Reviewerly/exHarmony huggingface dataset:
Using Fuzzy Logic to Learn Abstract Policies in Large-Scale Multiagent Reinforcement Learning

Large-scale multiagent reinforcement learning requires huge computation and space costs, and the too-long execution process makes it hard to train policies for agents. This work proposes a concept of fuzzy agent, which is a new paradigm for training homogeneous agents. Aiming at a lightweight and affordable reinforcement learning mechanism for large-scale homogeneous multiagent systems, we break the one-to-one correspondence between agent and policy, designing abstract agents as the substitute for the multiagent to interact with the environment. The Markov decision process models for these abstract agents are conducted by fuzzy logic, which also acts on the behavior mapping from abstract agent to entity. Specifically, just the abstract agents execute their policy at a time step, and the concrete behaviors are generated by simple matrix operations. The proposal has lower space and computation complexities because the number of abstract agents is far less than that of entities, and the coupling among agents is retained implicitly. Compared with other approximation and simplification methods, the proposed fuzzy agent not only greatly reduces required computing resources but also ensures the effectiveness of the learned policies. Several experiments are conducted to validate our method. The results show that the proposal outperforms the baseline methods, while it has satisfactory zero-shot and few-shot transfer abilities.
Characterizing Two‐Qubit Non‐Classical Correlations and Non‐Locality in Mixed Local Dephasing Noisy Channels

Abstract The dephasing effects of two independent local mixed static‐Ornstein Uhlenbeck (STC‐OUK) noisy channels on the dynamics of two‐qubit non‐classical correlations, non‐locality, and entanglement are addressed. Here, i) local quantum Fisher information; ii) local quantum uncertainty; iii) uncertainty‐induced non‐locality; iv) Bell non‐locality; and v) negativity to quantify two‐qubit non‐classical correlations, non‐locality and entanglement, respectively, and their loss under the influence of mixed noise are used. The findings show that measure (i) compared to measure (ii) remains more effective in demonstrating non‐classical correlations. For the two‐qubit state with maximum purity, measure (iii) remains less fragile to the mixed STC‐OUK noise than measure (iv) but gets more vulnerable as the initial purity of the two‐qubit state falls. It is intriguing that measures (i), (ii), and (v) completely freeze after a particular interval, yet measures (iii) and (iv) remains partially preserved indefinitely. When compared to other two‐qubit correlation quantifiers, measure (v) exhibits non‐Markovian behavior and the phenomenon of sudden death and birth. Markovian and non‐Markovian dynamical maps, and extended two‐qubit correlations, can be induced by optimizing the current configuration. Finally, under the influence of mixed dephasing effects, robust and strengthened quantum correlations beyond entanglement in the two‐qubit Werner state is shown.
Learning uncertainty with artificial neural networks for predictive process monitoring

The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertainty: model uncertainty due to a lack of training data and noise-induced observational uncertainty. Bayesian neural networks use solid mathematical foundations to learn the model uncertainties of their predictions. The observational uncertainty can be calculated by adding one layer to these networks and augmenting their loss functions. Our contribution is to apply these uncertainty concepts to predictive process monitoring tasks to train uncertainty-based models to predict the remaining time and outcomes. Our experiments show that uncertainty estimates allow more and less accurate predictions to be differentiated and confidence intervals to be constructed in both regression and classification tasks. These conclusions remain true even in early stages of running processes. Moreover, the deployed techniques are fast and produce more accurate predictions. The learned uncertainty could increase users’ confidence in their process prediction systems, promote better cooperation between humans and these systems, and enable earlier implementations with smaller datasets.
GreenABR

Adaptive bitrate (ABR) algorithms aim to make optimal bitrate decisions in dynamically changing network conditions to ensure a high quality of experience (QoE) for the users during video streaming. However, most of the existing ABRs share the limitations of predefined rules and incorrect assumptions about streaming parameters. They also come short to consider the perceived quality in their QoE model, target higher bitrates regardless, and ignore the corresponding energy consumption. This joint approach results in additional energy consumption and becomes a burden, especially for mobile device users. This paper proposes GreenABR, a new deep reinforcement learning-based ABR scheme that optimizes the energy consumption during video streaming without sacrificing the user QoE. GreenABR employs a standard perceived quality metric, VMAF, and real power measurements collected through a streaming application. GreenABR's deep reinforcement learning model makes no assumptions about the streaming environment and learns how to adapt to the dynamically changing conditions in a wide range of real network scenarios. GreenABR outperforms the existing state-of-the-art ABR algorithms by saving up to 57% in streaming energy consumption and 60% in data consumption while achieving up to 22% more perceptual QoE due to up to 84% less rebuffering time and near-zero capacity violations.
RT‐Unet: An advanced network based on residual network and transformer for medical image segmentation

