Reviewerly/exHarmony huggingface dataset:
A hybrid flower pollination with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si60.svg"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:math>-hill climbing algorithm for global optimization

In this paper, the β-hill climbing optimizer is hybridized with the flower pollination algorithm (FPA) as a local refinement operator for global optimization problems. The proposed method is called HyFPAβ-hc. Such hybridization aims to enhance the balance between exploration and exploitation processes during the search, thus improving the quality of the outcomes. β-hill climbing optimizer is a recent trajectory-based algorithm with a powerful digging the niche to search and find the local optimum, while FPA is a recent population-based algorithm with robust mining several niches in the search space without proper concentration. The proposed HyFPAβ-hc is evaluated using 15 unimodal and multimodal test functions established in IEEE-CEC2015. The results show significant improvement in the convergence behaviour of the proposed HyFPAβ-hc over FPA using different dimensions of the test function. The comparative evaluation is also conducted against 26 state-of-the-art methods. The experiments consider three problem sizes (with dimensions 10, 30, and 50) to show the proposed HyFPAβ-hc performance against all comparative methods, where the proposed method outperformed all compared methods in optimizing 8, 7, 4 out of 15 test functions for 10, 30, 50 dimensions, respectively. Accordingly, the achieved results prove the efficiency of the proposed HyFPAβ-hc in optimizing various problem dimensions. In conclusion, the proposed hybrid metaheuristic method can search powerfully in the niches of optimization problems search space and produces very fruitful outcomes.
A Trust based Privacy Providing Model for Online Social Networks

Online Social Networks (OSN) have become a central means of communication and interaction between people around the world. The essence of privacy has been challenged through the past two decades as technological advances enabled benefits and social visibility to active members that share content in online communities. While OSN users share personal content with friends and colleagues, they are not always fully aware of the potential unintentional exposure of their information to various people including adversaries, social bots, fake users, spammers, or data-harvesters. Preventing this information leakage is a key objective of many security models developed for OSNs including Access Control, Relationship based models, Trust based models and Information Flow control. Following previous research, we assert that a combined approach is required to overcome the shortcoming of each model. In this paper we present a new model to protect users' privacy that is composed of three main phases addressing three of its major aspects: trust, role-based access control and information flow. This model considers a user's sub-network and classifies the user's direct connections to roles. It relies on public information such as total number of friends, age of user account, and friendship duration to characterize the quality of the network connections. It also evaluates trust between a user and members of the user's network to estimates if these members are acquaintances or adversaries based on the paths of the information flow between them. Finally, it provides more precise and viable information sharing decisions and enables better privacy control in the social network. We have evaluated our model with extensive experiments using both synthetic and real users' networks to demonstrate its ability to provide a naïve user with a good means of privacy protection. We have validated separately every phase of the model and examined the decisions obtained by two different approaches. The results show a strong correlation between the decisions made by the algorithm and the users' decisions.
Real-time and Robust System for Counting Movement-Specific Vehicle at Crowded Intersections

In order to reduce traffic congestion and improve the efficiency of traffic light signals, intelligent traffic systems are being developed by researchers, and vehicle counting is one of the key techniques in the system. The traditional methods mostly focus on increasing the vehicle counting effectiveness without regard to the program execution efficiency. The practical value of these systems will be reduced if they cannot be operated in real-time on compact IoT device. Therefore, in this paper, we mainly focus on designing a real-time and robust system for the problem of counting specific-movement vehicles. The system is able to detect and track objects in the area of interest, then count those tracked trajectories using the movements. To improve performance of tracking multiple objects, a high recall detection method and an efficient feature matching strategy were proposed. Moreover, to minimize the wrong direction of movement prediction and improve the results of vehicle counting, a cosine similarity-based vehicle counting scheme is applied. Experiments are conducted on AI City 2021 Track-1 dataset. Our method is evaluated on both sides of efficiency and effectiveness.
Learning attention embeddings based on memory networks for neural collaborative recommendation

