Reviewerly/exHarmony huggingface dataset:
Combine Voxel Flow and Adaptive Convolutional Kernel for Video Colorization

In this paper, we propose a novel deep learning model for video colorization. The model consists of three parts: a local propagation network, a global propagation network and a refine network The local propagation network contains a voxel flow network and an adaptive convolutional kernel network. The voxel flow network learns the motion relationship of pixels between adjacent frames while the adaptive convolutional kernel network extent the ability of our model to handle occlusion/dis-occlusion problem. The global propagation network enhances the temporal continuity of colorized frames. The final refine network fine-tunes the colors from the local propagation network and the global propagation network to get the final result. The experimental results show that our model could handle large motion and occlusion problems in some degree, as a result, our method obtains a higher peak signal-to-noise ratio than other video colorization methods. The video sequences colorized by our method could maintain temporal and spatial continuity.
Miniaturized Devices for Free Space Quantum Key Distribution; in Your Pocket and in Space

Quantum Key Distribution (QKD) is a method of generating physically secure links over a quantum channel guarateed by the laws of physics rather than mathematical complexity. There exists commercial QKD systems which are designed for integration into data centres and as such are large and expensive. We have developed a miniaturized source which is suitable for integration into novel applications.
Adaptive Cuckoo Search Algorithm for Parameter Estimation of Heavy Oil Thermal Cracking Model

To overcome the shortcomings of cuckoo search, an adaptive cuckoo search (ACS) algorithm is proposed. In the ACS algorithm, the logarithmic adaptive step size is adopted and the random traction component is added to the Lévy flight formula to update the population. The effectiveness of the ACS algorithm is verified with four benchmark functions. The ACS algorithm is applied to estimate the parameters of the heavy oil thermal cracking model. The experimental results show that the established model has higher accuracy than the models obtained by the other optimization algorithms.
Construction of Intelligent Electricity Platform under the Background of Intelligent Fire Protection

In order to forecast and warn the electric fire accidents, and to provide support for the emergency rescue of the electric fire accidents, the intelligent electric monitoring system is applied to the electric fire. The system uses Internet of Things, Mobile Internet, Cloud Platform and Big Data Technology to realize real-time online monitoring and graphical display of current, leakage, temperature, harmonic and other parameters of electrical lines. Once the electrical parameters exceed the threshold, they will alarm at the mobile terminal, monitoring screen, computer and other terminals. After the intelligent electric fire alarm system is issued, the basic information of enterprises, surrounding environment information and emergency information can be viewed through the system, so as to improve the speed and efficiency of emergency rescue. The system can realize the early warning of electric fire and provide support for emergency rescue of electric fire accidents.
Influence of Power Line Termination Device Placed on Ground Plane to NSA Measurement

To improve the reproducibility of radiated emission measurement, a very high frequency line impedance stabilization network (VHF-LISN) has been proposed and has been under consideration for standardization into basic measurement standards (CISPR 16-2-3 and CISPR 16-1-4). Since a VHF-LISN can be placed on the ground plane (GP), we thought it was necessary to investigate the effects of placing it. Initially, the effects were investigated by normalized site attenuation (NSA) measurements. The influence of a common-mode absorbing device (CMAD) and an artificial mains network (AMN) were also investigated in the same way for comparison. Then, we created simulation models of antennas and these devices. Both actual measurement results and simulation results demonstrated that the influence of VHF-LISN was smaller than the influence of CMAD or AMN. The results suggest that evaluation of the influence on the NSA of a VHF-LISN placed on the GP is not needed if the evaluation is not required for a CMAD or an AMN.
Improvement of the quality of the interferogram based on the Gaussian process regression

Interferometric techniques are very important in the metrology field, while the quality of the interferogram will directly affect the retrieval phase of the tested object. This paper presents a method to improve the quality of the interferogram including restoration of noise aliasing and moiré distortion by using the Gaussian Process Regression (GPR). Through choosing a suitable covariance function to describe the relationship between points and points in the fringe pattern, we build a Gaussian process regression model of interferogram, denoise the interferogram and improve the resolution at the same time. The treated interferogram can predict and compensate the part of the fringe distortion and enlarge the depth range of the interferometric measurement. Besides, with the resolution elevated of the hologram, a wider spectrum range can be obtained. In order to verify the possibility of this method, several simulations have been done, which showed a good performance in the enhancement of the quality of interferogram.
BERT based Web Mining of Concerns and Reviews for TV Drama Audience

