Reviewerly/exHarmony huggingface dataset:
An Improved Style Transfer Algorithm Using Feedforward Neural Network for Real-Time Image Conversion

Creation of art is a complex process for its abstraction and novelty. In order to create those art with less cost, style transfer using advanced machine learning technology becomes a popular method in computer vision field. However, traditional transferred image still troubles with color anamorphosis, content losing, and time-consuming problems. In this paper, we propose an improved style transfer algorithm using the feedforward neural network. The whole network is composed of two parts, a style transfer network and a loss network. The style transfer network owns the ability of directly mapping the content image into the stylized image after training. Content loss, style loss, and Total Variation (TV) loss are calculated by the loss network to update the weight of the style transfer network. Additionally, a cross training strategy is proposed to better preserve the details of the content image. Plenty of experiments are conducted to show the superior performance of our presented algorithm compared to the classic neural style transfer algorithm.
Squeezing the Last MHz for CNN Acceleration on FPGAs

Neural networks especially the convolution neural networks (CNN) have become prevalent and numerous CNN accelerators have been developed to achieve higher performance. While clock frequency determines the operation speed and has direct influence on the performance of the accelerators, we propose to apply overclocking, a circuit optimization approach that enables higher clock frequency, on general CNN accelerators. This technique brings significant performance improvement, but it leads to moderate timing errors, wrong computing results and low prediction accuracy. By taking advantage of the inherent fault tolerance of neural networks, we opt to learn the computing errors together with the application data with additional on-accelerator training. In this case, the resulting models can be resilient to the errors and do not necessarily suffer considerable prediction accuracy loss. In addition, we also take the worst case of overclocking into consideration with a series of approaches ranging from fault detection to fault recovery in case of hardware crash. Finally, we demonstrate the use of overclocking on a CNN accelerator implemented on Xilinx KCU1500 with comprehensive experiments. The experiments show that overclocking in combination with the on-accelerator neural network training improves both the neural network performance and energy efficiency with small prediction accuracy loss.
Dynamic N-Gram System Based on an Online Croatian Spellchecking Service

As an infrastructure able to accelerate the development of natural language processing applications, large-scale lexical n-gram databases are at present important data systems.However, deriving such systems for world minority languages as it was done in the Google n-gram project leads to many obstacles.This paper presents an innovative approach to large-scale n-gram system creation applied to the Croatian language.Instead of using the Web as the world's largest text repository, our process of n-gram collection relies on the Croatian online academic spellchecker Hascheck, a language service publicly available since 1993 and popular worldwide.Our n-gram filtering is based on dictionary criteria, contrary to the publicly available Google n-gram systems in which cutoff criteria were applied.After 12 years of collecting, the size of the Croatian n-gram system reached the size of the largest Google Version 1 n-gram systems.Due to reliance on a service in constant use, the Croatian n-gram system is a dynamic one.System dynamics allowed modeling of n-gram count behavior through Heaps' law, which led to interesting results.Like many minority languages, the Croatian language suffers from a lack of sophisticated language processing systems in many application areas.The importance of a rich lexical n-gram infrastructure for rapid breakthroughs in new application areas is also exemplified in the paper.
CULTURE BASED TEACHING AND LEARNING FOR INDONESIAN AS A FOREIGN LANGUAGE IN YOGYAKARTA

The practice of culture-based teaching and learning Indonesian as a foreign language (T&amp;LIFL) has not been well managed and standardized. Therefore, this study attempted to investigate T&amp;LIFL at six universities in Yogyakarta. The investigation consisted of some cultural aspects in terms of program management, learning material, learning media, teaching approaches, methods, and strategies, learning assessment, and program evaluation. The research design was a mix method design. To collect data, it used questionnaires, interviews, and observations. The data then were analysis quantitatively and qualitatively. The research results were (1) T&amp;LIFL program management had not been culture-based, (2) T&amp;LIFL learning material had not been entirely culture-based, (3) there were not enough learning media reflected on culture, (4) approaches, methods, and strategies had not used learning models including language learning, language awareness, cultural awareness, and cultural experience, (5) the assessment of culture-based learning had not been carried out with process assessment, but some have already packed it in cultural festivals at the end of the program, and (6) there was no evaluation on T&amp;LIFL programs that explicitly evaluated program achievements on aspects of culture awareness, and experience.
The Effects of Globalization and Cyber Security on Smart Cities

