehicular Cloud

A future smart car will be more than a means of transportation, as it will not only move people to a destination without requiring them to drive but will enable people to work or enjoy a trip with entertainment. For this, smart vehicles need to deal with various types of data for safety and infotainment, such as real-time traffic, multi-media contents, documents and weather information. Recently, a fleet of vehicles connected to other vehicles and infrastructure (i.e., road side units) using a legacy or 5G mmWave spectrum has been considered as a platform to cooperate for those new tasks, known as the vehicular cloud or fog. Within the vehicular cloud, data management should consider security, high availability and interoperability between vehicles. However, these are not easily achievable without a centralized service provider; it is difficult for an autonomous P2P system to guarantee data integrity, and it cannot compensate drivers that actively participate in the vehicular cloud. Fortunately, the many successes achieved in the field of crypto-currency raise the possibility of defining incentives that are necessary for a sustainable digital economy. In this paper, we propose autoCoin—an approach that aims to encourage smart vehicles to cooperate to create and exchange infotainment data securely under the assumption of rationality. We introduce a scalable blockchain architecture for autoCoin and a smart contract to exchange contents without third-parties using an off-chain technique.
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation

We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation.NeuralWOZ has two pipelined models, Collector and Labeler.Collector generates dialogues from (1) user's goal instructions, which are the user context and task constraints in natural language, and (2) system's API call results, which is a list of possible query responses for user requests from the given knowledge base.Labeler annotates the generated dialogue by formulating the annotation as a multiple-choice problem, in which the candidate labels are extracted from goal instructions and API call results.We demonstrate the effectiveness of the proposed method in the zero-shot domain transfer learning for dialogue state tracking.In the evaluation, the synthetic dialogue corpus generated from NeuralWOZ achieves a new state-of-theart with improvements of 4.4% point joint goal accuracy on average across domains, and improvements of 5.7% point of zero-shot coverage against the MultiWOZ 2.1 dataset. 1