For the past several years, semantic segmentation method based on deep learning, especially Unet, have achieved tremendous success in medical image processing. The U-shaped topology of Unet can well solve image segmentation tasks. However, due to the limitation of traditional convolution operations, Unet cannot realize global semantic information interaction. To address this problem, this paper proposes RT-Unet, which combines the advantages of Transformer and Residual network for accurate medical segmentation. In RT-Unet, the Residual block is taken as the image feature extraction layer to alleviate the problem of gradient degradation and obtain more effective features. Meanwhile, Skip-Transformer is proposed, which takes Multi-head Self-Attention as the main algorithm framework, instead of the original Skip-Connection layer in Unet to avoid the influence of shallow features on the network's performance. Besides, we add attention module at the decoder to reduce semantic differences. According to the experiments on MoNuSeg data set and ISBI_2018cell data set, RT-Unet achieves better segmentation performance than other deep learning-based algorithms. In addition, a series of further ablation experiments were conducted on Residual network and Skip-Transformer, which verified the effectiveness and efficiency of the proposed methods in this paper.
RLogic: Recursive Logical Rule Learning from Knowledge Graphs

Logical rules are widely used to represent domain knowledge and hypothesis, which is fundamental to symbolic reasoning-based human intelligence. Very recently, it has been demonstrated that integrating logical rules into regular learning tasks can further enhance learning performance in a label-efficient manner. Many attempts have been made to learn logical rules automatically from knowledge graphs (KGs). However, a majority of existing methods entirely rely on observed rule instances to define the score function for rule evaluation and thus lack generalization ability and suffer from severe computational inefficiency. Instead of completely relying on rule instances for rule evaluation, RLogic defines a predicate representation learning-based scoring model, which is trained by sampled rule instances. In addition, RLogic incorporates one of the most significant properties of logical rules, the deductive nature, into rule learning, which is critical especially when a rule lacks supporting evidence. To push deductive reasoning deeper into rule learning, RLogic breaks a big sequential model into small atomic models in a recursive way. Extensive experiments have demonstrated that RLogic is superior to existing state-of-the-art algorithms in terms of both efficiency and effectiveness.
Improving Relevance Modeling via Heterogeneous Behavior Graph Learning in Bing Ads

As the fundamental basis of sponsored search, relevance modeling measures the closeness between the input queries and the candidate ads. Conventional relevance models solely rely on the textual data, which suffer from the scarce semantic signals within the short queries. Recently, user historical click behaviors are incorporated in the format of click graphs to provide additional correlations beyond pure textual semantics, which contributes to advancing the relevance modeling performance. However, user behaviors are usually arbitrary and unpredictable, leading to the noisy and sparse graph topology. In addition, there exist other types of user behaviors besides clicks, which may also provide complementary information. In this paper, we study the novel problem of heterogeneous behavior graph learning to facilitate relevance modeling task. Our motivation lies in learning an optimal and task-relevant heterogeneous behavior graph consisting of multiple types of user behaviors. We further propose a novel HBGLR model to learn the behavior graph structure by mining the sophisticated correlations between node semantics and graph topology, and encode the textual semantics and structural heterogeneity into the learned representations. Our proposal is evaluated over real-world industry datasets, and has been mainstreamed in the Bing ads. Both offline and online experimental results demonstrate its superiority.
Few-Shot Unsupervised Specific Emitter Identification Based on Density Peak Clustering Algorithm and Meta-Learning

Deep-learning methods are becoming increasingly popular for improving the efficiency of specific emitter identification (SEI). However, in noncooperative scenarios, it is difficult to both obtain a large number of signal samples for the training of deep-learning models and assign labels to the acquired samples. To solve these two problems, a few-shot unsupervised SEI method is proposed in this study. First, the original radio-frequency (RF) signal is preprocessed based on the Hilbert–Huang transform (HHT) to obtain the Hilbert time–frequency spectrum, which can highlight RF fingerprints (RFFs) that can be used as signal training samples. Then, the Hilbert time–frequency spectrum is input to an improved autoencoder network for training to obtain the latent vector that can represent hidden features of the received signal. Next, the clustering by fast search and find of a density peaks [i.e., density peak clustering (DPC)] algorithm is used to cluster and label the latent vector, which is later reconstructed by the improved autoencoder network to obtain training samples with labeled information. Finally, the meta-learning algorithm is used to train the few-shot SEI network under the few-shot condition, so that it can distinguish different types of RF signals, corresponding to specific emitters. The experimental results show that the unlabeled signal training samples can be clustered and labeled accurately with the proposed method to perform SEI efficiently in few-shot scenarios. In comparison with state-of-the-art solutions, the proposed method in this study exhibited its superiority with regard to few-shot unsupervised SEI.
Light Transport Induced Domain Adaptation for Semantic Segmentation in Thermal Infrared Urban Scenes

