Reviewerly/exHarmony huggingface dataset:
Comparative study of Centrality based Adversarial Attacks on Graph Convolutional Network model for Node classification

Graph Convolution Networks (GCNs) are neural networks that can be used to perform different kinds of analysis and mining activities on graphs. GCNs are neural networks that can be directly applied to graphs, and provide an easy way to do node-level, edge-level, and graph-level prediction tasks. They have many practical uses ranging from link prediction to image recognition, from machine translation to node classification. A lot of research is being conducted to research graph information and it has been found that GCNs are also vulnerable to adversarial attacks i.e. the performance of the algorithm can vary drastically based on whether any change has been made to the graph structurally or otherwise. One of the methods of knowing where to conduct the attack on the graph is by using centrality measures, which denote how important a certain node is. For any use case, different kinds of centralities exist which can be used to find the influential nodes in a graph. These measures all work at varying levels of effectiveness depending on their underlying algorithm and the graph structure or features. The Cora citations graph network and a GCN model that performs Node classification task on it is considered here. Before training and classification, uniform edge level adversarial attacks are conducted using four different centrality measures at different scales and the various accuracies are compared.
KGNER: Improving Chinese Named Entity Recognition by BERT Infused with the Knowledge Graph

Recently, the lexicon method has been proven to be effective for named entity recognition (NER). However, most existing lexicon-based methods cannot fully utilize common-sense knowledge in the knowledge graph. For example, the word embeddings pretrained by Word2vector or Glove lack better contextual semantic information usage. Hence, how to make the best of knowledge for the NER task has become a challenging and hot research topic. We propose a knowledge graph-inspired named-entity recognition (KGNER) featuring a masking and encoding method to incorporate common sense into bidirectional encoder representations from transformers (BERT). The proposed method not only preserves the original sentence semantic information but also takes advantage of the knowledge information in a more reasonable way. Subsequently, we model the temporal dependencies by taking the conditional random field (CRF) as the backend, and improve the overall performance. Experiments on four dominant datasets demonstrate that the KGNER outperforms other lexicon-based models in terms of performance.
MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data

In this paper, we address the challenging problem of learning to rank from highly imbalanced data. This scenario requires to resort to specific metrics able to account the scarcity of the so-called positive examples. We present MetaAP, a tree-based ranking algorithm, which induces meta-trees by optimizing directly during the learning process the Average Precision (AP). This latter has been shown to be more relevant than the area under the ROC curve (AUC–ROC) when the objective is to push the examples of interest at the very top of the list. This effect of the AP in tree-based ranking is particularly wished to address fraud detection tasks where (i) the budget is often constrained (in terms of possible controls) and (ii) the interpretability of the induced models is required to support decision making. After an extensive comparative study on 28 public datasets showing that MetaAP is significantly better than other tree-based ranking methods, we tackle a tax fraud detection task coming from a partnership with the French Ministry of Economy and Finance. The results show that MetaAP is able to make the tax audit process much more efficient.
Morphometric Integrated Classification Index: A Multisite Model-Based, Interpretable, Shareable and Evolvable Biomarker for Schizophrenia

Abstract Background and Hypothesis Multisite massive schizophrenia neuroimaging data sharing is becoming critical in understanding the pathophysiological mechanism and making an objective diagnosis of schizophrenia; it remains challenging to obtain a generalizable and interpretable, shareable, and evolvable neuroimaging biomarker for schizophrenia diagnosis. Study Design A Morphometric Integrated Classification Index (MICI) was proposed as a potential biomarker for schizophrenia diagnosis based on structural magnetic resonance imaging data of 1270 subjects from 10 sites (588 schizophrenia patients and 682 normal controls). An optimal XGBoost classifier plus sample-weighted SHapley Additive explanation algorithms were used to construct the MICI measure. Study Results The MICI measure achieved comparable performance with the sample-weighted ensembling model and merged model based on raw data (Delong test, P &amp;gt; 0.82) while outperformed the single-site models (Delong test, P &amp;lt; 0.05) in either the independent-sample testing datasets from the 9 sites or the independent-site dataset (generalizable). Besides, when new sites were embedded in, the performance of this measure was gradually increasing (evolvable). Finally, MICI was strongly associated with the severity of schizophrenia brain structural abnormality, with the patients’ positive and negative symptoms, and with the brain expression profiles of schizophrenia risk genes (interpretable). Conclusions In summary, the proposed MICI biomarker may provide a simple and explainable way to support clinicians for objectively diagnosing schizophrenia. Finally, we developed an online model share platform to promote biomarker generalization and provide free individual prediction services (http://micc.tmu.edu.cn/mici/index.html).
All-Photonic Artificial Neural Network Processor Via Nonlinear Optics

