Reviewerly/exHarmony huggingface dataset:
Attention to body parts prompts thermoregulatory reactions in Body Integrity Dysphoria

In healthy subjects, the transient perturbation of body part ownership is accompanied by regional skin temperature decrease. This observation leaves an open question about a possible body part-specific thermoregulatory response in pathological conditions, in which the sense of ownership over that body part is altered. For instance, Body Integrity Dysphoria (BID), a poorly understood neuropsychiatric disorder, is characterised by the non-acceptance of one or more of one's extremities. This unsettling feeling pervasively captures the individuals' attention towards the unwanted limb. Previous studies characterised BID in terms of absent ownership feeling with preserved ownership judgment. We explored for the first time whether this altered feeling is also associated with a specific thermoregulatory response. We recorded thermal image sequences of circumscribed regions of the limbs' skin in seven individuals with BID desiring to remove one leg while they were invited to focus their attention toward one particular limb (arm or leg). Their event-related thermoregulatory pattern was compared to a group of healthy matched controls. In individuals with BID but not in control persons, we found a bilateral decrease in leg temperature when focusing their attention on either the unwanted or accepted leg. The event-related thermoregulatory response for both upper limbs was similar between individuals with BID and healthy controls. Our results suggest that the alteration of the sense of body ownership in neuropsychiatric conditions such as BID may critically rest on specific event-related thermoregulatory patterns in response to modulation of attention to body parts.
Training Novel Adaptive Fuzzy Cognitive Map by Knowledge-Guidance Learning Mechanism for Large-Scale Time-Series Forecasting

A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve various modeling tasks including forecasting time series. In the original FCM-based forecasting model, causal relationships among concepts of the FCM remain unchanged. However, causal relationships may change in time. Therefore, we propose a new learning method for training an FCM resulting in an adaptive FCM which consists of several sub-FCMs. It can select different sub-FCMs at different moments. In an active processing scenario, in which we deal with a large-scale time series with new data being continuously generated, a forecasting model built on the old data should be updated when the new data arrive. Furthermore, retraining an FCM from scratch entails increasing computing overhead that will become a serious obstacle in many practical scenarios. To overcome the above-mentioned shortcomings, this study offers an original design setting in which the FCM is updated by knowledge-guidance learning mechanism for the first time. Compared with the existing classical forecasting models, the proposed model shows higher accuracy and efficiency. Its increased performance is demonstrated through a series of reported experimental studies.
Leveraging eye tracking to understand children’s attention during game-based, tangible robotics activities

The difficulty in maintaining attention can interfere with the acquisition of critical academic skills. Recently, researchers have used embodied and game-based learning to support skill acquisition for children with learning difficulties. In this context, robots can be an interesting asset to foster engagement and investigate game dynamics. However, it is still an open question of how to develop adaptive learning environments for children with learning difficulties. Before one can provide effective adaptation, a first step is needed to understand the differentiating behaviors during the activity for children with attention difficulties. Three such differentiating behaviors are how a child divides his or her attention during the learning activity, the child's level of cognitive load, and the child's physiological fatigue, which are the focus of our study Using a robot assisted, gamified activity, we conducted a user study with 18 children having difficulty in maintaining attention. Using process mining techniques and eye-tracking data, we found the importance of integrating the autonomous robots into the attention patterns to successfully complete a game and the influence their behaviors can have on the participant's attention. This importance was supported by the cognitive load of participants decreasing the more they focused on the autonomous robots in successful games. This work contributes to the understanding of children's behaviors during tangible game-based activities and can be used to build effective adaptation for children with attention difficulties.
ONEST (Observers Needed to Evaluate Subjective Tests) suggests four or more observers for a reliable assessment of the consistency of histological grading of invasive breast carcinoma: A reproducibility study with a retrospective view on previous studies

Histological grade is one of the most important prognosticators of breast cancer which is available for nearly all cases. It also makes part of several multivariable analysis derived combined prognostic profiles despite concerns about its reproducibility. The aims included a reproducibility study of grading in the light of a recently described statistical approach, ONEST (Observers Needed to Evaluate Subjective Tests) and review earlier reproducibility studies in the light of the ONEST analysis. Nine pathologists reviewed 50 core needle biopsies and 50 slides from different excision specimens and recorded the scores for gland (tubule) formation, nuclear pleomorphism and mitotic activity as well as histological grade. Overall percent agreement, Fleiss kappa and the intraclass correlation coefficient (ICC) were used for the analysis of reproducibility. ONEST data and curves were generated from 100 random permutations of the participants. ONEST suggested a minimum of 4 observers for the reliable evaluation of reproducibility for both the scored components and grade in either type of specimen. Our results suggested moderate or moderate to good reproducibility of grading (kappa values of 0.51 for excisions, and 0.54 for biopsies and ICCs of 0.70 and 0.69, respectively) with gland formation being the most and nuclear pleomorphism the worst consistently evaluated feature. In studies with sufficient participants (at least 4) and non-pairwise comparisons in the analysis, the reproducibility of histological grading is fair to moderate, whereas studies with fewer participants or pairwise kappa analysis suggest moderate to almost prefect agreement of the results. ONEST is a valuable complementation of reproducibility analyses.
Best Ratio Size of Image in Steganography using Portable Document Format with Evaluation RMSE, PSNR, and SSIM

