Reviewerly/exHarmony huggingface dataset:
Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously

We propose a novel framework for simultaneously generating and manipulating the face images with desired attributes. While the state-of-the-art attribute editing technique has achieved the impressive performance for creating realistic attribute effects, they only address the image editing problem, using the input image as the condition of model. Recently, several studies attempt to tackle both novel face generation and attribute editing problem using a single solution. However, their image quality is still unsatisfactory. Our goal is to develop a single unified model that can simultaneously create and edit high quality face images with desired attributes. A key idea of our work is that we decompose the image into the latent and attribute vector in low dimensional representation, and then utilize the GAN framework for mapping the low dimensional representation to the image. In this way, we can address both the generation and editing problem by learning the generator. For qualitative and quantitative evaluations, the proposed algorithm outperforms recent algorithms addressing the same problem. Also, we show that our model can achieve the competitive performance with the state-of-the-art attribute editing technique in terms of attribute editing quality.
Fast Modular Subset Sum using Linear Sketching

Given n positive integers, the Modular Subset Sum problem asks if a subset adds up to a given target t modulo a given integer m. This is a natural generalization of the Subset Sum problem (where m = +∞) with ties to additive combinatorics and cryptography.Recently, in [Bri17, KX17], efficient algorithms have been developed for the non-modular case, running in near-linear pseudo-polynomial time. For the modular case, however, the best known algorithm by Koiliaris and Xu [KX17] runs in time Õ(m5/4).In this paper, we present an algorithm running in Õ(m) randomized time, which matches a recent conditional lower bound of [ABHS17] based on the Strong Exponential Time Hypothesis. Interestingly, in contrast to most previous results on Subset Sum, our algorithm does not use the Fast Fourier Transform. Instead, it is able to simulate the “textbook” Dynamic Programming algorithm much faster, using ideas from linear sketching. This is one of the first applications of sketching-based techniques to obtain fast algorithms for exact combinatorial problems in an offline setting.
Applying PLEASE Strategy to Teach Writing Skill on Students with Different Linguistic Intelligence

PLEASE Strategy is a mnemonic that provides learners with a road map for writing a text. It is a strategy when a teacher teaches the students an acronym to remember each step in writing. PLEASE strategy will help students to write, to start the first sentence, and to put the data information in their writing. This article refers to an experimental study on the effectiveness of PLEASE Strategy to teach writing at the eleventh grade of SMA Negeri 1 Ngemplak. The samples were two classes, experimental class which was taught using PLEASE Strategy and control class which was taught using Guided Writing Strategy. To gain the data, two instruments were use, writing test and linguistic intelligence test. The data were, then, analyzed by using Multifactor Analysis of Variance ANOVA 2x2 and Tukey test. Before conducting the ANOVA test, pre-requisite test namely normality and homogeneity test were conducted. The findings of this research are: (1) PLEASE strategy is more effective than Guided Writing Strategy to teach writing; (2) The students having high linguistic intelligence have better writing skill than those having low linguistic intelligence; and (3) There is an interaction between teaching strategies and students’ linguistic intelligence in teaching writing.PLEASE Strategy is a mnemonic that provides learners with a road map for writing a text. It is a strategy when a teacher teaches the students an acronym to remember each step in writing. PLEASE strategy will help students to write, to start the first sentence, and to put the data information in their writing. This article refers to an experimental study on the effectiveness of PLEASE Strategy to teach writing at the eleventh grade of SMA Negeri 1 Ngemplak. The samples were two classes, experimental class which was taught using PLEASE Strategy and control class which was taught using Guided Writing Strategy. To gain the data, two instruments were use, writing test and linguistic intelligence test. The data were, then, analyzed by using Multifactor Analysis of Variance ANOVA 2x2 and Tukey test. Before conducting the ANOVA test, pre-requisite test namely normality and homogeneity test were conducted. The findings of this research are: (1) PLEASE strategy is more effective than Guided Writing Strategy to teach writing; (2) The students having high linguistic intelligence have better writing skill than those having low linguistic intelligence; and (3) There is an interaction between teaching strategies and students’ linguistic intelligence in teaching writing.
Unmixing for Causal Inference: Thoughts on McCaffrey and Danks

McCaffrey and Danks have posed the challenge of discovering causal relations in data drawn from a mixture of distributions as an impossibility result in functional magnetic resonance (fMRI). We give an algorithm that addresses this problem for the distributions commonly assumed in fMRI studies and find that in testing, it can accurately separate data from mixed distributions. As with other obstacles to automated search, the problem of mixed distributions is not an impossible one, but rather a challenge. 1Introduction2Background3Addressing the Problem4Discussion.
GBFHS: A Generalized Breadth-First Heuristic Search Algorithm

