Reviewerly/exHarmony huggingface dataset:
Syntax-type-aware graph convolutional networks for natural language understanding

The structure of a sentence conveys rich linguistic knowledge and has proven useful for natural language understanding. In this paper, we aim to incorporate syntactical constraints and long-range word dependencies into the sentence encoding procedure using the widely applied Graph Convolutional Network (GCN) and word dependency trees. Existing syntax-aware GCN methods construct the adjacency matrix by referring to whether two words are connected in the dependency tree. But they fail to model the word dependency type, which reflects how the words are linked in dependency trees. They cannot distinguish the different contributions of different word dependency paths. To avoid introducing redundant word dependencies that harm language understanding, we propose a GCN version that is extended by a novel Word Dependency Gate mechanism. Word Dependency Gate can adaptively maintain the balance between the inclusion and exclusion of specific word dependency paths based on the word dependency type and its word context. Experiments show that our approach can effectively incorporate the relevant syntactical dependency in BERT and achieve a state-of-the-art performance in the End-to-End Aspect-Based Sentiment Analysis and Relation Triple Extraction tasks.
This Face Does Not Exist... But It Might Be Yours! Identity Leakage in Generative Models

Generative adversarial networks (GANs) are able to generate high resolution photo-realistic images of objects that "do not exist." These synthetic images are rather difficult to detect as fake. However, the manner in which these generative models are trained hints at a potential for information leakage from the supplied training data, especially in the context of synthetic faces. This paper presents experiments suggesting that identity information in face images can flow from the training corpus into synthetic samples without any adversarial actions when building or using the existing model. This raises privacy-related questions, but also stimulates discussions of (a) the face manifold's characteristics in the feature space and (b) how to create generative models that do not inadvertently reveal identity information of real subjects whose images were used for training. We used five different face matchers (face_recognition, FaceNet, ArcFace, SphereFace and Neurotechnology MegaMatcher) and the StyleGAN2 synthesis model, and show that this identity leakage does exist for some, but not all methods. So, can we say that these synthetically generated faces truly do not exist? Databases of real and synthetically generated faces are made available with this paper to allow full replicability of the results discussed in this work.
Auto-ReID+: Searching for a multi-branch ConvNet for person re-identification

In the field of person re-identification (ReID), multi-branch models are more effective in learning robust features than single-branch models. The current popular multi-branch models are based on ResNet or GoogleNet. These networks are designed initially to solve classification problems. There is an essential difference between ReID and classification problems, so it is particularly important to find a corresponding multi-branch backbone for ReID tasks. We propose to automatically search for a multi-branch convolutional neural network (CNN) for ReID tasks utilizing neural architecture search (NAS). First, we designed a multi-resolution, multi-branch macro search architecture that can extract more abundant scale information. Then in the searching process, the early stopping mechanism is proposed to improve the effectiveness and efficiency of the entire searching process. Finally, we experimentally prove on four mainstream datasets that the searched model can achieve state-of-the-art performance with only 5.7 million parameters.
Strengthening women's participation in the traditional enterprises of sub‐saharan Africa: The role of corporate social responsibility initiatives in Niger delta, Nigeria

Abstract The purpose of this paper is to critically examine the multinational oil companies (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on development of rural women's traditional enterprises in the Niger Delta region. A total of 2,400 women were sampled across the region. Results from the use of a combined propensity score matching and logit model indicate that the GMoU model of the CSR has recorded significant success in supporting farming and fishing transformation generally, but has also undermined those initiatives that focused on empowering rural women in traditional enterprises, due to the cultural and traditional context in the region. This causes both direct harm to women and their children, and wider costs to African economies. It implies that if the GMoU interventions are not targeted to raise women's economic status and to deter aggression, invariably they may contribute towards reducing the participation of women in economic, political and social development and, by extension, dampen efforts in deterring poverty and achieving the sustainable development goals in sub‐Saharan Africa.
Blockchain-based federation of wireless sensor nodes

