Reviewerly/exHarmony huggingface dataset:
Prediction of the Influence of Navigation Scan-Path on Perceived Quality of Free-Viewpoint Videos

Free-viewpoint video (FVV) systems allow the viewers to freely change the viewpoints of the scene. In such systems, view synthesis and compression are the two main sources of artifacts influencing the perceived quality. To assess this influence, quality evaluation studies are often carried out using conventional displays and generating predefined navigation trajectories mimicking the possible movement of the viewers when exploring the content. Nevertheless, as different trajectories may lead to different conclusions in terms of visual quality when bench-marking the performance of the systems, methods to identify critical trajectories are needed. This paper aims at exploring the impact of exploration trajectories [defined as hypothetical rendering trajectories (HRT)] on the perceived quality of FVV subjectively and objectively, providing two main contributions. First, a subjective assessment test including different HRTs was carried out and analyzed. The results demonstrate and quantify the influence of HRT in the perceived quality. Second, we propose a new full-reference objective video quality assessment measure to objectively predict the impact of HRT. This measure, based on sketch-token representation, models how the categories of the contours change spatially and temporally from a higher semantic level. Performance in comparison with existing quality metrics for the FVV highlight promising results for the automatic detection of most critical HRTs for the benchmark of immersive systems.
A round-efficient distributed betweenness centrality algorithm

We present Min-Rounds BC (MRBC), a distributed-memory algorithm in the CONGEST model that computes the betweenness centrality (BC) of every vertex in a directed unweighted n-node graph in O(n) rounds. Min-Rounds BC also computes all-pairs-shortest-paths (APSP) in such graphs. It improves the number of rounds by at least a constant factor over previous results for unweighted directed APSP and for unweighted BC, both directed and undirected.
Rotation and scale invariant image watermarking based on polar harmonic transforms

Geometric attacks such as rotation and scale can destroy the synchronization of watermarking, designing a geometric-distortion resilient watermarking is currently a challenging work. In this paper, we propose a robust image watermarking technique based on polar harmonic transforms (PHTs) which are defined in a circle domain. The watermarking embedding process includes three principal steps: Firstly, we explicitly exploit invariant properties of PHTs' moments to compute orthogonal moment features. Next, according to the order and repetition of PHTs' moments, steady moments those are not sensitive to geometric attacks are picked out. Finally, watermark embedding is performed through quantization modulation on the selected steady moments, and the watermarked image is reconstructed by combining the original and the compensating image acquired from unmodulated and modulated PHTs' moments, respectively. Experimental results show that the proposed watermarking approach is robust to both geometric distortions (rotation and scaling) and general signal processing attacks (Gaussian filtering, median filtering, average filtering, Gaussian noise, salt & pepper noise, speckle noise, JPEG compression, etc.) and outperforms state-of-the-art watermarking methods.
EACD: evolutionary adaptation to concept drifts in data streams

This paper presents a novel ensemble learning method based on evolutionary algorithms to cope with different types of concept drifts in non-stationary data stream classification tasks. In ensemble learning, multiple learners forming an ensemble are trained to obtain a better predictive performance compared to that of a single learner, especially in non-stationary environments, where data evolve over time. The evolution of data streams can be viewed as a problem of changing environment, and evolutionary algorithms offer a natural solution to this problem. The method proposed in this paper uses random subspaces of features from a pool of features to create different classification types in the ensemble. Each such type consists of a limited number of classifiers (decision trees) that have been built at different times over the data stream. An evolutionary algorithm (replicator dynamics) is used to adapt to different concept drifts; it allows the types with a higher performance to increase and those with a lower performance to decrease in size. Genetic algorithm is then applied to build a two-layer architecture based on the proposed technique to dynamically optimise the combination of features in each type to achieve a better adaptation to new concepts. The proposed method, called EACD, offers both implicit and explicit mechanisms to deal with concept drifts. A set of experiments employing four artificial and five real-world data streams is conducted to compare its performance with that of the state-of-the-art algorithms using the immediate and delayed prequential evaluation methods. The results demonstrate favourable performance of the proposed EACD method in different environments.
Strengths and limitations of computer assisted telephone interviews (CATI) for nutrition data collection in rural Kenya