This paper proposes how to mine concerns and reviews that are relevant to TV dramas from the results of collecting Web pages on those TV dramas. The proposed framework consists of the natural language processing based techniques of analyzing sentence embeddings of those collected Web pages. More specifically, we apply the technique of BERT to the task of judging whether the theme of a collected Web page is relevant to the given drama. We also apply the technique of BERT to the task of judging whether a collected Web page includes any review or not. Results of evaluating those proposed models show that the proposed framework performs well in those tasks of relevance judgment and review detection.
Selection of Neural Network for Crime Time Series Prediction by Virtual Leave-One-Out Tests

The goal of this paper is the application of the virtual leave-one-out methodology to the selection of optimal neural network structure for time series prediction. The experiments are performed on the real dataset of spatiotemporal crime incidence for forecasting in the time coordinate. Due to the idea of local linearization, the estimation of generalization can be obtained in analytical form; hence, the method is computationally efficient.
Real-time Wildlife Detection on Embedded Systems

Modern machine learning algorithms are generally deployed on highperformance GPUs, and require significant power and memoryresources. However, such resources are not always available,especially when one wants to deploy such models on the ”edge.” In thisproject, we work with Jasper Ridge Biological Preserve in Stanford,CA, and develop an image classification system that can be deployed onthe 18 camera traps they have setup around the preserve to detectwildlife. We experiment with and deploy various image classificationmodels on a Raspberry Pi that can be connected to these cameratraps. We show that our best model achieves a mean-per-class accuracyof 87.6% and can quickly run inference in real-time, on-device, usingminimal power.
Local Low-rank Approach for Despeckling of Ocean Internal Wave on SAR Image

Speckle suppression is extremely important for understanding and utilizing synthetic aperture radar (SAR) images, while the emphasis of the traditional methods for speckle suppression is usually focused on removing the noise instead of keeping the feature characters of imaging objects, which has caused serious interference to the subsequent applications of SAR images, such as feature extraction, parameter estimation and so on. Generally speaking, the statistics of speckle is usually explored to remove the speckle, whereas in this paper the local low-rank character of ocean internal wave is also utilized to suppress the speckle and restore the destroyed waveform by exploring the structure information of internal wave. The results of real data validate the good performance of the proposed method.
Blockchain Based Decentralized Authentication and Licensing Process of Medicine

Counterfeit medicines are increasing day by day and these medicines are damaging the health of people. Drug Regulatory Authorities (DRAs) are trying to overcome this issue. Synchronized electronic medicine record can mitigate this risk. We proposed a decentralized Blockchain (BC) based medicine licensing and authentication system to stop production of counterfeit medicines. Our proposed system provides a convenient way to register medicines by manufacturers with DRA. Vendors will also be registered with DRA, they are intermediate who buy from manufacturer and sale to customers. Furthermore, every transaction between manufacturer and vendor will be saved to BC. We used Proof of Collaboration (PoC) as a consensus mechanism, the manufacturer deals with more vendors will have more mining p ower. DRA has a different department, i.e., Licensing Department (LD), regulatory Department (RD), and Quality Control Department (QAD). These departments perform many actions, which will be saved in the BC database. LD registers manufacturers and vendors, RD imposes rules and QAD makes random checks to test the quality of medicines. We also propose manufacturer profile scheme, QAD rates manufacturer according to its quality of medicines from the feedback of users. Moreover, we provide an interface to the users, through which they can check the authenticity of medicine. We compare results of traditional licensing system with BC based licensing system.
Remote State Preparation and Quantum Steering Based on Hybrid Entanglement of Light

