Reviewerly/exHarmony huggingface dataset:
Cross-Modal Image Retrieval Considering Semantic Relationships With Many-to-Many Correspondence Loss

A cross-modal image retrieval that explicitly considers semantic relationships between images and texts is proposed. Most conventional cross-modal image retrieval methods retrieve the target images by directly measuring the similarities between the candidate images and query texts in a common semantic embedding space. However, such methods tend to focus on a one-to-one correspondence between a predefined image-text pair during the training phase, and other semantically similar images and texts are ignored. By considering the many-to-many correspondences between semantically similar images and texts, a common embedding space is constructed to assure semantic relationships, which allows users to accurately find more images that are related to the input query texts. Thus, in this paper, we propose a cross-modal image retrieval method that considers semantic relationships between images and texts. The proposed method calculates the similarities between texts as semantic similarities to acquire the relationships. Then, we introduce a loss function that explicitly constructs the many-to-many correspondences between semantically similar images and texts from their semantic relationships. We also propose an evaluation metric to assess whether each method can construct an embedding space considering the semantic relationships. Experimental results demonstrate that the proposed method outperforms conventional methods in terms of this newly proposed metric.
Data Management Plans: Implications for Automated Analyses

Data management plans (DMPs) are an essential part of planning data-driven research projects and ensuring long-term access and use of research data and digital objects; however, as text-based documents, DMPs must be analyzed manually for conformance to funder requirements. This study presents a comparison of DMPs evaluations for 21 funded projects using 1) an automated means of analysis to identify elements that align with best practices in support of open research initiatives and 2) a manually-applied scorecard measuring these same elements. The automated analysis revealed that terms related to availability (90% of DMPs), metadata (86% of DMPs), and sharing (81% of DMPs) were reliably supplied. Manual analysis revealed 86% (n = 18) of funded DMPs were adequate, with strong discussions of data management personnel (average score: 2 out of 2), data sharing (average score 1.83 out of 2), and limitations to data sharing (average score: 1.65 out of 2). This study reveals that the automated approach to DMP assessment yields less granular yet similar results to manual assessments of the DMPs that are more efficiently produced. Additional observations and recommendations are also presented to make data management planning exercises and automated analysis even more useful going forward.
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning

When dealing with clinical text classification on a small dataset, recent studies have confirmed that a well-tuned multilayer perceptron outperforms other generative classifiers, including deep learning ones. To increase the performance of the neural network classifier, feature selection for the learning representation can effectively be used. However, most feature selection methods only estimate the degree of linear dependency between variables and select the best features based on univariate statistical tests. Furthermore, the sparsity of the feature space involved in the learning representation is ignored.Our aim is, therefore, to access an alternative approach to tackle the sparsity by compressing the clinical representation feature space, where limited French clinical notes can also be dealt with effectively.This study proposed an autoencoder learning algorithm to take advantage of sparsity reduction in clinical note representation. The motivation was to determine how to compress sparse, high-dimensional data by reducing the dimension of the clinical note representation feature space. The classification performance of the classifiers was then evaluated in the trained and compressed feature space.The proposed approach provided overall performance gains of up to 3% for each test set evaluation. Finally, the classifier achieved 92% accuracy, 91% recall, 91% precision, and 91% f1-score in detecting the patient's condition. Furthermore, the compression working mechanism and the autoencoder prediction process were demonstrated by applying the theoretic information bottleneck framework. Clinical and Translational Impact Statement- An autoencoder learning algorithm effectively tackles the problem of sparsity in the representation feature space from a small clinical narrative dataset. Significantly, it can learn the best representation of the training data because of its lossless compression capacity compared to other approaches. Consequently, its downstream classification ability can be significantly improved, which cannot be done using deep learning models.
Feedback Control of Quantum Systems to Stabilize Superposition States

As a specific trait of quantum mechanics, quantum superposition is exploited in many applications such as quantum computation. This paper proposes a feedback scheme to stabilize the desired quantum superposition states. To this aim, a new factorization is derived for the stochastic master equation of a quantum system which is driven by the field in the superposition of coherent states. Then, a feedback scheme is proposed, which consists of a quantum system driven by the field in the superposition of coherent states and a classical feedback channel proportional to the observations of a Homodyne detector. Afterward, the quantum filter of the closed loop system is attained using the SLH framework. Finally, the factorization of the closed loop quantum filter is obtained and the control law is calculated such that the proposed factorization satisfies the stabilization condition. Finally, as an example, the control method is simulated for a two-level atom and the stabilization of the superposition states is achieved.
Unsupervised Visual Representation Learning Based on Segmentation of Geometric Pseudo-Shapes for Transformer-Based Medical Tasks

