Reviewerly/exHarmony huggingface dataset:
Uncertainty quantification for semi-supervised multi-class classification in image processing and ego-motion analysis of body-worn videos

Semi-supervised learning uses underlying relationships in data with a scarcity of ground-truth labels. In this paper, we introduce an uncertainty quantification (UQ) method for graph-based semi-supervised multi-class classification problems. We not only predict the class label for each data point, but also provide a confidence score for the prediction. We adopt a Bayesian approach and propose a graphical multi-class probit model together with an effective Gibbs sampling procedure. Furthermore, we propose a confidence measure for each data point that correlates with the classification performance. We use the empirical properties of the proposed confidence measure to guide the design of a humanin-the-loop system. The uncertainty quantification algorithm and the human-in-the-loop system are successfully applied to classification problems in image processing and ego-motion analysis of body-worn videos.
Social distance, trust and getting “hooked”: A phishing expedition

Phishing is a message-based computer attack sent to deceive and exploit recipients via email. Phishing is a problem for organizations because getting “hooked,” or tricked into acting on behalf of the sender, can put sensitive personal and organizational data at risk. In this research, we draw from several theoretical perspectives concerning social distance and trust as an exploitable resource to provide evidence that, even in contexts where actual social distance and intentions are nearly impossible to determine, people look to category-based signals of social distance to establish trust, which increases the likelihood of deception. We present the results of a validation study and a randomized field experiment in which we sent phishing emails to all employees of an insurance company.
Multiobjective nonfragile fuzzy control for nonlinear stochastic financial systems with mixed time delays

In this study, a multiobjective nonfragile control is proposed for a class of stochastic Takagi and Sugeno (T–S) fuzzy systems with mixed time delays to guarantee the optimal H2 and H∞ performance simultaneously. Firstly, based on the T–S fuzzy model, two form of nonfragile state feedback controllers are designed to stabilize the T–S fuzzy system, that is to say, nonfragile state feedback controllers minimize the H2 and H∞ performance simultaneously. Then, by applying T–S fuzzy approach, the multiobjective H2/H∞ nonfragile fuzzy control problem is transformed into linear matrix inequality (LMI)-constrained multiobjective problem (MOP). In addition, we efficiently solve Pareto optimal solutions for the MOP by employing LMI-based multiobjective evolution algorithm (MOEA). Finally, the validity of this approach is illustrated by a realistic design example.
Conditional probability distributions of finite absorbing quantum walks

Quantum walks are known to have nontrivial interactions with absorbing boundaries. In particular it has been shown that an absorbing boundary in the one-dimensional quantum walk partially reflects information, as observed by absorption probability computations. In this paper, we shift our sights from the local phenomena of absorption probabilities to the global behavior of finite absorbing quantum walks in one dimension. We conduct our analysis by approximating the eigenbasis of the associated absorbing quantum walk operator matrix ${Q}_{n}$, where $n$ is the lattice size. The conditional probability distributions of these finite absorbing quantum walks exhibit distinct behavior at various timescales, namely wavelike reflections for times $t=O(n)$, fractional quantum revivals for $t=O({n}^{2})$, and stability for $t=O({n}^{3})$. At the end of this paper, we demonstrate the existence of fractional quantum revivals in other sufficiently regular quantum walk systems.
Construction of Certificateless Proxy Signcryption Scheme From CMGs

As a cryptography primitive for secure data transmission, certificateless proxy signcryption (CLPS) allows an original signcrypter to entrust his signing authority to a proxy signcrypter for signing specified message on his behalf. In this paper, we combine CLPS with cyclic multiplication groups (CMGs) to construct a new certificateless proxy signcryption scheme from CMGs (CMGs-CLPSS). CMGs-CLPSS will receive significant attention because it simplifies the traditional public key cryptosystem (PKC) and solves the key escrow issue suffered by identity-based public key cryptosystem (IB-PKC). In CMGs-CLPSS, an encrypted message can only be decrypted by a designated receiver who is also responsible for verifying the message; moreover, if a later dispute over repudiation occurs, the designated receiver can readily announce ordinary CLPS for public verification without any extra computation effort. CMGs-CLPSS is proved to have the indistinguishability under adaptive chosen-ciphertext attacks (IND-CCA2 security) and existential unforgeability under adaptive chosen-message attacks (UF-CMA security) in the random oracle model. CMGs-CLPSS outperforms the existing schemes on the basis of computational complexity and is suitable for applications in digital contract signing and online proxy auction, and so on.
Knowledge Harvesting: Achievements and Challenges

