Reviewerly/exHarmony huggingface dataset:
Rethinking Portrait Matting with Privacy Preserving

Recently, there has been an increasing concern about the privacy issue raised by identifiable information in machine learning. However, previous portrait matting methods were all based on identifiable images. To fill the gap, we present P3M-10k, which is the first large-scale anonymized benchmark for Privacy-Preserving Portrait Matting (P3M). P3M-10k consists of 10,421 high resolution face-blurred portrait images along with high-quality alpha mattes, which enables us to systematically evaluate both trimap-free and trimap-based matting methods and obtain some useful findings about model generalization ability under the privacy preserving training (PPT) setting. We also present a unified matting model dubbed P3M-Net that is compatible with both CNN and transformer backbones. To further mitigate the cross-domain performance gap issue under the PPT setting, we devise a simple yet effective Copy and Paste strategy (P3M-CP), which borrows facial information from public celebrity images and directs the network to reacquire the face context at both data and feature level. Extensive experiments on P3M-10k and public benchmarks demonstrate the superiority of P3M-Net over state-of-the-art methods and the effectiveness of P3M-CP in improving the cross-domain generalization ability, implying a great significance of P3M for future research and real-world applications. The dataset, code and models are available here (https://github.com/ViTAE-Transformer/P3M-Net).
Was a 3D-printed firearm discharged? Study of traces produced by the use of six fully 3D-printed firearms

Since the blueprints of the Liberator were published and successfully tested, countless new designs for said 3D-printed firearms and 3D-printed firearm components have been created and made publicly available. These new 3D-printed firearms, which are praised by their designers as ever more reliable, can be found on the Internet with little effort. Press reports have shown that various models of 3D-printed firearms have already been confiscated by law enforcement services around the world. So far, forensic studies have addressed this set of problems relatively little, whereby for the most part only the Liberator has been examined in detail and three other designs were only included a few times. The rapid pace of this development poses new challenges for forensic investigations and unveil new spheres of investigation regarding 3D-printed firearms. This research initiative aims to determine whether the results from previous studies on Liberators, are also reproducible and observable when using other models of 3D-printed firearms. In this respect six fully 3D-printed firearms - PM422 Songbird, PM522 Washbear, TREVOR, TESSA, Marvel Revolver and Grizzly - were produced on a material extrusion type Prusa i3 MK3S using PLA as the material. Test firings of these 3D-printed firearms have shown that they are indeed functional, but that, depending on the model, they suffer different levels of damage when fired. However, they were all rendered inoperative after one discharge and could not be used for further discharges unless the broken pieces were replaced. As in other studies, the firing process and the resulting ruptures on the 3D-printed firearm, projected polymer parts and fragments of different sizes and in different quantities into the immediate environment. The parts could be physically matched, allowing the reconstruction and identification of the 3D-printed firearms. Elements of ammunition also showed traces of melted polymer on the surface and cartridge cases bore tears or swellings.
Assistance from the Ambient Intelligence: Cyber–physical​ system applications in smart buildings for cognitively declined occupants

Caregivers have traditionally provided assistance and care to patients with cognitive decline, but this has resulted in financial and emotional burdens for both caregivers and patients, impacting their quality of life. To address this issue, Ambient Assistive Living (AAL) technologies that incorporate Internet of Things (IoT) and Artificial Intelligence (AI) can replace or complement caregivers by enabling intelligent learning in smart buildings. This review evaluates the intelligence complements provided by smart buildings enabled with such capabilities to increase the quality of life and autonomy of cognitively declined occupants. Existing contributions primarily focus on learning occupants' behavior to identify assistive services and solutions, which are delivered through technological interventions or caregivers. However, there are several key research gaps that need to be addressed. The most important is the lack of adequate implementation of technological interventions to fully support the occupants' autonomy and independence. Other gaps include challenges in usability and acceptability, ethical concerns, systems' comprehensiveness, and the need for human-in-the-loop. To address these gaps, a conceptual framework is proposed as future research directions for the applications of smart buildings supporting cognitively declined occupants. The framework aims to facilitate the implementation of technological interventions that can enhance occupants' autonomy and independence, address usability and acceptability challenges, and ensure ethical considerations and system comprehensiveness. This review provides insights into the current state-of-the-art of AAL technologies and highlights research directions for improving the quality of life and autonomy of cognitively declined occupants.
SSRT: A Sequential Skeleton RGB Transformer to Recognize Fine-Grained Human-Object Interactions and Action Recognition

