Reviewerly/exHarmony huggingface dataset:
Establish a Simulation Platform of DNS Root Server in Campus Network Laboratory

In the experiment teaching of computer network DNS course, domain names among students cannot communicate with each other, because the self-defined domain name is not authorized by the official ROOT DNS server of the Internet. The paper focuses on setting up a root domain simulation platform to simulate the root domain name and the top-level domain name server in the internal network of the campus. When students conduct DNS service experiments, they can achieve the same `real' effect as the resolution of internet domain name.
Negotiating Experiences and Design Directions through Object Theatre

The challenge of shaping human experiences with intelligent products has urged design researchers to move from a user-centred towards a thing-centred approach. By investigating use from the perspective of 'digital things', designers can better understand how objects influence human experience as part of socio-material systems. The research presented contributes with studying the role of theatre methods in facilitating this move. Whereas earlier work with scenario acting and role-play primarily focuses on use, my study shows that Object Theatre can help challenge stereotypical notions of product use to reveal sentimental human experiences with things. The paper analyses how two new object theatre methods, Object Cemetery and Talking Objects, develop shared understandings between designers on two projects: the design of booking systems and health tracking systems. The developed methods reveal the experienced qualities of digital products beyond efficiency and convenience, and enable a collaborative negotiation of design directions.
Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks

The Industry 4.0 revolution allows monitoring and intelligent processing of big amounts of data. When monitoring certain assets, very few data is found for operation under faulty conditions because the cost of not operating properly is unacceptable and thus preventive strategies are put in practice. Because machine learning algorithms are data exhaustive, synthetic data can be created for these cases. Deep learning techniques have been proven to work very well for these cases. Generative Adversarial Networks (GANs) have been deployed in numerous applications with data augmentation objectives, but not so much for balancing unidimensional series with few data. In this paper, a GAN is applied in order to augment data for assets operating under faulty conditions. The proposed method is validated on a real industrial case, yielding promising results with respect to the case with no strategy for class imbalance whatsoever.
Fast Cluster Tendency Assessment for Big, High-Dimensional Data

Assessment of clustering tendency is an important first step in crisp or fuzzy cluster analysis. One tool for assessing cluster tendency is the Visual Assessment of Tendency (VAT) algorithm. The VAT and improved VAT (iVAT) algorithms have been successful in determining potential cluster structure in the form of visual images for various datasets, but they can be computationally expensive for datasets with a very large number of samples and/or dimensions. Scalable versions of VAT/iVAT, such as sVAT/siVAT, have been proposed for iVAT approximation, but they also take a lot of time when the data is large both in the number of records and dimensions. In this chapter, we introduce two new algorithms to obtain approximate iVAT images that can be used to visually estimate the potential number of clusters in big data. We compare the two proposed methods with the original version of siVAT on five large, high-dimensional datasets, and demonstrate that both new methods provide visual evidence about potential cluster structure in these datasets in significantly less time than siVAT with no apparent loss of accuracy or visual acuity.
Anisotropic Graph Convolutional Network for Semi-Supervised Learning

Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results in semi-supervised learning tasks, such as node classification. However, these networks suffer from the issue of over-smoothing and shrinking effect of the graph due in large part to the fact that they diffuse features across the edges of the graph using a linear Laplacian flow. This limitation is especially problematic for the task of node classification, where the goal is to predict the label associated with a graph node. To address this issue, we propose an anisotropic graph convolutional network for semi-supervised node classification by introducing a nonlinear function that captures informative features from nodes, while preventing oversmoothing. The proposed framework is largely motivated by the good performance of anisotropic diffusion in image and geometry processing, and learns nonlinear representations based on local graph structure and node features. The effectiveness of our approach is demonstrated on three citation networks and two image datasets, achieving better or comparable classification accuracy results compared to the standard baseline methods.
Magic and Immersion in VR

The rise of the notion of “immersion”, thanks to interactive VR productions, provokes creators and experiencers to think of new design frameworks. The concept of immersion is frequently mentioned together with the notion of illusion. In this paper I will outline a way to think of the sense of immersion in VR that is based on the science of magic. Based on this particular view of immersion as a magical experiment I will present an outline of a possible parallel between experiencing immersion in VR and in a magic trick.
Developing Occupancy Grid with Automotive Simulation Environment