By 2050, 70% of all people will be living in towns and cities.In 1900, it was only 13%.This means that every year, the number of people living in cities increases by seven times the population of New York.Water usage is increasing rapidly too; it has sextupled in the past 100 years; this rate of increase is double the rate of population increase.We must all face the challenge of a large number of people living together efficiently, in an organized way, on Earth, and how they can all access the needed services with the required quality.The term smart city primarily emphasizes sustainability, efficiency, and wide participation in decision-making, infocommunication technology solutions and providing services.The term was coined based on the integration of digital technologies, and the important phenomenon of community development and economic innovation, cities.The main players in the economy of the future are cities.States and local governments alone cannot respond to the challenges of global urbanization and environmental issues.In the development of smart cities, players of the economy and the city dwellers themselves play a more and more important role.The European Union has started programs like this and it is of paramount importance that Hungary actively participates in these, both in central coordination and at the level of settlements.At the same time, it is great help for Hungarian enterprises, too because the products they develop in and for Hungarian towns can be competitive on the international market as well.
Sparse Temporal Causal Convolution for Efficient Action Modeling

Recently, spatio-temporal convolutional networks have achieved prominent performance in action classification. However, debates on the importance of temporal information lead to the rethinking of these architectures. In this work, we propose to employ sparse temporal convolutional operations in networks for efficient action modeling. We demonstrate that the explicit temporal feature interactions can be largely reduced without any degradation. And towards better scalability, we use causal convolutions for temporal feature learning. Under causality constraints, we replenish the model with auxiliary self-supervised tasks, namely video prediction and frame order discrimination. Besides, a gradient based multi-task learning algorithm is introduced for guaranteeing the dominance of action recognition task. The proposed model matches or outperforms the state-of-the-art methods on Kinetics, Something-Something V2, UCF101 and HMDB51 datasets.
Coaxial projective imaging system for surgical navigation and telementoring

A coaxial projective imaging (CPI) module acquires surgical scene images from the local site of surgery, transfers them wirelessly to the remote site, and projects instructive annotations to the surgical field. At the remote site, the surgical scene images are displayed, and the instructive annotations from a surgical specialist are wirelessly transferred back to the local site in order to guide the surgical intervention by a less experienced surgeon. The CPI module achieves seamless imaging of the surgical field and accurate projection of the instructive annotations, by a coaxial optical path design that couples the imaging arm with the projection arm and by a color correction algorithm that recovers the true color of the surgical scene. Our benchtop study of tele-guided intervention verifies that the proposed system has a positional accuracy of better than 1 mm at a working distance ranging from 300 to 500 mm. Our in vivo study of cricothyrotomy in a rabbit model proves the concept of tele-mentored surgical navigation. This is the first report of tele-guided surgery based on CPI. The proposed technique can be potentially used for surgical training and for telementored surgery in resource-limited settings.
The Hydrothermal System of Solfatara Crater (Campi Flegrei, Italy) Inferred From Machine Learning Algorithms

Two machine learning algorithms were applied to three multivariate datasets acquired at Solfatara volcano. Our aim was to find an unbiased and coherent synthesis among the large amount of data acquired within the crater and along two orthogonal vertical NNE- and WNW-trending cross-sections. The first algorithm includes a new approach for a soft K-means clustering based on the use of the silhouette index to control the color palette of the clusters. The second algorithm which uses the self-organizing maps incorporates an alternative method for choosing the number of nodes of the neural network which aims to avoid the need for downstream clustering of the results of the classification. Both methods achieved an objective characterization of the shallow hydrothermal system of the volcano, enhancing and highlighting subtle geophysical anomalies likely correlated to structural pathways of deep magmatic degassing. Comparison between the results of K-means and self-organizing maps on the datasets with the largest number of nodes confirms that, with respect to the K-means, self-organizing maps compress the data in a way that better highlights finer details of the original data. However, the choice of the coloring scheme of the neurons is critical for an effective visualization of the results. Unsupervised integration of the three multivariate datasets allowed us to spatially correlate, with a high-degree of confidence, the geophysical anomalies recorded at the surface of the crater with those recorded at the subsurface along the two cross-sections. it also allowed us to associate those anomalies to different hydrothermal features such as shallow gas-saturated and water-saturated zones and their underlying fractures/faults feeding system. Our results suggest that the main shallow structural patterns, which influence the hydrothermal dynamics at Solfatara volcano, remained substantially unchanged in the last fifteen years. Our approach shows that the use of clustering methods to interpret multivariate data reduces interpretation uncertainties and achieves an improved understanding of the complex dynamics occurring in volcanoes.
Behavior Analysis of Indoor Escape Route-Finding Based on Head-Mounted VR and Eye Tracking

