Reviewerly/exHarmony huggingface dataset:
SofGAN: A Portrait Image Generator with Dynamic Styling

Recently, Generative Adversarial Networks (GANs)} have been widely used for portrait image generation. However, in the latent space learned by GANs, different attributes, such as pose, shape, and texture style, are generally entangled, making the explicit control of specific attributes difficult. To address this issue, we propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space. The latent codes sampled from the two subspaces are fed to two network branches separately, one to generate the 3D geometry of portraits with canonical pose, and the other to generate textures. The aligned 3D geometries also come with semantic part segmentation, encoded as a semantic occupancy field (SOF). The SOF allows the rendering of consistent 2D semantic segmentation maps at arbitrary views, which are then fused with the generated texture maps and stylized to a portrait photo using our semantic instance-wise (SIW) module. Through extensive experiments, we show that our system can generate high quality portrait images with independently controllable geometry and texture attributes. The method also generalizes well in various applications such as appearance-consistent facial animation and dynamic styling.
Subjective Quality Database and Objective Study of Compressed Point Clouds With 6DoF Head-Mounted Display

In this paper, we focus on subjective and objective Point Cloud Quality Assessment (PCQA) in an immersive environment and study the effect of geometry and texture attributes in compression distortion. Using a Head-Mounted Display (HMD) with six degrees of freedom, we establish a subjective PCQA database, named SIAT Point Cloud Quality Database (SIAT-PCQD). Our database consists of 340 distorted point clouds compressed by the MPEG point cloud encoder with the combination of 20 sequences and 17 pairs of geometry and texture quantization parameters. The impact of distorted geometry and texture attributes is further discussed in this paper. Then, we propose two projection-based objective quality evaluation methods, i.e., a weighted view projection based model and a patch projection based model. Our subjective database and findings can be used in point cloud processing, transmission, and coding, especially for virtual reality applications. The subjective dataset <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://dx.doi.org/10.21227/ad8d-7r28</uri> <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://codec.siat.ac.cn/video_download_siat-pcqd.htmlhas</uri> been released in the public repository.
Unpacking Non-Dualistic Design: The Soma Design Case

We report on a somaesthetic design workshop and the subsequent analytical work aiming to demystify what is entailed in a non-dualistic design stance on embodied interaction and why a first-person engagement is crucial to its unfoldings. However, as we will uncover through a detailed account of our process, these first-person engagements are deeply entangled with second- and third-person perspectives, sometimes even overlapping. The analysis furthermore reveals some strategies for bridging the body-mind divide by attending to our inner universe and dissolving or traversing dichotomies between inside and outside ; individual and social ; body and technology . By detailing the creative process, we show how soma design becomes a process of designing with and through kinesthetic experience, in turn letting us confront several dualisms that run like fault lines through HCI’s engagement with embodied interaction.
Conflict-Based Search For Optimal Multi-Agent Path Finding

In the multi agent path finding problem (MAPF) paths should be found for several agents, each with a different start and goal position such that agents do not collide. Previous optimal solvers applied global A*-based searches. We present a new search algorithm called Conflict Based Search (CBS). CBS is a two-level algorithm. At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. In many cases this reformulation enables CBS to examine fewer states than A* while still maintaining optimality. We analyze CBS and show its benefits and drawbacks. Experimental results on various problems shows a speedup of up to a full order of magnitude over previous approaches.
POA: Puzzle Optimization Algorithm

Optimization plays a key role in various disciplines of science in order to achieve the optimal solution among all available solutions.Innovation and contribution of this paper is in introducing a new optimization algorithm called Puzzle Optimization Algorithm (POA) to solve various optimization problems.The main idea in the design of the proposed POA is the mathematical simulation of the process of solving a puzzle as an evolutionary optimizer.The various steps of POA are explained and then its mathematical model is presented.The main advantage and feature of the proposed POA is that it has no control parameters and therefore does not require parameter setting.The performance of the proposed POA is tested on twenty-three different objective functions.Also, in order to analyse the performance of POA in optimization, its results are compared with eight other optimization algorithms.The optimization results indicate the high and acceptable ability of the proposed POA to solve optimization problems.In addition, the simulation results show that the proposed POA is far better and much more competitive than the eight compared optimization algorithms.
KNN weighted reduced universum twin SVM for class imbalance learning

