Reviewerly/exHarmony huggingface dataset:
ICT, governance and inequality in Africa

We examine the direct effect of Information and Communication Technologies (ICTs) on inequality and investigate whether the ICT and inequality relationships can be reinforced by governance indicators of democracy, regulatory quality, rule of law and political stability. Using the panel dataset of 46 African countries over the period 1984–2018 and dynamic two-step system generalized method of moment estimator, we found that ICT measures of internet, fixed broadband and mobile cellular subscription directly reduce inequality, and good governance indicators can reinforce the ICT and inequality links. We execute a threshold analysis that illustrates the crucial role of democracy, regulatory quality, rule of law and political stability in facilitating the ICT and inequality relationships in Africa.
Meta-Learning-Based Incremental Few-Shot Object Detection

Recent years have witnessed meaningful progress in the task of few-shot object detection. However, most of the existing models are not capable of incremental learning with a few samples, <i>i.e.</i>, the detector can&#x2019;t detect novel-class objects by using only a few samples of novel classes (without revisiting the original training samples) while maintaining the performances on base classes. This is largely because of catastrophic forgetting, which is a general phenomenon in few-shot learning that the incorporation of the unseen information (<i>e.g.</i>, novel-class objects) will lead to a serious loss of the knowledge learnt before (<i>e.g.</i>, base-class objects). In this paper, a new model is proposed for incremental few-shot object detection, which takes CenterNet as the fundamental framework and redesigns it by introducing a novel meta-learning method to make the model adapted to unseen knowledge while overcoming forgetting to a great extent. Specifically, a meta-learner is trained with the base-class samples, providing the object locator of the proposed model with a good weight initialization, and thus the proposed model can be fine-tuned easily with few novel-class samples. On the other hand, the filters correlated to base classes are preserved when fine-tuning the proposed model with the few samples of novel classes, which is a simple but effective solution to mitigate the problem of forgetting. The experiments on the benchmark MS COCO and PASCAL VOC datasets demonstrate that the proposed model outperforms the state-of-the-art methods by a large margin in the detection performances on base classes and all classes while achieving best performances when detecting novel-class objects in most cases. The project page can be found in <uri>https://mic.tongji.edu.cn/e6/d5/c9778a190165/page.htm</uri>.
Intention-aware Heterogeneous Graph Attention Networks for Fraud Transactions Detection

Fraud transactions have been the major threats to the healthy development of e-commerce platforms, which not only damage the user experience but also disrupt the orderly operation of the market. User behavioral data is widely used to detect fraud transactions, and recent works show that accurate modeling of user intentions in behavioral sequences can propel further improvements on the performances. However, most existing methods treat each transaction as an independent data instance without considering the transaction-level interactions accessed by transaction attributes, e.g., information on remark, logistics, payment, device and etc., which may fail to achieve satisfactory results in more complex scenarios. In this paper, a novel heterogeneous transaction-intention network is devised to leverage the cross-interaction information over transactions and intentions, which consists of two types of nodes, namely transaction and intention nodes, and two types of edges, i.e., transaction-intention and transaction-transaction edges. Then we propose a graph neural method coined IHGAT(Intention-aware Heterogeneous Graph ATtention networks) that not only perceives sequence-like intentions, but also encodes the relationship among transactions. Extensive experiments on a real-world dataset of Alibaba platform show that our proposed algorithm outperforms state-of-the-art methods in both offline and online modes.
UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification

The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages. Its fundamental part is a system that can generate synthesized images of high-quality and from controllable distributions. Instance-level annotation goes with the synthesized data and is almost free. We point out some details in image synthesis that largely impact the data quality. With 3,000 IDs and 120,000 instances, our method achieves a 38.5% rank-1 accuracy when being directly transferred to MSMT17. It almost doubles the former record using synthesized data and even surpasses previous direct transfer records using real data. This offers a good basis for unsupervised domain adaption, where our pre-trained model is easily plugged into the state-of-the-art algorithms towards higher accuracy. In addition, the data distribution can be flexibly adjusted to fit some corner ReID scenarios, which widens the application of our pipeline. We publish our data synthesis toolkit and synthesized data in https://github.com/FlyHighest/UnrealPerson.
Cancer diagnosis using generative adversarial networks based on deep learning from imbalanced data