We propose an all-photonic architecture to accelerate linear matrix processing by encoding information in the amplitudes of frequency states. Our design is unique in providing a unitary, reversible mode of computation at high speeds.
OSNMAlib: An Open Python Library for Galileo OSNMA

Galileo has started authenticating its navigation message through OSNMA. In order to support OSNMA implementation by receiver manufacturers and application developers, this paper presents OSNMAlib, an open Python library implementing OSNMA functions. OSNMAlib processes the Galileo I/NAV pages in decoded SBF, hexadecimal or other formats, and performs the required operations to authenticate Galileo navigation data: OSNMA status handling, cryptographic functions required for the Merkle tree, digital signatures, keychain management and tag authentication. It handles the up-to-date data authentication status and performs the public key and chain renewal and revocation processes. This paper describes OSNMAlib architecture and its main functions and presents the first test results.
A blockchain-based spatial data trading framework

Abstract With the increasing utilization of space related data, the demand for spatial big data sharing and trading is growing rapidly, which promotes the emergence of spatial data market. However, in conventional data markets, both data buyers and data sellers have to use a centralized trading platform which might be dishonest. Blockchain is a decentralized distributed data storage technology, which uses the traceability and unforgeability to confirm and record each transaction, and can solve partial disadvantages of the centralized data market; unfortunately, it also introduces the problems of security and privacy. To address this issue, in this paper, we propose a blockchain-based spatial data trading framework with trusted execution environment to provide a trusted decentralized platform, including data storage, data query, data pricing, data reputation and security computing. Based on this framework, we use an auction pricing mechanism to ensure data trading authenticity and efficiency. At last, a spatial data trading framework was implemented and its effectiveness and security were verified.
How Virtual Walking Task Difficulty Design Influences on Task Performance and User Experience

In recent years, virtual reality (VR) has emerged as a novel paradigm for immersive applications for rehabilitation purposes. When designing a virtual rehabilitation application, difficulty customization and motivation improvement are two major concerns and are essential for ensuring a desirable rehabilitation outcome. It is significant to explore the impact of task difficulty on user experiences in virtual rehabilitation. This paper proposes a VR rehabilitation system for walking tasks with difficulty customization based on performance judgment (loose, standard, strict, and adaptive modes). In addition, this paper investigates the influence of walking task difficulties on users' task performance and user experience with sense of agency and emotion as two indexes, through an experimental study of users' subjective feelings and objective measured data. The results show that the task difficulties designed in this study have a noticeable influence on task performance and user experience. The proposed system and experimental study can provide a strong empirical basis for building a more effective and user-centered VR rehabilitation training system.
Altitude control of a quadcopter using interval type-2 fuzzy controller with dynamic footprint of uncertainty

Footprint of uncertainty (FOU) characteristics of interval type-2 membership functions (IT2-MFs) play an important role in the performance and robustness of interval type-2 fuzzy controllers (IT2-FCs). In literature, fixed FOU structures are used in almost all IT2-FC designs. In this study, an IT2-FC with dynamic FOU is proposed to provide high performance and robustness in the altitude control of a quadcopter. A proportional–derivative (PD) type FC with Takagi–Sugeno rule structure is used in the design procedure of the proposed controller. Additionally, input variables (error and derivative of error) are defined by using triangular IT2-MFs. To provide a dynamic FOU structure, the height of the lower MF (LMF) of each interval type-2 fuzzy set is defined as a function of the system error. In this way, FOU levels of IT2-MFs are adjusted dynamically since the height value of the LMF directly determines the FOU level of the IT2-MF. To evaluate the effectiveness of the proposed controller, comparison studies are performed under different conditions, including unmodeled measurement noises, an external disturbance, and a system parameter uncertainty. A classical PD, a type-1 fuzzy PD, and an interval type-2 fuzzy PD with fixed FOU controllers are used in the comparisons. The comparison results demonstrate that the proposed interval type-2 fuzzy PD controller with dynamic FOU exhibits better performance and more robustness than the other controllers.
Blockchain-enabled Internet of Things (IoTs) platforms for vehicle sensing and transportation monitoring

