Reviewerly/exHarmony huggingface dataset:
INTEGRATION OF PARTICIPATORY MAPPING, CROWDSOURCING AND GEOGRAPHIC INFORMATION SYSTEM IN FLOOD DISASTER MANAGEMENT (CASE STUDY CILEDUG LOR, CIREBON)

Ciledug Lor is a flood-prone area in Cirebon Regency. Flood disaster management can empower the community through participatory mapping and crowdsourcing activities. This study aims to analyze the level of floods, threats, vulnerabilities, capacities, risks and refuge locations in Ciledug Lor Village based on participatory mapping, crowdsourcing, and GIS. Various indicators of threat, vulnerability, and flood capacity are obtained from field survey, open data and official data that have been given a value and weight which are then processed using overlay analysis to obtain flood risk parameters. Determination of refuge locations used network analysis to find out the route, distance, and effective time. The results analysis and modeling showed the average flood level in Ciledug Lor reached 2.27 meters. The refugee location for Dusun Pamosongan and Dusun Kampung Baru are to the north close to the railway tracks. Meanwhile, Dusun Karanganyar and Dusun Genggong are in the Ciledug Bus Terminal. In the future, participatory mapping, crowdsourcing, and GIS are expected to build awareness and resilience of disaster.
Adversarial Lipschitz Regularization

Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality. However, Wasserstein GANs require the critic to be 1-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques. Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations. With a novel generalization of Virtual Adversarial Training, called Adversarial Lipschitz Regularization, we show that using an explicit Lipschitz penalty is indeed viable and leads to competitive performance when applied to Wasserstein GANs, highlighting an important connection between Lipschitz regularization and adversarial training.
RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.
Social Networks Fake Profiles Detection Using Machine Learning Algorithms

Fake profiles play an important role in advanced persisted threats and are also involved in other malicious activities. The present paper focuses on identifying fake profiles in social media. The approaches to identifying fake profiles in social media can be classified into the approaches aimed on analysing profiles data and individual accounts. Social networks fake profile creation is considered to cause more harm than any other form of cyber crime. This crime has to be detected even before the user is notified about the fake profile creation. Many algorithms and methods have been proposed for the detection of fake profiles in the literature. This paper sheds light on the role of fake identities in advanced persistent threats and covers the mentioned approaches of detecting fake social media profiles. In order to make a relevant prediction of fake or genuine profiles, we will assess the impact of three supervised machine learning algorithms: Random Forest (RF), Decision Tree (DT-J48), and Naïve Bayes (NB).
Transductive Few-shot Learning with Meta-Learned Confidence.

Transductive inference is an effective means of tackling the data deficiency problem in few-shot learning settings. A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples, or confidence-weighted average of all the query samples. However, a caveat here is that the model confidence may be unreliable, which may lead to incorrect predictions. To tackle this issue, we propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries such that they improve the model's transductive inference performance on unseen tasks. We achieve this by meta-learning an input-adaptive distance metric over a task distribution under various model and data perturbations, which will enforce consistency on the model predictions under diverse uncertainties for unseen tasks. Moreover, we additionally suggest a regularization which explicitly enforces the consistency on the predictions across the different dimensions of a high-dimensional embedding vector. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets, on which it largely outperforms strong recent baselines and obtains new state-of-the-art results. Further application on semi-supervised few-shot learning tasks also yields significant performance improvements over the baselines. The source code of our algorithm is available at this https URL.
Multi-Modal Memory Enhancement Attention Network for Image-Text Matching

Image-text matching is an attractive research topic in the community of vision and language.The key element to narrow the ''heterogeneity gap'' between visual and textual data lies in how to learn powerful and robust representations for both modalities.This paper proposes to alleviate this issue to achieve the fine-grained visual-textual alignment from two aspects: exploiting attention mechanism to locate the semantically meaningful portion and leveraging the memory network to capture the long-term contextual knowledge.Unlike most existing studies sorely focus on exploring the cross-modal associations at the fragment level, our designed Collaborative Dual Attention (CDA) module is able to model the semantic interdependencies from both perspectives of fragment and channel.Furthermore, considering the usage of long-term contextual knowledge contributes to compensate for detailed semantics concealed in the rarely appeared image-text pairs, we present to learn the joint representations by constructing a Multi-Modal Memory Enhancement (M3E) module.Specifically, it sequentially restores the intra-modal and multi-modal information into the memory items, and they conversely persistently memorize cross-modal shared semantics to improve the latent embeddings.By incorporating both CDA and M3E modules into a deep architecture, our approach generates more semantically consistent embeddings for representing images and texts.Extensive experiments demonstrate our model can achieve the state-of-the-art results on two public benchmark datasets.
Improved multi-view GEPSVM via Inter-View Difference Maximization and Intra-view Agreement Minimization

