Reviewerly/exHarmony huggingface dataset:
Neural Networks as Geometric Chaotic Maps

The use of artificial neural networks (NNs) as models of chaotic dynamics has been rapidly expanding. Still, a theoretical understanding of how NNs learn chaos is lacking. Here, we employ a geometric perspective to show that NNs can efficiently model chaotic dynamics by becoming structurally chaotic themselves. We first confirm NN's efficiency in emulating chaos by showing that a parsimonious NN trained only on few data points can reconstruct strange attractors, extrapolate outside training data boundaries, and accurately predict local divergence rates. We then posit that the trained network's map comprises sequential geometric stretching, rotation, and compression operations. These geometric operations indicate topological mixing and chaos, explaining why NNs are naturally suitable to emulate chaotic dynamics.
An SQL Domain Ontology Learning for Analyzing Hierarchies of Structures in Pre-Learning Assessment Agents

This paper presents the use of description logics (DL) in the definition and development of a Structured Query Language (SQL) domain ontology for a multi-agent based pre-assessment system. Description logics is a knowledge representation language for defining terms or classes, the relationships between classes, their instances, including individuals and literals. In a formal school curriculum, modules of learning are inter-dependent. So, teaching and learning follows an ordered sequence of learning from lower-level module(s) to higher-level ones. This process enables students to gain mastery of lower-level materials before moving up the ladder to higher-level learning. To describe an SQL ontology and its representation for a multi-agent based system application, this paper uses a description logic language to present the organization of learning modules into DesiredConcept $$<{{\varvec{D}}}>$$ , PrerequisiteConcept $$<{{\varvec{C}}}>$$ and LeafNodes $$<{{\varvec{N}}}>$$ as well as their associated relationships, namely, hasPrerequisite and hasKB between the learning modules. The paper thus presents a TBox and an Abox of a DL ontology and further transformation into a first-order predicate for a multi-agent based system that was implemented in Jason.
Angle-based cost-sensitive multicategory classification

Many real-world classification problems come with costs which can vary for different types of misclassification. It is thus important to develop cost-sensitive classifiers which minimize the total misclassification cost. Although binary cost-sensitive classifiers have been well-studied, solving multicategory classification problems is still challenging. A popular approach to address this issue is to construct K classification functions for a K-class problem and remove the redundancy by imposing a sum-to-zero constraint. However, such method usually results in higher computational complexity and inefficient algorithms. In this article, we propose a novel angle-based cost-sensitive classification framework for multicategory classification without the sum-to-zero constraint. Loss functions that included in the angle-based cost-sensitive classification framework are further justified to be Fisher consistent. To show the usefulness of the framework, two cost-sensitive multicategory boosting algorithms are derived as concrete instances. Numerical experiments demonstrate that the proposed boosting algorithms yield competitive classification performances against other existing boosting approaches.
Siamese Attentive Graph Tracking

Recently, deep Siamese matching networks have attracted increasing attention for visual tracking. Despite the demonstrated successes, Siamese trackers do not take full advantage of the structural information of target objects. They tend to drift in the presence of non-rigid deformation or partly occlusion. In this paper, we propose to advance Siamese trackers with graph convolutional networks, which pay more attention to the structural layout of target objects, to learn features robust to large appearance changes over time. Specifically, we divide the target object into several sub-parts and design an attentive graph convolutional network to model the relationship between parts. We incrementally update the attention coefficients of the graph with the attention scheme at each frame in an end-to-end manner. To further improve localization accuracy, we propose a learnable cascade regression algorithm based on deep reinforcement learning to refine the predicted bounding boxes. Extensive experiments on seven challenging benchmark datasets, i.e., OTB-100, TC-128, VOT2018, VOT2019, TrackingNet, GOT-10k and LaSOT, demonstrate that the proposed tracking method performs favorably against state-of-the-art approaches.
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition

Fine-grained visual recognition, which aims to identify subcategories of the same base-level category, is a challenging task because of its large intra-class variances and small inter-class variances. Human beings can perform object recognition task based on not only the visual appearance but also the knowledge from texts, as texts can point out the discriminative parts or characteristics which are always the key to distinguishing different subcategories. This is an involuntary transfer from human textual attention to visual attention, suggesting that texts are able to assist fine-grained recognition. In this paper, we propose a Text-Embedded Bilinear (TEB) model which incorporates texts as extra guidance for fine-grained recognition. Specially, we first conduct a text-embedded network to embed text feature into the discriminative image feature learning to get a embedded feature. In addition, since the cross-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other, we also extract a candidate feature from the text encoder and embed it into the inter-layer feature of the image encoder to get an embedded candidate feature. At last we utilize a cross-layer bilinear network to fuse the two embedded features. Comparing with state-of-the-art methods on the widely used CUB-200-2011 dataset and Oxford Flowers-102 dataset for fine-grained image recognition, the experimental results demonstrate our TEB model achieves the best performance.
Diverter-Guider Recurrent Network for Diverse Poems Generation from Image