This study presents the process employed in prototyping and early evaluation of automotive perception algorithms. The data generation was performed using an automotive virtual validation tool. The off-the-shelf simulation framework used was expanded to include phenomenological sensors model that allowed for a simplified simulation of radars, lidars, and cameras. This paper extends the description of the methods for the generation of control algorithms. The work presented also includes a description of relevant data fusion methods for building occupancy grids. Results were obtained by performing a comparison of algorithm results against ground-truth. This virtual validation was used to enable early definition and verification of system-level requirements, narrow down performance assessment methods, and identify performance limitations before data from real sensors are available.
Towards Exploring the Benefits of Augmented Reality for Patient Support During Radiation Oncology Interventions

Traditionally, patient education has been limited to verbal exchanges between providers and patients, along with paper handouts that summarise relevant information. While such exchanges are a natural step in educating patients, they are limited for several reasons, including the lack of time that provider teams are afforded, and the inherent challenge of communicating nuanced concepts related to complex medical procedures. A clear example of this is radiation oncology, in which traditional routes of patient education may not satisfy the patient’s needs. Although existing work has demonstrated the ability of audio-visual systems to improve patient engagement during medical procedures, the integration of emerging technologies such as Augmented Reality (AR) remains largely untapped. In this work, we propose an innovative proof-of-concept AR system as a first step towards exploring the benefits of using this technology during radiotherapy sessions. Our concept uses an AR headset to provide visual feedback of the patient's respiratory trace presented using two different forms: (i) a bi-dimensional graph and (ii) a game-based user interface. Moreover, we explore how interactive environments have the potential to contribute to better user experience and improve engagement, and discuss different challenges that must be addressed to deploy this technology to radiation treatment sessions.
Antisocial Behaviour Analyses Using Deep Learning

Online antisocial behaviour is a social problem and a public health threat. It is one of the ten personality disorders and entails a permeating pattern of violation of the rights of others, and disregard for safety. It prevails online in the form of aggression, irritability, lack of remorse, impulsivity, and unlawful behaviour. The paper introduces a deep learning-based approach to automatically detect and classify antisocial behaviour (ASB) from online platforms and to generate insights into its various widespread forms. Once detected, appropriate measures can be taken to eradicate such behaviour online and to encourage participation. The data for this paper was collected over a period of four months from the popular online social media platform Twitter by using pre-defined phrases linked to antisocial behaviour. Widely used machine learning classifiers: SVM, Decision tree, Random Forest, Linear regression, and deep learning architecture (CNN) were experimented with. CNN was implemented with both GloVe and Word2Vec embeddings and outperformed all the traditional machine algorithms used in the study. Standard performance metrics such as accuracy, recall, precision, and f-measures were used to evaluate classifiers and the CNN-GloVe combination (with 300 dimensions) produced the highest classification performance achieving 98.42% accuracy. Visually enhanced interpretation of the results is presented to demonstrate the inner workings of the classification process.
Outline of Tube Elbow Detection Based on GAC

We outline an algorithm for camera–based tube elbow detection provided that a moving camera is placed in the tube axis together with a light source. We claim that once an elbow is approached, circular contour is replaced by an elliptical one and the displacement of respective circle and ellipse centres is proportional to the elbow angle. We provide Python code for computation in Geometric Algebra for Conics (GAC), parameter extraction and an example of proportionality.
Reusable Data Visualization Patterns for Clinical Practice

Among clinical psychologists involved in guided internet-facilitated interventions, there is an overarching need to understand patients symptom development and learn about patients need for treatment support. Data visualizations is a technique for managing enormous amounts of data and extract useful information, and is often used in developing digital tool support for decision-making. Although there exists numerous data visualisation and analytical reasoning techniques available through interactive visual interfaces, it is a challenge to develop visualizations that are relevant and suitable in a healthcare context, and can be used in clinical practice in a meaningful way. For this purpose it is necessary to identify actual needs of healthcare professionals and develop reusable data visualization components according to these needs. In this paper we present a study of decision support needs of psychologists involved in online internet-facilitated cognitive behavioural therapy. Based on these needs, we provide a library of reusable visual components using a model-based approach. The visual components are featured with mechanisms for investigating data using various levels of abstraction and causal analysis.
Segmentation of Left Ventricle in Short-Axis MR Images Based on Fully Convolutional Network and Active Contour Model