Steganography is a data encryption method to hide messages into files. Files that are often used are images as a place to hide data. One type of steganography that is often used is Least Significant Bit (LSB). The LSB mechanism inserts data into the last bit of each pixel in the cover file. In this paper we build application system encryption to insert Portable Document Format (PDF) files into images. Every 700 characters stored in a PDF file are inserted into the image using LSB Steganography. The images used are divided into three size classes, namely 200*200 pixels, 400*400 pixels, and 1000*1000 pixels with 100 experimental images in each class. The results of the insertion are compared with the original image using the evaluation of Root Mean Squared Error (RMSE), Structural Similarity Index (SSIM), and Peak to Signal Ratio (PSNR). The experimental results show that an image with a size of 1000*1000 shows the best results with RMSE measurements showing an average result of 7.68e-06 dB, SSIM showing an average result of 0.999999985 dB, and PSNR showing an average result of 102.2891185 dB. From the measurement results of the evaluation method, the best ratio for inserting a PDF file into an image is that every two bits of an image can accommodate one bit of a PDF file.
A multi-system weighted fusion method to improve measurement accuracy of structured light 3D profilometry

Abstract Improving the accuracy of three-dimensional profile measurement based on binary coded structured light for complex surfaces is a challenge. A new method of weighted fusion using multiple systems is presented to reduce the measurement errors due to the stripe grayscale asymmetry. It is based on the analysis of stripe center deviation related to the surface normal and the directions of incident and reflected rays. First, the stripe center deviation model is established according to the geometric relationship between the stripe center deviation and the incident and reflected angles at any measured point. The influence of each variable on stripe center deviation is analyzed, and three subsystems are formed by a binocular structured light framework to achieve multiple measurements based on the influence regularity. Then, in order to improve the measurement accuracy, different weights are assigned to the measured point in the different subsystems according to the stripe center deviation model and its relationship with the measurement error, and the weighted data from the different subsystems are fused. Experiments are carried out to validate the presented method, and the experimental results demonstrate that it effectively improves the measurement accuracy of complex surfaces. The measurement accuracy is improved by about 27% compared with the conventional method.
AI-Based Video Clipping of Soccer Events

The current gold standard for extracting highlight clips from soccer games is the use of manual annotations and clippings, where human operators define the start and end of an event and trim away the unwanted scenes. This is a tedious, time-consuming, and expensive task, to the extent of being rendered infeasible for use in lower league games. In this paper, we aim to automate the process of highlight generation using logo transition detection, scene boundary detection, and optional scene removal. We experiment with various approaches, using different neural network architectures on different datasets, and present two models that automatically find the appropriate time interval for extracting goal events. These models are evaluated both quantitatively and qualitatively, and the results show that we can detect logo and scene transitions with high accuracy and generate highlight clips that are highly acceptable for viewers. We conclude that there is considerable potential in automating the overall soccer video clipping process.
Encrypted 5G Over- The- Top Voice Traffic Identification Based on Deep Learning

With the commercialization of fifth-generation (5G), the rapid popularity of mobile Over- The- Top (OTT) voice applications brings huge impacts on the traditional telecommunications voice call service. Tunnel encryption and anonymous network technologies allow OTT users to escape the supervision of network operators easily, which may cause potential security risks to cyberspace. To monitor harmful OTT applications in the context of 5G, it is critical to identify encrypted OTT voice traffic. However, there is no comprehensive study on typical OTT voice traffic identification. This is the first study to analyze OTT Virtual Private Network (VPN) voice traffic in the 5G network specifically. We propose to employ Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to classify encrypted 5G OTT VPN voice traffic, study the impact of the sample sizes and the deep learning methods on identification performance. To verify the performance of the proposed approach, we collect 10 types of typical OTT VPN voice traffic from the experimental 5G network. Extensive experimental results prove the effectiveness of the proposed approach in encrypted 5G OTT VPN voice traffic classification.
Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs: (Invited Paper)