Recently, transformer-based architectures have been shown to outperform classic convolutional architectures and have rapidly been established as state-of-the-art models for many medical vision tasks. Their superior performance can be explained by their ability to capture long-range dependencies of their multi-head self-attention mechanism. However, they tend to overfit on small- or even medium-sized datasets because of their weak inductive bias. As a result, they require massive, labeled datasets, which are expensive to obtain, especially in the medical domain. This motivated us to explore unsupervised semantic feature learning without any form of annotation. In this work, we aimed to learn semantic features in a self-supervised manner by training transformer-based models to segment the numerical signals of geometric shapes inserted on original computed tomography (CT) images. Moreover, we developed a Convolutional Pyramid vision Transformer (CPT) that leverages multi-kernel convolutional patch embedding and local spatial reduction in each of its layer to generate multi-scale features, capture local information, and reduce computational cost. Using these approaches, we were able to noticeably outperformed state-of-the-art deep learning-based segmentation or classification models of liver cancer CT datasets of 5,237 patients, the pancreatic cancer CT datasets of 6,063 patients, and breast cancer MRI dataset of 127 patients.
A Proposed Framework for Designing Blockchain Solutions for Logistics in post-Covid Scenario and Future Pandemics

Logistics companies, that are participating in the distribution, storage, and movement of goods, have been widely affected by the Covid-19 pandemic and it is also exposed that traditional logistics is not adaptable, agile, and resilient enough to manage a pandemic or another widespread disaster. The digital, and smart logistics infrastructure is required to uplift the firm's logistics performance. Blockchain technology is the most effective proposal for smart logistics networks because this technology has distinctive features; transparency, traceability, and visibility which are highly essential for future logistics management. This study proposed the initial framework which suggests the Blockchain relevancy with smart logistics networks. Moreover, this study guides managers of logistics companies to understand the weakness of traditional logistics and the use of Blockchain to modernize the firm's logistics function. This is a qualitative based study and focuses on an extensive literature review about traditional logistics, smart logistics, and emerging technologies especially Blockchain. This study designs the framework for the adoption of Blockchain in logistics to make it a more agile and resilient logistics network in the post-covid scenario.
Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment

Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation criteria. However, due to the rigid structure and learning framework, they cannot apply to the cross-task BIQA scenario, where the distortion types and evaluation criteria keep changing in practical applications. This paper proposes a scalable incremental learning framework (SILF) that could sequentially conduct BIQA across multiple evaluation tasks with limited memory capacity. More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other. Each parameter subset is temporarily settled to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Remember</i> one evaluation preference toward its corresponding task, and the previously settled parameter subsets can be adaptively reused in the following BIQA to achieve better performance based on the task relevance. To suppress the unrestrained expansion of memory capacity in sequential tasks learning, we develop a scalable memory unit by gradually and selectively pruning unimportant neurons from previously settled parameter subsets, which enable us to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Forget</i> part of previous experiences and free the limited memory capacity for adapting to the emerging new tasks. Extensive experiments on eleven IQA datasets demonstrate that our proposed method significantly outperforms the other state-of-the-art methods in cross-task BIQA. The source code of the proposed method is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">github.com/maruiperfect/SILF.</uri>
Shortest Path Finding in Quantum Networks With Quasi-Linear Complexity

