Reviewerly/exHarmony huggingface dataset:
Multi-objective evolutionary 3D face reconstruction based on improved encoder–decoder network

Three-dimensional (3D) face application has attracted significant attention the field of multimedia and image processing. However, the end-to-end 3D face reconstruction method is still immature, and there are some problems, such as overfitting caused by too few training sets and unacceptable 3D face texture alignment performance. Therefore, we design a novel approach to construct a 3D face, named multi-objective evolutionary 3D face reconstruction based on improved encoder–decoder network (MoEDN). This study introduces a regularization algorithm named feature map distortion (Disout); whose purpose is to strengthen the network generalization ability. Based on this, we construct a multi-objective evolutionary 3D face reconstruction model, in which decision variables are distortion probability, distorted block size, distorted intensity, probability step, and learning rate; and objective functions are loss and structural similarity (SSIM). We use four multi-objective evolutionary algorithms (NSGA-II, AGEII, NSLS, and MOEA/D) to optimize the proposed model. Experimental results demonstrate that NSLS has the best performance. In addition, compared with position map regression network (PRNet), 2D-assisted self-supervised learning (2DASL) and other state-of-the-art, the proposed model achieves better loss values and NME values. Therefore, the proposed multi-objective evolutionary 3D face reconstruction model has outstanding 3D facial reconstruction performance in large poses and face expression.
Learning Selective Communication for Multi-Agent Path Finding

Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF). However, existing communication based MAPF solvers focus on broadcast communication, where an agent broadcasts its message to all other or predefined agents. It is not only impractical but also leads to redundant information that could even impair the multi-agent cooperation. A succinct communication scheme should learn <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">which</i> information is relevant and influential to each agent’s decision making process. To address this problem, we consider a request-reply scenario and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decision Causal Communication</i> (DCC), a simple yet efficient model to enable agents to select neighbors to conduct communication during both training and execution. Specifically, a neighbor is determined as relevant and influential only when the presence of this neighbor causes the decision adjustment on the central agent. This judgment is learned only based on agent’s local observation and thus suitable for decentralized execution to handle large scale problems. Empirical evaluation in obstacle-rich environment indicates the high success rate with low communication overhead of our method.
Portuguese Primary and Secondary Education in Times of COVID-19 Pandemic: An Exploratory Study on Teacher Training and Challenges

The discussion about the use of digital technologies in education is not new. However, the COVID-19 pandemic and the total closure of schools around the world, that forced millions of students to attend their classes from home, has demonstrated the importance of this discussion. It has highlighted the need to revisit debates about the interactions between technology and education, and the added value of digital resources to enhance the educational process. This article, based on an exploratory analysis, aims to understand how the transition from face-to-face to digital was accomplished in Portuguese primary and secondary education, namely regarding teacher training and the difficulties experienced during the emergency remote education period. The data analysed in this article were collected through an online questionnaire, disseminated through online social networks, and answered by 136 Portuguese primary and secondary education teachers. The questions focused on this article were open-ended, and the information collected was analysed using content analysis methodology. The results show how teachers have been forced to modify their pedagogical work, the importance of training, and the inherent challenges and critical reflections associated with the process, as well as the opportunities presented in a post-pandemic educational reality.
Attentive Contextual Network for Image Captioning

Existing image captioning approaches fail to generate fine-grained captions due to the lack of rich encoding representation of an image. In this paper, we present an attentive contextual network (ACN) to learn the spatially transformed image features and dense multi-scale contextual information of an image to generate semantically meaningful captions. At first, we construct deformable network on intermediate layers of convolutional neural network (CNN) to cultivate spatial invariant features. And the multi-scale contextual features are produced by employing contextual network on top of last layers of CNN. Then, we exploit attention mechanism on contextual network to extract dense contextual features. Further, the extracted spatial and contextual features are combined to encode the holistic representation of an image. Finally, a multi-stage caption decoder with visual attention module is incorporated to generate fine-grained captions. The performance of the proposed approach is demonstrated on COCO dataset, the largest dataset for image captioning.
Blockchain Technology in the Tourism Industry: New Perspectives in Switzerland