Combining skeleton and RGB modalities in human action recognition (HAR) has garnered attention due to their ability to complement each other.However, previous studies did not address the challenge of recognizing fine-grained human-object interaction (HOI).To tackle this problem, this study introduces a new transformer-based architecture called Sequential Skeleton RGB Transformer (SSRT), which fuses skeleton and RGB modalities.First, SSRT leverages the strength of Long Short-Term Memory (LSTM) and a multi-head attention mechanism to extract high-level features from both modalities.Subsequently, SSRT employs a two-stage fusion method, including transformer cross-attention fusion and softmax layer late score fusion, to effectively integrate the multimodal features.Aside from evaluating the proposed method on fine-grained HOI, this study also assesses its performance on two other action recognition tasks: general HAR and cross-dataset HAR.Furthermore, this study conducts a performance comparison between a HAR model using single-modality features (RGB and skeleton) alongside multimodality features on all three action recognition tasks.To ensure a fair comparison, comparable state-of-the-art transformer architectures are employed for both the single-modality HAR model and SSRT.In terms of modality, SSRT outperforms the best-performing single-modality HAR model on all three tasks, with accuracy improved by 9.92% on fine-grained HOI recognition, 6.73% on general HAR, and 11.08% on cross-dataset HAR.Additionally, the proposed fusion model surpasses state-of-the-art multimodal fusion techniques like Transformer Early Concatenation, with an accuracy improved by 6.32% on fine-grained HOI recognition, 4.04% on general HAR, and 6.56% on cross-dataset.
Pre-processing and Resource Modelling for English-Assamese NMT System

Neural Machine Translation (NMT) modelling for low-resourced languages is usually under-explored lacking proper analysis in the pre-processing stages which could potentially influence the performance of the model. Assamese, an indigenous language of the North East India with over 16 million speakers, is no exception. This paper is an attempt towards exploration of the resources and tools necessary for developing a neural computational model for English-Assamese machine translation. Linguistic specialties are discussed. Resource identification and consolidation are highlighted. The paper highlights the technical intricacies associated with the language in the process of getting the input ready for NMT modelling. The various stages of preprocessing requirements to make the resources NMT ready are discussed. This includes normalization, tokenization, sub word tokenization and word embedding methods. With an open list of different NMT toolkits, this paper describes a roadmap for preparing Assamese machine translation.
Unsupervised Domain Adaptation for Remote Sensing Image Segmentation Based on Adversarial Learning and Self-Training

There is a large amount of out-of-distribution data (OOD) in remote sensing, which hinders high-accuracy segmentation models under the assumption of independent identical distribution (i.i.d.) from stable and reliable performance in real-world remote sensing applications. And Domain Adaptation (DA) is presented to seamlessly extend classifiers to the label-scarce target domain in the presence of the label-sufficient source domain with different data distributions. However, given that the domain shift, i.e. the distribution difference between the two domains, is more serious in remote sensing images, the current DA methods for image segmentation in Computer Vision (CV) typically perform unsatisfactorily in remote sensing, even suffering from the negative domain alignment. To this end, this paper proposes the Self-Training Adversarial Domain Adaptation (STADA) method for remote sensing image segmentation, which not only performs adversarial learning to extract domain-invariant features, but also implements Self-Training using pseudo-labels in the target domain denoised by the conditional adversarial loss for classifier adaptation. The ISPRS and WHU datasets are employed to conduct extensive experiments to investigate the effectiveness of STADA and the specific effect of its each DA component. And the experimental results demonstrate that STADA outperforms other state-of-the-art DA methods in the remote sensing image segmentation task.
Location-Based Augmented Reality for Cultural Heritage Communication and Education: The Doltso District Application