This article gives an overview on knowledge harvesting: automatically constructing large high-quality knowledge bases from Internet sources. The first part reviews key principles and best-practice methods. The second part points out open challenges for future research.
Dynamic Network Embedding by Semantic Evolution

Network embedding, which aims to learn the low-dimensional representations of nodes, has attracted increasing attention in various fields such as social networks, paper citation networks and knowledge graphs. At present, most of the network embedding works are based on static networks, that is, the evolution of networks over time is not taken into account. It is more realistic to consider temporal information in network embedding and it could also make the embedding get more abundant information. In this paper, we propose a dynamic network embedding model DynSEM with semantic evolution, to train node embeddings in a sequence of networks over time. The advantage of our method is that it presents an effective inheritance of historical information. Our method uses nonrandom initialization and orthogonal procrustes method to align the node embeddings into common space which makes node embedding able to inheritance information. In particular, in the common space, we train a model to capture the dynamics information of the networks and smooth temporal node embeddings. We evaluate our method comparing it with other methods on three real-world datasets. The experimental results prove the effectiveness of dynamic network embeddings generated by DynSEM model.
Comparison of Probabilistic Models and Neural Networks on Prediction of Home Sensor Events

We present results and comparative analysis on the prediction of sensor events in a smart home environment with a limited number of binary sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery - SPEED, and Active LeZi - ALZ, as well as Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) in order to predict the next sensor event in a sequence. Our dataset has been collected from a real home with one resident over a period of 30 weeks. The binary sensor events are converted to two different text sequences as dictated by SPEED and ALZ, which are also used as inputs for the LSTM networks. We compare the performance of the algorithms regarding the number of preceding sensor events required to predict the next one, the required amount of data for the model to reach peak accuracy and stability, and the execution time. In addition, we analyze these for two different sets of sensors. Our best implementation achieved a peak accuracy of 83% for a set with fifteen sensors including motion, magnetic and power sensors, and 87% for seven motion sensors.
Boosted GAN with Semantically Interpretable Information for Image Inpainting

Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal. However, current GAN-based inpainting models fail to explicitly consider the semantic consistency between restored images and original images. For example, given a male image with image region of one eye missing, current models may restore it with a female eye. This is due to the ambiguity of GAN-based inpainting models: these models can generate many possible restorations given a missing region. To address this limitation, our key insight is that semantically interpretable information (such as attribute and segmentation information) of input images (with missing regions) can provide essential guidance for the inpainting process. Based on this insight, we propose a boosted GAN with semantically interpretable information for image inpainting that consists of an inpainting network and a discriminative network. The inpainting network utilizes two auxiliary pretrained networks to discover the attribute and segmentation information of input images and incorporates them into the inpainting process to provide explicit semantic-level guidance. The discriminative network adopts a multi-level design that can enforce regularizations not only on overall realness but also on attribute and segmentation consistency with the original images. Experimental results show that our proposed model can preserve consistency on both attribute and segmentation level, and significantly outperforms the state-of-the-art models.
Utilizing Generative Adversarial Networks for Recommendation based on Ratings and Reviews

Many existing rating-based recommendation algorithms have achieved relative success. However, the real-world datasets are extremely sparse and most rating-based algorithms are still suffering from the data sparsity problem. Along with integer-valued ratings, we consider that the user-generated review is also an important user feedback. Furthermore, compared with the traditional recommendation algorithms which have the limited ability to learn the distributions of ratings and reviews simultaneously, the generative adversarial networks can learn better representations for data. In this paper, we propose Rating and Review Generative Adversarial Networks (RRGAN), an innovative framework for recommendation, in which the generative model and discriminative model play a minimax game. Specifically, the generative model predicts the ratings of top-N list for users or items based on reviews, while the discriminative model aims to distinguish the predicted ratings from real ratings. With the competition between these two models, RRGAN improves the ability of understanding users and items based on ratings and reviews. We introduce the user profiles, item representations and ratings into a matrix factorization model to predict the top-N list for the users. In addition, we study three different architectures to learn reasonable user profiles and item representations based on ratings and reviews to achieve better recommendations. To evaluate the performance of our model, we conduct the extensive experiments on three real-world amazon datasets in three parts, which are top-N recommendation analysis, case study and long-tail users analysis. The experimental results show that our method significantly outperforms various state-of-the-art methods, including LFM, LambdaFM, HFT, DeepCoNN and IRGAN methods.
DESCRIPTION OF EDUCATOR’S DIGITAL COMPETENCE

