Reviewerly/exHarmony huggingface dataset:
Enhanced Beetle Antennae Algorithm for Chemical Dynamic Optimization Problems’ Non-Fixed Points Discrete Solution

Dynamic optimization is an important research topic in chemical process control. A dynamic optimization method with good performance can reduce energy consumption and prompt production efficiency. However, the method of solving the problem is complicated in the establishment of the model, and the process of solving the optimal value has a certain degree of difficulty. Based on this, we proposed a non-fixed points discrete method of an enhanced beetle antennae optimization algorithm (EBSO) to solve this kind of problem. Firstly, we converted individual beetles into groups of beetles to search for the best and increase the diversity of the population. Secondly, we introduced a balanced direction strategy, which explored extreme values in new directions before the beetles updated their positions. Finally, a spiral flight mechanism was introduced to change the situation of the beetles flying straight toward the tentacles to prevent the traditional algorithm from easily falling into a certain local range and not being able to jump out. We applied the enhanced algorithm to four classic chemical problems. Meanwhile, we changed the equal time division method or unequal time division method commonly used to solve chemical dynamic optimization problems, and proposed a new interval distribution method—the non-fixed points discrete method, which can more accurately represent the optimal control trajectory. The comparison and analysis of the simulation test results with other algorithms for solving chemical dynamic optimization problems show that the EBSO algorithm has good performance to a certain extent, which further proves the effectiveness of the EBSO algorithm and has a better optimization ability.
Evading obscure communication from spam emails

Spam is any form of annoying and unsought digital communication sent in bulk and may contain offensive content feasting viruses and cyber-attacks. The voluminous increase in spam has necessitated developing more reliable and vigorous artificial intelligence-based anti-spam filters. Besides text, an email sometimes contains multimedia content such as audio, video, and images. However, text-centric email spam filtering employing text classification techniques remains today's preferred choice. In this paper, we show that text pre-processing techniques nullify the detection of malicious contents in an obscure communication framework. We use Spamassassin corpus with and without text pre-processing and examined it using machine learning (ML) and deep learning (DL) algorithms to classify these as ham or spam emails. The proposed DL-based approach consistently outperforms ML models. In the first stage, using pre-processing techniques, the long-short-term memory (LSTM) model achieves the highest results of 93.46% precision, 96.81% recall, and 95% F1-score. In the second stage, without using pre-processing techniques, LSTM achieves the best results of 95.26% precision, 97.18% recall, and 96% F1-score. Results show the supremacy of DL algorithms over the standard ones in filtering spam. However, the effects are unsatisfactory for detecting encrypted communication for both forms of ML algorithms.
Lattice-Based Proxy-Oriented Public Auditing Scheme for Electronic Health Record in Cloud-Assisted WBANs

The integrity auditing of outsourced data is becoming an essential process because an increasing number of people or organizations are opting to store health records in cloud servers to reduce local storage. Most existing public auditing schemes requiring complex certificate management are unsuitable for resource-limited mobile devices and are unable to resist quantum attacks; thus, we propose a lattice-based proxy-oriented public auditing scheme for electronic health records. We construct an identity-based auditing scheme based on the small integer solution to avoid complex certificate management issues. We also introduce the idea of proxy to reduce computing overhead from the user side significantly and enable the application of resource-limited mobile devices. Furthermore, we improve the proposed scheme by integrating the Ethereum blockchain to resist malicious proxies. We prove that the proposed scheme is proxy-protected, unforgeable, and privacy-protected. Results of an efficiency analysis show that our scheme demonstrates high computing efficiency on the user side and comprehensive functions.
Fully Unsupervised Person Re-Identification via Multiple Pseudo Labels Joint Training