Location-based Augmented Reality applications are increasingly used in many research and commercial fields. Some of the fields that these applications are used are recreational digital games, tourism, education, and marketing. This study aims to present a location-based augmented reality (AR) application for cultural heritage communication and education. The application was created to inform the public, especially K12 students, about a district of their city with cultural heritage value. Furthermore, Google Earth was utilized to create an interactive virtual tour for consolidating the knowledge acquired by the location-based AR application. A scheme for evaluating the AR application was also constructed using factors suitable for location-based applications: challenge, educational usefulness (knowledge), collaboration, and intention to reuse. A sample of 309 students evaluated the application. Descriptive statistical analysis showed that the application scored well in all factors, especially in challenge and knowledge (mean values 4.21 and 4.12). Furthermore, structural equation modeling (SEM) analysis led to a model construction that represents how the factors are causally related. Based on the findings, the perceived challenge significantly influenced the perceived educational usefulness (knowledge) (b = 0.459, sig = 0.000) and interaction levels (b = 0.645, sig = 0.000). Interaction amongst users also had a significant positive impact on users' perceived educational usefulness (b = 0.374, sig = 0.000), which in turn influenced users' intention to reuse the application (b = 0.624, sig = 0.000).
Natural image matting based on surrogate model

Image matting is an important process in digital image processing, with pixel pair optimization-based methods having distinct advantages in parallelization and handling mislabeled trimaps or spatially disconnected foregrounds. Nevertheless, such methods cannot provide high-quality alpha mattes under a limited computing time, limiting their computing time-sensitive applications. Thus, this paper presents a natural image matting method based on surrogate models to address this problem. Specifically, the surrogate models for pixel pair optimization is established to approximate a pixel pair evaluation function, and its optimal solution obtained efficiently is used as the approximate optimal solution of the pixel pair optimization problem, saving much computing time. Experimental results demonstrate that the image matting based on surrogate models provides high-quality matting mattes with a little computing time and a competitive image matting performance compared to state-of-the-art pixel pair optimization-based methods that impose an excessive computing time.
Deep learning-based classification of breast lesions using dynamic ultrasound video

We intended to develop a deep-learning-based classification model based on breast ultrasound dynamic video, then evaluate its diagnostic performance in comparison with the classic model based on ultrasound static image and that of different radiologists.We collected 1000 breast lesions from 888 patients from May 2020 to December 2021. Each lesion contained two static images and two dynamic videos. We divided these lesions randomly into training, validation, and test sets by the ratio of 7:2:1. Two deep learning (DL) models, namely DL-video and DL-image, were developed based on 3D Resnet-50 and 2D Resnet-50 using 2000 dynamic videos and 2000 static images, respectively. Lesions in the test set were evaluated to compare the diagnostic performance of two models and six radiologists with different seniority.The area under the curve of the DL-video model was significantly higher than those of the DL-image model (0.969 vs. 0.925, P = 0.0172) and six radiologists (0.969 vs. 0.779-0.912, P < 0.05). All radiologists performed better when evaluating the dynamic videos compared to the static images. Furthermore, radiologists performed better with increased seniority both in reading images and videos.The DL-video model can discern more detailed spatial and temporal information for accurate classification of breast lesions than the conventional DL-image model and radiologists, and its clinical application can further improve the diagnosis of breast cancer.
Generalized self-supervised contrastive learning with bregman divergence for image recognition

Contrastive learning techniques continue to receive a lot of attention in the self-supervised learning area. Specifically, the learned distance features can be further utilized to capture the distance between latent features in the embedding space and improve the performance of both supervised and unsupervised learning tasks. However, most contrastive learning efforts are focused on learning the geometric distance between the latent features, while the underlying probability distribution is usually ignored. To address this challenge, we propose a novel generalized contrastive loss for self-supervised learning using the Bregman divergence by investigating the hidden relationship between the contrastive loss and the Bregman divergence. Our method considers the hybrid divergence that leverages the Euclidean-based distance and probabilistic divergence, which improves the quality of self-supervised learned feature representation. Besides theory, extensive experimental results demonstrate the effectiveness of our method compared to other state-of-the-art self-supervised methods.
Adversarial Attacks on Graph Neural Network

Graph neural networks (GNNs) are a useful tool for analyzing graph-based data in areas like social networks, molecular chemistry, and recommendation systems. Adversarial attacks on GNNs include introducing malicious perturbations that manipulate the model's predictions without being detected. These attacks can be structural or feature-based depending on whether the attacker modifies the graph's topology or node/edge features. To defend against adversarial attacks, researchers have proposed countermeasures like robust training, adversarial training, and defense mechanisms that identify and correct adversarial examples. These methods aim to improve the model's generalization capabilities, enforce regularization, and incorporate defense mechanisms into the model architecture to improve its robustness against attacks. This chapter offers an overview of recent advances in adversarial attacks on GNNs, including attack methods, evaluation metrics, and their impact on model performance.
The unethical use of deepfakes