Wireless sensor networks (WSNs), as an integral part of most Internet of Things (IoT) devices, are currently proliferating providing a new paradigm of emerging technologies. It is estimated that the number of globally connected products will increase exponentially in the next decade. Therefore, it is not surprising finding applications in different areas such as smart homes, smart cities, industry, e-health, defense, security, vehicle networks, agriculture, and logistics among others. WSNs transmit the information gathered by the existing sensors in the IoT devices to header nodes acting as gateways to reach cloud computing nodes that will process this information. The processing of the data gathered through the sensors has made it possible to provide intelligence in strategical environments such as in the agrifood sector. Securing how this information is transmitted and assuring the integrity of the information is preserved. The use of blockchain technology has proven to be effective for securing the integrity of data transactions among entities. In this paper, we seize the advantage of this circumstance to design a robust mechanism based on smart contracts and blockchain technology that allow the reliable processing of data.
VD-GAN: A Unified Framework for Joint Prototype and Representation Learning From Contaminated Single Sample per Person

Single sample per person (SSPP) face recognition with a contaminated biometric enrolment database (SSPP-ce FR) is an emerging practical FR problem, where the SSPP in the enrolment database is no longer standard but contaminated by nuisance facial variations such as expression, lighting, pose, and disguise. In this case, the conventional SSPP FR methods, including the patch-based and generic learning methods, will suffer from serious performance degradation. Few recent methods were proposed to tackle SSPP-ce FR by either performing prototype learning on the contaminated enrolment database or learning discriminative representations that are robust against variation. Despite that, most of these approaches can only handle a specified single variation, e.g., pose, but cannot be extended to multiple variations. To address these two limitations, we propose a novel Variation Disentangling Generative Adversarial Network (VDGAN) to jointly perform prototype learning and representation learning in a unified framework. The proposed VD-GAN consists of an encoder-decoder structural generator and a multi-task discriminator to handle universal variations including single, multiple, and even mixed variations in practice. The generator and discriminator play an adversarial game such that the generator learns a discriminative identity representation and generates an identity-preserved prototype for each face image, while the discriminator aims to predict face identity label, distinguish real vs. fake prototype, and disentangle target variations from the learned representations. Qualitative and quantitative evaluations on various real-world face datasets containing single/multiple and mixed variations demonstrate the effectiveness of VD-GAN.
Improving Single Shot Object Detection With Feature Scale Unmixing

Due to the advantages of real-time detection and improved performance, single-shot detectors have gained great attention recently. To solve the complex scale variations, single-shot detectors make scale-aware predictions based on multiple pyramid layers. Typically, small objects are detected on shallow layers while large objects are detected on deep layers. However, the features in the pyramid are not scale-aware enough, which limits the detection performance. Two common problems in single-shot detectors caused by object scale variations can be observed: (1) false negative problem, i.e., small objects are easily missed due to the weak features; (2) part-false positive problem, i.e., the salient part of a large object is sometimes detected as an object. With this observation, a new Neighbor Erasing and Transferring (NET) mechanism is proposed for feature scale-unmixing to explore scale-aware features in this paper. In NET, a Neighbor Erasing Module (NEM) is designed to erase the salient features of large objects and emphasize the features of small objects in shallow layers. A Neighbor Transferring Module (NTM) is introduced to transfer the erased features and highlight large objects in deep layers. With this mechanism, a single-shot network called NETNet is constructed for scale-aware object detection. In addition, we propose to aggregate nearest neighboring pyramid features to enhance our NET. Experiments on MS COCO dataset and UAVDT dataset demonstrate the effectiveness of our method. NETNet obtains 38.5% AP at a speed of 27 FPS and 32.0% AP at a speed of 55 FPS on MS COCO dataset. As a result, NETNet achieves a better trade-off for real-time and accurate object detection.
AKM<sup>3</sup>C: Adaptive K-Multiple-Means for Multi-View Clustering

