Reviewerly/exHarmony huggingface dataset:
To Detect or not to Detect: The Right Faces to Morph

Recent works have studied the face morphing attack detection performance generalization over variations in morphing approaches, image re-digitization, and image source variations. However, these works assumed a constant approach for selecting the images to be morphed (pairing) across their training and testing data. A realistic variation in the pairing protocol in the training data can result in challenges and opportunities for a stable attack detector. This work extensively study this issue by building a novel database with three different pairing protocols and two different morphing approaches. We study the detection generalization over these variations for single image and differential attack detection, along with handcrafted and CNN-based features. Our observations included that training an attack detection solution on attacks created from dissimilar face images, in contrary to the common practice, can result in an overall more generalized detection performance. Moreover, we found that differential attack detection is very sensitive to variations in morphing and pairing protocols.
Using virtual reality for tourism marketing: A mediating role of self-presence

Despite increasing interest in using virtual reality (VR) for tourism marketing, limited empirical evidence exists regarding consumers' psychological response toward VR. The current study investigates the effectiveness of different media – head-mounted display (HMD) or computer – used to view 360-degree tourism promotional videos as well as the impact of different information sources featured in a VR tourism video on destination image formation and intention to visit. The experiment featured a 2 (media: HMD vs. computer) x 2 (information source: tour guide vs. peer) between-subjects design. Findings indicate that the type of media used to view VR promotional videos significantly impacts destination image but not intention to visit. The role of different types of information sources on destination image and intention to visit was found to be non-significant. The findings suggest that virtual reality offers an effective means to promote tourism, particularly because the enhanced sense of presence provided via HMD leads to a more positive image of the destination.
Facial Age Synthesis With Label Distribution-Guided Generative Adversarial Network

The existing research work on facial age synthesis has been mostly focused on long-term aging (e.g., over an age span of 10 years or more). In this paper, we employ generative adversarial networks (GANs) as a tool to investigate age synthesis over different age spans. Compared with long-term aging, short-term age synthesis suffers from the reduced amount of available training data, which can severely hinder the model training. We conduct a series of experiments to validate this. To facilitate short-term age synthesis, we further propose label distribution-guided generative adversarial network (ldGAN), where each sample is associated with an age label distribution (ALD) rather than a single age group. Accordingly, each sample can contribute not only to the learning of its own age group but also to neighbouring groups' learning. This is useful when addressing short-term aging to cope with the reduced amount of training data. In addition, unlike one-hot encoding which treats age groups as independent from one another, ldGAN can well capture the correlation among different age groups, so that smooth aging sequences can be achieved. The ALD model is integrated into GAN with a two-step process. Firstly, instead of the traditional one-hot encoding, ALD is applied as the condition of the generator. Secondly, we add a sequence of label distribution learners on top of several multi-scale discriminators, with the aim of minimizing the label distribution learning loss when optimizing both the generator and discriminators. Both qualitative and quantitative evaluations are conducted to assess ldGAN's ability in dealing with two core issues of face aging, i.e., aging effect generation and identity preservation. The obtained experimental results demonstrate the effectiveness of ldGAN in both learning short-term aging patterns and coping with the lack of training data.
Domain Adaptation via Teacher-Student Learning for End-to-End Speech Recognition

Teacher-student (T/S) has shown to be effective for domain adaptation of deep neural network acoustic models in hybrid speech recognition systems. In this work, we extend the T/S learning to large-scale unsupervised domain adaptation of an attention-based end-to-end (E2E) model through two levels of knowledge transfer: teacher's token posteriors as soft labels and one-best predictions as decoder guidance. To further improve T/S learning with the help of ground-truth labels, we propose adaptive T/S (AT/S) learning. Instead of conditionally choosing from either the teacher's soft token posteriors or the one-hot ground-truth label, in AT/S, the student always learns from both the teacher and the ground truth with a pair of adaptive weights assigned to the soft and one-hot labels quantifying the confidence on each of the knowledge sources. The confidence scores are dynamically estimated at each decoder step as a function of the soft and one-hot labels. With 3400 hours parallel close-talk and far-field Microsoft Cortana data for domain adaptation, T/S and AT/S achieves 6.3% and 10.3% relative word error rate improvement over a strong E2E model trained with the same amount of far-field data.
A Secure Energy-Saving Communication and Encrypted Storage Model Based on RC4 for EHR