Purpose The purpose of this study is to discuss the harmful use of deepfakes in an organizational context, based on the only two cases the authors found that were addressed by the media from the perspective of corporate fraud. This study offers an overview of deepfake technology, and in particular, examines five W questions to better decipher the impact of these tools on organizations: What is deepfake? Who is the fraudster and who is targeted? Why use them and how? And What after? Based on these five W questions, this study provides an in-depth discussion of the two cases identified. Even though this technology has several advantages, this study examines its dark side. Design/methodology/approach Using comparative analysis, the authors study the only two known and publicized fraud cases by using deepfakes that have targeted chief executive officers to date. Findings The paper provides an extensive picture of the unethical and illicit use of deepfakes in an organizational context and discusses how this technology could affect fraud risk. In addition, the analysis of cases shows that voice-generating software, combined with other fraud schemes such as business email compromise, facilitates the commission of the fraud, as the victims feel confident because they recognize the speaker’s voice and emails. The analysis shows that any organization could be vulnerable to this technology. The median costs of this type of fraud can be high. For the two cases identified, the estimated losses amounted to US$243,000 and US$35,000,000, respectively. Originality/value This paper adds new insights to the scarce research on deepfakes and financial crime by investigating the causes and consequences of the unethical and illicit use of deepfakes. It has several implications for organizations, boards of directors, management and regulatory authorities.
Digital Marketing in the Metaverse

Companies are targeting the metaverse to reach new generations. It allows marketers to create a virtual and augmented reality world to promote their brands and products. Digital marketing is a popular tool for promotion but has limitations in that customers are not able to get a 3D overview of the product. Metaverse offers an immersive experience to customers, and it is a future marketing platform that gives real-life experience to explore different brands, products, and services in the 3D interactive digital space. Many companies have started using metaverse to promote their products, especially real estate, gaming, fashion, automobile, etc. There is enormous scope for neuromarketing through the metaverse. Metaverse integrates the real physical world and virtual reality to create an environment where consumers and marketers can communicate the utility, application, attributes, and content of products.
Metaverse and Financial Inclusion Opportunities and Risks for the Banking Ecosystem

This chapter examines the potential of metaverse technology to transform the banking industry and promote financial inclusion. It provides an overview of the historical development of metaverse and its potential impact on the banking industry while identifying key opportunities to improve access to financial services, financial education, and customer engagement, as well as potential risks such as exacerbating inequalities and privacy concerns. The chapter also considers the ethical and societal implications of metaverse technology, emphasizing the importance of corporate social responsibility and stakeholder theory for responsible implementation. In conclusion, this chapter offers a comprehensive analysis of the opportunities and risks of metaverse technology for financial inclusion in the banking industry and highlights the need for responsible development and governance as well as stakeholder participation.
Potential-Field-RRT: A Path-Planning Algorithm for UAVs Based on Potential-Field-Oriented Greedy Strategy to Extend Random Tree

This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local errors, and introduces potential fields as an aid to the expansion process of random trees. It reasonably triggers a greedy strategy based on the principle of field strength descending gradient optimization, accelerating the process of random tree expansion to a better region and reducing path search time. Compared with other optimization algorithms that improve the sampling method to reduce the search time of the random tree, PF-RRT takes full advantage of the potential field without limiting the arbitrariness of random tree expansion. Secondly, the path construction process is based on the principle of triangle inequality for the root node of the new node to improve the quality of the path in one iteration. Simulation experiments of the algorithm comparison show that the algorithm has the advantages of fast acquisition of high-quality initial path solutions and fast optimal convergence in the path search process. Compared with the original algorithm, obtaining the initial solution using PF-RRT can reduce the time loss by 20% to 70% and improve the path quality by about 25%. In addition, the feasibility of PF-RRT for UAV path planning is demonstrated by actual flight test experiments at the end of the experiment.
The consequence of societal secrecy for the financial constraints faced by firms

Does the level of societal secrecy aggravate or alleviate access to finance? We explore this question for 51,249 firms in 39, predominantly developing, countries, from 2006 to 2015. We find a strong positive relationship between cultural orientation towards secrecy in a country and financial constraints faced by its firms. Our results are robust to several considerations and emphasize the adverse consequence of societal secrecy for perpetuating financing obstacles for firms.