Cancer is a serious global disease due to its high mortality, and the key to effective treatment is accurate diagnosis. However, limited by sampling difficulty and actual sample size in clinical practice, data imbalance is a common problem in cancer diagnosis, while most conventional classification methods assume balanced data distribution. Therefore, addressing the imbalanced learning problem to improve the predictive performance of cancer diagnosis is significant. In the study, we dissect the data imbalance prevalent in cancer gene expression data and present an improved deep learning based Wasserstein generative adversarial network (WGAN) model, which provides a reliable training progress indicator and deeply explores the characteristics of data. The WGAN generates new samples from the minority class and solves the imbalance problem at the data level. We analyze three publicly available data sets on RNA-seq of three kinds of cancer using the proposed WGAN and compare the results with those from two commonly adopted sampling methods. According to the results, through addressing the data imbalance problem, the balanced data distribution and the expanding sample size increase the prediction accuracy in all three data sets. Therefore, the proposed WGAN method is superior in solving the imbalanced learning problem of gene expression data, providing significantly better prediction performance in cancer diagnosis.
Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration

Recent years have witnessed significant progress in 3D hand mesh recovery. Nevertheless, because of the intrinsic 2D-to-3D ambiguity, recovering camera-space 3D information from a single RGB image remains challenging. To tackle this problem, we divide camera-space mesh recovery into two sub-tasks, i.e., root-relative mesh recovery and root recovery. First, joint landmarks and silhouette are extracted from a single input image to provide 2D cues for the 3D tasks. In the root-relative mesh recovery task, we exploit semantic relations among joints to generate a 3D mesh from the extracted 2D cues. Such generated 3D mesh coordinates are expressed relative to a root position, i.e., wrist of the hand. In the root recovery task, the root position is registered to the camera space by aligning the generated 3D mesh back to 2D cues, thereby completing camera-space 3D mesh recovery. Our pipeline is novel in that (1) it explicitly makes use of known semantic relations among joints and (2) it exploits 1D projections of the silhouette and mesh to achieve robust registration. Extensive experiments on popular datasets such as FreiHAND, RHD, and Human3.6M demonstrate that our approach achieves state-of-the-art performance on both root-relative mesh recovery and root recovery. Our code is publicly available at https://github.com/SeanChenxy/HandMesh.
Attacks and defenses in user authentication systems: A survey

User authentication systems (in short authentication systems) have wide utilization in our daily life. Unfortunately, existing authentication systems are prone to various attacks while both system security and usability are expected to be satisfied. But the current research still lacks a thorough survey on various types of attacks and corresponding countermeasures regarding user authentication, including traditional password-based and emerging biometric-based systems. In this paper, we make a comprehensive review on attacks and defenses of the authentication systems. We firstly introduce a number of common attacks by classifying them into different categories based on attacker knowledge, attack target, attack form and attack strength. Then, we propose a set of evaluation criteria for evaluating different kinds of attack defense mechanisms. Furthermore, we review and evaluate the existing methods of detecting and resisting attacks in the authentication systems by employing the proposed evaluation criteria as a common measure. Specifically, we focus on comparing and analyzing the performance of different defense mechanisms in different types of authentication systems. Through serious review and analysis, we put forward a number of open issues and propose some promising future research directions, hoping to inspire further research in this field.
A novel network with multiple attention mechanisms for aspect-level sentiment analysis

Aspect-level sentiment analysis aims at identifying the sentiment polarity of specific aspect words in a given sentence. Existing studies mostly use recurrent neural network (RNN) -based models. However, truncated backpropagation, gradient vanishing, and exploration problems often occur during the training process. To address these issues, this paper proposed a novel network with multiple attention mechanisms for aspect-level sentiment analysis. First, we apply the bidirectional encoder representations from transformers (BERT) model to construct word embedding vectors. Second, multiple attention mechanisms, including intra- and inter-level attention mechanisms, are used to generate hidden state representations of a sentence. In the intra-level attention mechanism, multi-head self-attention and point-wise feed-forward structures are designed. In the inter-level attention mechanism, global attention is used to capture the interactive information between context and aspect words. Furthermore, a feature focus attention mechanism is proposed to enhance sentiment identification. Finally, several classic aspect-level sentiment analysis datasets are used to evaluate the performance of our model. Experiments demonstrate that the proposed model can achieve state-of-the-art results compared to baseline models.
Representative Batch Normalization with Feature Calibration

Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable training and better representation of the network, while inevitably ignores the representation differences among instances. We propose to add a simple yet effective feature calibration scheme into the centering and scaling operations of BatchNorm, enhancing the instance-specific representations with the negligible computational cost. The centering calibration strengthens informative features and reduces noisy features. The scaling calibration restricts the feature intensity to form a more stable feature distribution. Our proposed variant of BatchNorm, namely Representative BatchNorm, can be plugged into existing methods to boost the performance of various tasks such as classification, detection, and segmentation. The source code is available in http://mmcheng.net/rbn.
A simulation-based framework for modulating the effects of subjectivity in greenfield Mineral Prospectivity Mapping with geochemical and geological data

Mineral Prospectivity Mapping (MPM) is a multifaceted process relying heavily on experts' judgments. Notwithstanding the importance of human interpretations and cognitive knowledge in the success of exploration projects, human input, granted, is material to cognitive heuristics, subjectivity, and mental traits of geologists involved in the various stages of MPM. Knowledge-driven MPM used in greenfield areas – where few or no mineral deposits are known – use experts' opinions for assigning weights to exploration targeting criteria. This issue introduces a systemic uncertainty that eventually propagates to the generated targets. This study, therefore, intends to propose a methodology for modulating the effects of this type of uncertainty in knowledge-driven MPM. Aiming to attain this objective, a procedure combining Monte Carlo simulation with fuzzy logic was articulated and applied to a suite of mineral systems-derived targeting criteria derived by geochemical and geological data in a case study. The proposed procedure returns three components, namely (a) the modulated prospectivity model, (b) uncertainty, and (c) confidence. In this method, plots of uncertainty and confidence versus prospectivity values are used for target generation; low-risk targets are those marked by low uncertainty, high confidence, and high prospectivity values. In this study, low-risk targets occupy merely 0.5% of the study area, showcasing the applied framework's efficacy for reducing the search space in greenfield exploration.
Federated Learning Systems for Healthcare: Perspective and Recent Progress

In the medical or healthcare industry, where, the already available information or data is never sufficient, excellence can be performed with the help of Federated Learning (FL) by empowering AI models to learn on private data without conceding privacy. It opened the door for ample research because of its high level of communication efficiency which is linked with distributed training problems. The primary objective of the chapter is to highlight the adaptability and working of the FL techniques in the healthcare system especially in drug development, clinical diagnosis, digital health monitoring, and various disease predictions and detection system. The first section of the chapter is comprised of a background study on an FL framework for healthcare, FL working model in healthcare, and various important benefits of FL. The next section of the chapter described the reported work which highlights different research works in the field of electronic health record systems, drug discovery, and disease prediction systems using the FL model. The final section of the chapter presented the comparative analysis, which shows the comparison between different FL algorithms for different health sectors by using parameters such as accuracy, the area under the curve, precision, recall, and F-score.
Robust Reference-based Super-Resolution via C<sup>2</sup>-Matching

Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e.g. scale and rotation) and the resolution gap (e.g. HR and LR). To tackle these challenges, we propose C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Matching in this work, which produces explicit robust matching crossing transformation and resolution. 1) For the transformation gap, we propose a contrastive correspondence network, which learns transformation-robust correspondences using augmented views of the input image. 2) For the resolution gap, we adopt a teacher-student correlation distillation, which distills knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation module to address the potential misalignment issue. In addition, to faithfully evaluate the performance of Ref-SR under a realistic setting, we contribute the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario. Extensive experiments demonstrate that our proposed C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Matching significantly outperforms state of the arts by over 1dB on the standard CUFED5 benchmark. Notably, it also shows great generalizability on WR-SR dataset as well as robustness across large scale and rotation transformations <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Review of cybersecurity assessment methods: Applicability perspective

