Reviewerly/exHarmony huggingface dataset:
Analysis of the Relationship between Corporate Social Responsibility and Customer Loyalty in the Egyptian Hypermarkets Sector

Hypermarkets have experienced an increasingly growing and competitive market in the recent past including many multinational and local companies all battling to achieve customer loyalty.Thus their condition has necessitated the development of new models that have modernized elements.Businesses all around the globe are recognizing the importance of Corporate Social Responsibility (CSR) in their survival and development (Cuesta-Valiño et al., 2019).The current study seeks to add information on the impact of CSR activities on improving trust and brand image, and simultaneously investigate the impact of these variables on customer satisfaction and loyalty.To accomplish this objective, a causal framework was developed and tested using a structured e-survey.The questionnaire was collected from a sample of 539 customers (of which 350 were valid responses) in hypermarkets in Egypt.An analysis using Statistical Package for the Social Sciences (SPSS) and Structural Equational Modeling (SEM) tests revealed that ethical, philanthropic, and economic CSR activities carried out by the hypermarket significantly impacted brand image, while only ethical and legal responsibilities had a direct and substantial influence on customer trust.Finally, it was revealed that trust and image impact customer satisfaction and eventually lead to loyalty.To sum up, these findings support CSR's indirect effect on customer loyalty, which is intervened by these relational variables.As a result, in the context of hypermarkets, CSR is a crucial method for creating a long-term consumer-company connection.
Teaching old presumptive tests new digital tricks with computer vision for forensic applications

By-eye forensic spot tests can be more quantifiably monitored and compared using non-contact computer vision methods.
Data Quality– and Utility-Compliant Anonymization of Common Data Model–Harmonized Electronic Health Record Data: Protocol for a Scoping Review

Background The anonymization of Common Data Model (CDM)–converted EHR data is essential to ensure the data privacy in the use of harmonized health care data. However, applying data anonymization techniques can significantly affect many properties of the resulting data sets and thus biases research results. Few studies have reviewed these applications with a reflection of approaches to manage data utility and quality concerns in the context of CDM-formatted health care data. Objective Our intended scoping review aims to identify and describe (1) how formal anonymization methods are carried out with CDM-converted health care data, (2) how data quality and utility concerns are considered, and (3) how the various CDMs differ in terms of their suitability for recording anonymized data. Methods The planned scoping review is based on the framework of Arksey and O'Malley. By using this, only articles published in English will be included. The retrieval of literature items should be based on a literature search string combining keywords related to data anonymization, CDM standards, and data quality assessment. The proposed literature search query should be validated by a librarian, accompanied by manual searches to include further informal sources. Eligible articles will first undergo a deduplication step, followed by the screening of titles. Second, a full-text reading will allow the 2 reviewers involved to reach the final decision about article selection, while a domain expert will support the resolution of citation selection conflicts. Additionally, key information will be extracted, categorized, summarized, and analyzed by using a proposed template into an iterative process. Tabular and graphical analyses should be addressed in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We also performed some tentative searches on Web of Science for estimating the feasibility of reaching eligible articles. Results Tentative searches on Web of Science resulted in 507 nonduplicated matches, suggesting the availability of (potential) relevant articles. Further analysis and selection steps will allow us to derive a final literature set. Furthermore, the completion of this scoping review study is expected by the end of the fourth quarter of 2023. Conclusions Outlining the approaches of applying formal anonymization methods on CDM-formatted health care data while taking into account data quality and utility concerns should provide useful insights to understand the existing approaches and future research direction based on identified gaps. This protocol describes a schedule to perform a scoping review, which should support the conduction of follow-up investigations. International Registered Report Identifier (IRRID) PRR1-10.2196/46471
Anonymous provision of privacy-sensitive services using blockchain and decentralised storage

