Reviewerly/exHarmony huggingface dataset:
Autonomous Navigation in Complex Environments with Deep Multimodal Fusion Network

Autonomous navigation in complex environments is a crucial task in time-sensitive scenarios such as disaster response or search and rescue. However, complex environments pose significant challenges for autonomous platforms to navigate due to their challenging properties: constrained narrow passages, unstable pathway with debris and obstacles, or irregular geological structures and poor lighting conditions. In this work, we propose a multimodal fusion approach to address the problem of autonomous navigation in complex environments such as collapsed cites, or natural caves. We first simulate the complex environments in a physics-based simulation engine and collect a large-scale dataset for training. We then propose a Navigation Multimodal Fusion Network (NMFNet) which has three branches to effectively handle three visual modalities: laser, RGB images, and point cloud data. The extensively experimental results show that our NMFNet outperforms recent state of the art by a fair margin while achieving real-time performance. We further show that the use of multiple modalities is essential for autonomous navigation in complex environments. Finally, we successfully deploy our network to both simulated and real mobile robots.
The impact of blockchain in banking processes: the Interbank Spunta case study

This paper discusses a case study of blockchain used to address real business problems in the Italian banking sector. We use an exploratory case of the banking research and innovation centre promoted by the Italian Banking Association (ABI Lab), and show how the Interbank Spunta project implies the application of blockchain in interbank processes, with the aim at providing greater data transparency and visibility, faster execution, and the possibility of transfer checks and money directly within the application. We shed light on the new challenges faced by bankers and develop several propositions as well as potential applications of blockchain in banking to guide future research on this topic and inspire bank managers to take advantages by new financial technologies applied to different banks’ business lines.
Research advances on blockchain-as-a-service: architectures, applications and challenges

Due to the complexity of blockchain technology, it usually costs too much effort to build, maintain and monitor a blockchain system that supports a targeted application. To this end, the emerging “Blockchain as a Service” (BaaS) makes the blockchain and distributed ledgers more accessible, particularly for businesses, by reducing costs and overheads. BaaS combines the high computing power of cloud computing, the pervasiveness of IoT and the decentralization of blockchain, allowing people to build their own applications while ensuring the transparency and openness of the system. This paper surveys the research outputs of both academia and industry. First, it introduces the representative architectures of BaaS systems and then summarizes the research contributions of BaaS from the technologies for service provision, roles, container and virtualization, interfaces, customization and evaluation. The typical applications of BaaS in both academic and practical domains are also introduced. At present, the research on the blockchain is abundant, but research on BaaS is still in its infancy. Six challenges of BaaS are concluded in this paper for further study directions.
Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction

Recently, image feature extraction algorithms based on 2D discriminant local preserving projection (2DDLPP) algorithms have been successfully applied in many fields. The 2DDLPP can maintain the discrimination information of the local intrinsic manifold structure using two-dimensional image representation data. However, the 2DDLPP algorithm encounters the problem of the sensitivity of overlapping points (outliers) and requires high computational cost in real-world applications. In order to resolve the problems mentioned above, we introduce a new elastic feature extraction algorithm called the sparse fuzzy 2D discriminant local preserving projection (SF2DDLPP). First, the membership matrix is calculated using the fuzzy k-nearest neighbours (FKNN), which is applied to the intraclass weighted matrix and the interclass weighted matrix. Second, two theorems are developed to directly solve the generalized eigenfunctions. Finally, the optimal sparse fuzzy 2D discriminant projection matrix is regressed using the elastic net regression. The experiments show the effectiveness and stability of this algorithm on several face (ORL, Yale, AR and Yale B), USPS and palm print datasets.
Cognitive structure learning model for hierarchical multi-label text classification