A fully-quantum network implies the creation of quantum entanglement between a given source node and some other destination node, with a number of quantum repeaters in between. This paper tackles the problem of quantum entanglement distribution by solving the routing problem over an infrastructure based on quantum repeaters and with a finite number of pairs of entangled qubits available in each link. The network model considers that link purification is available such that a nested purification protocol can be applied at each link to generate entangled qubits with higher fidelity than the original ones. A low-complexity multi-objective routing algorithm to find the shortest path between any two given nodes is proposed and assessed for random networks, using a fairly general path extension mechanism that can fit a large family of particular technological requirements. Different types of quantum protocols require different levels of fidelity for the entangled qubit pairs. For that reason, the proposed algorithm identifies the shortest path between two nodes that assures an end-to-end fidelity above a specified threshold. The minimum requirements for the end-to-end entanglement fidelity depend on the whole extension of the paths, and cannot be looked at as a local property of each link. Moreover, one needs to keep track not only of the shortest path, but also of longer paths holding more entangled qubits than the shorter paths in order to satisfy the fidelity criterion. Thus, standard single parameter shortest-path algorithms do not necessarily converge to the optimal solution. The problem of finding the best path in a network subject to multiple criteria (known as multi-objective routing) is, in general, an NP-hard problem due to the rapid growth of the number of stored paths. This work proposes a metric that identifies and discards paths that are objectively worse than others. By doing so, the time complexity of the proposed algorithm scales near-to-linearly with respect to the number of nodes in the network, showing that the shortest-path problem in quantum networks can be solved with a complexity very close to the one of the classical counterparts. That is analytically proved for the case where all the links of a path have the same fidelity (homogeneous model). The algorithm is also adapted to a particular type of path extension, where different links along a path can be purified to different degrees, asserting its flexibility and near-to-linearity even when heterogeneous fidelities along the sections of a path are considered.
Instance Segmentation Model Evaluation and Rapid Deployment for Autonomous Driving Using Domain Differences

This paper studies the model evaluation and rapid deployment problems when the model trained by the source domain is deployed to multiple other target domains in autonomous driving application scenarios. To address the problem that the existing methods for evaluating model accuracy require complete labeled test sets, this paper proposes an instance segmentation model evaluation method based on domain differences, which can give the prediction accuracy of the model on unlabeled test sets. Furthermore, to address the rapid model deployment, this paper proposes a calculation method for the minimum number of supplements based on domain differences. The method selects the fewest images from the target domain scene and realizes the rapid deployment of the model through further annotation and training. Finally, experiments are conducted on the public dataset Cityscapes. The model evaluation experiments show that the root mean square error (RMSE) of the proposed method on Cityscapes is about 4% smaller (from 6% to 2%) than that of other existing model evaluation methods, and it can be as small as 2.35%. Rapid deployment experiments show that the RMSE of the proposed method on Cityscapes is about 8%.
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

Recent advances in wearable devices and Internetof-Things (IoT) have led to massive growth in sensor data generated in edge devices.Labeling such massive data for classification tasks has proven to be challenging.In addition, data generated by different users bear various personal attributes and edge heterogeneity, rendering it impractical to develop a global model that adapts well to all users.Concerns over data privacy and communication costs also prohibit centralized data accumulation and training.We propose SemiPFL that supports edge users having no label or limited labeled datasets and a sizable amount of unlabeled data that is insufficient to train a well-performing model.In this work, edge users collaborate to train a Hypernetwork in the server, generating personalized autoencoders for each user.After receiving updates from edge users, the server produces a set of base models for each user, which the users locally aggregate them using their own labeled dataset.We comprehensively evaluate our proposed framework on various public datasets from a wide range of application scenarios, from wearable health to IoT, and demonstrate that SemiPFL outperforms state-of-art federated learning frameworks under the same assumptions regarding user performance, network footprint, and computational consumption.We also show that the solution performs well for users without label or having limited labeled datasets and increasing performance for increased labeled data and number of users, signifying the effectiveness of SemiPFL for handling data heterogeneity and limited annotation.We also demonstrate the stability of SemiPFL for handling user hardware resource heterogeneity in three real-time scenarios.
An Improved NSGA-III Algorithm Based on Deep Q-Networks for Cloud Storage Optimization of Blockchain