Despite progress in fighting undernutrition, Africa has the highest rates of undernutrition globally, exacerbated by drought and conflict. Mobile phones are emerging as a tool for rapid, cost effective data collection at scale in Africa, as mobile phone subscriptions and phone ownership increase at the highest rates globally. To assess the feasibility and biases of collecting nutrition data via computer assisted telephone interviews (CATI) to mobile phones, we measured Minimum Dietary Diversity for Women (MDD-W) and Minimum Acceptable Diet for Infants and Young Children (MAD) using a one-week test-retest study on 1,821 households in Kenya. Accuracy and bias were assessed by comparing individual scores and population prevalence of undernutrition collected via CATI with data collected via traditional face-to-face (F2F) surveys. We were able to reach 75% (n = 1366) of study participants via CATI. Women’s reported nutrition scores did not change with mode for MDD-W, but children’s nutrition scores were significantly higher when measured via CATI for both the dietary diversity (mean increase of 0.45 food groups, 95% confidence interval 0.34–0.56) and meal frequency (mean increase of 0.75 meals per day, 95% confidence interval 0.53–0.96) components of MAD. This resulted in a 17% higher inferred prevalence of adequate diets for infants and young children via CATI. Women without mobile-phone access were younger and had fewer assets than women with access, but only marginally lower dietary diversity, resulting in a small non-coverage bias of 1–7% due to exclusion of participants without mobile phones. Thus, collecting nutrition data from rural women in Africa with mobile phones may result in 0% (no change) to as much as 25% higher nutrition estimates than collecting that information in face-to-face interviews.
Impact of pre‐analytical variables on deep learning accuracy in histopathology

Aims Machine learning ( ML ) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many studies irrespective of the paucity of supporting evidence. We empirically compared training image file type, training set size, and two common convolutional neural networks ( CNN s) using transfer learning (ResNet50 and SqueezeNet). Methods and results Thirty haematoxylin and eosin (H&amp;E)‐stained slides with carcinoma or normal tissue from three tissue types (breast, colon, and prostate) were photographed, generating 3000 partially overlapping images (1000 per tissue type). These lossless Portable Networks Graphics ( PNG s) images were converted to lossy Joint Photographic Experts Group ( JPG ) images. Tissue type‐specific binary classification ML models were developed by the use of all PNG or JPG images, and repeated with a subset of 500, 200, 100, 50, 30 and 10 images. Eleven models were generated for each tissue type, at each quantity of training images, for each file type, and for each CNN , resulting in 924 models. Internal accuracies and generalisation accuracies were compared. There was no meaningful significant difference in accuracies between PNG and JPG models. Models trained with more images did not invariably perform better. ResNet50 typically outperformed SqueezeNet. Models were generalisable within a tissue type but not across tissue types. Conclusions Lossy JPG images were not inferior to lossless PNG images in our models. Large numbers of unique H&amp;E‐stained slides were not required for training optimal ML models. This reinforces the need for an evidence‐based approach to best practices for histopathological ML .
Quantum synchronization in a collision model

We reveal the emergence of environment-induced spontaneous synchronization between two spin-$\frac{1}{2}$ quantum objects in a collision model setting. In particular, we determine the conditions for the dynamical establishment of synchronous evolution between local spin observables of a pair of spins undergoing open-system dynamics in the absence of an external drive. Exploiting the versatility of the collision model framework, we show that the formation of quantum or classical correlations between the principal spin pair is of no significant relevance to the manifestation of spontaneous quantum synchronization between them. Furthermore, we discuss the consequences of thermal effects on the environmental spins for the emergence of quantum synchronization.
Identity-based proxy-oriented outsourcing with public auditing in cloud-based medical cyber–physical systems

Abstract Cloud-based medical cyber–physical system (MCPS) relies on cloud computing to provide powerful data storage and computing services. Based on the vital outsourced medical data, doctors can perform precise medical diagnosis for patients, thus the integrity verification of medical data has become increasingly important. In this paper, we propose an identity-based proxy-oriented outsourcing with public auditing scheme in cloud-based MCPS using elliptic curve cryptography. Our scheme enables a patient to authorize the proxy to generate and upload the signatures of medical data and corresponding encrypted medical data to cloud-based MCPS. Any third party auditor (TPA) can audit the medical data efficiently, without retrieving the entire medical data set. We provide the security proof of the proposed scheme, including the storage correctness guarantee and proxy-oriented privacy-preserving property. Moreover, our scheme is designed on identity-based systems, which can avoid complex certificates management. The efficiency comparison shows that our scheme is much more light-weight, and more suitable in cloud-based MCPS.
Dynamic Gesture Recognition Based on MEMP Network