With the popularity of cameras and sensors, massive data are captured from various view angles or modalities, which provide abundant complementary information and also bring great challenges for traditional clustering methods. In this article, we propose a novel Adaptive K-Multiple-Means for multi-view clustering method (AKM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C). Unlike traditional multi-view K-means methods by grouping samples into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> clusters each with a cluster center in every view, the proposed AKM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C employs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M (M&gt;C)$ </tex-math></inline-formula> sub-cluster centers in each view to reveal the sub-cluster structure in the multi-view data thus enhances the clustering performance. Additionally, to distinguish the importance of different views, instead of using empirical weights, AKM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C exploits the multi-view combination weights strategy to assign a weight to each view automatically and thus fuses the complementary information of different views properly to get an optimally shared bipartite graph, on which the Laplacian rank constraint is executed and the final clusters are obtained by directly partitioning. An efficient optimization algorithm proposed with complexity and convergence analysis is used to solve the proposed AKM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C method. The extensive experimental results on eight public datasets show that the proposed AKM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> C performs better than state-of-the-art multi-view clustering methods. The code can be downloaded at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://drive.google.com/file/d/1CQ0royrYxKFJdNLnbBQSbDrohtfH71di/view?usp=sharing</uri> .
Feature Selection Methods Based on Symmetric Uncertainty Coefficients and Independent Classification Information

Feature selection is a critical step in the data preprocessing phase in the field of pattern recognition and machine learning.The core of feature selection is to analyze and quantify the relevance, irrelevance, and redundancy between features and class labels.While existing feature selection methods give multiple explanations for these relationships, they ignore the multi-value bias of class-independent features and the redundancy of class dependent features.Therefore, a feature selection method (Maximal independent classification information and minimal redundancy, MICIMR) is proposed in this paper.Firstly, the relevance and redundancy terms of class independent characteristics are calculated respectively based on the symmetric uncertainty coefficient.Secondly, it calculates the relevance and redundancy terms of class-dependent features according to the independent classification information criterion.Finally, the selection criteria for these two characteristics are combined.To verify the effectiveness of the MICIMR algorithm, five feature selection methods are compared with the MICIMR algorithm on fifteen real datasets.The experimental results demonstrate that the MICIMR algorithm outperforms the other feature selection algorithms in terms of redundancy rate as well as classification accuracy (Gmean_macro and F1_macro).
Local Memory Attention for Fast Video Semantic Segmentation

We propose a novel neural network module that transforms an existing single-frame semantic segmentation model into a video semantic segmentation pipeline. In contrast to prior works, we strive towards a simple, fast, and general module that can be integrated into virtually any single-frame architecture. Our approach aggregates a rich representation of the semantic information in past frames into a memory module. Information stored in the memory is then accessed through an attention mechanism. In contrast to previous memory-based approaches, we propose a fast local attention layer, providing temporal appearance cues in the local region of prior frames. We further fuse these cues with an encoding of the current frame through a second attention-based module. The segmentation decoder processes the fused representation to predict the final semantic segmentation. We integrate our approach into two popular semantic segmentation networks: ERFNet and PSPNet. We observe an improvement in segmentation performance on Cityscapes by 1.7% and 2.1% in mIoU respectively, while increasing inference time of ERFNet by only 1.5ms. Source code is available at https://github.com/mattpfr/lmanet.
Multi-GPU approach to global induction of classification trees for large-scale data mining

Abstract This paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.
The Fourth Industrial Revolution and private commercial banks: the good, bad and ugly