Multiview Generalized Eigenvalue Proximal Support Vector Machine (MvGEPSVM) is an effective method for multiview data classification proposed recently. However, it ignores discriminations between different views and the agreement of the same view. Moreover, there is no robustness guarantee. In this paper, we propose an improved multiview GEPSVM (IMvGEPSVM) method, which adds a multi-view regularization that can connect different views of the same class and simultaneously considers the maximization of the samples from different classes in heterogeneous views for promoting discriminations. This makes the classification more effective. In addition, L1-norm rather than squared L2-norm is employed to calculate the distances from each of the sample points to the hyperplane so as to reduce the effect of outliers in the proposed model. To solve the resulting objective, an efficient iterative algorithm is presented. Theoretically, we conduct the proof of the algorithm’s convergence. Experimental results show the effectiveness of the proposed method.
An Integrated TOE-DoI Framework for Cloud Computing Adoption in Higher Education: The Case of Sub-Saharan Africa, Ethiopia

This paper aims to propose an integrated framework based on the technology-organization-environment (TOE) framework and diffusion of innovation (DoI) theory and to show the significant factors that are pertinent to adopt cloud computing in the Ethiopian higher education (EHE) sector. Emerging technology literature based on adoption theories and the framework studied to identify a set of factors and sub-factors relevant to cloud computing adoption. It resulted in conceptualizing an integrated TOE-DoI framework for cloud computing adoption in higher education at the university in Ethiopia and in developing its reliable measures. A qualitative study is done with a questionnaire survey comprising 35 staff respondents in connection with four items (the technological factor, the organizational factor, the environmental factor, and the sociocultural factor), and cloud computing adoption in Ethiopia was established using factors and concepts adopted from studies. The study confirmed that the TOE-DoI approach education in Ethiopia is authenticated. Thus, the four-factor reliability statistics is validated with Cronbach’s alpha a = 0.918 and Cronbach’s alpha ‘a’ based on standard items: a = 0.990. This indicates that scaling the four items there subsists strong reasons to determine a cloud computing adoption in EHE with TOE-DoI integration. This research paper has a novel contribution in integrating TOE-DoI framework.
Speedup in classical simulation of Gaussian boson sampling

Gaussian boson sampling is an alternative model for demonstrating quantum computational supremacy, where squeezed states are injected into every input mode, instead of applying single photons as in the case of standard boson sampling. Here by analyzing numerically the computational costs, we establish a lower bound for achieving quantum computational supremacy for a class of Gaussian boson-sampling problems. Specifically, we propose a more efficient method for calculating the transition probabilities, leading to a significant reduction of the simulation costs. Particularly, our numerical results indicate that one can simulate up to 18 photons for Gaussian boson sampling at the output subspace on a normal laptop, 20 photons on a commercial workstation with 256 cores, and about 30 photons for supercomputers. These numbers are significantly smaller than those in standard boson sampling, suggesting that Gaussian boson sampling could be experimentally-friendly for demonstrating quantum computational supremacy.
Adsorption difference of Cu2+ on the compositely modified amphipathic maifanites

