Reviewerly/exHarmony huggingface dataset:
The sense of being there during online classes: analysis of usability and presence in web-conferencing systems and virtual reality social platforms

Due to the Covid-19 pandemic, millions of people had to stay at home for several months and to move their everyday activities online. From smart-working to online learning, also considering entertainment activities, people started to extensively use platforms for remote communication and for sharing video, text, and audio content. Such systems are able to cope with the need of communicating, but the common feeling is that people, and especially students, are missing the physical relationships with classmates and colleagues. In this paper, we analyse two modalities that can be used to give an online University lecture, by focusing on the ‘sense of presence’, i.e. on the ability to produce into students the psychological sense of being in a classroom together with the teacher and their classmates. We consider a standard web-conferencing system, and a Virtual Reality social platform. We also take into account the ‘usability’ factors, to understand if extended reality systems could be, in a near future, a possible alternative to improve distance lectures and meetings.
DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images

The analysis of histopathological images is the core way for detecting breast cancer, the most insidious type of cancer for women. Artificial intelligence-based applications are used as an effective and supportive tool for automated breast cancer detection. Especially, deep learning models are among the most popular approaches due to their high performances in classification problems of medical images. In this study, a novel and robust approach, based on the convolutional-LSTM (CLSTM) learning model, the pre-processing technique using marker-controlled watershed segmentation algorithm (MWSA), and the optimized SVM classifier, was proposed for detecting breast cancer automatically from histopathological images (HPIs). The CLSTM model trained on the BreakHis dataset, which is popular in the research community, composes of binary and eight-class classification tasks. The classification performance of the CLSTM model was significantly increased by using the processed HPIs with MWSA. For binary and eight-class classification tasks, the best scores were obtained by using the optimized SVM classifier with Bayesian optimization instead of the softmax classifier of the CLSTM model. The proposed approach, which provided very high performance for both classification tasks, was compared to the existing approaches using the BreakHis dataset.
NFDDE: A novelty-hybrid-fitness driving differential evolution algorithm

In differential evolution algorithm (DE), it is a widely accepted method that selecting individuals with higher fitness to generate a mutant vector. In this case, the population evolution is under a fitness-based driving force. Although the driving force is beneficial for the exploitation, it sacrifices performance on the exploration. In this paper, a novelty-hybrid-fitness driving force is introduced to trade off contradictions between the exploration and the exploitation of DE. In the new proposed DE, named as NFDDE, both fitness and novelty values of individuals are considered when choosing individuals to create mutant vectors. In addition, two adaptive scaling factors are proposed to adjust the weights of the fitness-based driving force and the novelty-based driving force, respectively, and then distinct properties of the two driving forces can be effectively utilized. At last, to save computational resources, some individuals with lower novelty are deleted when the population has converged to a certain extent. The comprehensive performance of NFDDE is extensively evaluated by comparisons between it and other 9 state-of-art DE variants based on CEC2017 test suite. In addition, distinct properties of the newly introduced strategies and involved parameters are further confirmed by a set of experiments.
Is the suggested food your desired?: Multi-modal recipe recommendation with demand-based knowledge graph

Personalized recipe recommender systems help users mine certain dishes they want to find and even really desire, which play a significant role in matching dishes, balancing nutrients, and preventing non-communicable diseases. Generally, customer’s preferences or needs vary from person to person, and people are often reluctant to accept recommended food without reasonable explanation, especially when their demands are not explicitly addressed. In this paper, we are devoted to providing recipe suggestions accompanied by rational interpretations generated from images or videos. First, we construct a recipe knowledge graph (RcpKG) through the use of multi-modality and hierarchical thought, which focuses on the underlying demands of users and the consideration of multiple fine-grained factors. On this basis, a novel multi-modal recipe recommendation method via the knowledge graph (RcpMKR) is proposed, which represents nodes in multiple aspects and performs multi-relational graph structure extraction of the RcpKG. It not only takes into account local associations within the graph but also global information, and incorporates user concerns at different levels. Then, we adopt BERT-based multi-modal models and generative adversarial networks to generate interpretations. Additionally, dynamic convolution and random synthetic attention are utilized in our work to discriminate among features. Experimental results show that the proposed method and BERT-based fusion models improve recipe recommendation performance and explanation generation. Specifically, the precision of the RcpMKR method through RcpKG, user concerns and graph convolutional network improves by 7.82%, and the viExpCBTBERT method via 2D&3D convolutional neural networks for developing text interpretations enhances the F1-score by 10% compared with the baseline. • Propose an extendable multi-modal recipe fusion framework based on knowledge graph. • Constructing a recipe knowledge graph based on user preferences. • Use multi-modality to improve recommendation results. • Utilizing deep learning to generate explanations from multimedia data.
Literature Review on the Applications of Machine Learning and Blockchain Technology in Smart Healthcare Industry: A Bibliometric Analysis