In real world problems, imbalance of data samples poses major challenge for the classification problems as the data samples of a particular class are dominating. Problems like fault and disease detection involve imbalance data and hence need attention to avoid the bias towards a particular class. The classification models like support vector machines (SVM) get biased to majority class samples and hence results in misclassification of the minority class samples. SVM suffers as no prior information related to the data is involved in the generation of hyperplanes. Also, local information of the neighbourhood is ignored in SVM samples and thus treats each sample equally for generating the hyperplanes. However, the data points may be contaminated and may mislead the generation of hyperplanes. Inspired by the idea of prior data information and local neighbourhood information, we propose K-nearest neighbour based weighted reduced universum twin SVM for class imbalance learning (KWRUTSVM-CIL). The proposed KWRUTSVM-CIL embodies the local neighbourhood information and uses universum data to balance the classes in class imbalance problems. Local neighbourhood information is incorporated via weight matrix in the objective function. In proposed KWRUTSVM-CIL model, weight vectors are used in the corresponding constraints of the objective functions to exploit the interclass information. The oversampling and undersampling approaches are followed to balance the data in class imbalance problems. Universum data gives prior information of the data. Twin SVM, universum twin SVM, and reduced universum twin SVM for class imbalance implement empirical risk minimization principle and thus may lead to overfitting. However, the proposed KWRUTSVM-CIL model embodies regularization term to maximize the margin and implement the structural risk minimization principle which is the marrow of statistical learning and overcomes the issues of overfitting. Experimental results and the statistical analysis signify that the generalization ability of proposed KWRUTSVM-CIL model is superior in comparison to other twin SVM based models. As an application, we use the proposed KWRUTSVM-CIL model for the diagnosis of Alzheimer’s disease and breast cancer disease. The proposed KWRUTSVM-CIL model showed better generalization performance compared to other twin SVM based models in biomedical datasets.
Development of a teaching discipline program "Fundamentals of scientific communications" in the specialty "System analysis and information processing"

The main way to organize the interaction of scientists is to provide each participant in the scientific process with highly operational and high-quality information about the state of affairs in science in general and at its forefront in particular. This function is performed by the system of scientific communication – a set of types and forms of professional communication of scientists, carried out with the help of both standardized regular publications and a wide range of oral, written, printed and electronic means. The article describes the implementation of work program "Fundamentals of Scientific Communications" within the framework of teaching the specialty "System Analysis and Information Processing" at the Moscow Technical University of Communications and Informatics. Considered: program mastering objectives, list of planned learning outcomes for the discipline, discipline structure and content, thematic work plan, evaluative properties fund, as well as methodological recommendations for students and graduate students on mastering the discipline.
Deep Learning for Spatio-Temporal Modeling of Dynamic Spontaneous Emotions

Facial expressions involve dynamic morphological changes in a face, conveying information about the expresser's feelings. Each emotion has a specific spatial deformation over the face and temporal profile with distinct time segments. We aim at modeling the human dynamic emotional behavior by taking into consideration the visual content of the face and its evolution. But emotions can both speed-up or slow-down, therefore it is important to incorporate information from the local neighborhood frames (short-term dependencies) and the global setting (long-term dependencies) to summarize the segment context despite of its time variations. A 3D-Convolutional Neural Networks (3D-CNN) is used to learn early local spatiotemporal features. The 3D-CNN is designed to capture subtle spatiotemporal changes that may occur on the face. Then, a Convolutional-Long-Short-Term-Memory (ConvLSTM) network is designed to learn semantic information by taking into account longer spatiotemporal dependencies. The ConvLSTM network helps considering the global visual saliency of the expression. That is locating and learning features in space and time that stand out from their local neighbors in order to signify distinctive facial expression features along the entire sequence. Non-variant representations based on aggregating global spatiotemporal features at increasingly fine resolutions are then done using a weighted Spatial Pyramid Pooling layer.
Solving Trajectory Optimization Problems in the Presence of Probabilistic Constraints