Semantic segmentation in urban scenes is widely used in applications of intelligent transportation systems (ITS). In urban scenes, thermal infrared (TIR) images can be captured in weak illumination conditions or in the presence of obscuration (e.g., light fog, smoke). Therefore, TIR images have great potential to endow automated intelligent vehicles or assist navigation systems. However, TIR imaging is blurry and low-contrast due to the absorption by atmospheric gases and heat transfer effect. Hence, TIR semantic segmentation in urban scenes has rarely been explored even though it has a wide range of scenarios in ITS. To overcome this limitation, we analyze the light transport of TIR light. Our analysis reveals that contours are the reliable features shared by TIR and Visible Spectrum (VS) light. Inspired by this, we attempt to transfer joint features from VS domain to TIR domain. Thus, we propose a curriculum domain adaptation method to guide the TIR urban scene semantic segmentation task from VS domain through contours. Moreover, to evaluate the proposed model, we build TIR-SS: an open-for-request dataset consisting of TIR images and pixel level annotations of 8 classes in urban scenes. Qualitative and quantitative experimental results on the dataset indicate that the proposed domain adaptation method outperforms related methods on this TIR semantic segmentation task.
Robust multi-view learning via adaptive regression

As data collected from different sources have multiple representations, multi-view learning has become an important paradigm of machine learning. To exploit multi-view data, previous works either tackle each view separately or concatenate all views directly, such that the distinctions as well as correlations of different views are often ignored. Furthermore, existing models usually involve intractable parameters that need to be manually determined to balance the contributions of different views, degrading the efficiency and applicability of models. In this paper, a novel multi-view learning framework, namely Robust Multi-view learning via Adaptive Regression (RMAR), is derived to discriminate diverse views in a self-supervised weighting manner without extra parameters. Meanwhile, RMAR coalesces multiple feature projections with adaptive view-wise weights and adopts L2,1-norm regression loss to learn a joint projection subspace compatible across all views, not only increasing the robustness of model but also preserving the consistency and diversity among views. Furthermore, RMAR can be naturally extended for feature selection by imposing L2,1-norm constraint on feature projections. Additionally, an efficient convergent algorithm is developed to solve RMAR. Extensive experiments have been performed to validate the effectiveness of RMAR for classification and feature selection and show its superiority over state-of-the-arts.
Asymmetric and threshold effects of FinTech on poverty in SSA countries

Purpose The purpose of this paper is to analyze the dynamic asymmetric relationship between financial technology (FinTech) adoption and poverty alleviation on annual data for the Sub-Saharan Africa (SSA) region over the period from 2004 to 2020. Design/methodology/approach This study adopted the general method of moments (GMM) method on annual data for 127 countries including 45 countries from the SSA region over the period from 2004 to 2020. Findings The study’s findings show that improvement in FinTech may initially decrease the rate of extreme poverty, leading to a decrease in total poverty as a percent of the population. While there is an initial decrease in the rate of extreme poverty with improvements of FinTech, once the FinTech index reaches its threshold level of 37.18 points, further improvement in FinTech tends to decrease as penetration increases, giving rise to an decrease in the rate of poverty alleviation. Research limitations/implications Policymakers should design more aggressive and comprehensive policies directed at recouping the maximum gains of FinTech adoption, with a reasonable threshold target. Practical implications Policymakers in the SSA region must be aware of a FinTech threshold level of 37.18 points. To ensure the highest reduction in extreme poverty, policymakers must keep investing in FinTech to reach this threshold level. Social implications FinTech improvement leads to poverty alleviation. Policymakers in the SSA region can fully recoup the benefits of FinTech by achieving a pre-set threshold level. Originality/value This paper addresses that gap in the literature by studying the impact of FinTech, instead of the traditional financial inclusion measures, on poverty in the 45 countries in the SSA region, exploring the potential dynamic asymmetry of this poverty-FinTech link, and testing the presence and statistical significance of the threshold level of FinTech.
PPNNP: A privacy-preserving neural network prediction with separated data providers using multi-client inner-product encryption