Abstract Lawyers, laboratories, auditors or banks often need data containing sensitive personal information to provide their services. Examples of sensitive services include genetic testing, paternity testing, STD testing, credit scoring or legal advice. The processing of personal data, especially when providing services involving sensitive data such as health records, biological material or legal documents, exposes both users and service providers (SPs) to privacy risks. SPs are at risk of violating GDPR, CPAA and other legal regulations, while customers are at risk of losing their privacy. We observe that personal data is often only used for logistical purposes, such as payment or communication, and could be provided anonymously if such methods were available. We present a solution that allows services to be provided without collecting personal data. We use anonymous payment methods such as cash and anonymous cryptocurrencies, blockchain to achieve fairness, and distributed content-addressable storage networks to deliver results. Compared to other solutions, our protocol achieves anonymity with weaker assumptions, supports physical materials and conflict resolution, and does not require the customer to interact with the blockchain in conflict-free transactions, demonstrating better practicality. In this work, we analyse the fairness of our protocol and implement it using Ethereum technology as a message board, Monero as an anonymous payment method, and Powergate (IPFS and Filecoin) as a decentralised storage network.
Compositional Prototypical Networks for Few-Shot Classification

It is assumed that pre-training provides the feature extractor with strong class transferability and that high novel class generalization can be achieved by simply reusing the transferable feature extractor. In this work, our motivation is to explicitly learn some fine-grained and transferable meta-knowledge so that feature reusability can be further improved. Concretely, inspired by the fact that humans can use learned concepts or components to help them recognize novel classes, we propose Compositional Prototypical Networks (CPN) to learn a transferable prototype for each human-annotated attribute, which we call a component prototype. We empirically demonstrate that the learned component prototypes have good class transferability and can be reused to construct compositional prototypes for novel classes. Then a learnable weight generator is utilized to adaptively fuse the compositional and visual prototypes. Extensive experiments demonstrate that our method can achieve state-of-the-art results on different datasets and settings. The performance gains are especially remarkable in the 5-way 1-shot setting. The code is available at https://github.com/fikry102/CPN.
RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. Imitating the diagnostic logic of human pathologists, our RL agent learns how to find regions of observation value and obtain representative features across multiple resolution levels, without having to analyze each part of the WSI at the high magnification. We benchmark our method on two whole-slide level classification tasks, including detection of metastases in WSIs of lymph node sections, and subtyping of lung cancer. Experimental results demonstrate that RLogist achieves competitive classification performance compared to typical multiple instance learning algorithms, while having a significantly short observation path. In addition, the observation path given by RLogist provides good decision-making interpretability, and its ability of reading path navigation can potentially be used by pathologists for educational/assistive purposes. Our code is available at: https://github.com/tencent-ailab/RLogist.
Pixel-Wise Warping for Deep Image Stitching

Existing image stitching approaches based on global or local homography estimation are not free from the parallax problem and suffer from undesired artifacts. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. The proposed deep image stitching framework consists of a Pixel-wise Warping Module (PWM) and a Stitched Image Generating Module (SIGMo). For PWM, we obtain pixel-wise warp in a similar manner as estimating an optical flow (OF). In the stitching scenario, the input images usually include non-overlap (NOV) regions of which warp cannot be directly estimated, unlike the overlap (OV) regions. To help the PWM predict a reasonable warp on the NOV region, we impose two geometrical constraints: an epipolar loss and a line-preservation loss. With the obtained warp field, we relocate the pixels of the target image using forward warping. Finally, the SIGMo is trained by the proposed multi-branch training framework to generate a stitched image from a reference image and a warped target image. For training and evaluating the proposed framework, we build and publish a novel dataset including image pairs with corresponding pixel-wise ground truth warp and stitched result images. We show that the results of the proposed framework are quantitatively and qualitatively superior to those of the conventional methods.
Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer

Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding (e.g., GloVe). However, such methods only exploit single-modal knowledge from a language model, while ignoring the rich semantic information inherent in image-text pairs. Instead, recently developed open-vocabulary (OV) based methods succeed in exploiting such information of image-text pairs in object detection, and achieve impressive performance. Inspired by the success of OV-based methods, we propose a novel open-vocabulary framework, named multi-modal knowledge transfer (MKT), for multi-label classification. Specifically, our method exploits multi-modal knowledge of image-text pairs based on a vision and language pre-training (VLP) model. To facilitate transferring the image-text matching ability of VLP model, knowledge distillation is employed to guarantee the consistency of image and label embeddings, along with prompt tuning to further update the label embeddings. To further enable the recognition of multiple objects, a simple but effective two-stream module is developed to capture both local and global features. Extensive experimental results show that our method significantly outperforms state-of-the-art methods on public benchmark datasets.
Real-World Deep Local Motion Deblurring

Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. Extensive experiments prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves better performance than state-of-the-art global deblurring methods and our proposed local blur-aware techniques are effective.
Adapting Object Size Variance and Class Imbalance for Semi-supervised Object Detection

Semi-supervised object detection (SSOD) attracts extensive research interest due to its great significance in reducing the data annotation effort. Collecting high-quality and category-balanced pseudo labels for unlabeled images is critical to addressing the SSOD problem. However, most of the existing pseudo-labeling-based methods depend on a large and fixed threshold to select high-quality pseudo labels from the predictions of a teacher model. Considering different object classes usually have different detection difficulty levels due to scale variance and data distribution imbalance, conventional pseudo-labeling-based methods are arduous to explore the value of unlabeled data sufficiently. To address these issues, we propose an adaptive pseudo labeling strategy, which can assign thresholds to classes with respect to their “hardness”. This is beneficial for ensuring the high quality of easier classes and increasing the quantity of harder classes simultaneously. Besides, label refinement modules are set up based on box jittering for guaranteeing the localization quality of pseudo labels. To further improve the algorithm’s robustness against scale variance and make the most of pseudo labels, we devise a joint feature-level and prediction-level consistency learning pipeline for transferring the information of the teacher model to the student model. Extensive experiments on COCO and VOC datasets indicate that our method achieves state-of-the-art performance. Especially, it brings mean average precision gains of 2.08 and 1.28 on MS-COCO dataset with 5% and 10% labeled images, respectively.
Ch(e)atGPT? An Anecdotal Approach Addressing the Impact of ChatGPT on Teaching and Learning GIScience

Natural language processing systems like ChatGPT have recently attracted enormous attention in the field of higher education.We aim to contribute to this discussion by scrutinizing the suitability of current testing methods and potentially necessary shifts in learning objectives in GIScience.This paper presents an anecdotal approach to the impact of ChatGPT on teaching and learning based on a real-world use case.It focuses on the results of a fictional student who used ChatGPT for the completion of applicationdevelopment assignments, including coding.The solutions were submitted to the instructor, who assessed the results in a single-blind experiment.The instructor's feedback and grading as well as the AI-plagiarism results were part of our evaluation of the testing methods applied.This triggered a discussion on the adequacy of current learning objectives in the development of GIS applications and the integration of AI into the learning process.
Unbiased Heterogeneous Scene Graph Generation with Relation-Aware Message Passing Neural Network

Recent scene graph generation (SGG) frameworks have focused on learning complex relationships among multiple objects in an image. Thanks to the nature of the message passing neural network (MPNN) that models high-order interactions between objects and their neighboring objects, they are dominant representation learning modules for SGG. However, existing MPNN-based frameworks assume the scene graph as a homogeneous graph, which restricts the context-awareness of visual relations between objects. That is, they overlook the fact that the relations tend to be highly dependent on the objects with which the relations are associated. In this paper, we propose an unbiased heterogeneous scene graph generation (HetSGG) framework that captures relation-aware context using message passing neural networks. We devise a novel message passing layer, called relation-aware message passing neural network (RMP), that aggregates the contextual information of an image considering the predicate type between objects. Our extensive evaluations demonstrate that HetSGG outperforms state-of-the-art methods, especially outperforming on tail predicate classes. The source code for HetSGG is available at https://github.com/KanghoonYoon/hetsgg-torch
HybridCap: Inertia-Aid Monocular Capture of Challenging Human Motions