Recently, deep learning has dominated the recommender system, as it is able to effectively capture nonlinear and nontrivial user–item relationships, and perform complex nonlinear transformations. However, there are still some issues with respects to the existing methods. Firstly, they always treat user–item interactions independently, and may fail to cover more complex and hidden information that is inherently implicit in the local neighborhood surrounding an interaction sample. Secondly, by quantifying the dependence degree of user–item sequences, it demonstrates that both short-term and long-term dependent behavioral patterns co-exist. Unfortunately, typical deep learning methods might be problematic when coping with very long-term sequential dependencies. To address these issues, we propose a novel unified neural collaborative recommendation algorithm that capitalizes on memory networks for learning attention embedding from implicit interaction (NCRAE). Particularly, the attention is capable of learning the relative importance of different users and items from user–item interaction sequences, which provides a better solution for concentrating on inputs and helps to better memorize long-term sequential dependencies. Extensive experiments on three real-world datasets show significant improvements of our proposed NCRAE algorithm over the competitive methods. Empirical evidence shows that using memory networks for learning attention embeddings of users’ implicit interaction yields better recommendation performance.
Customized Impression Prediction From Radiology Reports Using BERT and LSTMs

Clinical language processing has become an attractive field with the improvements of deep learning applications and the abundance of large unstructured narratives in the healthcare records. The capability to extract unstructured information from raw text to provide actionable information for healthcare personnel plays a vital role in healthcare workflows. In this article, we introduce a deep learning approach to automate the generation of radiology impressions by analyzing radiology findings and patient background information of each examination. Since the impression section of a radiology report is an essential conclusion, any errors can prove to be detrimental. Thus, we developed a deep learning system to prevent important clinical findings from being overlooked by using almost 1 million de-identified radiology reports obtained from the University of Chicago Medicine over the last 12 years. We propose to automate the generation of radiology reports by incorporating sequence-to-sequence neural network models with the power of bidirectional encoder representations from transformers (BERT). We tested our model in a real-time experimental setup with radiologists in a top tier academic institution and statistically validated the performance by using ROUGE metrics. Clinical validations have shown that 76% of our predictions are at least as accurate as human-generated impressions by radiologists. Furthermore, statistical validation metrics demonstrated higher ROUGE scores compared to previously published studies over two different test sets.
Multi-language static code analysis on the LARA framework

We propose a mechanism to raise the abstraction level of source-code analysis and robustly support multiple languages. Built on top of the LARA framework, it allows sharing language specifications between LARA source-to-source compilers, and enables the mapping of a virtual AST over the nodes of ASTs provided by different, unrelated parsers.
Blind Inference of Eigenvector Centrality Rankings

We consider the problem of estimating a network's eigenvector centrality only from data on the nodes, with no information about network topology. Leveraging the versatility of graph filters to model network processes, data supported on the nodes is modeled as a graph signal obtained via the output of a graph filter applied to white noise. We seek to simplify the downstream task of centrality ranking by bypassing network topology inference methods and, instead, inferring the centrality structure of the graph directly from the graph signals. To this end, we propose two simple algorithms for ranking a set of nodes connected by an unobserved set of edges. We derive asymptotic and non-asymptotic guarantees for these algorithms, revealing key features that determine the complexity of the task at hand. Finally, we illustrate the behavior of the proposed algorithms on synthetic and real-world datasets.
Quantum Key-Length Extension

Should quantum computers become available, they will reduce the effective key length of basic secret-key primitives, such as blockciphers. To address this we will either need to use blockciphers with inherently longer keys or develop key-length extension techniques to amplify the security of a blockcipher to use longer keys.
Joint optimization of an autoencoder for clustering and embedding