The human mind grows in learning new knowledge, which finally organizes and develops a basic mental pattern called cognitive structure. Hierarchical multi-label text classification (HMLTC), a fundamental but challenging task in many real-world applications, aims to classify the documents with hierarchical labels to form a resembling cognitive structure learning process. Existing approaches for HMLTC mainly focus on partial new knowledge learning or the global cognitive-structure-like label structure utilization in a cognitive view. However, the complete cognitive structure learning model is a unity that is indispensably constructed by the global label structure utilization and partial knowledge learning, which is ignored among those HMLTC approaches. To address this problem, we will imitate the cognitive structure learning process into the HMLTC learning and propose a unified framework called Hierarchical Cognitive Structure Learning Model (HCSM) in this paper. HCSM is composed of the Attentional Ordered Recurrent Neural Network (AORNN) submodule and Hierarchical Bi-Directional Capsule (HBiCaps) submodule. Both submodules utilize the partial new knowledge and global hierarchical label structure comprehensively for the HMLTC task. On the one hand, AORNN extracts the semantic vector as partial new knowledge from the original text by the word-level and hierarchy-level embedding granularities. On the other hand, AORNN builds the hierarchical text representation learning corresponding to the global label structure by the document-level neurons ordering. HBiCaps employs an iteration to form a unified label categorization process similar to cognitive-structure learning: firstly, using the probability computation of local hierarchical relationships to maintain partial knowledge learning; secondly, modifying the global hierarchical label structure based on the dynamic routing mechanism between capsules. Moreover, the experimental results on four benchmark datasets demonstrate that HCSM outperforms or matches state-of-the-art text classification methods.
Standing Up or Standing By: Understanding Bystanders’ Proactive Reporting Responses to Social Media Harassment

Social media harassment, a cyberbullying behavior, poses a serious threat to users and platform owners of social media. In this paper, we contextualize the bystander intervention framework and reporting literature to social media in order to understand why bystanders report social media harassment. Our contextualized intervention framework focuses on three sociotechnical aspects—the online social environment, characteristics of the technology platform, and their interplay—that explain bystander reporting on social media platforms. We tested the model using data gathered from active Facebook users. Our findings direct practitioners’ attention to the role of the platform in encouraging bystanders to help stop social media harassment. For policy makers, our findings direct attention to supporting programs that encourage social media users to feel responsible for reporting harassment and making transparent the outcomes of reporting social media harassment using anonymous reporting tools. For platform owners, our findings direct attention to investing in tools that enable anonymous reporting, to fostering a climate that encourages reporting, and to ensuring that all users understand that reporting social media harassment results in swift, effective responses from platform owners. Taken together, we believe our research offer insight into how to build safer and secure social media platforms.
Systematic Literature Review of Dialectal Arabic: Identification and Detection

It is becoming increasingly difficult to know who is working on what and how in computational studies of Dialectal Arabic. This study comes to chart the field by conducting a systematic literature review that is intended to give insight into the most and least popular research areas, dialects, machine learning approaches, neural network input features, data types, datasets, system evaluation criteria, publication venues, and publication trends. It is a review that is guided by the norms of systematic reviews. It has taken account of all the research that adopted a computational approach to dialectal Arabic identification and detection and that was published between 2000 and 2020. It collected, analyzed, and collated this research, discovered its trends, and identified research gaps. It revealed, inter alia, that our research effort has not been directed evenly between speech and text or between the vernaculars; there is some bias favoring text over speech, regional varieties over individual vernaculars, and Egyptian over all other vernaculars. Furthermore, there is a clear preference for shallow machine learning approaches, for the use of n-grams, TF-IDF, and MFCC as neural network features, and for accuracy as a statistical measure of validation of results. This paper also pointed to some glaring gaps in the research: (1) total neglect of Mauritanian and Bahraini in the continuous Arabic language area and of such enclave varieties as Anatolian Arabic, Khuzistan Arabic, Khurasan Arabic, Uzbekistan Arabic, the Subsaharan Arabic of Nigeria and Chad, Djibouti Arabic, Cypriot Arabic and Maltese; (2) scarcity of city dialect resources; (3) rarity of linguistic investigations that would complement our research; (4) and paucity of deep machine learning experimentation.
TanhExp: A smooth activation function with high convergence speed for lightweight neural networks

Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance. Herein, a novel activation function named as Tanh Exponential Activation Function (TanhExp) is proposed which can improve the performance for these networks on image classification task significantly. The definition of TanhExp is f(x) = x tanh(ex). The simplicity, efficiency, and robustness of TanhExp on various datasets and network models is demonstrated and TanhExp outperforms its counterparts in both convergence speed and accuracy. Its behaviour also remains stable even with noise added and dataset altered. It is shown that without increasing the size of the network, the capacity of lightweight neural networks can be enhanced by TanhExp with only a few training epochs and no extra parameters added.
Heterogeneous Hierarchical Feature Aggregation Network for Personalized Micro-Video Recommendation