Eye tracking technology can help to explore user psychology and is widely used in design evaluation, user gaze and behavior analysis. In this paper, we propose a new method to analyze indoor fire escape behavior using head-mounted virtual environment combined with eye tracking. The eyeball tracking module is installed in HTC Vive VR helmet glasses by building indoor fire scene. We collect the three-dimensional position, movement and gaze information of eyes and head simultaneously in the virtual environment. The spatial cognitive process of participants escaping from fire emergency scene is analyzed by the combination of scene walkthrough location information and eye movement information. In order to verify the applicability of the method, we conducted an experiment to collect the movement information of 20 participants. The results show that our method can create qualitative attention maps and quantitative eye movement information, and provide technical support for the evaluation of indoor escape identification system.
Capsule Network With Identifying Transferable Knowledge for Cross-Domain Sentiment Classification

Domain adaptation tasks have raised much attention in recent years, especially, the task of cross-domain sentiment classification, and remarkable success has been achieved on specific domains with large amounts of labeled data. However, annotating enough data in each domain is still expensive and time-consuming, which will produce difficulty in the application of domain adaptation. In this paper, we proposed a Capsule network method with Identifying Transferable Knowledge (CITK) as common knowledge for cross-domain sentiment classification. CITK model uses capsule network to encode the intrinsic spatial part-whole relationship constituting domain invariant knowledge, which bridges the knowledge gap between the source and target domains. In addition, we use Bidirectional Encoder Representations from Transformers (BERT) to convert sentences to equal length, which is called pre-training, in order to obtain more complete semantic embedded representation, so that Significant Consistent Polarity (SCP) words can be more accurate. Extensive experiments are conducted to evaluate the effectiveness of the proposed CITK model on a real world data set of four domains. Experimental results demonstrate that CITK can significantly outperform the state-of-the-art methods for the cross-domain sentiment classification task.
Nature Inspired Instance Selection Techniques for Support Vector Machine Speed Optimization

Due to the fast-growing rate of information sources, many organizations and individuals are overwhelmed with vast amounts of data. The rate of data growth is very alarming, and it is already going beyond the Exabyte limit. Hence, there is an obvious need for fast and accurate big data classification tools. Machine Learning (ML) based solutions are very useful and reliable data classification tools, however, they cannot effectively handle large-scale datasets. This paper therefore proposes two intelligent instance selection techniques for optimizing the training and classification speed of ML algorithms, with a specific focus on Support Vector Machine (SVM). Furthermore, this paper considers two different approaches to instance selection namely: filter-based and wrapper-based. Different sets of experiments are performed on 20 small-scale datasets and 10 large- or medium-scale datasets. The results show that the proposed techniques improved SVM training speed in 100% (30 out of 30) of the datasets used for evaluation and simultaneously improved SVM predictive accuracy in some cases. Furthermore, statistical analysis test is carried out and the results reveal that the training speed of the proposed techniques is statistically significantly faster than the training speed of standard SVM and some other existing instance selection techniques. In real life application, such as video surveillance and intrusion detection systems, that require a classifier to be trained quickly for speedy classification of new target concepts, the filter-based techniques provide the best solutions; while the wrapper-based techniques are better suited for applications such as email filters, that are very sensitive to slight changes in predictive accuracy.
DCM: D Number Extended Cognitive Map. Application on Location Selection in SCM

Offshore outsourcing is a widely used management technique for performing business functions with the aim of reducing labor and transportation costs. The selection of locations has a significant influence on the supply chain’s resilience and qualities, but the influence of multiple external factors on the supply chain’s performance in local places in a complex and uncertain environment has not been examined. In this study, we investigated the influence of external factors in a highly uncertain and complicated situation in which relationships between external factors and supply chain resilience are complicated. Furthermore, we proposed a novel model to select locations from a comprehensive perspective. Specifically, the fuzzy cognitive map (FCM) is utilized to simulate the dynamic influence process where the adjacency is aggregated by D numbers. The weights of different resilience capabilities are considered from the perspective of maximizing benefits by using the decision-making trial and evaluation laboratory-analytic network processes (DEMATEL-ANP) model. By comparing the distance to the ideal solutions, we selected the best alternative location. Our results differ from the general case, which reveals that the weights of different capabilities influence selections.
Evading Real-Time Person Detectors by Adversarial T-shirt.