Person re-identification (re-ID) is the task of finding the matched person in a non-overlapping and multi-camera system. Because annotating images across multiple cameras is difficult and time-consuming, this paper focuses on fully unsupervised learning person re-ID that can learn person re-ID on unlabeled data. The unsupervised re-ID needs to self-generate pseudo labels to make training possible. Unlike human-annotated true labels, the pseudo labels contain noise labels which substantially hinder the network’s capability on feature learning. In order to refine the predicted pseudo labels, we introduce a novel unsupervised re-ID method named Multiple pseudo Labels Joint Training (MLJT) in this paper. Different from the existing works, the MLJT predicts multiple pseudo labels for each image by mining potential similarities in multiple ways. Based on invariance constraints among multiple pseudo labels, the MLJT is jointly optimized under the supervision of multiple pseudo labels to ease the impact of noises in the single pseudo label. The proposed MLJT predicts three types of pseudo labels for one input image. The first one is the clustering-based pseudo label. The second one is adaptive similarity measurement-based pseudo label. The third one is pseudo sub-labels which are predicted by mining channel-based self-similarities. The proposed MLJT has been extensively evaluated on two mainstream and public person re-ID datasets and outdoor real-world videos. Experiments demonstrate the effectiveness of the proposed multiple pseudo labels joint training strategy and the practicality of the proposed MLJT in real-world unsupervised person re-ID applications. The testing demo can be found at https://drive.google.com/drive/folders/1RvNaEiy6tF18_RcgTNcjE7jJ6eGy8sZL?usp=sharing.
Neural Photometry-Guided Visual Attribute Transfer

We present a deep learning-based method for propagating spatially-varying visual material attributes (e.g., texture maps or image stylizations) to larger samples of the same or similar materials. For training, we leverage images of the material taken under multiple illuminations and a dedicated data augmentation policy, making the transfer robust to novel illumination conditions and affine deformations. Our model relies on a supervised image-to-image translation framework and is agnostic to the transferred domain; we showcase a semantic segmentation, a normal map, and a stylization. Following an image analogies approach, the method only requires the training data to contain the same visual structures as the input guidance. Our approach works at interactive rates, making it suitable for material edit applications. We thoroughly evaluate our learning methodology in a controlled setup providing quantitative measures of performance. Last, we demonstrate that training the model on a single material is enough to generalize to materials of the same type without the need for massive datasets.
A Keyword-Enhanced Approach to Handle Class Imbalance in Clinical Text Classification

Recent applications ofdeep learning have shown promising results for classifying unstructured text in the healthcare domain. However, the reliability of models in production settings has been hindered by imbalanced data sets in which a small subset of the classes dominate. In the absence of adequate training data, rare classes necessitate additional model constraints for robust performance. Here, we present a strategy for incorporating short sequences of text (i.e. keywords) into training to boost model accuracy on rare classes. In our approach, we assemble a set of keywords, including short phrases, associated with each class. The keywords are then used as additional data during each batch of model training, resulting in a training loss that has contributions from both raw data and keywords. We evaluate our approach on classification of cancer pathology reports, which shows a substantial increase in model performance for rare classes. Furthermore, we analyze the impact of keywords on model output probabilities for bigrams, providing a straightforward method to identify model difficulties for limited training data.
IEEE Access Special Section: Privacy Preservation for Large-Scale User Data in Social Networks

Social networks have become one of the most popular platforms for people to communicate and interact with their friends and share personal information and experiences (e.g., Facebook owns over 1.23 billion monthly active users). The increasing popularity of social networks has generated extremely large-scale user data (e.g., Twitter generates 500 million tweets per day and around 200 billion tweets per year). These data can help improve people’s quality of life as well as benefit various interest groups such as advertisers, application developers, and so on. However, privacy may be compromised if learning algorithms are used to infer unpublished privacy information from published data. Hence, user data privacy preservation has become one of the most urgent research issues in social networks.
Action-Entropy Approach to Modeling of ‘Infodemic-Pandemic’ System on the COVID - 19 Cases