The commercialization of 5G has greatly promoted the development of medical Internet of Things (IoT). More medical devices connected to the Internet may further increase the communication power consumption. Meanwhile, privacy protection technique in cloud computing cannot match the rapid development of medical applications. Therefore, exploring secure, balanced and energy-efficient data transmission between medical devices and cloud servers is extremely challenging. This paper focuses on the security and energy consumption of medical electronic health record (EHR) data transmission and storage between cloud server and IoT device users. We build a secure energy-saving communication and encrypted storage model by adding secure energy-saving communication scheme and encryption algorithm to the traditional medical cloud model. Specifically, we propose a communication authentication algorithm MedGreen based on elliptic curve and bilinear pair. In the algorithm, the two communication parties can complete the key establishment and identity authentication only after one communication, which effectively balances the resource overhead of the key center and the user, and resists the Man-in-the-middle attack. Aiming at the characteristics of large repetition and high sensitivity of medical data, we present a secure data storage algorithm MedSecrecy based on Huffman compression and RC4. The algorithm not only maintains the RC4 encryption efficiency, reduces the amount of cipher text data, but also improves confidentiality, randomness and security of the key stream. Comprehensive analysis and simulations show that our system is secure, energy-saving and highly efficient for EHR.
Monotonic Recurrent Neural Network Transducer and Decoding Strategies

Recurrent Neural Network Transducer (RNNT) is an end-to-end model which transduces discrete input sequences to output sequences by learning alignments between the sequences. In speech recognition tasks we generally have a strictly monotonic alignment between time frames and label sequence. However, the standard RNNT loss does not enforce this constraint. This can cause some anomalies in alignments such as the model outputting a sequence of labels at a single time frame. There is also no bound on the decoding time steps. To address these problems, we introduce a monotonic version of the RNNT loss. Under the assumption that the output sequence is not longer than the input sequence, this loss can be used with forward-backward algorithm to learn strictly monotonic alignments between the sequences. We present experimental studies showing that speech recognition accuracy for monotonic RNNT is equivalent to standard RNNT. We also explore best-first and breadth-first decoding strategies for both monotonic and standard RNNT models. Our experiments show that breadth-first search is effective in exploring and combining alternative alignments. Additionally, it also allows batching of hypotheses during search label expansion, allowing better resource utilization, and resulting in decoding speedup.
Modeling Quality of IoT Experience in Autonomous Vehicles

Today's research on Quality of Experience (QoE) mainly addresses multimedia services. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as autonomous vehicles (AVs), are more complex and involve additional quality requirements, such as those related to machine-to-machine communication that enables self-driving. In fully autonomous cases, it is the intelligent machines operating the vehicles. Thus, it is not clear how intelligent machines will impact end-user QoE, but also how end users can alter and affect a self-driving vehicle. This article argues for a paradigm shift in the QoE area to cover the relationship between humans and intelligent machines. We introduce the term Quality of IoT-experience (QoIoT) within the context of AV, where the quality evaluation, besides end users, considers quantifying the perspectives of intelligent machines with objective metrics. Hence, we propose a novel architecture that considers Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC) to determine the overall QoIoT in the context of AVs. Finally, we present a case study to illustrate the use of QoIoT.
PartMitosis: A Partially Supervised Deep Learning Framework for Mitosis Detection in Breast Cancer Histopathology Images

Detection of mitotic tumor cells per tissue area is one of the critical markers of breast cancer prognosis.The aim of this paper is to develop a method for the automatic detection of mitotic figures from breast cancer histological slides using a partially supervised deep learning framework.Unlike the previous literature, which has focused on solving the problem of mitosis detection in the weakly annotated datasets using centroid pixel labels (weak labels) only without taking advantage of the available pixel-level labels (strong labels) of other datasets, in this paper, we design a novel partially supervised framework based on two parallel deep fully convolutional networks.One of them is trained using weak labels and the other is trained using strong labels, together with a weight transfer function.In the detection phase, we fuse the segmentation maps produced by the two networks to obtain the final mitosis detections.Our system exploits the available large sets of mitosis detection samples with mitosis centroid annotation, such as the 2014 ICPR dataset and the AMIDA13 dataset, and only a small set of samples with the annotation of all mitosis pixels, such as the 2012 ICPR dataset, to perform a more accurate mitosis detection on weakly labeled data.This enables us to outperform all previous mitosis detection systems by achieving F-scores of 0.575 and 0.698 on the 2014 ICPR dataset and the AMIDA13 dataset respectively.
A Systematic Literature Review of Integration of Blockchain and Artificial Intelligence