Recently there has been renewed interest in bidirectional heuristic search. New algorithms, e.g., MM, MMe, and NBS, have been introduced which seem much closer to refuting the accepted wisdom that "any front-to-end bidirectional heuristic search algorithm will likely be dominated by unidirectional heuristic search or bidirectional brute-force search". However, MM and MMe can still be dominated by their bidirectional brute-force versions, i.e., they can show a "hump-in-the-middle". We introduce a novel general breadth-first heuristic search algorithm, GBFHS, that unifies both unidirectional and bidirectional search into a single algorithm. It uses knowledge of the edge cost in unit cost domains to stop on first-collision in unidirectional search and in bidirectional search, unlike MM, MMe, and NBS. With no heuristic it expands fewer nodes bidirectionally than Nicholson's blind bidirectional search algorithm. GBFHS expands substantially fewer nodes than MM0, MM, MMe, and NBS. Additionally, GBFHS does not show a "hump-in-the-middle". GBFHS run bidirectionally is not dominated by bidirectional brute-force search, likewise, GBFHS run unidirectionally is not dominated by A*.
A Neural Network for Decision Making in Real-Time Heuristic Search

Most real-time heuristic search algorithms solve search problems by executing a series of episodes. During each episode the algorithm decides an action for execution. Such a decision is usually made using information gathered by running a bounded, heuristic-search algorithm. In this paper we report on a real-time search algorithm that does not use a search algorithm to choose the next action to be applied. Rather, it uses a neural network whose input is local information about the search graph, comparable to the information that would be used by a bounded search algorithm. We describe a supervised learning approach to training such a network. Our three types of maps from the Moving AI benchmarks, shows that our algorithm is, in some cases, substantially superior to algorithms that have access to the same information about the graph. One of our most important conclusions is that our extended set of features important: indeed, using features beyond the heuristic seems key to achieving good performance.
Quality control of solar radiation data within the South African Weather Service solar radiometric network

This study reports on the performance results of the Baseline Surface Radiation Network (BSRN) quality control procedures applied to the solar radiation data, from September 2013 to December 2017, within the South African Weather Service radiometric network. The overall percentage performance of the SAWS solar radiation network based on BSRN quality control methodology was 97.79%, 93.64%, 91.60% and 92.23% for long wave downward irradiance (LWD), global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI) and direct normal irradiance (DNI), respectively, with operational problems largely dominating the percentage of bad data. The overall average performance of the surface solar radiation dataset – Heliosat data records for the GHI estimation for all stations showed a mean bias deviation of 8.28 Wm-2, a mean absolute deviation of 9.06 Wm-2 and the root mean square deviation of 11.02 Wm-2. The correlation, quantified by the square of correlation coefficient (R2), between ground-based and Heliosat-derived GHI time series was ~0.98. The established network has the potential to provide high quality minute solar radiation data sets (GHI, DHI, DNI and LWD) and auxiliary hourly meteorological parameters vital for scientific and practical applications in renewable energy technologies.
Irony, deception and theory of mind in people with intellectual disabilities and dual diagnoses

Background/Aim. The ability to comprehend and produce irony and deception is rarely explored in people with intellectual disability (ID) or dual diagnoses (DD). The ability to understand irony and deception appears to be related to many cognitive skills, but some authors point out that the theory of mind is one of the most important factors for this ability. This research was conducted to determine the linguistic aspects of production and comprehension of irony and deception in adults with ID and DD, as well as the relationship of these abilities with theory of mind. Methods. The sample consisted of 120 people with ID aged between 20 and 56. Half of the sample comprised people with DD. Four subscales from the Assessment Battery for Communication were used to assess the participants? abilities to produce and comprehend irony and deception. False-belief tasks from ?appearance-reality? category were used in theory of mind assessment. The level of intellectual functioning was measured by the Raven?s progressive matrices, while the Peabody Picture Vocabulary Test was used to assess speech comprehension ability. Results. The results show that participants with DD and ID comprehend and produce false statements better than ironic ones. Participants with ID were more successful in production than in comprehension tasks of both false and ironic statements, while the same was true for participants with DD only for ironic statements. Participants with ID were significantly more successful than participants with DD in irony comprehension tasks. In participants with ID, first-order theory of mind significantly correlated only with the ability to produce irony, and second-order theory of mind significantly correlated with producing irony and deception. There were no significant correlations between theory of mind and producing and comprehending irony and deception in participants with DD. Conclusion. Although differences in some aspects of assessed abilities were found between the two groups of participants, the similarities in the profile of these abilities were dominant. Results of variability can be explained by differences in speech comprehension ability more than by differences in nonverbal intellectual functioning or theory of mind acquisition. Future studies should assess the influence of other cognitive factors.
Hot-N-Cold model for energy aware cloud databases