The objective of this paper is to present an approximation-based strategy for solving the problem of nonlinear trajectory optimization with the consideration of probabilistic constraints. The proposed method defines a smooth and differentiable function to replace probabilistic constraints by the deterministic ones, thereby converting the chance-constrained trajectory optimization model into a parametric nonlinear programming model. In addition, it is proved that the approximation function and the corresponding approximation set will converge to that of the original problem. Furthermore, the optimal solution of the approximated model is ensured to converge to the optimal solution of the original problem. Numerical results, obtained from a new chance-constrained space vehicle trajectory optimization model and a 3-D unmanned vehicle trajectory smoothing problem, verify the feasibility and effectiveness of the proposed approach. Comparative studies were also carried out to show the proposed design can yield good performance and outperform other typical chance-constrained optimization techniques investigated in this paper.
Real-time control and application with self-tuning PID-type fuzzy adaptive controller of an inverted pendulum

Purpose This paper aims to keep the pendulum on the linear moving car vertically balanced and to bring the car to the equilibrium position with the designed controllers. Design/methodology/approach As inverted pendulum systems are structurally unstable and nonlinear dynamic systems, they are important mechanisms used in engineering and technological developments to apply control techniques on these systems and to develop control algorithms, thus ensuring that the controllers designed for real-time balancing of these systems have certain performance criteria and the selection of each controller method according to performance criteria in the presence of destructive effects is very helpful in getting information about applying the methods to other systems. Findings As a result, the designed controllers are implemented on a real-time and real system, and the performance results of the system are obtained graphically, compared and analyzed. Originality/value In this study, motion equations of a linear inverted pendulum system are obtained, and classical and artificial intelligence adaptive control algorithms are designed and implemented for real-time control. Classic proportional-integral-derivative (PID) controller, fuzzy logic controller and PID-type Fuzzy adaptive controller methods are used to control the system. Self-tuning PID-type fuzzy adaptive controller was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of real-time with self-tuning PID-type fuzzy adaptive controller.
Fault Diagnosis of Delta 3D Printers Using Transfer Support Vector Machine With Attitude Signals

In traditional machine learning algorithms, more labeled data are required in model training for better classification accuracy. However, collecting labeled data is an expensive task for real applications. In this paper, a transfer support vector machine (TSVM) technique is introduced to tackle this problem with application to the fault diagnosis of delta 3D printers. Transfer component analysis is first proposed to capture shared features for representing the source and target domains, by cross-domain feature extraction from less labeled data in the source domain and massive unlabeled data in the target domain. Support vector machine is then applied to the fault diagnosis using the transferred features of attitude signals. In the experiments, fault classification rate achieves 83.79% using only 6.7% of the dataset for model training. Compared with peer methods, the results show that the present TSVM exhibits the best performance in extracting the cross-domain feature and improves the accuracy of the 3D printer fault diagnosis.
An efficient semantic recommender method forArabic text

Purpose This paper aims to propose a new efficient semantic recommender method for Arabic content. Design/methodology/approach Three semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature. Findings Experiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time. Originality/value Although many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.
A survey of leakage-resilient cryptography

research-article Share on A survey of leakage-resilient cryptography Authors: Yael Tauman Kalai View Profile , Leonid Reyzin View Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 727–794https://doi.org/10.1145/3335741.3335768Published:04 October 2019Publication History 20citation149DownloadsMetricsTotal Citations20Total Downloads149Last 12 Months17Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Augmented Reality in Healthcare

a focus rivalry between real and virtual, that can compromise their efficacy in a real scenario.
The neural and cognitive architecture for learning from a small sample

Artificial intelligence algorithms are capable of fantastic exploits, yet they are still grossly inefficient compared with the brain’s ability to learn from few exemplars or solve problems that have not been explicitly defined. What is the secret that the evolution of human intelligence has unlocked? Generalization is one answer, but there is more to it. The brain does not directly solve difficult problems, it is able to recast them into new and more tractable problems. Here, we propose a model whereby higher cognitive functions profoundly interact with reinforcement learning to drastically reduce the degrees of freedom of the search space, simplifying complex problems, and fostering more efficient learning.
Asymmetric Valleys: Beyond Sharp and Flat Local Minima

Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectures that SGD with proper conﬁguration is able to ﬁnd wide and ﬂat local minima, which are correlated with good generalization performance. In this paper, we observe that local minima of modern deep networks are more than being ﬂat or sharp. Instead, at a local minimum there exist many asymmetric directions such that the loss increases abruptly along one side, and slowly along the opposite side – we formally deﬁne such minima as asymmetric valleys. Under mild assumptions, we ﬁrst prove that for asymmetric valleys, a solution biased towards the ﬂat side generalizes better than the exact empirical minimizer. Then, we show that performing weight averaging along the SGD trajectory implicitly induces such biased solutions. This provides theoretical explanations for a series of intriguing phenomena observed in recent work [25, 5, 51]. Finally, extensive empirical experiments on both modern deep networks and simple 2 layer networks are conducted to validate our assumptions and analyze the intriguing properties of asymmetric valleys.
Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images

Currently generative adversarial networks (GANs) are rarely applied to medical images of large sizes, especially 3D volumes, due to their large computational demand. We propose a novel multi-scale patch-based GAN approach to generate large high resolution 2D and 3D images. Our key idea is to first learn a low-resolution version of the image and then generate patches of successively growing resolutions conditioned on previous scales. In a domain translation use-case scenario, 3D thorax CTs of size $$512^3$$ and thorax X-rays of size $$2048^2$$ are generated and we show that, due to the constant GPU memory demand of our method, arbitrarily large images of high resolution can be generated. Moreover, compared to common patch-based approaches, our multi-resolution scheme enables better image quality and prevents patch artifacts.
Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments

Multiview video summarization (MVS) has not received much attention from the research community due to inter-view correlations and views' overlapping, etc. The majority of previous MVS works are offline, relying on only summary, and require additional communication bandwidth and transmission time, with no focus on foggy environments. We propose an edge intelligence-based MVS and activity recognition framework that combines artificial intelligence with Internet of Things (IoT) devices. In our framework, resource-constrained devices with cameras use a lightweight CNN-based object detection model to segment multiview videos into shots, followed by mutual information computation that helps in a summary generation. Our system does not rely solely on a summary, but encodes and transmits it to a master device using a neural computing stick for inter-view correlations computation and efficient activity recognition, an approach which saves computation resources, communication bandwidth, and transmission time. Experiments show an increase of 0.4 unit in F-measure on an MVS Office dateset and 0.2% and 2% improved accuracy for UCF-50 and YouTube 11 datesets, respectively, with lower storage and transmission times. The processing time is reduced from 1.23 to 0.45 s for a single frame and optimally 0.75 seconds faster MVS. A new dateset is constructed by synthetically adding fog to an MVS dateset to show the adaptability of our system for both certain and uncertain IoT surveillance environments.
Significantly improving lossy compression quality based on an optimized hybrid prediction model

With the ever-increasing volumes of data produced by today's large-scale scientific simulations, error-bounded lossy compression techniques have become critical: not only can they significantly reduce the data size but they also can retain high data fidelity for postanalysis. In this paper, we design a strategy to improve the compression quality significantly based on an optimized, hybrid prediction model. Our contribution is fourfold. (1) We propose a novel, transform-based predictor and optimize its compression quality. (2) We significantly improve the coefficient-encoding efficiency for the data-fitting predictor. (3) We propose an adaptive framework that can select the best-fit predictor accurately for different datasets. (4) We evaluate our solution and several existing state-of-the-art lossy compressors by running real-world applications on a supercomputer with 8,192 cores. Experiments show that our adaptive compressor can improve the compression ratio by 112~165% compared with the second-best compressor. The parallel I/O performance is improved by about 100% because of the significantly reduced data size. The total I/O time is reduced by up to 60X with our compressor compared with the original I/O time.