Blockchain and artificial intelligence (AI) have gain the most research attention during recent years. Blockchain is a distributed ledger of trustworthy digital records shared by a network of participants. Blockchain technology has the potential capacity in many fields such as international payment, secure data sharing and marketing, and supply chain management. On the other side, Artificial Intelligence (AI) is used to develop the creation of machines capable of performing tasks that need intelligence. This paper aims to determine the current state of the art within the field of AI with Blockchain technology. In particular, we investigated the latest articles on this integration and carried out an analysis to determine what applications can benefit from it. We identified 23 articles that comply with the assessment criteria. The review research demonstrates that distributed management, security and efficiency improvement, prediction and decision making are among the most popular types of applications that benefit from the integration of AI and Blockchain, while security is the hottest topic. In general, AI algorithms can improve Blockchain design and operation. The combination of these two technologies increases security, efficiency and, productivity of applications.
PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics

Federated learning has emerged as a promising solution for big data analytics, which jointly trains a global model across multiple mobile devices. However, participants' sensitive data information may be leaked to an untrusted server through uploaded gradient vectors. To address this problem, we propose a privacy-enhanced federated learning (PEFL) scheme to protect the gradients over an untrusted server. This is mainly enabled by encrypting participants' local gradients with Paillier homomorphic cryptosystem. In order to reduce the computation costs of the cryptosystem, we utilize the distributed selective stochastic gradient descent (DSSGD) method in the local training phase to achieve the distributed encryption. Moreover, the encrypted gradients can be further used for secure sum aggregation at the server side. In this way, the untrusted server can only learn the aggregated statistics for all the participants' updates, while each individual's private information will be well-protected. For the security analysis, we theoretically prove that our scheme is secure under several cryptographic hard problems. Exhaustive experimental results demonstrate that PEFL has low computation costs while reaching high accuracy in the settings of federated learning.
An Ensemble of Locally Reliable Cluster Solutions

Clustering ensemble indicates to an approach in which a number of (usually weak) base clusterings are performed and their consensus clustering is used as the final clustering. Knowing democratic decisions are better than dictatorial decisions, it seems clear and simple that ensemble (here, clustering ensemble) decisions are better than simple model (here, clustering) decisions. But it is not guaranteed that every ensemble is better than a simple model. An ensemble is considered to be a better ensemble if their members are valid or high-quality and if they participate according to their qualities in constructing consensus clustering. In this paper, we propose a clustering ensemble framework that uses a simple clustering algorithm based on kmedoids clustering algorithm. Our simple clustering algorithm guarantees that the discovered clusters are valid. From another point, it is also guaranteed that our clustering ensemble framework uses a mechanism to make use of each discovered cluster according to its quality. To do this mechanism an auxiliary ensemble named reference set is created by running several kmeans clustering algorithms.
Federated Cooperation and Augmentation for Power Allocation in Decentralized Wireless Networks

Emerging mobile edge techniques and applications such as Augmented Reality (AR)/Virtual Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of power and computing resource consumptions.In the meantime, the volume of data generated at the edge networks is also increasing rapidly.Under this circumstance, building energy-efficient and privacy-protected communications is imperative for 5G and beyond wireless communication systems.The recent emerging distributed learning methods such as federated learning (FL) perform well in improving resource efficiency while protecting user privacy with low communication overhead.Specifically, FL enables edge devices to learn a shared network model by aggregating local updates while keeping all the training processes on local devices.This paper investigates distributed power allocation for edge users in decentralized wireless networks with aim to maximize energy/spectrum efficiency while preventing privacy leakage based on a FL framework.Due to the dynamics and complexity of wireless networks, we adopt an on-line Actor-Critic (AC) architecture as the local training model, and FL performs cooperation for edge users by sharing the gradients and weightages generated in the Actor network.Moreover, in order to resolve the over-fitting problem caused by data leakages in Non-independent and identically distributed (Non-i.i.d) data environment, we propose a federated augmentation mechanism with Wasserstein Generative Adversarial Networks (WGANs) algorithm for data augmentation.Federated augmentation empowers each device to replenish the data buffer using a generative model of WGANs until accomplishing an i.i.d training dataset, which significantly reduces the communication overhead in distributed learning compared to direct data sample exchange method.Numerical results reveal that the proposed federated learning based cooperation and augmentation (FL-CA) algorithm possesses a good convergence property, high robustness and achieves better accuracy of power allocation strategy than other three benchmark algorithms.
Adaptive Pruning of Transfer Learned Deep Convolutional Neural Network for Classification of Cervical Pap Smear Images

