Reviewerly/exHarmony huggingface dataset:
Autoencoders reloaded

In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called "auto-associative multilayer perceptrons") were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least squares solutions, pp 134-151. Springer, 1971), equivalent to principal component analysis (PCA) Hotelling (Educ Psychol 24(6/7):417-441, 1993); Jolliffe (Principal component analysis, springer series in statistics, 2nd edn. Springer, New York ). That is, AE are able to derive the eigenvalues that represent the amount of variance covered by each component even with the presence of the nonlinear function (sigmoid-like, or any other nonlinear functions) present on their hidden units. Today, with the renewed interest in "deep neural networks" (DNN), multiple types of (deep) AE are being investigated as an alternative to manifold learning Cayton (Univ California San Diego Tech Rep 12(1-17):1, 2005) for conducting nonlinear feature extraction or fusion, each with its own specific (expected) properties. Many of those AE are currently being developed as powerful, nonlinear encoder-decoder models, or used to generate reduced and discriminant feature sets that are more amenable to different modeling and classification tasks. In this paper, we start by recalling and further clarifying the main conclusions of Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), supporting them by extensive empirical evidences, which were not possible to be provided previously (in 1988), due to the dataset and processing limitations. Upon full understanding of the underlying mechanisms, we show that it remains hard (although feasible) to go beyond the state-of-the-art PCA/SVD techniques for auto-association. Finally, we present a brief overview on different autoencoder models that are mainly in use today and discuss their rationale, relations and application areas.
Multi-UAV Coverage through Two-Step Auction in Dynamic Environments

The cooperation of multiple unmanned aerial vehicles (Multi-UAV) can effectively solve the area coverage problem. However, developing an online multi-UAV coverage approach remains a challenge due to energy constraints and environmental dynamics. In this paper, we design a comprehensive framework for area coverage with multiple energy-limited UAVs in dynamic environments, which we call MCTA (Multi-UAV Coverage through Two-step Auction). Specifically, the online two-step auction mechanism is proposed to select the optimal action. Then, an obstacle avoidance mechanism is designed by defining several heuristic rules. After that, considering energy constraints, we develop the reverse auction mechanism to balance workload between multiple UAVs. Comprehensive experiments demonstrate that MCTA can achieve a high coverage rate while ensuring a low repeated coverage rate and average step deviation in most circumstances.
Mask-guided cycle-GAN for specular highlight removal

Specular highlight removal is an important yet challenging problem in image enhancement. Recent methods based on deep learning and trained by massive paired or unpaired data have demonstrated promising performance for this task. Methods based on unpaired data have recently gained more attention for easier training data collection. In this paper, we present a Mask-Guided Cycle-GAN for specular highlight removal on unpaired data. Incorporating the idea that specular highlight mainly has characteristics in lightness, we attempt to train a module only on luminance channel before considering all channels, and then adopt the training results to guide the subsequent highlight removal module. We further convert the highlight removal problem to image-to-image translation by using cycle-consistent adversarial network (Cycle-GAN). In the proposed network, a non-negative matrix factorization (NMF) based method is incorporated to obtain accurate highlight masks. The proposed method is evaluated using the specular highlight image quadruples (SHIQ) and the LIME datasets, and the advantages are demonstrated via comparative experimental results.
Denseformer: A dense transformer framework for person re‐identification

Abstract Transformer has shown its effectiveness and advantage in many computer vision tasks, for example, image classification and object re‐identification (ReID). However, existing vision transformers are stacked layer by layer, lacking direct information exchange among every layer. Inspired by DenseNet, we propose a dense transformer framework (termed Denseformer) that connects each layer to every other layer through class tokens. We demonstrate that Denseformer can consistently achieve better performance on person ReID tasks across datasets (Market‐1501, DukeMTMC, MSMT17, and Occluded‐Duke), only at a negligible increase of computation. We show that Denseformer has several compelling advantages: it pays more attention to the main parts of human bodies and obtains discriminative global features.
PB-GCN: Progressive binary graph convolutional networks for skeleton-based action recognition