Purpose This study aims to assess how new and emerging technologies can contribute to achieving the financial goals of the private commercial banking sector in Bangladesh. It considers the perception among the top management about the Fourth Industrial Revolution (4IR) and further measures the readiness of private commercial banks (PCBs) to become resilient. Design/methodology/approach This study attempts to measure the perception and readiness of the commercial banking sector because of the 4IR based on the stratified sampling method. The research is qualitative and selected PCBs listed in the Dhaka Stock Exchange. 4IR in the context of the banking sector in Bangladesh is a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final research design. Findings This research has identified a significant gap of study in the preparedness among the private commercial banking sector in Bangladesh to confront the 4IR while indicating the most significant risks and managerial insights. The findings show technologies will dramatically change the nature of work. Traditional system of banking from the branch will be shifting into banking from everywhere. Hence, digital products and services will foster value-driven business. The result of the study also states the readiness of the banking sector is in the preliminary stage and endorses some of the coping approaches. Research limitations/implications Different schools of thought regarding the role of the 4IR and its future consequences have been observed. The corporate sector in Bangladesh has an inclusive lack of understanding regarding the 4IR. Practical implications The insights may provide directions to banking financial institutions of Bangladesh to thrive during the 4IR. This study is intended to assist policymakers, decision-makers and employees of PCBs to increase awareness and preparedness for future challenges that may appear from the 4IR where the 41 competitive PCBs play vital role in turning the fast emerging Bangladesh economy. Originality/value The contribution of this paper associates with academics and bankers to increase understanding of coping in the context of the escalating use of emerging technology-driven banking services within the PCBs in Bangladesh by determining perception and testing different forms of readiness including a variety of important outcomes such as risks.
Deep Active Inference for Partially Observable MDPs

Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully observable domains. In this paper, we describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. The deep learning architecture optimizes a variant of the expected free energy and encodes the continuous state representation by means of a variational autoencoder. We show, in the OpenAI benchmark, that our approach has comparable or better performance than deep Q-learning, a state-of-the-art deep reinforcement learning algorithm.
Hyperrealistic Image Inpainting with Hypergraphs

Image inpainting is a non-trivial task in computer vision due to multiple possibilities for filling the missing data, which may be dependent on the global information of the image. Most of the existing approaches use the attention mechanism to learn the global context of the image. This attention mechanism produces semantically plausible but blurry results because of incapability to capture the global context. In this paper, we introduce hypergraph convolution on spatial features to learn the complex relationship among the data. We introduce a trainable mechanism to connect nodes using hyperedges for hypergraph convolution. To the best of our knowledge, hypergraph convolution have never been used on spatial features for any image-to-image tasks in computer vision. Further, we introduce gated convolution in the discriminator to enforce local consistency in the predicted image. The experiments on Places2, CelebA-HQ, Paris Street View, and Facades datasets, show that our approach achieves state-of-the-art results.
Boosting Monocular Depth with Panoptic Segmentation Maps

Monocular depth prediction is ill-posed by nature; hence successful approaches need to exploit the available cues to the fullest. Yet, real-world training data with depth ground-truth suffers from limited variability and data acquired from depth sensors is also sparse and prone to noise. While available datasets with semantic annotations might help to better exploit semantic cues, they are not immediately usable for depth prediction. We show how to leverage panoptic segmentation maps to boost monocular depth predictors in stereo training setups. In particular, we augment a self-supervised training scheme through panoptic-guided smoothing, panoptic-guided alignment, and panoptic left-right consistency from ground truth or inferred panoptic segmentation maps. Our approach incurs only a minor overhead, can easily be applied to a wide range of depth estimation methods that are trained at least partially using stereo pairs, providing a substantial boost in accuracy.
Quantum Reinforcement Learning with Quantum Photonics

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside this field, quantum reinforcement learning aims at designing and building quantum agents that may exchange information with their environment and adapt to it, with the aim of achieving some goal. Different quantum platforms have been considered for quantum machine learning and specifically for quantum reinforcement learning. Here, we review the field of quantum reinforcement learning and its implementation with quantum photonics. This quantum technology may enhance quantum computation and communication, as well as machine learning, via the fruitful marriage between these previously unrelated fields.