As the underlying technology of cryptocurrencies, blockchain has gained a lot of attention in recent years. However, the storage problem needs to be solved with the increasing number of blocks in the blockchain network. Cloud storage optimization is an effective way to solve the storage issue, which selects and stores parts of blocks to the cloud. Precisely, block selection can be described as a multiobjective optimization problem (MOP) and solved by evolutionary algorithms (EAs). To obtain well results of block selection, an improved NSGA-III algorithm based on deep Q-networks (DQN), termed NSGA-DQN, is proposed in this paper, which aims to maintain well convergence and diversity of the population. This way, a set of suitable solutions is obtained to determine the number of blocks stored to the cloud, and the storage problem can be solved effectively. To be specific, DQN creates a decision-making agent to maximize the expected reward by learning a policy that evaluates <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> values of each action in each state. In the proposed selection mechanism, the reward values are set according to the convergence and diversity of the population, and the actions correspond to the individuals. This way, our method can determine a set of individuals that maximizes the convergence and diversity of the population. In addition, an adaptive maximum reward enhancement module (AMREM) is developed to further enhance the maximum expected reward by updating the new better reward and modifying the replay memory. We conduct the experimental study on block selection, and the results demonstrate that the proposed algorithm is superior to five state-of-the-art algorithms.
Neuromorphic Speech Recognition With Photonic Convolutional Spiking Neural Networks

Spiking neural network (SNN) have attracted lots of attention due to its event-driven nature and powerful computation capability. However, it is still limited to simple task due to the training difficulty. In this work, we propose a hybrid architecture of photonic convolutional spiking neural network (PCSNN) to realize the speech recognition task. In the PCSNN, the feature extraction is realized by a convolution SNN with unsupervised learning algorithm, the classification is realized by a photonic SNN with modified time-based supervised training algorithm. The TIDIGITS dataset is used to test the speech recognition performance of the proposed PCSNN, and the highest testing accuracy is 93.75%. The proposed PCSNN provides a solution for architecture and algorithm co-design for the speech recognition task, which is helpful for extending the applications of photonic SNN.
Identifying, exploring, and interpreting time series shapes in multivariate time intervals

We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes. A data structure representing a single episode is a multivariate time series. To analyse collections of episodes, we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes. Each episode is thus represented by a combination of patterns. Using this representation, we apply visual analytics techniques to fulfil a set of analysis tasks, such as investigation of the temporal distribution of the patterns, frequencies of transitions between the patterns in episode sequences, and co-occurrences of patterns of different variables within same episodes. We demonstrate our approach on two examples using real-world data, namely, dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover.
High Dynamic Range Image Quality Assessment Based on Frequency Disparity

In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM). Motivated by the assumption that the human visual system (HVS) is highly adapted for extracting structural information and partial frequencies when perceiving the visual scene, the Gabor and the Butterworth filters are applied to the luminance component of the HDR image to extract the local and global frequency features, respectively. The similarity measurement and feature pooling strategy are sequentially performed on the frequency features to obtain the predicted single quality score. The experiments evaluated on four widely used benchmarks demonstrate that the proposed LGFM can provide a higher consistency with the subjective perception compared with the state-of-the-art HDR IQA methods. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/eezkni/LGFM</uri> .
Fairness, explainability, privacy, and robustness for trustworthy algorithmic decision-making

With the rapid increase in the use and deployment of machine learning (ML) systems in the world, concomitant concerns on the ethical implications of their downstream effect have surfaced in recent years. Responding to this challenge, the field of trustworthy ML has grown rapidly and resulted in a large body of methods and algorithms that embody desirable qualities such as fairness, transparency, privacy, and robustness. In this chapter, we survey the current landscape of trustworthy ML methods, introduce fundamental concepts, and summarize research directions. To bridge the gap between theory and practice, we provide implementation details of each category of methods that are currently available publicly.
Feature engineering methods

Feature engineering is one of the steps in any project which utilizes machine learning to solve business problem. It is a set of steps to prepare the raw data collected from the real-world objects under investigation to the use by algorithms in automated analysis. In this chapter, the place of feature engineering in the machine learning projects is described based on CRISP-DM framework, and then the types of the input data are described. In exploratory data analysis and data preprocessing, encoding the variables, treatment of outliers and missing values, binning, and variable transformation are presented. Feature extraction methods are mentioned briefly in context of transition from data to features, and the problem of curse of dimensionality is explained. To avoid the curse, two types of feature reduction techniques are overviewed. First, supervised and unsupervised feature selection is presented. Then the main approaches of feature dimensionality reduction techniques are described, such as principal and independent component analysis, nonnegative matrix factorization, self-organized maps, and autoencoder neural networks. The reasoning of important aspects related to feature engineering (interpretability, feature importance, data augmentation) concludes the chapter.