In this paper, the action-entropy concept is applied to the modelling of the “infodemic-pandemic” systems. From the uncertainty-action-entropy concept, the specificity of the “infodemic-pandemic” systems is that each project forms a certain contribution to the formation and dynamics of action entropy. According to this concept, “infodemic-pandemic” is considered as a system that exchanges biological resources, action and information with the environment, which determines the presence of informational (structural) entropy and action entropy. Moreover, the latter is the result of the existing order in the organization, which is determined by the structure and implementation of business processes. The action-entropy concept operates with such categories as “uncertainty”, “action”, “entropy”, “free action”, “dissipation” and allows using action-entropy to assess the state and dynamics of the “infodemic-pandemic” systems. This study presents the universal formalization of action entropy for various organizations and its expression for project-oriented organizations. The action entropy of organizations is determined by information entropy, total action and the ratio of the achieved level of action efficiency to the “ideal” level. The relationship between action entropy and informational (structural) entropy of project-oriented organizations is established. It is determined that when adding a new project to the current totality, the organization must ensure a balance between the growth of uncertainty (information entropy) and action efficiency. The method of “decomposition” of the total action entropy of the “infodemic-pandemic” systems is presented in the form of the sum of local action entropies for projects and programs. The effect of adding a new project to the structure of the “infodemic-pandemic” systems on its integrated action entropy is investigated. The presented results form a new look at the qualitative assessment of both an individual project and the entire set of projects of the “infodemic-pandemic” systems.
Effects of Out-of-Hospital Continuous Nursing on Postoperative Breast Cancer Patients by Medical Big Data

This study aimed to explore the application value of the intelligent medical communication system based on the Apriori algorithm and cloud follow-up platform in out-of-hospital continuous nursing of breast cancer patients. In this study, the Apriori algorithm is optimized by Amazon Web Services (AWS) and graphics processing unit (GPU) to improve its data mining speed. At the same time, a cloud follow-up platform-based intelligent mobile medical communication system is established, which includes the log-in, my workstation, patient records, follow-up center, satisfaction management, propaganda and education center, SMS platform, and appointment management module. The subjects are divided into the control group (routine telephone follow-up, 163) and the intervention group (continuous nursing intervention, 216) according to different nursing methods. The cloud follow-up platform-based intelligent medical communication system is used to analyze patients’ compliance, quality of life before and after nursing, function limitation of affected limb, and nursing satisfaction under different nursing methods. The running time of Apriori algorithm is proportional to the data amount and inversely proportional to the number of nodes in the cluster. Compared with the control group, there are statistical differences in the proportion of complete compliance data, the proportion of poor compliance data, and the proportion of total compliance in the intervention group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). After the intervention, the scores of the quality of life in the two groups are statistically different from those before treatment ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ), and the scores of the quality of life in the intervention group were higher than those in the control group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The proportion of patients with limited and severely limited functional activity of the affected limb in the intervention group is significantly lower than that in the control group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). The satisfaction rate of postoperative nursing in the intervention group is significantly higher than that in the control group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>P</mi> <mo>&lt;</mo> <mn>0.001</mn> </math> ), and the proportion of basically satisfied and dissatisfied patients in the control group was higher than that in the intervention group ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ).
WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data

Detecting relevant changes in dynamic time series data in a timely manner is crucially important for many data analysis tasks in real-world settings. Change point detection methods have the ability to discover changes in an unsupervised fashion, which represents a desirable property in the analysis of unbounded and unlabeled data streams. However, one limitation of most of the existing approaches is represented by their limited ability to handle multivariate and high-dimensional data, which is frequently observed in modern applications such as traffic flow prediction, human activity recognition, and smart grids monitoring. In this paper, we attempt to fill this gap by proposing WATCH, a novel Wasserstein distance-based change point detection approach that models an initial distribution and monitors its behavior while processing new data points, providing accurate and robust detection of change points in dynamic high-dimensional data. An extensive experimental evaluation involving a large number of benchmark datasets shows that WATCH is capable of accurately identifying change points and outperforming state-of-the-art methods.
Construction for both self-dual codes and LCD codes

From a given [n, k] code C, we give a method for constructing many [n, k] codes C such that the hull dimensions of C and C are identical.This method can be applied to constructions of both self-dual codes and linear complementary dual codes (LCD codes for short).Using the method, we construct 661 new inequivalent extremal doubly even [56, 28,12] codes.Furthermore, constructing LCD codes by the method, we improve some of the previously known lower bounds on the largest minimum weights of binary LCD codes of length 26 ≤ n ≤ 40.
The recursive variational Gaussian approximation (R-VGA)