Monocular 3D motion capture (mocap) is beneficial to many applications. The use of a single camera, however, often fails to handle occlusions of different body parts and hence it is limited to capture relatively simple movements. We present a light-weight, hybrid mocap technique called HybridCap that augments the camera with only 4 Inertial Measurement Units (IMUs) in a novel learning-and-optimization framework. We first employ a weakly-supervised and hierarchical motion inference module based on cooperative pure residual recurrent blocks that serve as limb, body and root trackers as well as an inverse kinematics solver. Our network effectively narrows the search space of plausible motions via coarse-to-fine pose estimation and manages to tackle challenging movements with high efficiency. We further develop a hybrid optimization scheme that combines inertial feedback and visual cues to improve tracking accuracy. Extensive experiments on various datasets demonstrate HybridCap can robustly handle challenging movements ranging from fitness actions to Latin dance. It also achieves real-time performance up to 60 fps with state-of-the-art accuracy.
Learning with Partial Labels from Semi-supervised Perspective

Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, contrastive learning, or class activate values, can achieve promising performance. Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP). Specifically, we first form the pseudo-labeled dataset by selecting a small number of reliable pseudo-labeled instances with high-confidence prediction scores and treating the remaining instances as pseudo-unlabeled ones. Then we design a SS learning objective, consisting of a supervised loss for pseudo-labeled instances and a semantic consistency regularization for pseudo-unlabeled instances. We further introduce a complementary regularization for those non-candidate labels to constrain the model predictions on them to be as small as possible. Empirical results demonstrate that PLSP significantly outperforms the existing PL baseline methods, especially on high ambiguity levels. Code available: https://github.com/changchunli/PLSP.
CEE-Net: Complementary End-to-End Network for 3D Human Pose Generation and Estimation

The limited number of actors and actions in existing datasets make 3D pose estimators tend to overfit, which can be seen from the performance degradation of the algorithm on cross-datasets, especially for rare and complex poses. Although previous data augmentation works have increased the diversity of the training set, the changes in camera viewpoint and position play a dominant role in improving the accuracy of the estimator, while the generated 3D poses are limited and still heavily rely on the source dataset. In addition, these works do not consider the adaptability of the pose estimator to generated data, and complex poses will cause training collapse. In this paper, we propose the CEE-Net, a Complementary End-to-End Network for 3D human pose generation and estimation. The generator extremely expands the distribution of each joint-angle in the existing dataset and limits them to a reasonable range. By learning the correlations within and between the torso and limbs, the estimator can combine different body-parts more effectively and weaken the influence of specific joint-angle changes on the global pose, improving the generalization ability. Extensive ablation studies show that our pose generator greatly strengthens the joint-angle distribution, and our pose estimator can utilize these poses positively. Compared with the state-of-the-art methods, our method can achieve much better performance on various cross-datasets, rare and complex poses.
Exploring Stroke-Level Modifications for Scene Text Editing

Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. Due to the lack of edited real scene text images, the network can only be well trained on synthetic pairs and performs poorly on real-world images. To handle the above problems, we propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL). Firstly, we generate stroke guidance maps to explicitly indicate regions to be edited. Different from the implicit one by directly modifying all the pixels at image level, such explicit instructions filter out the distractions from background and guide the network to focus on editing rules of text regions. Secondly, we propose a Semi-supervised Hybrid Learning to train the network with both labeled synthetic images and unpaired real scene text images. Thus, the STE model is adapted to real-world datasets distributions. Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets. Extensive experiments demonstrate that our MOSTEL outperforms previous methods both qualitatively and quantitatively. Datasets and code will be available at https://github.com/qqqyd/MOSTEL.