Left ventricle (LV) segmentation from cardiac MRI images plays an important role in clinical diagnosis of the LV function. In this study, we proposed a new approach for left ventricle segmentation based on deep neural network and active contour model (ACM). The paper proposed a coarse-to-fine segmentation framework. In the first step of the framework, the fully convolutional network was employed to achieve coarse segmentation of LV from input cardiac MR images. Especially, instead of using cross entropy loss function, we propose to utilize Tversky loss that is known to be suitable for the unbalance data-an issue in medical images, to train the network. The coarse segmentation in the first step is then used to create initial curves for ACM. Finally, active contour model was performed to further optimize the energy functional in order to get fine segmentation of LV. Comparative experiments with other state of the arts on ACDCA and Sunnybrook challenge databases, in terms of Dice coefficient and Jaccard indexes, show the advantages of the proposed approach.
De-Noising of MRI Brain Tumor Image using Deep Convolutional Neural Network

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Hopfield Networks for Vector Quantization

We consider the problem of finding representative prototypes within a set of data and solve it using Hopfield networks. Our key idea is to minimize the mean discrepancy between kernel density estimates of the distributions of data points and prototypes. We show that this objective can be cast as a quadratic unconstrained binary optimization problem which is equivalent to a Hopfield energy minimization problem. This result is of current interest as it suggests that vector quantization can be accomplished via adiabatic quantum computing.
Cross-Modality Person ReID with Maximum Intra-class Triplet Loss

Compared with conventional ReID task, RGB-IR person re-identification is more significant and challenging owing to enormous cross-modality variations between RGB and infrared images. Most existing cross-modality person re-identification approaches in the infrared and visible domains use the joint training of traditional triplet loss and CE loss to reduce the discrepancy between two modalities. However, as the model gradually converges, the number of positive and negative sample pairs that can be optimized by traditional triplet loss also decreases. In this paper, we adopt a dual-stream CNN structure and propose a maximum intra-class distance to triplet loss (MICT loss) for RGB-IR ReID task. The proposed method enjoys several advantages. First, it can learn specific modal information and shared information better through the CNN structure of the dual-stream branch. Second, it can add the optimization of intra-class cross-modality distance to the traditional triplet loss, which can effectively reduce the intra-class distance and increase the training difficulty of the model. Experimental results demonstrate that our proposed algorithm performs favorably against the state-of-the-art methods that rank-1 accuracy is 61.71% and mAP is 58.06% on SYSU-MM01 dataset.
Physics-Guided Sound Synthesis for Rotating Blades

This paper focuses on sound synthesis for rotating blades such as fans, helicopters and wind turbines, which is common in both real world and computer games though has received little attention until now. In this paper, we propose a novel physics-guided sound synthesis method for rotating blades. First, we propose an efficient rotating blade sound solver for Ffowcs Williams-Hawkings (FW-H) equation, which can greatly reduce the computational complexity. Then, inspired by the good expression of Mel-scale Frequency Cepstral Coefficients (MFCC) in speech recognition, we design a new sound parameter A_MFCC to enrich the sound. Specifically, while ensuring the sensitivity of MFCC to formants, we improve MFCC to make it well show the properties of sound timbre and loudness, so that it can be well applied in sound synthesis. Finally, based on the observation that rotating blade sound has similar qualities with noise, we specially devise a method to further enrich the sounding result by combining noise and A_MFCC. Experimental results demonstrated that our method can achieve great sounding results for various rotating blades.
Evaluation of Feature Selections on Movie Reviews Sentiment

In the Text classification task, feature selections are one of the methods to improve classifier performance. With dimension reduction of the original features, it usually used to get better performance of accuracy, precision, recall, or maybe to accelerate computation time. In this paper, we applied several feature selections method such as Kbest with Chi-Squared Selection, Linear SVC, and Tree-based Selection into five classifiers: Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) dan Neural Network (NN). Datasets that we used are collected from Kaggle, Imdb Movie Review 5000 records, and the best F1-Score results are on Linear SVC that running on SVM Classifier 92,32%.
Design and optimization of high density wireless network in gymnasium of Beijing Normal University

With the explosive growth of mobile Internet, Internet of things and intelligent terminal technology and application, wireless network has become an indispensable infrastructure for teaching, scientific research and life of teachers and students in Colleges and universities. At present, colleges and universities have completed the deployment of wireless network in teaching, scientific research, office and dormitory scenes, and the deployment of wireless network in stadium and other high-density scenes has become an urgent problem to be solved. Based on the design, deployment and management of the wireless network in the gymnasium of Beijing Normal University, this paper expounds how to build a safe, reliable, high-speed and stable high-speed wireless network in high-density venues. The stadium high-density wireless network adopts high-density three radio frequency AP. Through directional antenna, it can accurately locate the coverage, avoid the interference of the same channel wireless signal, and realize high-speed Internet access for high-density scene users. In terms of deployment mode, wireless AP and antenna are deployed along the raceway of the stadium for later operation and management.
Hierarchical Agglomerative Clustering approach for Automated Attribute Classification of the Health Care Domain from User Generated Reviews on Web 2.0