We consider the problem of computing a Gaussian approximation to the posterior distribution of a parameter given N observations and a Gaussian prior. Owing to the need of processing large sample sizes N, a variety of approximate tractable methods revolving around online learning have flourished over the past decades. In the present work, we propose to use variational inference to compute a Gaussian approximation to the posterior through a single pass over the data. Our algorithm is a recursive version of variational Gaussian approximation we have called recursive variational Gaussian approximation. We start from the prior, and for each observation, we compute the nearest Gaussian approximation in the sense of Kullback–Leibler divergence to the posterior given this observation. In turn, this approximation is considered as the new prior when incorporating the next observation. This recursive version based on a sequence of optimal Gaussian approximations leads to a novel implicit update scheme which resembles the online Newton algorithm and which is shown to boil down to the Kalman filter for Bayesian linear regression. In the context of Bayesian logistic regression, the implicit scheme may be solved, and the algorithm is shown to perform better than the extended Kalman filter, while being less computationally demanding than its sampling counterparts.
Relation Extraction with Word Graphs from N-grams

Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for indomain scenarios.To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods with an unsupervised manner to build the context graph, without relying on the existence of a dependency parser.Specifically, we construct the graph from n-grams extracted from a lexicon built from pointwise mutual information (PMI) and apply attention over the graph.Therefore, different word pairs from the contexts within and across n-grams are weighted in the model and facilitate RE accordingly.Experimental results with further analyses on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.
On Generalization in Coreference Resolution

While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains.We consolidate a set of 8 coreference resolution datasets targeting different domains to evaluate the offthe-shelf performance of models.We then mix three datasets for training; even though their domain, annotation guidelines, and metadata differ, we propose a method for jointly training a single model on this heterogeneous data mixture by using data augmentation to account for annotation differences and sampling to balance the data quantities.We find that in a zeroshot setting, models trained on a single dataset transfer poorly while joint training yields improved overall performance, leading to better generalization in coreference resolution models.This work contributes a new benchmark for robust coreference resolution and multiple new state-of-the-art results.
Learning to Ground Visual Objects for Visual Dialog

Visual dialog is challenging since it needs to answer a series of coherent questions based on understanding the visual environment.How to ground related visual objects is one of the key problems.Previous studies utilize the question and history to attend to the image and achieve satisfactory performance, however these methods are not sufficient to locate related visual objects without any guidance.The inappropriate grounding of visual objects prohibits the performance of visual dialog models.In this paper, we propose a novel approach to Learn to Ground visual objects for visual dialog, which employs a novel visual objects grounding mechanism where both prior and posterior distributions over visual objects are used to facilitate visual objects grounding.Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process.Meanwhile, a prior distribution, which is inferred from context only, is used to approximate the posterior distribution so that appropriate visual objects can be grounded even without answers during the inference process.Experimental results on the VisDial v0.9 and v1.0 datasets demonstrate that our approach improves the previous strong models in both generative and discriminative settings by a significant margin.
ReasonBERT: Pre-trained to Reason with Distant Supervision

We present ReasonBERT, a pre-training method that augments language models with the ability to reason over long-range relations and multiple, possibly hybrid, contexts.Unlike existing pre-training methods that only harvest learning signals from local contexts of naturally occurring texts, we propose a generalized notion of distant supervision to automatically connect multiple pieces of text and tables to create pre-training examples that require long-range reasoning.Different types of reasoning are simulated, including intersecting multiple pieces of evidence, bridging from one piece of evidence to another, and detecting unanswerable cases.We conduct a comprehensive evaluation on a variety of extractive question answering datasets ranging from single-hop to multi-hop and from text-only to table-only to hybrid that require various reasoning capabilities and show that ReasonBERT achieves remarkable improvement over an array of strong baselines.Fewshot experiments further demonstrate that our pre-training method substantially improves sample efficiency.
From axioms to synthetic inference rules via focusing

An important application of focused variants of Gentzen's sequent calculus proof rules is the construction of (possibly) large synthetic inference rules. This paper examines the synthetic inference rules that arise when using theories composed of bipolars, and we do this in both classical and intuitionistic logics. A key step in transforming a formula into synthetic inference rules involves attaching a polarity to atomic formulas and some logical connectives. Since there are different choices in how polarity is assigned, it is possible to produce different synthetic inference rules for the same formula. We show that this flexibility allows for the generalization of different approaches for transforming axioms into sequent rules present in the literature. We finish the paper showing how to apply these results to organize the proof theory of labeled sequent systems for several propositional modal logics.