Automatic classification of cervical Pap smear images plays a key role in computer-aided cervical cancer diagnosis. Conventional classification approaches rely on cell segmentation and feature extraction methods. Due to overlapping cells, dust, impurities and uneven irradiation, the accurate segmentation and feature extraction of Pap smear images are still challenging. To overcome the difficulties of the feature-based approaches, deep learning is becoming more important alternative. Since the number of cervical cytological images is limited, an adaptive pruning deep transfer learning model (PsiNet-TAP) is proposed for Pap smear images classification. We designed a novel network to classify Pap smear images. Due to the limited number of images, we adopted transfer learning to obtain the pre-trained model. Then it was optimized by modifying the convolution layer and pruning some convolution kernels that may interfere with the target classification task. The proposed method PsiNet-TAP was tested on 389 cervical Pap smear images. The method has achieved remarkable performance (accuracy: more than 98%), which demonstrates the strength of the proposed method for providing an efficient tool for cervical cancer classification in clinical settings.
An Efficient and Parallel R-LWE Cryptoprocessor

Lattice-based cryptography (LBC) is a promising and efficient public key cryptography scheme whose theoretical foundation usually lies in Learning with Error (LWE) problem and its variant such as Ring-LWE (R-LWE) is the most studied cryptosystem which allows for more efficient implementation while maintaining the hardness of an original problem. Polynomial multiplication is the bottleneck of R-LWE, that can either be done using Number Theoretic Transform (NTT) or schoolbook polynomial multiplication (SPM) algorithm. The use of SPM is wider and possible for all parameters of R-LWE schemes. This brief proposes an efficient and parallel strategy for SPM in R-LWE; by successfully reducing its time complexity from n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /4 (making it 1.8× faster and 1.4× hardware efficient). Furthermore, by adjusting the bit width for the error terms, the polynomial multiplication and addition blocks are reused for both encryption and decryption modules resulting in 14% reduced area and 1.7× better throughput in comparison to state-of-art SPM based R-LWE designs.
Dress With Style: Learning Style From Joint Deep Embedding of Clothing Styles and Body Shapes

Body shape is about proportion, and fashion style is all about dressing those proportions to look their very best. Figuring out the styles to suit a body shape can be a daunting task for many people. It is, therefore, essential to develop a framework for learning the compatibility of body shapes and clothing styles. Though fashion designers and fashion stylists have analyzed the correlation between human body shapes and fashion styles for a long time, this issue did not receive much attention in multimedia science. In this paper, we present a novel style recommender, on the basis of the user's body attributes. The rich amount of fashion styling knowledge from social big data is exploited for this purpose. We first construct a joint embedding of clothing styles and human body measurements with deep multimodal representation learning on a reference dataset that has been sorted to meet the fashion rules. We then discover the relevant semantic features by propagation and selection in clothing style and body shape graphs. Experiments demonstrate the effectiveness of the proposed framework when compared with several baseline methods.
Hybrid Model Featuring CNN and LSTM Architecture for Human Activity Recognition on Smartphone Sensor Data

The traditional methods of recognizing human activities involve typical machine learning (ML) algorithms which uses heuristic engineered features. Human activities are dynamic in nature and are encoded with a sequence of actions. ML methods are able to perform activity recognition tasks but may not exploit the temporal correlations of the input data. Therefore, in this paper, we proposed and showed the effectiveness of employing a new combination of deep learning (DL) methods for human activity recognition (HAR). DL methods are capable of extracting discriminative features automatically from the raw sensor data. Specifically, in this paper, we proposed a hybrid architecture which features a combination of Convolutional neural networks (CNN) and Long short-term Memory (LSTM) networks for HAR task. The model is tested on UCI HAR dataset which is a benchmark dataset and comprises of accelerometer and gyroscope data obtained from a smartphone. Our experimental results showed that our proposed method outperformed the recent results which used pure LSTM and bidirectional LSTM networks on the same dataset.
What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking

Abstract Technologies to measure gaze direction and pupil reactivity have become efficient, cheap, and compact and are finding increasing use in many fields, including gaming, marketing, driver safety, military, and healthcare. Besides offering numerous useful applications, the rapidly expanding technology raises serious privacy concerns. Through the lens of advanced data analytics, gaze patterns can reveal much more information than a user wishes and expects to give away. Drawing from a broad range of scientific disciplines, this paper provides a structured overview of personal data that can be inferred from recorded eye activities. Our analysis of the literature shows that eye tracking data may implicitly contain information about a user’s biometric identity, gender, age, ethnicity, body weight, personality traits, drug consumption habits, emotional state, skills and abilities, fears, interests, and sexual preferences. Certain eye tracking measures may even reveal specific cognitive processes and can be used to diagnose various physical and mental health conditions. By portraying the richness and sensitivity of gaze data, this paper provides an important basis for consumer education, privacy impact assessments, and further research into the societal implications of eye tracking.