Due to Web2.0, large masses of healthcare product opinions are freely available. These enormous amounts of opinion contain valuable information for industries, consumers, and upgrading healthcare of patients. One of the major challenges in healthcare domain to find out valuable information hidden in a large volume of data. Thus data science succors us as many kinds as a decision it quicker or better as well as. Thinking so most of the popular companies invest much money in data scientists so that they could get the right information to provide an accurate result. It is the hollow which can show us all detail as by data science. In this paper, we use Data Science Hierarchical Agglomerative Clustering (HAC) system for classifying adopting the contraceptive method. In this system, we are using Frequent Pattern-growth for association rule mining for frequent feature extraction and corpus, Part Of Speech (POS), and interaction information used for infrequent feature identification and opinion words extraction, therefore, it is required to use both features as well as sentiment word for extracting the orientation. We use Thesaurus and Negation rule to identify the polarity of sentiment words. Finally, Hierarchical agglomerative clustering is used to categorize the contraceptive method.
Approximate Bayesian computation for finite mixture models

Finite mixture models are used in statistics and other disciplines, but inference for mixture models is challenging due, in part, to the multimodality of the likelihood function and the so-called label switching problem. We propose extensions of the Approximate Bayesian Computation–Population Monte Carlo (ABC–PMC) algorithm as an alternative framework for inference on finite mixture models. There are several decisions to make when implementing an ABC–PMC algorithm for finite mixture models, including the selection of the kernels used for moving the particles through the iterations, how to address the label switching problem and the choice of informative summary statistics. Examples are presented to demonstrate the performance of the proposed ABC–PMC algorithm for mixture modelling. The performance of the proposed method is evaluated in a simulation study and for the popular recessional velocity galaxy data.
Encrypted subblock design aided OFDM with all index modulation

In recent years, orthogonal frequency division multiplexing with index modulation (OFDM-IM) has been proposed due to its extra diversity gain. Specially, OFDM with all index modulation (OFDM-AIM) was proposed, which removed symbol bits and enhanced the diversity order compared to typical OFDM. However, the security issue of OFDM-AIM has not been given full attention. This paper proposes an encrypted subblock design aided OFDM with all index modulation (ESD-OFDMAIM) scheme to enhance physical layer security. Firstly, an encryption process of subblock design was put forward, which creates a random look-up table and thus the mapping rule of ESD-OFDM-AIM between information bits and subblock realisations becomes random and time varying. Moreover, the scrambling and rotation encryption based on chaotic sequences from channel state information is utilised to prevent the eavesdropper from stealing the information via wireless channel. For eavesdroppers, parameters of ESD-OFDM-AIM such as spectral efficiency (SE), constellation points and the length of look-up table are concealed by the encryption. Finally, analyses of attack models and the information entropy of constellations are also provided to measure the security of ESD-OFDM-AIM. Simulation results show that ESD-OFDM-AIM can effectively improve the system security without the affecting bit error rate performance.
Performance Analysis of LSB Color Image Steganography based on Embedding Pattern of the RGB Channels

This research aims to analyze three patterns of embedding messages based on the Least Significant Bit (LSB) method on RGB color images. Previous research has suggested a pattern of LSB x-y-z embedding, where the value of x-y-z is 2-3-3 or 3-2-3 or 3-3-2, where the sum of the x-y-z value is 8-bits. The x value represents the number of message bits embedded on the red channel, y on the green channel, and z on the blue channel. Each research claims that the pattern has its advantages, especially to increase payload and imperceptibility. Because of this, a third method is used to compile the three methods using the same dataset, both the host image and the message. To increase the security of the message, encryption is performed using the RSA method before embedded. Stego image quality is measured by comparing it with the host image with four kinds of measuring tools, namely mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index measurement (SSIM), and histogram analysis. The results showed that all methods were of good quality and identical. It's just that LSB 3-2-3 is slightly superior when measured based on MSE and PSNR values, but the average difference in value does not reach 0.1dB. Whereas based on measuring SSIM LSB 3-3-2 get the best results. Where the difference is not more than 0.001. While at the message extraction stage the value of the bit error ratio (BER) between the original message and the extracted message yields a value of 0, which indicates that all methods can extract the message perfectly.