It is known that deep neural networks (DNNs) could be vulnerable toadversarial attacks. The so-called physical adversarial examples deceiveDNN-based decision makers by attaching adversarial patches to real objects.However, most of the existing works on physical adversarial attacks focus onstatic objects such as glass frame, stop sign and image attached to acardboard. In this work, we proposed adversarial T-shirt, a robust physicaladversarial example for evading person detectors even if it suffers fromdeformation due toa moving person's pose change. To the best of our knowledge,the effect of deformation is first modeled for designing physical adversarialexamples with respect to non-rigid objects such as T-shirts. We show that theproposed method achieves 79% and 63% attack success rates in digital andphysical worlds respectively against YOLOv2. In contrast, the state-of-the-artphysical attack method to fool a person detector only achieves 27% attacksuccess rate. Furthermore, by leveraging min-max optimization, we extend ourmethod to the ensemble attack setting against object detectors YOLOv2 andFaster R-CNN simultaneously.
Visual Tracking with Levy Flight Grasshopper Optimization Algorithm

Grasshopper optimization algorithm (GOA) is a new meta-heuristic optimization algorithm that it simulates behavior of grasshopper swarms in nature. In this paper, a tracking framework called improved levy flight grasshopper optimization algorithm (LGOA) tracker is proposed. The levy flight can increase the diversity of population, prevent premature convergence and enhance the capability of jumping out of local optimal optima, thus improving the tracking accuracy. In addition, GOA has been applied to visual tracking for the first time as far as we know. Finally, compared with other optimization-based trackers, experimental results show that our tracker has obvious advantages.
Ontology-Based System for Dynamic Risk Management in Administrative Domains

With the increasing complexity of cyberthreats, it is necessary to have tools to understand the changing context in real-time. This document will present architecture and a prototype designed to model the risk of administrative domains, exemplifying the case of a country in real-time, specifically, Spain. In order to carry out this task, a modeling of the assets and threats detected by various sources of information has been carried out. All this information is stored as knowledge making use of ontologies, which enables the application of reasoning engines in order to infer new knowledge that can be used later in the following reasoning. This modeling and reasoning have been enriched with a dynamic system for managing the trust of the different sources of information and capabilities for increased reliability with the inclusion of additional threat intelligence information.
Legendre equivalences of spherical Boltzmann machines

We study either fully visible and restricted Boltzmann machines with sub-Gaussian random weights and spherical or Gaussian priors. We prove that the free energies of the spherical and Gaussian models are related by a Legendre transformation. Incidentally our analysis brings also a new purely variational derivation of the free energy of the spherical models.
Retinex theory for color image enhancement: A systematic review

&lt;span&gt;A short but comprehensive review of Retinex has been presented in this paper. Retinex theory aims to explain human color perception. In addition, its derivation on modifying the reflectance components has introduced effective approaches for images contrast enhancement. In this review, the classical theory of Retinex has been covered. Moreover, advance and improved techniques of Retinex, proposed in the literature, have been addressed. Strength and weakness aspects of each technique are discussed and compared. An optimum parameter is needed to be determined to define the image degradation level. Such parameter determination would help in quantifying the amount of adjustment in the Retinex theory. Thus, a robust framework to modify the reflectance component of the Retinex theory can be developed to enhance the overall quality of color images.&lt;/span&gt;
TUPA at MRP 2019: A Multi-Task Baseline System

This paper describes the TUPA system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).TUPA provides a baseline point of comparison and is not considered in the official ranking of participating systems.While originally developed for UCCA only, TUPA has been generalized to support all MRP frameworks included in the task, and trained using multi-task learning to parse them all with a shared model.It is a transition-based parser with a BiLSTM encoder, augmented with BERT contextualized embeddings.
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation

Regarding the problem of automatically generating paraphrases with modified styles or attributes, the difficulty lies in the lack of parallel corpora. Numerous advances have been proposed for the generation. However, significant problems remain with the auto-evaluation of style transfer tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019), style transfer evaluations rely on three metrics: post-transfer style classification accuracy, content or semantic similarity, and naturalness or fluency. We elucidate the dangerous current state of style transfer auto-evaluation research. Moreover, we propose ways to aggregate the three metrics into one evaluator. This abstract aims to bring researchers to think about the future of style transfer and style transfer evaluation research.
Unsupervised Neologism Normalization Using Embedding Space Mapping

This paper presents an approach for detecting and normalizing neologisms in social media content.Neologisms refer to recent expressions that are specific to certain entities or events and are being increasingly used by the public, but have not yet been accepted in mainstream language.Automated methods for handling neologisms are important for natural language understanding and normalization, especially for informal genres with user generated content.We present an unsupervised approach for detecting neologisms and then normalizing them to canonical words without relying on parallel training data.Our approach builds on the text normalization literature and introduces adaptations to fit the specificities of this task, including phonetic and etymological considerations.We evaluate the proposed techniques on a dataset of Reddit comments, with detected neologisms and corresponding normalizations.