In a neural network of deep learning, it needs a series of algorithms that endeavor to recognize underlying relationships in a set of data. In order to protect the privacy of user’s datasets, traditional schemes can perform the prediction task by setting only a single data provider in the system. However, the data may come from multiple separated data providers rather than single data source in real world since each data provider might hold partial features of a complete prediction sample. It requires that multiple data providers cooperate to perform the prediction for the neural networks by sending their own local data to a well-trained prediction model deployed on a remote cloud server to obtain a predictive label. However, the data owned by multiple data providers usually contain a large amount of private information, which can lead to serious security problems once leaked. To resolve the security and privacy issues of the data owned by multiple data providers, in this paper, we propose a Privacy-Preserving Neural Network Prediction model (PPNNP) that deploys multi-client inner-product functional encryption to the first layer of prediction model. Multiple data providers encrypt their data and upload it to a well-trained model deployed on cloud server, and the server makes predictions by calculating inner-products related to them. It can provide sufficient privacy and security for the data while deploying different neural network architectures with activation functions that are even non-linear on the remote server. We evaluate our scheme based on the real datasets and provide a comparison with the related schemes. Experimental results demonstrate that our scheme can reduce the computational cost of the whole process while significantly reducing the encryption time. It can obtain an accuracy of over 90% in different network architectures with even non-linear activation functions. Meanwhile, our solution can reduce communication overhead in the whole protocol.
A robust noise tolerant zeroing neural network for solving time-varying linear matrix equations

A robust noise-tolerant zeroing neural network (ZNN) is introduced for solving time-varying linear matrix equations (TVLME). The convergence speed of designed neural dynamics is analyzed theoretically and compared with the convergence of neural networks which include traditional activation functions, such as the tunable activation function, versatile activation function, and the modified sign-bi-power activation function. The proposed activation is utilized in the development of nonlinear ZNN dynamics for solving time-varying linear matrix equations and the Stein equation. We investigate theoretically and experimentally the behavior of the proposed robust noise-tolerant ZNN with the novel effective activation function. In particular, the convergence analysis of proposed ZNN flows is studied both in the presence of noise and without noise. Simulation tests demonstrate the effectiveness and domination of the suggested activation over already existing activation functions. Further, the introduced noise-tolerant ZNN model is applied in solving the Wheatstone bridge and output tracking control problem.
UBAR: User Behavior-Aware Recommendation with knowledge graph

The recommendation system is widely used in many aspects of digital economy to offer personalized services, in which efficient capture of user–item relations is of critical importance. However, there are two inevitable challenges in this task. On the one hand, the extraction of complicated associations is not easy among multiple users' actions such as searching, browsing or purchasing. On the other hand, the integration of numerous items' connections is indispensable for the recommendation framework. To address the stated challenges, we propose a User Behavior-Aware Recommendation method with knowledge graph (UBAR) consisting of a user behavior-aware module and an item knowledge graph module. The performance of the proposed UBAR method is evaluated on four datasets (i.e., Tmall, Taobao, Amazon, and Movie-Lens), and the experimental results demonstrate that the proposed UBAR outperforms state-of-the-art methods. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed UBAR method.
Multiobjective whale optimization algorithm‐based feature selection for intelligent systems

With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading more computational complexity. Also, to make the proposed algorithm as light as possible, this paper considers K-nearest neighbor algorithm as the main classifier. The proposed light feature selection algorithm is run on different data sets. Experimental results prove that this algorithm is able to reduce the number of features meanwhile it retains, and in some cases even increases, the accuracy of classification.
Email Spam Detection using Deep Learning Approach

Email is one of the most popular and efficient methods of internet communication and data or message sharing. Considering the significance and high usage of emails, the number of spam emails has also increased rapidly. Spam emails are unwanted emails that contains various contents like advertisements, offers, malicious links, malware, trojan, etc. Spammers send junk mails with an intention of committing email fraud, thus it is important to filter spam emails from emails. The motivation of this research is to build email spam detection models by using machine learning and deep learning techniques so that spam emails can be distinguished from legitimate emails with high accuracy. The Enron email dataset has been used and deep learning models are developed to detect and classify new email spam using LSTM and BERT. NLP approach was applied to analyze and perform data preprocessing of the text of the email. The results are compared to the previous models in email spam detection. The proposed deep learning approach obtained the highest accuracy of 99.14% using BERT, 98.34% using BiLSTM and 97.15% using LSTM. Python is utilized for all implementations.