The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of “smart healthcare.” This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.
A Blockchain-Enabled Privacy-Preserving Verifiable Query Framework for Securing Cloud-Assisted Industrial Internet of Things Systems

Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduces several data privacy and integrity issues related to verifying the reliability of users’ queries and aggregated outcomes. In this article, we propose a blockchain-based framework for provisioning a privacy-preserving and verifiable query facility to end-users in IIoT systems. The framework uses blockchain to store IoT data as on-chain data and the cloud to store extensive data (e.g., image) as off-chain data and provisioning search services to users by executing a query in both on-chain and off-chain data and generating an aggregated result. Besides, it introduces a new privacy-preserving query mechanism for ensuring sensitive data privacy during query execution. A data owner encrypts both on-chain and off-chain data in the privacy-preserving query mechanism before sending it to the blockchain and cloud. A CSP can perform search operations on the encrypted on-chain and off-chain data to ensure sensitive data privacy. A multisignature-powered query verification model is also built for the blockchain. The query verification model allows each blockchain node to endorse the query result individually and a user to verify the endorsement of the query result before use. The experiments revealed the high efficiency and scalability of the proposed framework.
TMS-GAN: A Twofold Multi-Scale Generative Adversarial Network for Single Image Dehazing

In recent years, learning-based single image dehazing networks have been comprehensively developed. However, performance improvement is limited due to domain shift between trained synthetic hazy images and untrained real-world hazy images. To alleviate this issue, this paper proposes a real-world dehazing targeted training scheme which nearly realizes paired real-world data training. As a result, a Twofold Multi-scale Generative Adversarial Network (TMS-GAN) consisting of a Haze-generation GAN (HgGAN) and a Haze-removal GAN (HrGAN) is designed. HgGAN attributes real haze properties to synthetic images and HrGAN removes haze from both synthetic and generated fake realistic data under supervision. Thus, the proposed method can better adapt to real-world image dehazing using this cooperative training scheme. Meanwhile, several structural advances of TMS-GAN also improve dehazing performance. Specifically, a haze residual map based on atmospheric scattering model is deduced in HgGAN for fake realistic data generation. The dual-branch generator in HrGAN draws attention to detail restoration by one branch along with another color-branch. A plug-and-play Multi-attention Progressive Fusion Module (MAPFM) is proposed and inserted in both HgGAN and HrGAN. MAPFM incorporates multi-attention mechanism to guide multi-scale feature fusion in a progressive manner, in which Adjacency-attention Block (AAB) can capture contributing features of each level and Self-attention Block (SAB) can establish non-local dependency of feature fusion. Experiments on mainstream benchmarks show that the proposed framework is superior especially on real-world hazy images among single image dehazing methods.
Doubly Geometric Quantum Control

In holonomic quantum computation, single-qubit gates are performed using driving protocols that trace out closed loops on the Bloch sphere, making them robust to certain pulse errors. However, dephasing noise that is transverse to the drive, which is significant in many qubit platforms, lies outside the family of correctable errors. Here, we present a general procedure that combines two types of geometry -- holonomy loops on the Bloch sphere and geometric space curves in three dimensions -- to design gates that simultaneously suppress pulse errors and transverse noise errors. We demonstrate this doubly geometric control technique by designing explicit examples of such dynamically corrected holonomic gates.
Exploiting foreground and background separation for prohibited item detection in overlapping X-Ray images

X-ray imagery security screening is an essential component of transportation and logistics. In recent years, some researchers have used computer vision algorithms to replace inefficient and tedious manual baggage inspection. However, X-ray images are complicated, and objects overlap with one another in a semi-transparent state, which underperforms the existing object detection frameworks. To solve the severe overlapping problem of X-ray images, we propose a foreground and background separation (FBS) X-ray prohibited item detection framework, which separates prohibited items from other items to exclude irrelevant information. First, we design a target foreground and use recursive training to adaptively approximate the real foreground. Thereafter, with the constraints of X-ray imaging characteristics, a decoder is employed to separate the prohibited items from other irrelevant items to obtain the foreground and background (FB). Finally, we use the attention module to make the detection framework focus more on the foreground. Our method is evaluated on a synthetic dataset with FB ground truth and two public datasets with only bounding box annotations. Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art solutions. Furthermore, experiments are performed in the case where only a small number of images contain the FB ground truth. The results indicate that our method requires only a small number of FB ground truths to obtain a performance equivalent to that of all FB ground truths.
Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images

Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet.
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