Skeleton-based action recognition is an essential yet challenging visual task, whose accuracy has been remarkably improved due to the successful application of graph convolutional networks (GCNs). However, high computation cost and memory usage hinder their deployment on resource-constrained environment. To deal with the issue, in this paper, we introduce two novel progressive binary graph convolutional network for skeleton-based action recognition PB-GCN and PB-GCN * , which can obtain significant speed-up and memory saving. In PB-GCN, the filters are binarized, and in PB-GCN * , both filters and activations are binary. Specifically, we propose a progressive optimization, i.e., employing ternary models as the initialization of binary GCNs (BGCN) to improve the representational capability of binary models. Moreover, the center loss is exploited to improve the training procedure for better performance. Experimental results on two public benchmarks (i.e., Skeleton-Kinetics and NTU RGB + D) demonstrate that the accuracy of the proposed PB-GCN and PB-GCN * are comparable to their full-precision counterparts and outperforms the state-of-the-art methods, such as BWN, XNOR-Net, and Bi-Real Net.
Spatial analysis of global Bitcoin mining

Bitcoin mining is not only the fundamental process to maintain Bitcoin network, but also the key linkage between the virtual cryptocurrency and the physical world. A variety of issues associated with it have been raised, such as network security, cryptoasset management and sustainability impacts. Investigating Bitcoin mining from a spatial perspective will provide new angles and empirical evidence with respect to extant literature. Here we explore the spatial distribution of Bitcoin mining through bottom-up tracking and geospatial statistics. We find that mining activity has been detected at more than 6000 geographical units across 139 countries and regions, which is in line with the distributed design of Bitcoin network. However, in terms of computing power, it has demonstrated a strong tendency of spatial concentration and association with energy production locations. We also discover that the spatial distribution of Bitcoin mining is dynamic, which fluctuates with diverse patterns, according to economic and regulatory changes.
Research on the Formation Mechanism of Health Insurance Fraud in China: From the Perspective of the Tripartite Evolutionary Game

The problem of fraud in China's health insurance has existed for a long time and is becoming more serious, which needs to be solved urgently. This article constructs a tripartite evolutionary game model to study the formation mechanism of the Health Insurance fraud, game participants including medical administrative organization (MAO, a government department responsible for health insurance supervision), medical institutions (MI, such as hospitals), and insured individuals (II, who participating in medical and healthcare insurance). By analyzing the equilibrium of the tripartite evolutionary game, this paper makes an in-depth study on the formation and resolution of health insurance fraud. The results show that: (1) How to prevent the fraud behavior of the medical institutions is the difficulty and core of the problem. It is necessary to achieve effective supervision of the MAO, improve the internal management of the MI and give play to the supervisory role of the II. (2) The regulatory behavior of the MAO needs to focus on protecting the interests of the II, not only to encourage them to actively play the role of supervision and reporting but also to prevent their collusion with MI. (3) On the one hand, the MAO needs to strengthen supervision and increase the punishment for fraud. On the other hand, they also need to take incentive measures to guide all subjects to form a sound internal management mechanism.
Exploring Blockchain Technology and Enterprise Resource Planning System: Business and Technical Aspects, Current Problems, and Future Perspectives

An Enterprise Resource-Planning (ERP) system is a complex, highly integrated software package for business organizations. However, an ERP system still has its limits, since the ERP business model is centered around individual enterprises. Today there is a great need for closer interactions between enterprises worldwide, a necessity that ERP systems face great difficulties with, when expanding to a full-on ecosystem. By integrating Blockchain Technology (BT) with ERP, we are entering a new epoch of decentralization, allowing organizations to function frictionless in a unified ecosystem. This study highlights the importance of integrating BT and ERP systems as well as explores the major prospective pillars for utilizing BT and ERP. This paper contributes to the academic literature, since there is little research evidence concerning the technical and business aspects of BT and ERP, so it fills the gap by exploring the main potential areas for deploying BT and ERP systems.
Disentangled Representations and Hierarchical Refinement of Multi-Granularity Features for Text-to-Image Synthesis

In this paper, we focus on generating photo-realistic images from given text descriptions. Current methods first generate an initial image and then progressively refine it to a high-resolution one. These methods typically indiscriminately refine all granularity features output from the previous stage. However, the ability to express different granularity features in each stage is not consistent, and it is difficult to express precise semantics by further refining the features with poor quality generated in the previous stage. Current methods cannot refine different granularity features independently, resulting in that it is challenging to clearly express all factors of semantics in generated image, and some features even become worse. To address this issue, we propose a Hierarchical Disentangled Representations Generative Adversarial Networks (HDR-GAN) to generate photo-realistic images by explicitly disentangling and individually modeling the factors of semantics in the image. HDR-GAN introduces a novel component called multi-granularity feature disentangled encoder to represent image information comprehensively through explicitly disentangling multi-granularity features including pose, shape and texture. Moreover, we develop a novel Multi-granularity Feature Refinement (MFR) containing a Coarse-grained Feature Refinement (CFR) model and a Fine-grained Feature Refinement (FFR) model. CFR utilizes coarse-grained disentangled representations (e.g., pose and shape) to clarify category information, while FFR employs fine-grained disentangled representations (e.g., texture) to reflect instance-level details. Extensive experiments on two well-studied and publicly available datasets (i.e., CUB-200 and CLEVR-SV) demonstrate the rationality and superiority of our method.
Redeemable Platform Currencies