Blockchain is often presented in the tourism industry as being a technology in a global approach that will enable the sector to make its digital transformation and bring a whole series of advantages, both financial and logistical. The reality is different, however. Indeed, research on Blockchain tends to focus on essentially technical aspects and takes little account of the customer experience in the sector in which it will be integrated. The aim of this exploratory research, based on 18 semi-directive interviews, is to understand the sociological obstacles to the adoption of Blockchain by tourism professionals in Switzerland. Our generalized findings are presented in the form of four research proposals that argue that, without the intervention of the State, it is hard to see how such disruptive innovation can radically change the highly fragmented tourism sector.JEL Codes: O33, O35
EGAT: Edge-Featured Graph Attention Network

Most state-of-the-art Graph Neural Networks focus on node features in the learning process but ignore edge features. However, edge features also contain essential information in real-world, such as financial graphs. Node-centric approaches are suboptimal in edge-sensitive graphs since edge features are not adequately utilized. To address this problem, we present the Edge-Featured Graph Attention Network (EGAT) to leverage edge features in the graph feature representation. Our model is based on the edge-integrated attention mechanism, where both node and edge features are included in the calculation of the message and attention weights. In addition, the importance of edge information suggests that the edge features should be updated to learn high-level representation. So we perform edge updating with the integration of the features of connected nodes. In contrast to edge-node switching, our model acquires the adjacent edge features with the node-transit strategy, avoiding significant lift of computational complexity. Then we employ a multi-scale merge strategy, which concatenates features of every layer to construct hierarchical representation. Moreover, our model can be adapted to domain-specific graph neural networks, which further extends the application scenarios. Experiments show that our model achieves or matches the state-of-the-art on both node-sensitive and edge-sensitive datasets.
Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles

In this paper, we propose a new privacy solution for the data used to train a recommender system, i.e., the user–item matrix. The user–item matrix contains implicit information, which can be inferred using a classifier, leading to potential privacy violations. Our solution, called Personalized Blurring (PerBlur), is a simple, yet effective, approach to adding and removing items from users’ profiles in order to generate an obfuscated user–item matrix. The novelty of PerBlur is personalization of the choice of items used for obfuscation to the individual user profiles. PerBlur is formulated within a user-oriented paradigm of recommender system data privacy that aims at making privacy solutions understandable, unobtrusive, and useful for the user. When obfuscated data is used for training, a recommender system algorithm is able to reach performance comparable to what is attained when it is trained on the original, unobfuscated data. At the same time, a classifier can no longer reliably use the obfuscated data to predict the gender of users, indicating that implicit gender information has been removed. In addition to introducing PerBlur, we make several key contributions. First, we propose an evaluation protocol that creates a fair environment to compare between different obfuscation conditions. Second, we carry out experiments that show that gender obfuscation impacts the fairness and diversity of recommender system results. In sum, our work establishes that a simple, transparent approach to gender obfuscation can protect user privacy while at the same time improving recommendation results for users by maintaining fairness and enhancing diversity.
Deformed2Self: Self-supervised Denoising for Dynamic Medical Imaging

Image denoising is of great importance for medical imaging system, since it can improve image quality for disease diagnosis and downstream image analyses. In a variety of applications, dynamic imaging techniques are utilized to capture the time-varying features of the subject, where multiple images are acquired for the same subject at different time points. Although signal-to-noise ratio of each time frame is usually limited by the short acquisition time, the correlation among different time frames can be exploited to improve denoising results with shared information across time frames. With the success of neural networks in computer vision, supervised deep learning methods show prominent performance in single-image denoising, which rely on large datasets with clean-vs-noisy image pairs. Recently, several self-supervised deep denoising models have been proposed, achieving promising results without needing the pairwise ground truth of clean images. In the field of multi-image denoising, however, very few works have been done on extracting correlated information from multiple slices for denoising using self-supervised deep learning methods. In this work, we propose Deformed2Self, an end-to-end self-supervised deep learning framework for dynamic imaging denoising. It combines single-image and multi-image denoising to improve image quality and use a spatial transformer network to model motion between different slices. Further, it only requires a single noisy image with a few auxiliary observations at different time frames for training and inference. Evaluations on phantom and in vivo data with different noise statistics show that our method has comparable performance to other state-of-the-art unsupervised or self-supervised denoising methods and outperforms under high noise levels.
The UK COVID‐19 app: The failed co‐production of a digital public service