Poem generation from image aims to automatically generate the poetic sentences for presenting the image content or overtone. Previous works focused on 1-to-1 image-poem generation with the demands of poeticness and content relevance. This paper proposes the paradigm of multiple poems generation from one image, which is closer to human poetizing but more challenging. Its key problem is to simultaneously guarantee the diversity of multiple poems with poeticness and relevance. To this end, we propose an end-to-end probabilistic Diverter-Guider Recurrent Network (DG-Net), which is a context-based encoder-decoder generative model with the hierarchical stochastic variables. Specifically, the diverter-variable represents the decoding-context inferred from the input image to diversify the poem themes; the guider-variable is introduced as an attribute decoder to restricts the word-choice with supervised information. Extensive experiments on automatic evaluations and human judgments demonstrate the superior performance of DG-Net than existing poem generation methods. Qualitative study show that our model can generate diverse poems with the poeticness and relevance.
Modified Ranking With Temporal Association Rule Mining in Supply Chains

This article deals with data mining applications for the supply chain inventory management. ABC classification is usually used for inventory items classification because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. Moreover, in ABC classification, the inter-relationship between items is not considered. But practically, the sale of one item could affect the sale of other items (cross selling effect). Hence, within time-periods, the inventories should be classified. In this article, a modified approach is proposed considering both time-periods and cross-selling effect to rank inventory items. A numerical example and an empirical study with a data set are used to evaluate the proposed approach. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.
Sensor-Enabled Context-Aware and Pro-Active Queue Management Systems in Intelligent Environments

Queue systems are practically used in various institutions and commercial enterprises constituting a challenge for the intelligent environments in smart cities. The management of the flow of customers guarantees the elimination or reduction of the queues as well as the economic benefits which follow the clients' satisfaction of a better quality of service. An intelligent queue management system has been proposed which is designed as the pro-active and context-aware ecosystem based on multiple low-level sensors and devices constituting the IoT (Internet of Things) network. The designed context-driven system is characterised by user friendliness, as well as the client behaviour recognition and understanding which generate actions that support clients, establishing wealthy environments. A prototype version of the system has been proposed which has been validated by formal analysis and simulation. This prototype can be used as a necessary experience and as a reference point when building a target system and meeting requirements typical for context-aware and pro-active systems based on IoT networks which process massive data streams.
Estimating software development effort using fuzzy clustering‐based analogy

Abstract During the past decades, many studies have been carried out in an attempt to build accurate software development effort estimation techniques. However, none of the techniques proposed has proven to be successful at predicting software effort in all circumstances. Among these techniques, analogy‐based estimation has gained significant popularity within software engineering community because of its outstanding performance and ability to mimic the human problem solving approach. One of the challenges facing analogy‐based effort estimation is how to predict effort when software projects are described by a mixture of continuous and categorical features. To address this issue, the present study proposes an improvement of our former 2FA‐kprototypes technique referred to as 2FA‐cmeans. 2FA‐cmeans uses a clustering technique, called general fuzzy c‐means, which is a generalization of the fuzzy c‐means clustering technique to cluster objects with mixed attributes. The performance of 2FA‐cmeans was evaluated and compared with that of our former 2FA‐kprototypes technique as well as classical analogy over six datasets that are quite diverse and have different sizes. Empirical results showed that 2FA‐cmeans outperforms the two other analogy techniques using both all‐in and jackknife evaluation methods. This was also confirmed by the win–tie–loss statistics and the Scott–Knott test.
A Generalized Stacking for Implementing Ensembles of Gradient Boosting Machines

The gradient boosting machine is one of the powerful tools for solving regression problems. In order to cope with its shortcomings, an approach for constructing ensembles of gradient boosting models is proposed. The main idea behind the approach is to use the stacking algorithm in order to learn a second-level meta-model which can be regarded as a model for implementing various ensembles of gradient boosting models. First, the linear regression of the gradient boosting models is considered as the simplest realization of the meta-model under the condition that the linear model is differentiable with respect to its coefficients (weights). Then it is shown that the proposed approach can be simply extended on arbitrary differentiable combination models, for example, on neural networks that are differentiable and can implement arbitrary functions of gradient boosting models. Various numerical examples illustrate the proposed approach.
Local Processing of Massive Databases with R: A National Analysis of a Brazilian Social Programme