A lot of cloud computing and cloud database techniques are adopted in industry and academia to face the explosion of the arrival of the big data era. Meanwhile, energy efficiency and energy saving become a major concern in data centers, which are in charge of large distributed systems and cloud databases. However, the phenomenon of energy wasting is related to resource provisioning. Hot-N-Cold model is introduced in this paper, which uses workload predictions and DVFS(Dynamic Voltage and Frequency Scaling) to cope with the resource provisioning problem within energy aware cloud database systems. In this model, the resource provisioning problem is considered as two bounded problems. A nonlinear programming algorithm and a multi-phase algorithm are proposed to solve them. The experimental results show that one of the proposed algorithms has great scalability which can be applied to a cloud database system deployed on 70 nodes. Using Hot-N-Cold model can save up to 21.5% of the energy of the running time.
MADP: An Open and Scalable Medical Auxiliary Diagnosis Platform

In some developing countries, the accessibility of excellent therapeutic resources is inadequate in rural areas. We propose an open and scalable medical auxiliary diagnosis platform, assigning training tasks and storage to large hospitals and conducting the trained disease discrimination models on rural clients. We test the performance of the platform with some common diseases.
Automatic Construction of OWL Ontologies From Petri Nets

Ontology, as a formal representation method of domain knowledge, plays a particular important key role in semantic web. How to construct ontologies has become a key technology in the semantic web, especially constructing ontologies from existing domain knowledge. Currently, Petri nets have been a mathematical modeling tool, and have been widely studied and successfully applied in modeling of software engineering, database and artificial intelligence. In particular, PNML (Petri Net Markup Language) language has been a part of ISO/IEC Petri nets standard for representing and exchanging data on Petri nets. Therefore, how to construct ontologies from PNML model of Petri nets needs to be investigated. In this article, the authors investigate a method for automatic construction of web ontology language (OWL) ontologies from PNML of Petri nets. Firstly, this paper gives a formal definition and the semantics of PNML models of Petri nets. On this basis, a formal approach for constructing OWL ontologies from PNML model of Petri nets is proposed, i.e., this paper transforms Petri nets (including PNML model and PNML document of the Petri nets) into OWL ontologies at both structure and instance levels. Furthermore, the correctness of the transformation is proved. Finally, a prototype construction tool called PN2OWL is developed to transform Petri nets models into OWL ontologies automatically.
Removal-based multi-view stereo using a window-based matching method

In this paper, we present a very simple and effective removal-based multi-view stereo (MVS) algorithm for application to the images of large outdoor objects. Owing to the complexity of outdoor illumination and occlusion, it is challenging to guarantee the accuracy of the estimated depth of every pixel in a stereo image. The key idea of the proposed algorithm for addressing this issue is to calculate the 3D coordinates of every pixel by attempting to reconstruct highly accurate point-cloud data (PCD) using only the appropriate corresponding points from a stereo pair of two views. All erroneous corresponding points in the pair are removed. The proposed algorithm was validated by conducting a set of experiments using several datasets for different environments. The algorithm for removing incorrectly matched points, which is referred to as R-NCC (removal normalized cross-correlation), was found to have a high reconstruction accuracy comparable to those of current state-of-the-art sparse algorithms.
Dynamic Migration Algorithm of Marine Big Data in Cloud Computing Environment

Cui, J., 2018. Dynamic migration algorithm of marine big data in cloud computing environment. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 706–712. Coconut Creek (Florida), ISSN 0749-0208.Aiming at the shortcomings of the current migration algorithm based on the importance of data, like poor load balancing and large consumption of resources, the paper proposes a dynamic migration algorithm for marine big data in cloud computing environment. Firstly, the characteristics of marine big data are analyzed. And then the Moran's I index is used to analyze the correlation index, and the storage of marine big data in cloud computing environment is positioned. Finally, adaptive inertia weight is introduced based on the firefly algorithm, through the objective function of the bandwidth to calculate the minimum value of the bandwidth and use as the optimal solution, and the dynamic migration of marine big data is realized. Experiments show that the proposed algorithm not only has better migration performance, but also takes into account migration costs and load balancing, resource utilization and bandwidth utilization.
Design of Simulation Training System for Remote Sensing Large Data Processing of Natural Disasters