Description of educator’s digital competence contains standards of structure and digital competence levels required for successful professional engagement of educators under the conditions of digital society development as well as dictionary of used terms. Description of educator’s digital competence is developed according to Concept of pedagogical education development, European framework documents on digital competence - DigComp 2.1: The Digital Competence Framework for Citizens with eight proficiency levels and examples of use, Digital Competence Framework for Educators (DigCompEdu); Report made by participants of the project Erasmus+ Modernization of Pedagogical Higher Education by Innovative Teaching Instruments (MoPED).
Enabling secure wireless multimedia resource pricing using consortium blockchains

Wireless Service Providers (WSPs) and Content Providers (CPs) charge common data price according to the flat rate, the transmission time, or the data usage, irrespective of the content characteristics. The multimedia compression parameters that determine the multimedia importance diversities among frames have largely been ignored in media pricing research. We establish a new economics-driven resource allocation framework for multimedia transmissions among WSPs, CPs, and End Users (EUs) based on Smart Media Pricing (SMP). The WSPs transmit media streams provided by CPs to the EUs to earn money. However, facing with the transaction security and privacy protection issues, we undertake promising consortium blockchain to improve resource pricing transaction security without reliance on a trusted third party. A novel Stackelberg game with consortium blockchain is firstly established to allocate transmission resources such as Transmission Times’ Limitations (TTLs) from an economic point of view. Then we investigate the utility maximization problems of the leader (WSP and CP) and the follower (EU) in terms of TTLs respectively. Finally, the optimal TTL and the optimal price are non-uniformly determined for each frame according to the game equilibria, by analyzing the payment from EUs, the channel conditions, and the media characteristics in terms of frame importance. Security analysis shows that our proposed scheme achieves better transaction security and privacy protection. Experimental comparison with uniform pricing based on equal TTL or equal frame importance validates the effectiveness of the proposed economic model.
Emotions Detection of User eXperience (UX) for Mobile Augmented Reality (MAR) Applications

Mobile Augmented Reality (MAR) is becoming a major interactive technology for cross-domain and destination applications.The number of using MAR apps around the world in excess of 2.2 billion by 2019.Despite the recent advances in MAR technology and tools, the User eXperience (UX) design perspective has not been seriously taken into consideration.In this paper, an MAR application production scheme using UX is practically investigated.Particularly, "GlassOn" app which provides visual information on how the user looks like with various types of glasses, is utilized in accordance with the user's emotion raised by the interaction.To verify the effectiveness of this scheme, an evaluation experiment was conducted with 10 participants.The numerical values that indicate the rates of each emotion were taken from Microsoft Azure Face API.Meanwhile, a questionnaire survey was conducted to collect each participant's perception of each pair of glasses as subjective evaluation.The comparison between subjective evaluation and the numerical results shows that the more the emotion of surprise is raised rather than happiness, the higher the user's purchase rate.It is revealed that UX is useful to inspire the user's purchase desire for glasses online.
Smartphone-Supported Malaria Diagnosis Based on Deep Learning

Malaria remains a major burden on global health, causing about half a million deaths every year. The objective of this work is to develop a fast, automated, smartphone-supported malaria diagnostic system. Our proposed system is the first system using both image processing and deep learning methods on a smartphone to detect malaria parasites in thick blood smears. The underlying detection algorithm is based on an iterative method for parasite candidate screening and a convolutional neural network model (CNN) for feature extraction and classification. The system runs on Android phones and can process blood smear images taken by the smartphone camera when attached to the eyepiece of a microscope. We tested the system on 50 normal patients and 150 abnormal patients. The accuracies of the system on patch-level and patient-level are 97% and 78%, respectively. AUC values on patch-level and patient-level are, respectively, 98% and 85%. Our system could aid in malaria diagnosis in resource-limited regions, without depending on extensive diagnostic expertise or expensive diagnostic equipment.
A novel effective and efficient capsule network via bottleneck residual block and automated gradual pruning

Capsule Network (CapsNet) complements the invariance properties of the convolutional neural network with equivariance through pose estimation. While CapsNet achieves a very decent performance with a shallow architecture, it suffers from heavily parameters learning in the primary capsule layer, and has a poor performance on large or complex datasets. To tackle these problems, this paper benefits CapsNet from two techniques of model compression: bottleneck residual block and automated gradual pruning. Specifically, this paper designs an end-to-end framework, denoted as Deft Capsule (DCaps). In order to reduce the number of need-to-learn parameters, this paper applies the bottleneck residual blocks to the primary capsule layer. Furthermore, this paper obtains automated gradual pruning techniques in dynamic routing to improve the performance of DCaps. Experiment results on four classic datasets demonstrate that DCaps learns fast and significantly outperforms CapsNet in image classification tasks.
Toward fast and accurate emergency cases detection in BSNs