Abstract The COVID‐19 pandemic put governments under pressure to make radical and urgent decisions, and to implement new digital solutions to steer society and deliver public services. Our study analyzes social media discourse to understand the co‐production of a digital public service in an emergency situation. Empirically, we mobilize Twitter netnography and discourse analysis to examine citizens’ perceptions of the contact tracing app (CTA) introduced by the UK government to tackle the pandemic and save lives. Our study contributes to research on public sector accountability for digital transformations by advancing scholarly understanding of how societal concerns and public perceptions impact the co‐production of digital services. Our findings reveal a high level of public skepticism toward the app and a general distrust of the UK government among the main social challenges of the CTA's implementation. Furthermore, we evidence widespread public distress over the potential violation of democratic freedoms and misuse of the data collected by the app. Finally, we reflect on the linkages between the lack of governmental accountability and the difficulties in mitigating the expressed societal concerns, causing a corresponding resistance on the part of the public to engage in and support co‐production.
Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation

Domain adaptation is critical for success when confronting with the lack of annotations in a new domain. As the huge time consumption of labeling process on 3D point cloud, domain adaptation for 3D semantic segmentation is of great expectation. With the rise of multi-modal datasets, large amount of 2D images are accessible besides 3D point clouds. In light of this, we propose to further leverage 2D data for 3D domain adaptation by intra and inter domain cross modal learning. As for intra-domain cross modal learning, most existing works sample the dense 2D pixel-wise features into the same size with sparse 3D point-wise features, resulting in the abandon of numerous useful 2D features. To address this problem, we propose Dynamic sparse-to-dense Cross Modal Learning (DsCML) to increase the sufficiency of multi-modality information interaction for domain adaptation. For inter-domain cross modal learning, we further advance Cross Modal Adversarial Learning (CMAL) on 2D and 3D data which contains different semantic content aiming to promote high-level modal complementarity. We evaluate our model under various multi-modality domain adaptation settings including day-to-night, country-to-country and dataset-to-dataset, brings large improvements over both uni-modal and multi-modal domain adaptation methods on all settings. Code is available at https://github.com/leolyj/DsCML
A Pyramid Architecture-Based Deep Learning Framework for Breast Cancer Detection

Breast cancer diagnosis is a critical step in clinical decision making, and this is achieved by making a pathological slide and gives a decision by the doctors, which is the method of final decision making for cancer diagnosis. Traditionally, the doctors usually check the pathological images by visual inspection under the microscope. Whole-slide images (WSIs) have supported the state-of-the-art diagnosis results and have been admitted as the gold standard clinically. However, this task is time-consuming and labour-intensive, and all of these limitations make low efficiency in decision making. Medical image processing protocols have been used for this task during the last decades and have obtained satisfactory results under some conditions; especially in the deep learning era, it has exhibited the advantages than those in the shallow learning period. In this paper, we proposed a novel breast cancer region mining framework based on deep pyramid architecture from multilevel and multiscale breast pathological WSIs. We incorporate the tissue- and cell-level information together and integrate these into a LSTM model for the final sequence modelling, which successfully keeps the WSIs' integration and is not mentioned by the prevalence frameworks. The experiment results demonstrated that our proposed framework greatly improved the detection accuracy than that only using tissue-level information.
Romoa: Robust Model Aggregation for the Resistance of Federated Learning to Model Poisoning Attacks

Training a deep neural network requires substantial data and intensive computing resources. Unaffordable price holds back many potential applications of deep learning. Besides, it is risky to gather user’s private data for training centrally. Then federated learning appears as a promising solution to having users learned jointly while keeping training data local. However, security issues keep coming up in federated learning applications. One of the most threatening attacks is the model poisoning attack which can manipulate the inference result of a jointly learned model. Some recent studies show that elaborate model poisoning approaches can even breach the existing Byzantine-robust federated learning solutions. Hence, it is critical to discuss alternative solutions to secure federated learning. In this paper, we propose to protect federated learning against model poisoning attacks by introducing a robust model aggregation solution named Romoa. Unlike previous studies, Romoa can deal with targeted and untargeted poisoning attacks with a unified approach. Moreover, Romoa achieves more precise attack detection and better fairness for federated learning participants by constructing a new similarity measurement. We conclude that through a comprehensive evaluation of standard datasets, Romoa can provide a satisfying defense effect against model poisoning attacks, including those attacks breaching Byzantine-robust federated learning solutions.
Fall Detection with CNN-Casual LSTM Network