The analysis of massive databases is a key issue for most applications today and the use of parallel computing techniques is one of the suitable approaches for that. Apache Spark is a widely employed tool within this context, aiming at processing large amounts of data in a distributed way. For the Statistics community, R is one of the preferred tools. Despite its growth in the last years, it still has limitations for processing large volumes of data in single local machines. In general, the data analysis community has difficulty to handle a massive amount of data on local machines, often requiring high-performance computing servers. One way to perform statistical analyzes over massive databases is combining both tools (Spark and R) via the sparklyr package, which allows for an R application to use Spark. This paper presents an analysis of Brazilian public data from the Bolsa Família Programme (BFP—conditional cash transfer), comprising a large data set with 1.26 billion observations. Our goal was to understand how this social program acts in different cities, as well as to identify potentially important variables reflecting its utilization rate. Statistical modeling was performed using random forest to predict the utilization rated of BFP. Variable selection was performed through a recent method based on the importance and interpretation of variables in the random forest model. Among the 89 variables initially considered, the final model presented a high predictive performance capacity with 17 selected variables, as well as indicated high importance of some variables for the observed utilization rate in income, education, job informality, and inactive youth, namely: family income, education, occupation and density of people in the homes. In this work, using a local machine, we highlighted the potential of aggregating Spark and R for analysis of a large database of 111.6 GB. This can serve as proof of concept or reference for other similar works within the Statistics community, as well as our case study can provide important evidence for further analysis of this important social support programme.
Exploiting Multi-Emotion Relations at Feature and Label Levels for Emotion Tagging

The dependence among emotions is crucial to boost emotion tagging. In this paper, we propose a novel emotion tagging method, that thoroughly explores emotion relations from both the feature and label levels. Specifically, a graph convolutional network is introduced to inject local dependence among emotions into the model at the feature level, while an adversarial learning strategy is applied to constrain the joint distribution of multiple emotions at the label level. In addition, a new balanced loss function that mitigates the adverse effects of intra-class and inter-class imbalance is introduced to deal with the imbalance of emotion labels. Experimental results on several benchmark databases demonstrate the superiority of the proposed method compared to state-of-the-art works.
Cross-modal Non-linear Guided Attention and Temporal Coherence in Multi-modal Deep Video Models

Videos have data in multiple modalities, e.g., audio, video, text (captions). Understanding and modeling the interaction between different modalities is key for video analysis tasks like categorization, object detection, activity recognition, etc. However, data modalities are not always correlated --- so, learning when modalities are correlated and using that to guide the influence of one modality on the other is crucial. Another salient feature of videos is the coherence between successive frames due to continuity of video and audio, a property that we refer to as temporal coherence. We show how using non-linear guided cross-modal signals and temporal coherence can improve the performance of multi-modal machine learning (ML) models for video analysis tasks like categorization. Our experiments on the large-scale YouTube-8M dataset show how our approach significantly outperforms state-of-the-art multi-modal ML models for video categorization. The model trained on the YouTube-8M dataset also showed good performance on an internal dataset of video segments from actual Samsung TV Plus channels without retraining or fine-tuning, showing the generalization capabilities of our model.
Memory Recursive Network for Single Image Super-Resolution

Recently, extensive works based on convolutional neural network (CNN) have shown great success in single image super-resolution (SISR). In order to improve the SISR performance while reducing the number of model parameters, some methods adopt multiple recursive layers to enhance the intermediate features. However, in the recursive process, these methods only use the output features of current stage as the input of the next stage and neglect the output features of historical stages, which degrades the performance of the recursive blocks. The long-term dependencies can only be learned implicitly during the recursive processes. To address these issues, we propose the memory recursive network (MRNet) to make full use of the output features at each stage. The proposed MRNet utilizes a memory recursive module (MRM) to generate features for each recursive stage, and then these features are fused by our proposed ShuffleConv block. Specifically, MRM adopts a memory updater block to explicitly model the long-term dependencies between the output features of historical recursive stages. The output features from the memory updater will be used as the input of the next recursive stage and will be continuously updated during the recursions. To reduce the number of parameters and ease the training difficulty, we introduce a ShuffleConv module to fuse the features from different recursive stages, which is much more effective than using plain convolutional combinations. Comprehensive experiments demonstrate that the proposed MRNet achieves state-of-the-art SISR performance while using much fewer parameters.
An R-Convolution Graph Kernel Based on Fast Discrete-Time Quantum Walk