In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples’ lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data variability, and adapt the sampling rate accordingly. Modified CUSUM is then applied to the adaptively sampled data to detect anomalies, and the correlation property between physiological parameters will be used to identify emergency cases from false alarms. Several experiments are performed and compared to evaluate the efficiency of their approach, and different parameters are considered.
Identifying Fake Facebook Profiles Using Data Mining Techniques

Facebook, the popular online social network, has changed our lives. Users can create a customized profile to share information about themselves with others that have agreed to be their 'friend'. However, this gigantic social network can be misused for carrying out malicious activities. Facebook faces the problem of fake accounts that enable scammers to violate users' privacy by creating fake profiles to infiltrate personal social networks. Many techniques have been proposed to address this issue. Most of them are based on detecting fake profiles/accounts, considering the characteristics of the user profile. However, the limited profile data made publicly available by Facebook makes it ineligible for applying the existing approaches in fake profile identification. Therefore, this research utilized data mining techniques to detect fake profiles. A set of supervised (ID3 decision tree, k-NN, and SVM) and unsupervised (k-Means and k-medoids) algorithms were applied to 12 behavioral and non-behavioral discriminative profile attributes from a dataset of 982 profiles. The results showed that ID3 had the highest accuracy in the detection process while k-medoids had the lowest accuracy.
Remarks on the Preservation of the Almost Fixed Point Property Involving Several Types of Digitizations

This paper explores a certain relationship between the almost fixed point property (AFPP for short) of a compact and n-dimensional Euclidean space and that of its digitized space. Based on several types of digitizations, we prove that the AFPP of a compact and n-dimensional Euclidean cube is preserved by each of the U ( k ) , the L ( k ) and the Khalimsky digitizations, k ∈ 3 n - 1 , n ∈ N .
Automatic Annotation and Segmentation of Object Instances With Deep Active Curve Network

Object instance segmentation is a challenging task in computer vision research, and instance annotation is its key sub-task and a basic component of segmentation models. In this paper, we propose the Deep Active Curve Network (DACN) which combines powerful ResNet models with GCN-based active curves. This new method regards instance segmentation as a points/vertices, edges and object masks prediction task instead of only a pixel-labeling problem. The multi-scale encoder firstly predicts a coarse result through a combination of the edge and segmentation features in a multi-task learning framework, which is effective dealing with objects with rough boundaries. In order to generate an accurate object annotation, an iterative Graph Convolutional Network (GCN) is used to correct the encoder's feature map and move all vertices of the predicted coarse result to the edge of the corresponding ground truth. A novel weighted loss function further estimates the location of points, edges, and segmented areas, and optimizes the annotation results. Finally, we use the improved 5-interpolation Catmull-Rom spline (CRS) algorithm which exploits key points to control the active curves around objects. In the experimental analysis, we demonstrate the effectiveness of our proposed method on three datasets in automatic mode, including Cityscapes, ADE20K and Rooftop. We further show the generalization ability of our approach on two novel cross-domain datasets.
Inter-modality Dependence Induced Data Recovery for MCI Conversion Prediction

Learning complementary information from multi-modality data often improves diagnostic performance of brain disorders. However, it is challenging to obtain this complementary information when the data are incomplete. Existing methods, such as low-rank matrix completion (which imputes the missing data) and multi-task learning (which restructures the problem into the joint learning of multiple tasks, with each task associated with a subset of complete data), simply concatenate features from different modalities without considering their underlying correlations. Furthermore, most methods conduct multi-modality fusion and prediction model learning in separated steps, which may render to a sub-optimal solution. To address these issues, we propose a novel diagnostic model that integrates missing data recovery, latent space learning and prediction model learning into a unified framework. Specifically, we first recover the missing modality by maximizing the dependency among different modalities. Then, we further exploit the modality correlation by projecting different modalities into a common latent space. Besides, we employ an $$\ell _1$$ -norm to our loss function to mitigate the influence of sample outliers. Finally, we map the learned latent representation into the label space. All these tasks are learned iteratively in a unified framework, where the label information (from the training samples) can also inherently guide the missing modality recovery. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method.