In recent years, gesture recognition has been used in many fields, such as games, robotics and sign language recognition. Human computer interaction (HCI) has been significantly improved by the development of gesture recognition, and now gesture recognition in video is an important research direction. Because each kind of neural network structure has its limitation, we proposed a neural network with alternate fusion of 3D CNN and ConvLSTM, which we called the Multiple extraction and Multiple prediction (MEMP) network. The main feature of the MEMP network is to extract and predict the temporal and spatial feature information of gesture video multiple times, which enables us to obtain a high accuracy rate. In the experimental part, three data sets (LSA64, SKIG and Chalearn 2016) are used to verify the performance of network. Our approach achieved high accuracy on those data sets. In the LSA64, the network achieved an identification rate of 99.063%. In SKIG, this network obtained the recognition rates of 97.01% and 99.02% in the RGB part and the rgb-depth part. In Chalearn 2016, the network achieved 74.57% and 78.85% recognition rates in RGB part and rgb-depth part respectively.
Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

The use of mobile robots for teaching automatic control is becoming more popular in engineering curricula. Currently, many robot simulators with high-graphical capabilities can be easily used by instructors to teach control engineering. However, the use of real robots is not as straightforward as simulations. There are many hardware and software details that must be considered before applying control. This paper presents the development of an easy-to-use platform for teaching control of mobile robots. The laboratory has been carefully designed to conceal all technical issues, such as communications or the localization that do not address the fundamental concepts of control engineering. To this end, a position sensor based on computer vision has been developed to provide the positions of the robots on the platform in real time. The Khepera IV robot has been selected for this platform because of its flexibility and advanced built-in sensors but the laboratory could be easily adapted for similar robots. The platform offers the opportunity to perform laboratory practices to test many different control strategies within a real experimental multi-agent environment. A methodology for using the platform in the lab is also provided.
A Quantitative Approach for Evaluating the Quality of Experience of Smart-Wearables From the Quality of Data and Quality of Information: An End User Perspective

A rapid growth in the smart-wearable industry is making it increasingly important to cater to the quality of experience (QoE) requirements of the end users. In this paper, we try to model the relationship between human experience and quality perception in relation to the smart-wearable segment. For this, the concepts of quality of data (QoD) and quality of information (QoI) are used. While QoD is concerned with the accuracy and precision of the data collected by the smart-wearables, QoI relates to the useful information that is obtained from the raw data captured by the devices via the companion applications of each wearable installed on a smartphone. A subjective experiment comprising of 40 participants and 5 wearable devices is performed in a free-living condition in order to create the QoE model. Four different approaches for weight determination are presented in this paper: balanced weight distribution, correlation-based distribution, hybrid distribution, and priority-based distribution while proposing the QoE model. Our results show that the priority-based distribution approach performs slightly better than the rest of the techniques and has the best correlation to the subjective QoE when compared against others. Based on the results, the appropriate recommendations are provided to the different smart-wearable vendors for improving their products, thereby ensuring greater user adoption.
A New Pseudorandom Bit Generator Based on Mixing Three-Dimensional Chen Chaotic System with a Chaotic Tactics

In this paper, a new chaotic system is proposed based on mixing three-dimensional Chen chaotic system with a chaotic tactics. This new system is proved to be chaotic under Wiggins’ chaos definition and can generate chaotic sequences with high complexity. Furthermore, we propose a new pseudorandom bit generator (PRBG) based on this new system. A coding algorithm is used to make the sequences uniform. Both statistical and security tests are provided to show the generated sequences are with good randomness and high complexity to withstand attacks.
DeepMorse: A Deep Convolutional Learning Method for Blind Morse Signal Detection in Wideband Wireless Spectrum