Recently, the generalization behavior of Convolutional Neural Networks (CNN) is gradually transparent through explanation techniques with the frequency components decomposition. However, the importance of the phase spectrum of the image for a robust vision system is still ignored. In this paper, we notice that the CNN tends to converge at the local optimum which is closely related to the high-frequency components of the training images, while the amplitude spectrum is easily disturbed such as noises or common corruptions. In contrast, more empirical studies found that humans rely on more phase components to achieve robust recognition. This observation leads to more explanations of the CNN’s generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image. That is, the generated samples force the CNN to pay more attention to the structured information from phase components and keep robust to the variation of the amplitude. Experiments on several image datasets indicate that the proposed method achieves state-of-the-art performances on multiple generalizations and calibration tasks, including adaptability for common corruptions and surface variations, out-of-distribution detection, and adversarial attack. The code is released on github/iCGY96/APR.
Data Quality for Machine Learning Tasks

The quality of training data has a huge impact on the efficiency, accuracy and complexity of machine learning tasks. Data remains susceptible to errors or irregularities that may be introduced during collection, aggregation or annotation stage. This necessitates profiling and assessment of data to understand its suitability for machine learning tasks and failure to do so can result in inaccurate analytics and unreliable decisions. While researchers and practitioners have focused on improving the quality of models, there are limited efforts towards improving the data quality.
Healthcare Insurance Frauds: Taxonomy and Blockchain-Based Detection Framework (Block-HI)

Medical health insurance fraud has been a major concern for the healthcare industry and governmental institutions. In the United States, the health insurance companies recorded a loss of tens of billions yearly due to healthcare fraud. Some types of fraud are at the risk of the patient's health. This is because the system that performs the manual processing of medical insurance claims frequently misses the endorsement of some stakeholders (such as the patient, pharmaceutical companies, wholesale dealers, and medical equipment suppliers) in a claim's validation process. Blockchain is a peer-to-peer distributed system that can enable the validation of healthcare claims in a secure, immutable, and transparent manner. We present a taxonomy of healthcare insurance claims frauds, and we propose and evaluate a blockchain-based healthcare insurance claims fraud detection framework.
Learning type annotation: is big data enough?

TypeScript is a widely used optionally-typed language where developers can adopt “pay as you go” typing: they can add types as desired, and benefit from static typing. The “type annotation tax” or manual effort required to annotate new or existing TypeScript can be reduced by a variety of automatic methods. Probabilistic machine-learning (ML) approaches work quite well. ML approaches use different inductive biases, ranging from simple token sequences to complex graphical neural network (GNN) models capturing syntax and semantic relations. More sophisticated inductive biases are hand-engineered to exploit the formal nature of software. Rather than deploying fancy inductive biases for code, can we just use “big data” to learn natural patterns relevant to typing? We find evidence suggesting that this is the case. We present TypeBert, demonstrating that even with simple token-sequence inductive bias used in BERT-style models and enough data, type-annotation performance of the most sophisticated models can be surpassed.
Interactive Multimodal Lifelog Retrieval with vitrivr at LSC 2021

The Lifelog Search Challenge (LSC) is an annual benchmarking competition for interactive multimedia retrieval systems, where participating systems compete in finding events based on textual descriptions containing hints about structured, semi-structured, and/or unstructured data. In this paper, we present the multimedia retrieval system vitrivr, a long-time participant to LSC, with a focus on new functionality. Specifically, we introduce the image stabilisation module which is added prior to the feature extraction to reduce the image degradation caused by lifelogger movements, and discuss how geodata is used during query formulation, query execution, and result presentation.
A2W: Context-Aware Recommendation System for Mobile Augmented Reality Web Browser

Augmented Reality (AR) offers new capabilities for blurring the boundaries between physical reality and digital media. However, the capabilities of integrating web contents and AR remain underexplored. This paper presents an AR web browser with an integrated context-aware AR-to-Web content recommendation service named as A2W browser, to provide continuously user-centric web browsing experiences driven by AR headsets. We implement the A2W browser on an AR headset as our demonstration application, demonstrating the features and performance of A2W framework. The A2W browser visualizes the AR-driven web contents to the user, which is suggested by the content-based filtering model in our recommendation system. In our experiments, 20 participants with the adaptive UIs and recommendation system in A2W browser achieve up to 30.69% time saving compared to smartphone conditions. Accordingly, A2W-supported web browsing on workstations facilitates the recommended information leading to 41.67% faster reaches to the target information than typical web browsing.
BCC: Blockchain-Based Collaborative Crowdsensing in Autonomous Vehicular Networks

The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.
Quantum inspired meta-heuristic approach for optimization of genetic algorithm

An optimization search technique evaluates the potential solution and frames the better solution by any of its befitting categories such as quantum-based approaches, meta-heuristic-based approaches, and multi-objective-based approaches. The Quantum Inspired Meta-Heuristic (QIMH) Algorithm is a synergistic combination of evolutionary algorithm, quantum computing, meta-heuristic, and multi-objective approaches to solve hard, exhaustive problems, and to get the best optimization. Techniques such as intensification or exploitation and diversification or exploration are useful in analyzing the optimum solution for the QIMH algorithm. The current research with QIMH algorithm provides a methodology for real time corona virus analysis from its prodigious parameters.