Abstract Can massive online retailers such as Amazon and Alibaba issue digital tokens that potentially compete with bank debit accounts? There is a long history of trading stamps and loyalty points, but new technologies are poised to sharply raise the significance of redeemable assets as a store of value. Here, we develop a simple stylized model of redeemable tokens that can be used to study sales and pricing strategies for issuing tokens, including ICOs. Our central finding is that platforms can potentially earn higher revenues by making tokens non-tradable unless they can generate a sufficiently high outside-platform convenience yield.
From Traditional Product Lifecycle Management Systems to Blockchain-Based Platforms

Background: Several product lifecycle management systems (PLMs) have been implemented in the industrial sector for managing the data of the product from the design up to the disposal or recycling stage. However, these PLMs face certain challenges in managing the complex and decentralized product lifecycles. Methods: To this aim, this work investigates the currently implemented PLMs used in industries through the exploration of various software reviews and selection websites. Accordingly, these existing PLMs are quantitatively compared and analyzed. Results: The analysis shows that most of the existing PLMs do not contain all the required features; therefore, industries integrate different software to create a full-fledged PLM system. However, this practice results in reducing the overall system efficiency. In this context, this paper assesses and recommends a blockchain-based innovative solution that overcomes the challenges of existing PLMs, hence increasing the overall system efficiency. Furthermore, this work argues, in a logical way, that the recommended blockchain-based platform provides a secure and connected infrastructure for data handling, processing, and storage at different stages of the product lifecycle. Conclusions: This work can be considered among the first to compare the currently implemented PLMs with a novel blockchain-based method. Thus, the stakeholders can utilize the outputs of this research in their analysis and decision-making processes for implementing the blockchain in their organizations.
Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition

The training of Human Activity Recognition (HAR) models requires a substantial amount of labeled data. Unfortunately, despite being trained on enormous datasets, most current models have poor performance rates when evaluated against anonymous data from new users. Furthermore, due to the limits and problems of working with human users, capturing adequate data for each new user is not feasible. This paper presents semi-supervised adversarial learning using the LSTM (Long-short term memory) approach for human activity recognition. This proposed method trains annotated and unannotated data (anonymous data) by adapting the semi-supervised learning paradigms on which adversarial learning capitalizes to improve the learning capabilities in dealing with errors that appear in the process. Moreover, it adapts to the change in human activity routine and new activities, i.e., it does not require prior understanding and historical information. Simultaneously, this method is designed as a temporal interactive model instantiation and shows the capacity to estimate heteroscedastic uncertainty owing to inherent data ambiguity. Our methodology also benefits from multiple parallel input sequential data predicting an output exploiting the synchronized LSTM. The proposed method proved to be the best state-of-the-art method with more than 98% accuracy in implementation utilizing the publicly available datasets collected from the smart home environment facilitated with heterogeneous sensors. This technique is a novel approach for high-level human activity recognition and is likely to be a broad application prospect for HAR.
Integrating Energy Smart Grid’s ontologies through multi-objective particle swarm optimization algorithm with competitive mechanism

Smart city seeks safer, resource-oriented, environment friendly, and cost-effective energy solutions, and due to the distributed and independent nature of energy management systems, Energy Smart Grid (ESG) quickly evolves from a widely accepted concept to an industrial reality. To monitor the energy consumption across various domains and assist efficient energy distribution and storage, it is necessary to integrate various ESG agents’ ontologies. To this end, this work models ESG’s Ontology Matching Problem (ESG-OMP) as a discrete Multi-Objective Optimization Problem (MOOP), which simultaneously optimizes the alignment’s completeness and correctness through determining an optimal entity corresponding set. After that, a Multi-Objective Particle Swarm Optimization Algorithm With Competitive Mechanism (MOPSO-CM) is proposed to address this issue by trading off the alignment’s completeness and correctness, which introduces the CM to update the particles with the pairwise competition that performed in the swarm of current generation. Moreover, an instance-based hybrid similarity measure is used to distinguish the heterogeneous ontology entities. The experiment uses OAEI’s testing datasets, 13 smart grid’s ontology matching tasks and 5 energy smart grid’s ontology matching tasks to test MOPSO-CM’s performance. The experimental results show that MOPSO-CM can effectively address various heterogeneous ontology matching problems and determine high-quality ESG’s ontology alignments.
Locality-aware Load-Balancing For Serverless Clusters