Falls are one of the main causes of elderly injuries. If the faller can be found in time, further injury can be effectively avoided. In order to protect personal privacy and improve the accuracy of fall detection, this paper proposes a fall detection algorithm using the CNN-Casual LSTM network based on three-axis acceleration and three-axis rotation angular velocity sensors. The neural network in this system includes an encoding layer, a decoding layer, and a ResNet18 classifier. Furthermore, the encoding layer includes three layers of CNN and three layers of Casual LSTM. The decoding layer includes three layers of deconvolution and three layers of Casual LSTM. The decoding layer maps spatio-temporal information to a hidden variable output that is more conducive relative to the work of the classification network, which is classified by ResNet18. Moreover, we used the public data set SisFall to evaluate the performance of the algorithm. The results of the experiments show that the algorithm has high accuracy up to 99.79%.
Re-designing the pension business processes for achieving technology-driven reforms through blockchain adoption: A proposed architecture

High administrative costs and associated operational inefficiencies present multiple challenges for pension industries across different countries. Although blockchain is considered a potential solution for these concerns, the pension industry has not fully leveraged this disruptive technology. This paper provides a blueprint for a blockchain-based end-to-end digital transformation of the pension industry. First, we identified the pre-requisites for blockchain adoption in pension. Then, we developed an architectural design of blockchain-based re-designed pension business processes. Subsequently, we elaborated on how smart contracts can make pension transactions in such redesigned processes optimized, automated, and error-free. Finally, we presented how blockchain can be integrated with the existing pension IT systems, using an API layer, to enable seamless onboarding of all pension participants to a single digital platform – a critical ask for any blockchain implementation. We concluded with an elucidation of the potential of such blockchain-based digital transformation of the pension industry in reducing turnaround time, lowering operating expenses, and facilitating the achievement of other pension reform agendas. This architecture of a blockchain-enabled pension network represents a flexible and scalable knowledge construct that can act as a foundation for further investigations by pension regulators or pension industry participants interested in achieving technology-driven pension operations reforms.
Blockchain Software Selection as a Fuzzy Multi-Criteria Problem

Increased consumer requirements for quality, safety and traceability of goods in supply chains has accelerated the implementation of blockchain during the COVID-19 pandemic. The right choice of blockchain software is a complicated task and an important prerequisite for successful deployment. In this study, we propose a conceptual framework for group multi-criteria selection of blockchain software in fuzzy environment according to organization needs and experts’ judgements. The applicability of the new framework has been verified through an illustrative example for ranking blockchain systems. The evaluations of compared alternatives were calculated by using measurement of alternatives and ranking according to the compromise solution (MARCOS) method. The robustness of the new framework was proven by sensitivity analysis in which two (crisp and fuzzy) MARCOS models with two different sets of weighting coefficients were compared.
A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques

In the new era, multimedia technology is used extensively. Multimedia data transfers over the Internet are not adequately stable. Data protection is needed for digital images transfer to avoid unauthorized entities. Many encryption strategies have been established for multimedia data over a medium that is not stable. This paper aims to compare the techniques used in encoding multimedia content. This paper explains the process for assessment for chaos image encryption algorithm. The chaos-based image encryption algorithm is used mainly because of its high protection and efficiency. Parameters such as MSC, encryption quality and avalanche effect check the image's quality. This paper presents a comparative review of non-chaotic and chaotic image encryption techniques.
Asleep at the wheel? Responsible Innovation in quantum computing

Quantum computing is an emerging set of technologies which promise to transform aspects of computing in ways that, though increasingly defined, are still largely theoretical. Responsible Innovation (RI) asserts that technologies with potentially transformative capacity on society should be approached with care and forethought; this paper is based on applying RI in one of the UK's National Quantum Technology Hubs.Quantum computing is at a key juncture as it emerges from the laboratory to be of interest commercially. This provides an opportunity to observe and influence the trajectory of this technology. Quantum computing is widely envisioned to have major impacts on computing and society; there are, however, great uncertainties about development timescales and the scope and impact of applications.From experiences with a major quantum computing project in the UK, we discuss the challenges in applying RI to quantum computing. Existing RI practices struggle to address the societal implications of such a complex and innovative technology. We argue that uncovering the visions and sociotechnical imaginaries that inform the development this technology enables RI to make valuable insights into future societal implications of quantum computing. This provides lessons for RI in emerging technologies more widely.