To determine the effect of compositely modified amphipathic clay on Cu 2+ adsorption, 50% and 100% cation exchange capacity (CEC) of anionic polyacrylamide (PA in the modified amphipathic maifanite (M 50x )), citric acid (CA), ethylenediaminetetraacetic acid (ED in M 50x ), and sodium dodecyl sulfate (SD in M 50x ) were used to remodify the 50% CEC of dodecyl dimethyl betaine (BS-12 or BS)-modified maifanite (M 50BS ) and form four types of compositely modified amphipathic maifanite (M 50x ), such as M 50BS/PA (including M 50BS/50PA and M 50BS/100PA ; the same goes for the other samples), M 50BS/CA , M 50BS/ED , and M 50BS/SD .The adsorption and thermodynamic characteristics of Cu 2+ on the various compositely modified M 50BS were investigated by using the batch method, and the adsorption difference in the pH value, temperature, and ion strength were discussed.The results showed that (1) the adsorption isotherms of Cu 2+ on the test samples were all "L" type and combined with the Langmuir model.The maximum adsorption amount of Cu 2+ on the different samples changed between 109.09 and 200.87 mmol/kg and presented the trend M 50BS/PA > M 50BS/CA > M 50BS/SD > M 50BS/ED > M 50BS > M. M 50BS remodified by 100% CEC of the composite modifier obtained superior Cu 2+ adsorption, and the adsorption amount of Cu 2+ was 1.10-1.20 times higher than that of M BS remodified by 50% CEC of the composite modifier.(2) In the range of 20°C to 40°C, the adsorption amount of Cu 2+ in the tested samples increased with the increase in temperature, except for M 50BS/PA and M 50BS/SD , and the increasing range was 7.56%-16.79%.(3) Within the pH range of 3 to 5, the Cu 2+ adsorbed by each test sample increased with increasing pH value.The adsorption amount of Cu 2+ on the tested maifanites (except M 50BS ) decreased with increasing ionic strength, wherein the best amount was at 0.01 mol/L.(4) The adsorption of Cu 2+ on the tested maifanites was a spontaneous, endothermic (except M BS/PA and M 50BS/SD ), and entropic-adding process.
Teaching virtual apparel technology through industry collaboration: an assessment of pedagogical process and outcomes

Graduates of apparel programmes are entering a workforce that requires the use of emerging technologies that are relevant to performing job requirements. Among apparel companies, virtual technologies are increasingly being used in the product development process. Virtual technologies are also becoming important in higher education, as programmes seek to incorporate them into the teaching and learning environment. The two-fold purpose of this study was to develop an approach to teaching virtual technology that is apparel industry-specific, and to evaluate outcomes using a framework of learner-centered curriculum design combined with Kirkpatrick's [(1994). Evaluating training programs. San Francisco, CA: Berrett-Koehler] training evaluation model. A mixed-methods research design was employed, beginning with a presentation to students followed by two weeks of in-class training. Assessment of outcomes was conducted via pre- and post-test comparisons and in-depth interviews. Results indicated improvement in students' attitudes toward the technology and skills important to using it. Findings contribute to the growing literature on teaching virtual technology.
Bosonic Quantum Communication Across Arbitrarily High Loss Channels

A general attenuator $\Phi_{\lambda, \sigma}$ is a bosonic quantum channel that acts by combining the input with a fixed environment state $\sigma$ in a beam splitter of transmissivity $\lambda$. If $\sigma$ is a thermal state the resulting channel is a thermal attenuator, whose quantum capacity vanishes for $\lambda\leq 1/2$. We study the quantum capacity of these objects for generic $\sigma$, proving a number of unexpected results. Most notably, we show that for any arbitrary value of $\lambda>0$ there exists a suitable single-mode state $\sigma(\lambda)$ such that the quantum capacity of $\Phi_{\lambda,\sigma(\lambda)}$ is larger than a universal constant $c>0$. Our result holds even when we fix an energy constraint at the input of the channel, and implies that quantum communication at a constant rate is possible even in the limit of arbitrarily low transmissivity, provided that the environment state is appropriately controlled. We also find examples of states $\sigma$ such that the quantum capacity of $\Phi_{\lambda,\sigma}$ is not monotonic in $\lambda$. These findings may have implications for the study of communication lines running across integrated optical circuits, of which general attenuators provide natural models.
Proposing a new feature descriptor for moving object detection

Segmenting moving objects from various video streams especially in the presence of complex dynamic backgrounds is one of the most pivotal and fundamental tasks in computer vision. Although there are many high quality proposals for moving object detection and segmentation, still some issues such as sudden changes in illumination, camera jitter, swaying vegetation, rippling water, etc need to be resolved. This paper proposes a new method for moving object detection using background subtraction technique. To this end, a new color feature descriptor and a new model updating strategy are proposed for ameliorating the effects of these natural challenges. The proposed descriptor is a local fuzzy descriptor that tries to extract the color feature of a pixel by looking at its neighbors. Because of its local nature, this descriptor is able to accurately detect and extract moving objects. The fuzzy nature of the descriptor helps it to be robust against diverse challenging situations. In order to validate the functionality and show the supremacy of the proposed method, comprehensive experiments were conducted on various challenging datasets. From the experimental results, it is observed that the proposed method achieves acceptable results for moving object detection and segmentation.
Output Feedback and Stability Analysis of Positive Polynomial Fuzzy Systems