With the recent advances in wireless technologies, high frequency radio has become the primary medium for long-distance communication.Among various types of modulation in signal transmission, morse code stands out due to its simplicity and efficiency in information transmission while costing small bandwidth.In practice, however, it is extremely laborious to locate morse signals in wideband communication.It's a needle in a haystack, if morse code is sent in random carrier waves at random period.To avoid this, automatic morse signal detection has become a challenging task in wireless morse communications, and new solutions could be derived from the latest machine learning techniques.In this paper, we propose a deep learning framework, namely DeepMorse, to blindly detect morse signals in wideband spectrum data.In particular, we first develop a multi-signal sensing module to retrieve signal candidates from wideband spectrum without prior knowledge.Then, we construct a CNN-based module to extract informative features from the located candidates, in order to distinguish the morse signal from other types of modulation.To evaluate the proposed DeepMorse model, we set up a testbed utilizing commercialized long-distance wireless communication devices.The experimental results demonstrate that DeepMorse is able to effectively detect morse signals and outperform the state-of-the-art methods on four real-world datasets.
A Multi-Label A* Algorithm for Multi-Agent Pathfinding

Given a set of agents, the multi-agent pathfinding problem consists in determining, for each agent, a path from its start location to its assigned goal while avoiding collisions with other agents. Recent work has studied variants of the problem in which agents are assigned a sequence of goals (tasks) that become available over time, such as the online multi-agent pickup and delivery (MAPD) problem. In this paper, we propose a multi-label A* algorithm (MLA*) for this problem. It extends the classic A* algorithm by allowing the computation of paths with multiple ordered goals (such as a pickup and delivery). Moreover, we develop a new h-value-based centralized heuristic for the MAPD. Computational experiments show that our proposed MLA* obtains substantial improvements in terms of makespan and service time as compared to existing methods, while being more computationally efficient. On instances with a thousand tasks and hundreds of agents, our method reduces the average service time by 43% compared to the state of the art, with considerably less computational effort.
Clinical Information Systems and Artificial Intelligence: Recent Research Trends

Objectives: This survey aims at reviewing the literature related to Clinical Information Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) systems, and how collected data can be analyzed by Artificial Intelligence (AI) techniques. Methods: We selected the major journals (11 journals) collecting papers (more than 7,000) over the last five years from the top members of the research community, and read and analyzed the papers (more than 200) covering the topics. Then, we completed the analysis using search engines to also include papers from major conferences over the same five years. Results: We defined a taxonomy of major features and research areas of CIS, HIS, EHR systems. We also defined a taxonomy for the use of Artificial Intelligence (AI) techniques on healthcare data. In the light of these taxonomies, we report on the most relevant papers from the literature. Conclusions: We highlighted some major research directions and issues which seem to be promising and to need further investigations over a medium- or long-term period.
Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph

Symptom diagnosis is a challenging yet profound problem in natural language processing.Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between doctors and patients that contain more rich information are not well studied.In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors.Then, we provide some benchmark models on this dataset to boost the research of dialogue symptom diagnosis.In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build a symptom graph to model the associations between symptoms rather than treating each symptom independently.Experimental results show that both the global attention and symptom graph are effective to boost dialogue symptom diagnosis.In particular, our proposed model achieves the state-of-the-art performance on the constructed dataset.
Metal artifact reduction for the segmentation of the intra cochlear anatomy in CT images of the ear with 3D-conditional GANs

Cochlear implants (CIs) are surgically implanted neural prosthetic devices that are used to treat severe-to-profound hearing loss. These devices are programmed post implantation and precise knowledge of the implant position with respect to the intra cochlear anatomy (ICA) can help the programming audiologists. Over the years, we have developed algorithms that permit determining the position of implanted electrodes relative to the ICA using pre- and post-implantation CT image pairs. However, these do not extend to CI recipients for whom pre-implantation CT (Pre-CT) images are not available. This is so because post-operative images are affected by strong artifacts introduced by the metallic implant. To overcome this issue, we have proposed two methods to segment the ICA in post-implantation CT (Post-CT) images, but they lead to segmentation errors that are substantially larger than errors obtained with Pre-CT images. Recently, we have proposed an approach that uses 2D-conditional generative adversarial nets (cGANs) to synthesize pre-operative images from post-operative images. This permits to use segmentation algorithms designed to operate on Pre-CT images even when these are not available. We have shown that it substantially and significantly improves the results obtained with methods designed to operate directly on post-CT images. In this article, we expand on our earlier work by moving from a 2D architecture to a 3D architecture. We perform a large validation and comparative study that shows that the 3D architecture improves significantly the quality of the synthetic images measured by the commonly used MSSIM (Mean Structural SIMilarity index). We also show that the segmentation results obtained with the 3D architecture are better than those obtained with the 2D architecture although differences have not reached statistical significance.