Abstract Deep embedded clustering has become a dominating approach to unsupervised categorization of objects with deep neural networks. The optimization of the most popular methods alternates between the training of a deep autoencoder and a k -means clustering of the autoencoder’s embedding. The diachronic setting, however, prevents the former to benefit from valuable information acquired by the latter. In this paper, we present an alternative where the autoencoder and the clustering are learned simultaneously. This is achieved by providing novel theoretical insight, where we show that the objective function of a certain class of Gaussian mixture models (GMM’s) can naturally be rephrased as the loss function of a one-hidden layer autoencoder thus inheriting the built-in clustering capabilities of the GMM. That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding. Experiments confirm the equivalence between the clustering module and Gaussian mixture models. Further evaluations affirm the empirical relevance of our deep architecture as it outperforms related baselines on several data sets.
Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

Radar signal sorting is the core part of electronic support measures, which is responsible for deinterleaving the overlapping pulse sequences received by the receiver from the complex environment, separating different radiation source signals, and providing support for radiation source identification.Particle swarm optimization (PSO) is a population-based global optimization algorithm with great advantages in intelligent sorting of complex signals, which can adapt to the electromagnetic environment with complex and variable radiation source signals and high pulse stream density.However, the PSO-based sorting method is prone to premature convergence and cannot adaptively adjust particle swarm parameters and positions.In this paper, a dynamic modified chaotic PSO algorithm (DMCPSO) is proposed.Chaotic search is used to increase the diversity of particle swarm in the later iteration to avoid premature convergence and falling into local optimum.Adaptive adjustment parameters related to the particle fitness value are adopted to balance the ability of global search and local search.A new fitness function is proposed and the particle position is dynamically corrected by clustering analysis to improve the accuracy of particle position optimization and avoid the influence from the distribution of feature parameters.The simulation results show that the DMCPSO algorithm provides stable and fast performance with excellent sorting indexes in complex, variable, and dense signal environment.
AIBM: Accurate and Instant Background Modeling for Moving Object Detection

Detecting moving objects has been widely studied since it plays vital many applications, such as video surveillance and intelligent transportation systems. It is necessary to accurately differentiate the foreground and the background in this technology to analyze object motions in the scene. Conventional detection methods use many reference frames to model the background to detect moving objects; however, the detection is inaccurate when immediate changes occur in the scene because the instant update of the background model is impossible. To be robust illumination changes and dynamic backgrounds, we propose an accurate and instant background modeling (AIBM) method that inpaints the background with superpixels and enhances it in detail with pixel-levels. Unlike the previous approaches, the proposed AIBM method utilizes spatio-temporal information of only two consecutive frames to eliminate the lengthy initialization and update period of the model. In this paper, we illustrate the importance of accurate and instant background modeling in detecting moving objects. The performance of our method is evaluated with three benchmark datasets (CDnet2014, LASIESTA, and SBI). The experimental results show that our AIBM method is robust to sudden changes in the scene and outperforms the other conventional methods with F-measure of 0.8911 and 0.9682 in detection accuracy in CDNet2014 and LASIESTA datasets, respectively. The accuracy of the generated background is also measured on the SBI dataset, which demonstrates the importance of high-quality background modeling.
Prediction error distribution with dynamic asymmetry for reversible data hiding

Prediction-error expansion (PEE) by employing asymmetric prediction-error distribution (PED) provides an opportunity to decrease the embedding distortion in reversible data hiding (RDH). Nevertheless, its performance is completely dependent on the asymmetry rate of utilized PED which has never been studied before despite its crucial role in the performance of RDH algorithms. In this paper, a new prediction scheme called dynamic asymmetric distribution of error (DADE) is proposed for PEE. DADE predictor produces error distribution with dynamic asymmetry rate proportionate to embedding capacity (EC). In the proposed predictor several PEDs with a variety of asymmetry rates are composed and for a given EC, the PED with the highest asymmetry rate is selected, provided that it fulfills the EC. By increasing the asymmetry rate of the PED, the length of its short tail and the number of prediction errors in that part are reduced. Consequently, data embedding through single side expansion of prediction-error in the shorter side significantly decreases the number of shifted pixels which leads to the reduction of embedding distortion. Experimental results prove that the proposed method reduces the embedding distortion while slightly increases the EC of PEE based methods with asymmetric PED, and outperforms some state-of-the-art methods.
Multi-agent reinforcement learning by the actor-critic model with an attention interface