Technical evolution of Internet of Things (IoTs) shifts its power, storage, and computational capabilities away from centralized cloud platforms to a decentralized IoT edge. Traditional security policies may not be effective against modification attacks, eavesdropping, distributed denial-of-service (DDoS) because of large attack surface area in IoT where devices cannot have their own ID and cannot keep their privacy and resiliency to the cyber-attacks. Blockchain can support secure, transparent, immutable data and computation-intensive applications such as services for factory automation, assisted living and automotive driving, transportation monitoring and vehicular networks. In addition, for facilitating blockchain applications at low-power mobile IoT systems, mobile edge computing (MEC) can be a convenient alternative for solving consensus protocols for mobile IoT users (offloading to MEC providers).In this chapter, blockchain-enabled IoT platforms and solutions for vehicle sensing and transportation monitoring from various aspects are investigated. After providing a more general background, we consider vehicular networks and blockchain applications. In a typical vehicular network, a large number of vehicles need ultra-reliable, low-latency communications and secure, transparent, immutable data sharing to avoid multiple-vehicle collisions. Therefore, we consider blockchain applications in vehicular networks for more scalable, transparent, and secure Internet of Vehicles (IoV). In addition, we consider cybersecurity issues in Internet of Drones (IoD) and unmanned traffic monitoring (UTM) systems and then provide the existing blockchain-based solutions/platforms for these problems.With 5G of mobile broadband systems, blockchain needs to deal with scalability problems due to a very large number of users in IoV. Artificial intelligence (AI) techniques can be applied with blockchain in connected vehicles in this manner. Implementing AI techniques can provide more scalable, transparent, and secure blockchain applications in vehicular networks. AI techniques also help blockchain achieve privacy and personalization for the users in IoV at the same time. In the sequel, we consider more lightweight protocols for more scalable blockchains. Moreover, we consider the solutions of game-theoretic models to jointly maximize the profit of the MEC service provider and the individual utilities of the miners. Then, we can consider resource-provision problems under different pricing schemes offered by MEC service providers. Finally, we conclude the chapter.
Fisher information analysis on weak-value-amplification metrology using optical coherent states

The weak-value-amplification (WVA) technique has been extensively considered and debated in the field of quantum precision measurement, largely owing to the reduced Fisher information caused by the low probability of successful postselection. In this work we show that, rather than the transverse-spatial-wave-function meter typically considered, using the optical coherent state as a meter, the WVA measurement can outperform the conventional measurement not involving the strategy of postselection. We also show that the postselection procedure involved in the WVA scheme can make a mixture of coherent states work better than a pure coherent state with identical average photon numbers. This is in contrast to the main result proved in the absence of postselection. The postselection strategy can also result in the precision of Heisenberg scaling with the photon numbers without using expensive quantum resources. The present work may stimulate further interest in postselection strategy for quantum precision measurements.
Predict-and-Drive: Avatar Motion Adaption in Room-Scale Augmented Reality Telepresence with Heterogeneous Spaces

Avatar-mediated symmetric Augmented Reality (AR) telepresence has emerged with the ability to empower users located in different remote spaces to interact with each other in 3D through avatars. However, different spaces have heterogeneous structures and features, which bring difficulties in synchronizing avatar motions with real user motions and adapting avatar motions to local scenes. To overcome these issues, existing methods generate mutual movable spaces or retarget the placement of avatars. However, these methods limit the telepresence experience in a small sub-area space, fix the positions of users and avatars, or adjust the beginning/ending positions of avatars without presenting smooth transitions. Moreover, the delay between the avatar retargeting and users' real transitions can break the semantic synchronization between users' verbal conversation and perceived avatar motion. In this paper, we first examine the impact of the aforementioned transition delay and explore the preferred transition style with the existence of such delay through user studies. With the results showing a significant negative effect of avatar transition delay and providing the design choice of the transition style, we propose a Predict-and-Drive controller to diminish the delay and present the smooth transition of the telepresence avatar. We also introduce a grouping component as an upgrade to immediately calculate a coarse virtual target once the user initiates a transition, which could further eliminate the avatar transition delay. Once having the coarse virtual target or an exactly predicted target, we find the corresponding target for the avatar according to the pre-constructed mapping of objects of interest between two spaces. The avatar control component maintains an artificial potential field of the space and drives the avatar towards the target while respecting the obstacles in the physical environment. We further conduct ablation studies to evaluate the effectiveness of our proposed components.
Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings

As the implementation of artificial intelligence (AI)-enabled tools is realized across diverse clinical environments, there is a growing understanding of the need for ongoing monitoring and updating of prediction models. Dataset shift—temporal changes in clinical practice, patient populations, and information systems—is now well-documented as a source of deteriorating model accuracy and a challenge to the sustainability of AI-enabled tools in clinical care. While best practices are well-established for training and validating new models, there has been limited work developing best practices for prospective validation and model maintenance. In this paper, we highlight the need for updating clinical prediction models and discuss open questions regarding this critical aspect of the AI modeling lifecycle in three focus areas: model maintenance policies, performance monitoring perspectives, and model updating strategies. With the increasing adoption of AI-enabled tools, the need for such best practices must be addressed and incorporated into new and existing implementations. This commentary aims to encourage conversation and motivate additional research across clinical and data science stakeholders.
A method for generating training data for a protective face mask detection system