While serverless computing provides more convenient abstractions for developing and deploying applications, the Function-as-a-Service (FaaS) programming model presents new resource management challenges for the FaaS provider. In this paper, we investigate load-balancing policies for serverless clusters. Locality, i.e., running repeated invocations of a function on the same server, is a key determinant of performance because it increases warm-starts and reduces cold-start overheads. We find that the locality vs. load tradeoff is crucial and presents a large design space.
Digital Teaching Assistant for the Python Programming Course

The digitalization of modern economic sectors leads to the lack of software developers in many industries. Hence, there is the increasing demand for mass training of IT specialists in colleges and universities. With the increasing students count, the workload for teachers of disciplines connected with IT tremendously increases. The automation of software developers training allows to reduce the burden on teachers by saving them from routine work. In this paper, we provide the details regarding the development of the Digital Teaching Assistant (DTA), the information system used in Federal State Budget Educational Institution of Higher Education “MIREA – Russian Technological University”, integrated into the Python programming course. The considered system consists of two components, namely, the DTA core, and the DTA web application, both components are implemented using the Python programming language. The DTA core generates tasks and tests for students, and the DTA web application allows students to interact with the core. We describe the architecture of DTA and visualize the statistics collected during system operation.
Domain Adaptational Text Steganalysis Based on Transductive Learning

Traditional text steganalysis methods rely on a large amount of labeled data. At the same time, the test data should be independent and identically distributed with the training data. However, in practice, a large number of text types make it difficult to satisfy the i.i.d condition between the training set and the test set, which leads to the problem of domain mismatch and significantly reduces the detection performance. In this paper, we draw on the ideas of domain adaptation and transductive learning to design a novel text steganalysis method. In this method, we design a distributed adaptation layer and adopt three loss functions to achieve domain adaptation, so that the model can learn the domain-invariant text features. The experimental results show that the method has better steganalysis performance in the case of domain mismatch.
Weakly-supervised Cerebrovascular Segmentation Network with Shape Prior and Model Indicator

Labeling cerebral vessels requires domain knowledge in neurology and could be extremely laborious, and there is a scarcity of public annotated cerebrovascular datasets. Traditional machine learning or statistical models could yield decent results on thick vessels with high contrast while having poor performance on those regions of low contrast. In our work, we employ a statistic model as noisy labels and propose a Transformer-based architecture which utilizes Hessian shape prior as soft supervision. It enhances the learning ability of the network to tubular structures, so that the model can make more accurate predictions on refined cerebrovascular segmentation. Furthermore, to combat the overfitting towards noisy labels as model training, we introduce an effective label extension strategy that only calls for a few manual strokes on one sample. These supplementary labels are not used for supervision but only as an indicator to tell where the model keeps the most generalization capability, so as to further guide the model selection in validation. Our experiments are carried out on a public TOF-MRA dataset from MIDAS data platform, and the results demonstrate that our method shows superior performance on cerebrovascular segmentation which achieves Dice of 0.831±0.040 in the dataset.
Super-robust digital image correlation based on learning template

Digital image correlation (DIC) is a well-established noncontact displacement measurement method. Nevertheless, there are still three aspects that can be further improved: accuracy, efficiency, and robustness. Compared with accuracy and efficiency, there are few studies on the robustness of DIC method. However, there are many factors that make the DIC algorithm no longer robust, such as cracks, discontinuous displacements, large inter-frame deformation, partial occlusion or contamination, perspective changes, image noise, and illumination variations. This work proposes a learning template based super-robust DIC method which mitigates the influence of all these disadvantage factors in a straightforward and unified approach, significantly improving DIC measurement robustness compared to classic approaches. During the implementation of this method, the online learning and offline learning methods are adopted to keep the correlation at a high level. Interestingly, the learning template can be seen as a deep learning method with only one layer, so this work also explicitly explains why deep learning can be applied to the DIC field. Uniaxial tension tests were conducted and showed that the matching error can meet the accuracy requirements of the initial value. Experiments on landslide images also show that our method significantly improves the matching robustness.