Wang, Y.; Yang, L.; Zhao, L., and Deng, Y., 2018. Design of simulation training system for remote sensing large data processing of natural disasters. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 328–334. Coconut Creek (Florida), ISSN 0749-0208.Satellite monitoring is an important means of monitoring forest fire, it has the characteristics of aging fast, wide monitoring range. However, on the one hand,the operation of ground receiving equipment is so complex that can be only mastered by powerful training. On the other hand, the equipment is too expensive to meet so many trainers' requirement of actual operation. The simulation training system can solve this problem preferable. The system utilizes the virtual simulation technology to simulate the satellite monitoring in forest fire and complies with satellite orbiting around the earth, HD video capturing,image receiving, image storing,image decoding, fire identifing,fire alarm and so on.Combined with particle animation, skeletal animation and other technologies,it can achieve both static and dynamic, immersive effect.
BUM: Bayesian User Model for Distributed Learning of User Characteristics From Heterogeneous Information

This paper presents a Bayesian user model able to learn and estimate user characteristics in a distributed manner using heterogeneous information. A unified user representation is obtained from an inference process, receiving a set of independently estimated user characteristics from different sources. The independence of characteristic models enables the system to be modular, with each module estimating one characteristic. The proposed model is iterative, fusing new observations, and measurements with previous information in a process regulated entropy. The system allows diverse implementations, such as the combination of multiple robots with a cloud infrastructure or distributed ambient sensors. This paper aims to enable the system to perform online learning while interacting with users. The system is also able to obtain a correct user representation from heterogeneous information, even when some user characteristics cannot be computed. To demonstrate its functionality, the system is tested on two experimental datasets, obtained from simulated experiments and with real users. This technique advances the state of the art in the areas of AAL and user-adaptive systems, and in cloud-connected robots and Internet of Things, allowing for these heterogeneous and naturally distributed teams of devices to better model their users, potentially achieving higher interaction autonomy.
Robust geometric model fitting based on iterative Hypergraph Construction and Partition

In this paper, we propose a novel Iterative Hypergraph Construction and Partition based model fitting method (termed IHCP), for handling multiple-structure data. Specifically, IHCP initially constructs a small-sized hypergraph, and then it performs hypergraph partition. Based on the partitioning results, IHCP iteratively updates the hypergraph by a novel guided sampling algorithm, and performs hypergraph partition. After a few iterations, IHCP is able to construct an effective hypergraph to represent the complex relationship between data points and model hypotheses, and obtain good partitioning results for model fitting as well. IHCP is very efficient since it avoids generating a large number of model hypotheses, and it is also very effective due to the excellent ability of the novel iterative strategy. Experimental results on real images show the superiority of the proposed IHCP method over several state-of-the-art model fitting methods.
Factor-based structural equation modelling: going beyond PLS and composites

Partial least squares (PLS) methods offer many advantages for path modelling, such as fast convergence to solutions and relaxed requirements in terms of sample size and multivariate normality. However, they do not deal with factors, but with composites. As a result, they typically underestimate path coefficients and overestimate loadings. Given these, it is difficult to fully justify their use for confirmatory factor analyses or factor-based structural equation modelling (SEM). We addressed this problem through the development of a new method that generates estimates of the true composites and factors, potentially placing researchers in a position where they can obtain consistent estimates of a wide range of model parameters in SEM analyses. A Monte Carlo experiment suggests that this new method represents a solid step in the direction of achieving this ambitious goal.
Item life cycle based collaborative filtering

To address the problem that the existing time-aware methods always recommend out-of-fashion items to users, this paper proposes a novel method named Item Life Cycle based Collaborative Filtering (ItemLC-CF), taking both user’s preference and item’s popularity into consideration. This method present s a life cycle function to simulate the variation of item’s popularity and integrates such variation with CF to learn the relative preference for items by reranking the candidate set of CF according to item’s vitality. Given that it is extremely complicated to construct a life cycle function for each item, items are clustered into different categories via their popularities instead of original product categories. Meanwhile, a SVM ensemble classifier based on AdaBoost is employed to find an appropriate category for new arrival items. Evaluation on MovieLens-1M dataset demonstrates that the proposed method can improve the performance of recommendation list, in turn providing more reasonable and popular recommendation lists for users.
3D reconstruction under light ray distortion from parametric focal cameras

In this paper, we propose a new method for reconstructing 3D objects under light ray distortion caused by unknown refractive media. The proposed method can reconstruct 3D scene, even if light rays projected into the cameras are refracted by unknown refractive media, such as glasses and raindrops. For this objective, we represent light ray projection of multiple cameras by using a pair of planes shared by the multiple cameras in the scene. By using this model, intrinsic and extrinsic camera parameters as well as the refractive properties of the refractive media can be represented efficiently. By using the newly defined camera model, we propose a method for recovering 3D points and camera parameters with refractive properties simultaneously. The experimental results show the efficiency of the proposed camera model and reconstruction method.