Monitoring and evaluation of the safety level of individuals is one of the most important problems of the modern world, which was forced to change due to the emergence of the COVID-19 virus. To increase the safety level of individuals, new information technologies are needed that can stop the spread of infection by minimizing the threat of outbreaks and monitor compliance with recommended measures. These technologies, in particular, include intelligent tracking systems of the presence of protective face masks. For these systems, this article proposes a new method for generating training data that combines data augmentation techniques, such as Mixup and Insert. The proposed method is tested on two datasets, namely, the MAsked FAce dataset and the Real-World Masked Face Recognition Dataset. For these datasets, values of the unweighted average recalls of 98.51% and 98.50% are obtained. In addition, the effectiveness of the proposed method is tested on images with face mask imitation on people's faces, and an automated technique is proposed for reducing type I and II errors. Using the proposed automated technique, it is possible to reduce the number of type II errors from 174 to 32 for the Real-World Masked Face Recognition Dataset, and from 40 to 14 for images with painted protective face masks.
An Optimized Continuous Dragonfly Algorithm Using Hill Climbing Local Search to Tackle the Low Exploitation Problem

Optimization problems are usually solved using heuristic algorithms such as swarm intelligence algorithms owing to their ability of providing near-optimal solutions in a feasible amount of time. An example of optimization problem is the training of artificial neural networks to obtain the most optimal connection weights. Artificial Neural Network (ANN), being the most prominent machine learning algorithm, has a multitude of applications in a myriad of areas. Recently, the use of ANNs has risen exponentially owing to its effective ability of making conclusions based on certain inputs. This ability is primarily achieved during the training phase of the ANN, which is a vital process prior to being able to use the ANN. Gradient descent-based algorithms, which are usually used for the training process, often encounter the problem of local optima, thus being unable to obtain the optimal connection weights of the ANN. Metaheuristic algorithms, including swarm intelligence algorithms, have been found to be a better alternative to train ANNs. The Dragonfly Algorithm (DA) is a swarm intelligence algorithm that has been found to be more effective than multiple swarm intelligence algorithms. However, despite having a good performance, it still suffers from a low exploitation. In this paper, we propose to further improve the performance of DA by using hill climbing as a local search so as to enhance its low exploitation. The optimized DA algorithm is then used for training artificial neural networks which are employed for classification problems. Based on the experimental results, the optimized DA algorithm has a higher effectiveness than the original DA as the ANNs trained by the optimized DA have a lower root mean squared error and a higher classification accuracy than the ones trained by the original DA.
WBC Image Segmentation Based on Residual Networks and Attentional Mechanisms

White blood cell (WBC) morphology examination plays a crucial role in diagnosing many diseases. One of the most important steps in WBC morphology analysis is WBC image segmentation, which remains a challenging task. To address the problems of low segmentation accuracy caused by color similarity, uneven brightness, and irregular boundary between WBC regions and the background, a WBC image segmentation network based on U-Net combining residual networks and attention mechanism was proposed. Firstly, the ResNet50 residual block is used to form the main unit of the encoder structure, which helps to overcome the overfitting problem caused by a small number of training samples by improving the network's feature extraction capacity and loading the pretraining weight. Secondly, the SE module is added to the decoder structure to make the model pay more attention to useful features while suppressing useless ones. In addition, atrous convolution is utilized to recover full-resolution feature maps in the decoder structure to increase the receptive field of the convolution layer. Finally, network parameters are optimized using the Adam optimization technique in conjunction with the binary cross-entropy loss function. Experimental results on BCISC and LISC datasets show that the proposed approach has higher segmentation accuracy and robustness.
Automatic Hate Speech Detection Using CNN Model and Word Embedding

Hatred spreading through the use of language on social media platforms and in online groups is becoming a well-known phenomenon. By comparing two text representations: bag of words (BoW) and pre-trained word embedding using GloVe, we used a binary classification approach to automatically process user contents to detect hate speech. The Naive Bayes Algorithm (NBA), Logistic Regression Model (LRM), Support Vector Machines (SVM), Random Forest Classifier (RFC) and the one-dimensional Convolutional Neural Networks (1D-CNN) are the models proposed. With a weighted macro-F1 score of 0.66 and a 0.90 accuracy, the performance of the 1D-CNN and GloVe embeddings was best among all the models.