With the constant increase of the number of quantum bits (qubits) in the actual quantum computers, implementing and accelerating the prevalent deep learning on quantum computers are becoming possible. Along with this trend, there emerge quantum neural architectures based on different designs of quantum neurons. A fundamental question in quantum deep learning arises: what is the best quantum neural architecture? Inspired by the design of neural architectures for classical computing which typically employs multiple types of neurons, this paper makes the very first attempt to mix quantum neuron designs to build quantum neural architectures. We observe that the existing quantum neuron designs may be quite different but complementary, such as neurons from variational quantum circuits (VQC) and Quantumflow. More specifically, VQC can apply real-valued weights but suffer from being extended to multiple layers, while QuantumFlow can build a multi-layer network efficiently, but is limited to use binary weights. To take their respective advantages, we propose to mix them together and figure out a way to connect them seamlessly without additional costly measurement. We further investigate the design principles to mix quantum neurons, which can provide guidance for quantum neural architecture exploration in the future. Experimental results demonstrate that the identified quantum neural architectures with mixed quantum neurons can achieve 90.62% of accuracy on the MNIST dataset, compared with 52.77% and 69.92% on the VQC and QuantumFlow, respectively.
Hate Speech and Offensive Language Detection from Social Media

In recent years, the advent of social media platforms has led users to freely express their opinions on various subjects, including politics, society, health, education, finance, and even business-related issues. However, this widespread usage of social media has also increased the risk of its misuse by some groups resulting in spreading hate speeches or offensive language. This paper investigates machine learning methods for hate speech classification and compared its results with advanced deep learning models to evaluate its efficiency. The data from Twitter, a popular microblogging social media platform for sharing short digital content, was used for experiments in this study. Each tweet was labeled into one of three categories: hate speech, offensive, neutral. Four machine learning methods were investigated: logistic regression (LR), random forest (RF), naive Bayes (NB), and support vector machine (SVM). The results were compared with two deep learning-based models: recurrent neural networks (RNN) and bidirectional encoder representations (BERT). The overall results indicated that both machine learning and deep learning models were effective for hate speech recognition. The highest overall accuracy was obtained using BERT (87.78%), while SVM produced the best (84.66%) among traditional classifiers.
Curved LCD based deflectometry method for specular surface measurement

Phase measuring deflectometry (PMD) technique is widely used to measure the specular surface. However, the Current PMD methods are generally based on the planar LCD for the measurement, which limits the measurable curvature of the surface under test (SUT). To increase the measurable range of curvature, this paper proposes a novel PMD method by using a curved LCD monitor. A calibration method based on the cylindrical coordinate system is introduced for the proposed system calibration. To extend the measurable range further, the curved LCD is applied in stereoscopic PMD. The partial surface of SUT in the field of view (FOV) of each camera and curved LCD are reconstructed with iterative algorithm respectively, the calibration parameters as well as the seed point are used for point cloud registration. Simulations show the advantage of a curved LCD over a planar one and the feasibility of the proposed method. The actual performance is demonstrated in the experiments for specular optics and a phone touchscreen.
Survey of recommender systems based on federated learning

随着互联网和移动计算等技术的发展, 人们的在线行为产生了越来越多的数据, 想要从海量数据中挑选出用户可能喜欢的物品, 推荐系统不可或缺. 然而传统的推荐算法需要将用户数据收集到服务端才能构建模型, 这会泄露用户隐私. 最近, 谷歌针对机器学习任务中需要收集用户数据才能进行建模的问题, 提出了一种新的学习范式—联邦学习. 联邦学习与推荐系统相结合, 使得联邦推荐算法能够在模型构建过程中, 始终将用户数据保留在客户端本地, 从而保护了用户隐私. 本文主要对联邦学习与推荐系统相结合的研究工作进行综述, 并从架构设计、系统的联邦化和隐私保护技术的应用三个角度重点分析联邦推荐算法的研究进展. 最后, 对基于联邦学习的推荐系统可研究的方向进行展望.
Hybrid Deep Learning Framework for Privacy Preservation in Geo-Distributed Data Centre