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. Traditional approaches consider micro-video recommendation as a matching task and ignore the rich relationships among users and micro-videos from various modalities (e.g., visual, acoustic, and textual). Recently, GNN-based approaches show promising performance for the micro-video recommendation task. However, they mainly focus on the homogeneous graph which includes only one type of nodes or relations, and cannot be applied to the heterogeneous graph which consists of users, micro-videos, and related multi-modal information. In this paper, a novel Heterogeneous Hierarchical Feature Aggregation Network (HHFAN) is proposed for personalized micro-video recommendation. Our goal is to explore the highly complicated relationship information among users, micro-videos and related multi-modal information from a modality-aware Heterogeneous Information Graph (M-HIG), and thus generate high-quality user and micro-video embeddings for recommendation. The proposed model consists of two key components: (1) In data structure level, we build a heterogeneous graph and utilize a random walk based sampling strategy to sample neighbors for users and micro-videos. (2) In representation learning level, we design a hierarchical feature aggregation network including the intra- and inter-type feature aggregation networks to better capture the complex structure and rich semantic information in the heterogeneous graph. We evaluate our method on two real-world datasets and the results demonstrate that the proposed model outperforms the baseline methods.
Robust light beam diffractive shaping based on a kind of compact all-optical neural network

A kind of compact all-optical learning-based neural network has been constructed and characterized for efficiently performing a robust layered diffractive shaping of laser beams. The data-driven control lightwave strategy demonstrates some particular advantages such as smart or intelligent light beam manipulation, optical data statistical inference and incident beam generalization. Based on the proposed method, several typical aberrated light fields can be effectively modulated into the desired fashion including the featured flat-top beams, an arrayed sub-beam arrangement and complex annular fringes compared with conventional GS-based DOEs. An actual THz laser is utilized to evaluate the effectiveness of the method developed.
A Generative Adversarial Network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images

In this paper, we proposed a Denoising Super-resolution Generative Adversarial Network (DnSRGAN) method for high-quality super-resolution reconstruction of noisy cardiac magnetic resonance (CMR) images.The proposed method is based on feed-forward denoising convolutional neural network (DnCNN) and SRGAN architecture. Firstly, we used a feed-forward denoising neural network to pre-denoise the CMR image to ensure that the input is a clean image. Secondly, we use the gradient penalty (GP) method to solve the problem of the discriminator gradient disappearing, which improves the convergence speed of the model. Finally, a new loss function is added to the original SRGAN loss function to monitor GAN gradient descent to achieve more stable and efficient model training, thereby providing higher perceptual quality for the super-resolution of CMR images.We divided the tested cardiac images into 3 groups, each group of 25 images. Then, we calculated the Peak Signal to Noise Ratio (PSNR) /Structural Similarity (SSIM) between Ground Truth (GT) and the images generated by super-resolution, used them to evaluate our model. We compared with the current widely used method: Bicubic ESRGAN and SRGAN, our method has better reconstruction quality and higher PSNR/SSIM score.We used DnCNN to denoise the CMR image, and then using the improved SRGAN to perform super-resolution reconstruction of the denoised image, we can solve the problem of high noise and artifacts that cause the cardiac image to be reconstructed incorrectly during super-resolution.
Densely nested top-down flows for salient object detection