In this article, a novel R-convolution kernel, named the fast quantum walk kernel (FQWK), is proposed for unattributed graphs. In FQWK, the similarity of the neighborhood-pair substructure between two nodes is measured via the superposition amplitude of quantum walks between those nodes. The quantum interference in this kind of local substructures provides more information on the substructures so that FQWK can capture finer-grained local structural features of graphs. In addition, to efficiently compute the transition amplitudes of multistep discrete-time quantum walks, a fast recursive method is designed. Thus, compared with all the existing kernels based on the quantum walk, FQWK has the highest computation speed. Extensive experiments demonstrate that FQWK outperforms state-of-the-art graph kernels in terms of classification accuracy for unattributed graphs. Meanwhile, it can be applied to distinguish a larger family of graphs, including cospectral graphs, regular graphs, and even strong regular graphs, which are not distinguishable by classical walk-based methods.
A Trust Game Model of Service Cooperation in Cloud Computing

With the development of cloud services, many network fraudulent incidents have damaged the interests of transaction subjects and brought a great crisis to online service. To analyze and solve the key conditions for different participants to achieve mutual trust and win-win situations in the cloud computing, we propose a trust prediction game model composed of the supervisor, cloud service provider (CSP), and user. Firstly, we use the Bayesian formula to predict the behavior trust attribute and propose the trust service map function and weight algorithm by the principle of maximum dispersion. Secondly, based on the perspective of the user, we establish a two-party dynamic game model and discuss the existence possibility of game equilibrium. Thirdly, based on the perspective of CSP, we establish a tripartite game model, analyze several possible equilibria, and obtain the equilibrium strategy from a mixed perspective. Finally, the experimental results show that the supervision has a significant impact on the strategy selection of the players, verify the correctness and effectiveness of various game parameters, exhibition the better advantage than several articles under some different standards, and make a useful exploration for building a secure and trusted cloud service environment.
LAL: Linguistically Aware Learning for Scene Text Recognition

Scene text recognition is the task of recognizing character sequences in images of natural scenes. The considerable diversity in the appearance of text in a scene image and potentially highly complex backgrounds make text recognition challenging. Previous approaches employ character sequence generators to analyze text regions and, subsequently, compare the candidate character sequences against a language model. In this work, we propose a bimodal framework that simultaneously utilizes visual and linguistic information to enhance recognition performance. Our linguistically aware learning (LAL) method effectively learns visual embeddings using a rectifier, encoder, and attention decoder approach, and linguistic embeddings, using a deep next-character prediction model. We present an innovative way of combining these two embeddings effectively. Our experiments on eight standard benchmarks show that our method outperforms previous methods by large margins, particularly on rotated, foreshortened, and curved text. We show that the bimodal approach has a statistically significant impact. We also contribute a new dataset, and show robust performance when LAL is combined with a text detector in a pipelined text spotting framework.
VVSec

Volumetric video (VV) streaming has drawn an increasing amount of interests recently with the rapid advancements in consumer VR/AR devices and the relevant multimedia and graphics research. While the resource and performance challenges in volumetric video streaming have been actively investigated by the multimedia community, the potential security and privacy concerns with this new type of multimedia have not been studied. We for the first time identify an effective threat model that extracts 3D face models from volumetric videos and compromises face ID-based authentications To defend against such attack, we develop a novel volumetric video security mechanism, namely VVSec, which makes benign use of adversarial perturbations to obfuscate the security and privacy-sensitive 3D face models. Such obfuscation ensures that the 3D models cannot be exploited to bypass deep learning-based face authentications. Meanwhile, the injected perturbations are not perceivable by the end-users, maintaining the original quality of experience in volumetric video streaming. We evaluate VVSec using two datasets, including a set of frames extracted from an empirical volumetric video and a public RGB-D face image dataset. Our evaluation results demonstrate the effectiveness of both the proposed attack and defense mechanisms in volumetric video streaming.
Beautify As You Like

Customizable makeup transfer, which aims to transfer the makeup from an arbitrary reference face to a source face, is widely demanded in many applications such as short video platforms and online meeting applications. However, existing methods are neither user-friendly nor sufficiently fast. In this demo, we present the first fast makeup transfer system named as Fast Pose and expression robust Spatial-Aware GAN (FPSGAN). With a novel Attentive Makeup Morphing (AMM) module, FPSGAN is robust to face pose and expression. Moreover, it can achieve shade-controllable and partial makeup, improving the system's user-friendliness. In addition, FPSGAN is light-weighted and fast. To sum up, FPSGAN is the first fast customizable makeup transfer system to enable users to beautify themselves as they like.