In recent times, a huge amount of data is being created from different sources and the size of the data generated on the Internet has already surpassed two Exabytes. Big Data processing and analysis can be employed in many disciplines which can aid the decision-making process with privacy preservation of users’ private data. To store large quantity of data, Geo-Distributed Data Centres (GDDC) are developed. In recent times, several applications comprising data analytics and machine learning have been designed for GDDC. In this view, this paper presents a hybrid deep learning framework for privacy preservation in distributed DCs. The proposed model uses Deep Neural Network (DNN) for the feature extractor and classifier operations. In addition, Siamese training method is applied to fine-tune the prevention of secondary inference on the data. Moreover, gradient descent approach is employed to reduce the loss function of the DNN model. Furthermore, Glow-worm Swarm Optimization (GSO) algorithm is utilized to fine tune the hyperparameters of the DNN model to improve the overall efficiency. The proposed model is executed on a Hadoop based environment, i.e., Hadoop Distributed File System (HDFS), which has two nodes namely master node and slave nodes. The master node is considered as the main user node to get the services from the service provider. Every slave node behaves as per master node’s instruction for data storage. In order to validate the enhanced performance of the proposed model, a series of simulations take place and the experimental results demonstrate the promising performance of the proposed model. The simple technique has reached a maximum gender recognition accuracy of 95, 90 and 79 on the applied data 1, 2 and 3 respectively. Also, the reduced simple approach has attained reduced gender recognition with the accuracy of 91, 84 and 74 on the applied data 1, 2 and 3 respectively.
Self-Adaptive Spherical Search With a Low-Precision Projection Matrix for Real-World Optimization

Since the last three decades, numerous search strategies have been introduced within the framework of different evolutionary algorithms (EAs). Most of the popular search strategies operate on the hypercube (HC) search model, and search models based on other hypershapes, such as hyper-spherical (HS), are not investigated well yet. The recently developed spherical search (SS) algorithm utilizing the HS search model has been shown to perform very well for the bound-constrained and constrained optimization problems compared to several state-of-the-art algorithms. Nevertheless, the computational burdens for generating an HS locus are higher than that for an HC locus. We propose an efficient technique to construct an HS locus by approximating the orthogonal projection matrix to resolve this issue. As per our empirical experiments, this technique significantly improves the performance of the original SS with less computational effort. Moreover, to enhance SS's search capability, we put forth a self-adaptation technique for choosing the effective values of the control parameters dynamically during the optimization process. We validate the proposed algorithm's performance on a plethora of real-world and benchmark optimization problems with and without constraints. Experimental results suggest that the proposed algorithm remains better than or at least comparable to the best-known state-of-the-art algorithms on a wide spectrum of problems.
Approaching The Anonymous Deployment Of Blockchain-Based Fair Advertising On Vehicle Networks

Distributed business advertising has proliferated in related vehicles, to advance their goods through vehicle communications or frameworks. Despite the possibility of spreading promotions in the vehicle network, but facing difficulties in the organization, especially on security and protection. In particular, vehicles may be sneaky to trick sponsors into getting rewards without spreading advertisements, which can cause &#x201C;free driving&#x201D; problems in this exercise. In addition, concerns about possible spill protection may disinterest the vehicle during the time spent on ad implementation. In addition, outside DDoS attacks and interior disappointment purposes can also affect administrative accessibility. To solve this problem, we investigated the ability of blockchain innovations to develop plausible and unknown plans for publication on the vehicle network. We first present a review of a blockchain based advertising deployment system.
Deep Face Recognition for Imperfect Human Face Images on Social Media using the CNN Method

Face recognition systems nowadays are applied in any gadget. The usual purpose of this technology is to recognize people's information for security purposes on a smartphone. Unfortunately, this face recognition is only available for perfect facial data. In some cases, there are situations where full faces may not be available on social media and recognizing imperfect human facial structure is not a simple task. The aim of this study is to provide the capability to recognize human faces based on partial or imperfect human facial data. This study explores the idea of face recognition based on partial or imperfect human facial data using the state art convolutional neural network-based architecture, especially for the CNN model with the pre-trained VGG-Face model which promises a good recognizing imperfect image of human face results.
Disentangling Task-Oriented Representations for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to address the domain-shift problem between a labeled source domain and an unlabeled target domain. Many efforts have been made to eliminate the mismatch between the distributions of training and testing data by learning domain-invariant representations. However, the learned representations are usually not task-oriented, i.e., being class-discriminative and domain-transferable simultaneously. This drawback limits the flexibility of UDA in complicated open-set tasks where no labels are shared between domains. In this paper, we break the concept of task-orientation into task-relevance and task-irrelevance, and propose a dynamic task-oriented disentangling network (DTDN) to learn disentangled representations in an end-to-end fashion for UDA. The dynamic disentangling network effectively disentangles data representations into two components: the task-relevant ones embedding critical information associated with the task across domains, and the task-irrelevant ones with the remaining non-transferable or disturbing information. These two components are regularized by a group of task-specific objective functions across domains. Such regularization explicitly encourages disentangling and avoids the use of generative models or decoders. Experiments in complicated, open-set scenarios (retrieval tasks) and empirical benchmarks (classification tasks) demonstrate that the proposed method captures rich disentangled information and achieves superior performance.