Cybersecurity assessments are crucial in building the assurance that vital cyberassets are effectively protected from threats. Multiple assessment methods have been proposed during the decades of the cybersecurity field. However, a systematic literature search described in this paper reveals that their reviews are practically missing. Thus, the primary objective of this research was to fulfil this gap by comprehensively identifying and analysing cybersecurity assessment methods described in the scientific literature. A structured research method and transparent criteria were applied for this purpose. As a result, thirty-two methods are presented in this paper. Particular attention is paid to the question of the methods’ applicability in realistic contexts and environments. In that regard, the challenges and limitations associated with the methods’ application as well as potential approaches to addressing them have been indicated. Besides, the paper systematises the terminology and indicates complementary studies which can be helpful during assessments. Finally, the areas that leave space for improvement and directions for further research and development are indicated. The intention is to support researchers and practitioners in choosing the method to be applied in their assessments and to indicate the areas that can be further explored.
Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes

There have been many successful implementations of neural style transfer in recent years. In most of these works, the stylization process is confined to the pixel domain. However, we argue that this representation is unnatural because paintings usually consist of brushstrokes rather than pixels. We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce a simple differentiable rendering mechanism.Our approach significantly improves visual quality and enables additional control over the stylization process such as controlling the flow of brushstrokes through user input.We provide qualitative and quantitative evaluations that show the efficacy of the proposed parameterized representation. Code is available at https://github.com/CompVis/brushstroke-parameterized-style-transfer.
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDE-Tracker (without detection module) to track single-camera vehicles and generate single-camera tracklets. (3) According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering in Adjacent Cameras for multi-camera tracklets matching. Through the above techniques, our method obtained an IDF1 score of 0.8095, ranking first on the leaderboard <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The code will be released later.
Not everything is as it seems: Digital technology affordance, pandemic control, and the mediating role of sociomaterial arrangements

An overly favorable narrative has developed around the role played by digital technologies in containing Covid-19, which oversimplifies the complexity of technology adoption. This narrative takes sociomaterial arrangements for granted and conceptualizes technology affordance - the problem-solving capability of a technology - as a standard built-in feature that automatically activates during technology deployment, leading to undiversified and predetermined collective benefits. This paper demonstrates that not everything is as it seems; implementing a technology is a necessary but insufficient condition for triggering its potential problem-solving capability. The potential affordance and effects of a technology are mediated by the sociomaterial arrangements that users assemble to connect their goals to the materiality of technological artifacts and socio-organizational context in which technology deployment takes place. To substantiate this argument and illustrate the mediating role of sociomaterial arrangements, we build on sociomateriality and technology affordance theory, and we present the results of a systematic review of Covid-19 literature in which 2187 documents are examined. The review combines text data mining, co-occurrence pattern recognition, and inductive coding, and it focuses on four digital technologies that public authorities have deployed as virus containment measures: infrared temperature-sensing devices; ICT-based surveillance and contact-tracing systems; bioinformatic tools and applications for laboratory testing; and electronic mass communications media. Reporting on our findings, we add nuances to the academic debate on sociomateriality, technology affordance, and the governance of technology in public health crises. In addition, we provide public authorities with practical recommendations on how to strengthen their approach to digital technology deployment for pandemic control.
Multi-label feature selection considering label supplementation

Multi-label feature selection is an efficient technique to alleviate the high dimensionality for multi-label learning. Existing multi-label feature selection methods based on information theory either deal with labels individually or treat all label relationships as redundancy. However, two important and being ignored issues are the different effects of label relationships and the dynamic changes of label relationships in measuring different candidate features. To address these issues, we first distinguish three types of label relationships: label independence, label redundancy and label supplementation. Second, we consider the changes of label relationships based on different features. By analyzing the differences and the changes of label relationships, two new methods named LSMFS and MLSMFS are proposed, which extracts all supplementary information and the maximum supplementary information of features for each label from other labels, respectively. Finally, experiments on fifteen benchmark multi-label data sets demonstrate the effectiveness of the proposed methods against nine other methods.