Multi-agent reinforcement learning algorithms have achieved satisfactory performances in various scenarios, but many of them encounter difficulties in partially observable environments. In partially observable environments, the inability to perceive environment states results in unsteadiness and misconvergence, especially in large-scale multi-agent environments. To improve interactions among homogeneous agents in a partially observable environment, we propose a novel multi-agent actor-critic model with a visual attention interface to solve this problem. First, a recurrent visual attention interface is used to extract a latent state from each agent’s partial observation. These latent states allow agents to focus on several local environments, in which each agent has a complete perception of a local environment and the intricate multi-agent environment is teased out by the interaction among several agents in the same local environment. The proposed method trains multi-agent systems with a centralized training and decentralized execution mechanism. The joint action of agents is approximated by the mean-field theory because the number of agents in a local environment is uncertain. Experimental results on the simulation platform suggest that our model performs better when training large-scale multi-agent systems in partially observable environments than baselines.
Assuming Just Enough Fairness to make Session Types Complete for Lock-freedom

We investigate how different fairness assumptions affect results concerning lock-freedom, a typical liveness property targeted by session type systems.We fix a minimal session calculus and systematically take into account all known fairness assumptions, thereby identifying precisely three interesting and semantically distinct notions of lock-freedom, all of which having a sound session type system.We then show that, by using a general merge operator in an otherwise standard approach to global session types, we obtain a session type system complete for the strongest amongst those notions of lock-freedom, which assumes only justness of execution paths, a minimal fairness assumption for concurrent systems.
Progressive Bilateral-Context Driven Model for Post-Processing Person Re-Identification

Most existing person re-identification methods compute pairwise similarity by extracting robust visual features and learning the discriminative metric. Owing to visual ambiguities, these content-based methods that determine the pairwise relationship only based on the similarity between them, inevitably produce a suboptimal ranking list. Instead, the pairwise similarity can be estimated more accurately along the geodesic path of the underlying data manifold by exploring the rich contextual information of the sample. In this paper, we propose a lightweight post-processing person re-identification method in which the pairwise measure is determined by the relationship between the sample and the counterpart's context in an unsupervised way. We translate the point-to-point comparison into the bilateral point-to-set comparison. The sample's context is composed of its neighbor samples with two different definition ways: the first order context and the second order context, which are used to compute the pairwise similarity in sequence, resulting in a progressive post-processing model. The experiments on four large-scale person re-identification benchmark datasets indicate that (1) the proposed method can consistently achieve higher accuracies by serving as a post-processing procedure after the content-based person re-identification methods, showing its state-of-the-art results, (2) the proposed lightweight method only needs about 6 milliseconds for optimizing the ranking results of one sample, showing its high-efficiency. Code is available at: https://github.com/123ci/PBCmodel.
Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation

Citation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods.
PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code

The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representations. Nowadays, a popular starting point to represent code is abstract syntax trees (ASTs). Abstract syntax trees have been used for a long time in various software engineering domains, and in particular in IDEs. The API of modern IDEs allows to manipulate and traverse ASTs, resolve references between code elements, etc. Such algorithms can enrich ASTs with new data and therefore may be useful in ML-based code analysis. In this work, we present PSIMiner- a tool for processing PSI trees from the IntelliJ Platform. PSI trees contain code syntax trees as well as functions to work with them, and therefore can be used to enrich code representation using static analysis algorithms of modern IDEs. To showcase this idea, we use our tool to infer types of identifiers in Java ASTs and extend the code2seq model for the method name prediction problem.