With the goal of identifying pixel-wise salient object regions from each input image, salient object detection (SOD) has been receiving great attention in recent years. One kind of mainstream SOD method is formed by a bottom-up feature encoding procedure and a top-down information decoding procedure. While numerous approaches have explored the bottom-up feature extraction for this task, the design of top-down flows remains under-studied. To this end, this paper revisits the role of top-down modeling in salient object detection and designs a novel densely nested top-down flows (DNTDF)-based framework. In every stage of DNTDF, features from higher levels are read in via the progressive compression shortcut paths (PCSPs). The notable characteristics of our proposed method are as follows. (1) The propagation of high-level features which usually have relatively strong semantic information is enhanced in the decoding procedure. (2) With the help of PCSP, the gradient vanishing issues caused by non-linear operations in top-down information flows can be alleviated. (3) Thanks to the full exploration of high-level features, the decoding process of our method is relatively memory-efficient compared to those of existing methods. Integrating DNTDF with EfficientNet, we construct a highly light-weighted SOD model, with very low computational complexity. To demonstrate the effectiveness of the proposed model, comprehensive experiments are conducted on six widely-used benchmark datasets. The comparisons to the most state-of-the-art methods as well as the carefully-designed baseline models verify our insights on the top-down flow modeling for SOD.
Few-Shots Parallel Algorithm Portfolio Construction via Co-Evolution

Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of “training” problem instances, which is often referred to as PAP construction. However, compared to the traditional machine learning, PAP construction often suffers from the lack of training instances, and the obtained PAPs may fail to generalize well. This article proposes a novel competitive co-evolution scheme, named co-evolution of parameterized search (CEPS), as a remedy to this challenge. By co-evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable PAPs with few training instances. The advantage of CEPS in improving generalization is analytically shown in this article. Two concrete algorithms, namely, CEPS-TSP and CEPS-VRPSPDTW, are presented for the traveling salesman problem (TSP) and the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW), respectively. The experimental results show that CEPS has led to better generalization, and even managed to find new best-known solutions for some instances.
Dense Residual Network: Enhancing global dense feature flow for character recognition

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply stacking the convolutional layers fail to enable them to fully discover local and global feature information between layers. In this paper, we mainly investigate how to enhance the local and global feature learning abilities of DenseNet by fully exploiting the hierarchical features from all convolutional layers. Technically, we propose an effective convolutional deep model termed Dense Residual Network (DRN) for the task of optical character recognition. To define DRN, we propose a refined residual dense block (r-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts of inner layers at the same time. After fully capturing local residual dense features, we utilize the sum operation and several r-RDBs to construct a new block termed global dense block (GDB) by imitating the construction of dense blocks to adaptively learn global dense residual features in a holistic way. Finally, we use two convolutional layers to design a down-sampling block to reduce the global feature size and extract more informative deeper features. Extensive results show that our DRN can deliver enhanced results, compared with other related deep models.
A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring

The credit scoring system has been revolutionized with the development of the financial system and has received increasing attention from the academia and industry. Artificial intelligence technology has reshaped credit scoring through predictive classification. In this study, a new hybrid ensemble model with voting-based outlier detection and balanced sampling is proposed to achieve superior predictive power for credit scoring. To avoid noise-filled data from misleading the classifier training, a new voting-based outlier detection method is proposed to enhance the classic outlier detection algorithms with the weighted voting mechanism and boost the outlier scores into the training set to form an outlier-adapted training set. To reduce the information loss caused by under-sampling when dealing with imbalanced data, a new bagging-based balanced sampling method is proposed to enhance the traditional under-sampling methods with the bagging strategy to obtain a balanced training set. To further improve the performance of the proposed model, a stacking-based ensemble modeling method is proposed to first perform parametrical optimization and then construct the stacking-based multi-stage ensemble model. Five datasets from the UC Irvine machine learning repository and five evaluation indicators were adopted to evaluate the model performance. The experimental results indicate the superior performance of the proposed model and prove its robustness and effectiveness.
MESHFREEFLOWNET: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

We propose MESHFREEFLOWNET, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. While being computationally efficient, MESHFREEFLOWNET accurately recovers the fine-scale quantities of interest. MESHFREEFLOWNET allows for: (i) the output to be sampled at all spatio-temporal resolutions, (ii) a set of Partial Differential Equation (PDE) constraints to be imposed, and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal domains owing to its fully convolutional encoder. We empirically study the performance of MESHFREEFLOWNET on the task of super-resolution of turbulent flows in the Rayleigh-Bénard convection problem. Across a diverse set of evaluation metrics, we show that MESHFREEFLOWNET significantly outperforms existing baselines. Furthermore, we provide a large scale implementation of MESHFREEFLOWNET and show that it efficiently scales across large clusters, achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of less than 4 minutes. We provide an opensource implementation of our method that supports arbitrary combinations of PDE constraints.