This article investigates the polynomial fuzzy output feedback (PFOF) control synthesis problem for positive polynomial fuzzy systems. When the states of a positive system cannot be fully obtained, the output feedback control strategy is a good method to stabilize the control system. From fuzzy control point of view, we employ the polynomial fuzzy model rather than the Takagi–Sugeno fuzzy model, and this kind of models can express a wider range of nonlinear positive systems. However, the polynomials in the system matrices and the feedback control gain matrices will make the nonconvex stability conditions more difficult to deal with. Thereby, in order to crack the hard nut, a nonzero transformation vector is introduced in this article to deal with the nonconvex problem skillfully. Furthermore, the imperfect premise matching technique is taken into account so that the implement of the controller is more simple and money-saving. In addition, the augmented dynamic of the positive polynomial fuzzy-model-based (PPFMB) control system is investigated to facilitate the stability and positivity analysis, and the basic conditions in the light of sum of squares (SOSs) are derived. Besides, the advanced membership function dependent (MFD) technique is used so that a great of useful information of MFs is extracted to improve the relaxation of the results. Finally, the effectiveness of the theoretical findings is illustrated by a simulation example.
Tracking your browser with high-performance browser fingerprint recognition model

As the cyber security has attracted great attention in recent years, and with all kinds of tools' (such as Network Agent, VPN and so on) help, traditional methods of tracking users like log analysis and cookie have been not that effective. Especially for some privacy sensitive users who changed their browser configuration frequently to hide themselves. The Browser Fingerprinting technology proposed by Electronic Frontier Foundation (EFF) gives a new approach of tracking users, and then our team designed an enhanced fingerprint dealing solution based on browser fingerprinting technology. Our enhanced solution plays well in recognizing the similar fingerprints, but it is not that efficient. Nowadays we improve the algorithm and propose a high-performance, efficient Browser Fingerprint Recognition Model. Our new model reforms the fingerprint items set by EFF and propose a Fingerprint Tracking Algorithm (FTA) to deal with collected data. It can associate users with some browser configuration changes in different periods of time quickly and precisely. Through testing with the experimental website built on the public network, we prove the high-performance and efficiency of our algorithm with a 20% time-consuming decrease than ever.
Motion Primitives Classification Using Deep Learning Models for Serious Game Platforms

Serious games are receiving increasing attention in the field of cultural heritage (CH) applications. A special field of CH and education is intangible cultural heritage and particularly dance. Machine learning (ML) tools are necessary elements for the success of a serious game platform since they introduce intelligence in processing and analysis of users’ interactivity. ML provides intelligent scoring and monitoring capabilities of the user's progress in a serious game platform. In this article, we introduce a deep learning model for motion primitive classification. The model combines a convolutional processing layer with a bidirectional analysis module. This way, RGB information is efficiently handled by the hierarchies of convolutions, while the bidirectional properties of a long short term memory (LSTM) model are retained. The resulting convolutionally enhanced bidirectional LSTM (CEBi-LSTM) architecture is less sensitive to skeleton errors, occurring using low-cost sensors, such as Kinect, while simultaneously handling the high amount of detail when using RGB visual information.
SRVAE: super resolution using variational autoencoders

The emergence of Generative Adversarial Network (GAN)-based single-image super-resolution (SISR) has allowed for finer textures in the super-resolved images, thus making them seem realistic to humans. However, GANbased models may depend on extensive high-quality data and are known to be very costly and unstable to train. On the other hand, Variational Autoencoders (VAEs) have inherent mathematical properties, and they are relatively cheap and stable to train; but VAEs produce blurry images that prevent them from being used for super-resolution. In this paper, we propose a first of its kind SISR method that takes advantage of a selfevaluating Variational Autoencoder (IntroVAE). Our network, called SRVAE, judges the quality of generated high-resolution (HR) images with the target images in an adversarial manner, which allows for high perceptual image generation. First, the encoder and the decoder of our introVAE-based method learn the manifold of HR images. In parallel, another encoder and decoder are simultaneously learning the reconstruction of the lowresolution (LR) images. Next, reconstructed LR images are fed to the encoder of the HR network to learn a mapping from LR images to corresponding HR versions. Using the encoder as a discriminator allows SRVAE to be a fast single-stream framework that performs super-resolution through generating photo-realistic images. Moreover, SRVAE has the same training stability and "nice" latent manifold structure as of VAEs, while playing a max-min adversarial game between the generator and the encoder like GANs. Our experiments show that our super-resolved images are comparable to the state-of-the-art GAN-based super-resolution.