The wave-particle duality of light has led to two different encodings of optical quantum information. One approach, refered as the discrete-variable (DV) one, relies on finite dimensional quantum systems, while the other one, refered as the continuous-variable (CV) one, is based on wave-like states belonging to an infinite dimensional Hilbert space. These two encodings have historically been separated, but recently, new hybrid protocols which aim at combining the two complementary encodings have emerged [1]. In that prospect our recent demonstration of the measurement-induced generation of hybrid entanglement between discrete and continuous-variable quantum states generated using optical parametric oscillators [2], located at distant places and connected by a lossy channel, has opened the way to the implementation of hybrid protocols and heterogeneous quantum networks. Here we will report the first protocols based on this resource: the remote preparation of continuous-variable qubits [3], and a violation of an Einstein-Podolosky-Rosen steering inequality [4].
A Standardization Method of Individual Rating Fluctuation in Social Listening Data

In recent years, social listening data analysis is adopted in various business areas as a measure to analyze customers’ voices. While sentiment information systems in social listening data analysis have been highly developed, their verification data requires sentiment score by humans. Sentiment means emotions such as Joy, anger and so on. Sentiment score by humans, however, depends on individuals and is not consistent enough. Thus, the accuracy of evaluation is uncertain. If verification data has uncertainty, it is apparent that uncertainty also occurs in the verification of systems. It is therefore important to exclude the uncertainty of verification data as much as possible. In this paper, we studied a standardization method for reducing such uncertainty and its effect. We set three choices, “Positive,” “Negative,” and “Not Sure,” for the verification data used in this study, and sentiment score evaluated the data as social listening data. We first showed an example of uncertainty in the verification data. We then proposed a correction method for reducing uncertainty in the verification data. The method adopted correction formulas using the average sentiment score of all the subjects as the norm. We used these formulas to suppress the bias of sentiment score of each individual subject. Consequently, compared with the result processed by simple decision by a majority, the frequency of “Not Sure” and the amount of mixed data of different sentiment scores decreased. Namely the method was effective in reducing the discarded data of indecisive sentiment score. Furthermore, the variance of data decreased consequently. These results showed that the standardization method was effective in reducing uncertainty in verification data.
Graph Regularized Domain Adaptation for Cross Domain Image Recognition

Visual domain adaptation can utilize similar but different source data to solve the scarcity problem of labeled data in target domain, therefore it has received more and more attention. The main difficulty of this task is the distribution discrepancy between target data and source data. Hence, a series of works have been proposed to reduce the distribution discrepancy by seeking cross-domain feature invariant, or reducing the weight of source instances that are not related to the target data. The major disadvantage is that most works fail to consider the intrinsic geometric manifold structure, which is important for effective learning. This paper considers the intrinsic geometric manifold structure, and the obtained domain invariant features can be more discriminating. Our method effectively integrates distribution matching and preserving the structure consistency into one framework, and obtains the optimal solution by solving a generalized eigen-decomposition problem. Comprehensive experimental results verify that proposed method can significantly outperform state-of-the-art methods in cross domain image recognition problems.
Efficient loose image content classifier

In this Letter, the authors put forward a loose classifier to determine whether the content of a given image belongs to screen content or natural scene. In the proposed classifier, they first extract 14 features by means of three typical histograms, the greyscale image histogram, local binary pattern histograms, and histograms of the oriented gradient, respectively. Then, in the second step, they try to build a non-linear mapping, with stacking-based integrated learners, from extracted features to the classification label. Experimental results prove that the effectiveness of their proposed method as compared with popular algorithms.
A study for categorizing relations between headword and aliases

ABSTRACT In this study, we propose a method of categorizing relations between a headword and its aliases using Korean Wikipedia data. We propose orthographically similar types and orthographically different types. Orthographically similar types are divided into 5 types: Word space, Pronunciation difference, Omission, Abbreviation, and Word order change while different types are also classified into 4 types: Foreign word, Acronym, Paraphrased expression and Call name. We show examples to verify the proposed method.
Long-Term Care (LTC) Monitoring System for Caregivers Based on Wireless Sensing Technology

Continued decline in fertility rates and increased life expectancy cause serious population ageing in the world. As the older population increases, the demand of caregivers increases also, long-term care (LTC) is now to become a serious topic for each country who is facing this problem undoubtedly. In Taiwan, caregivers who need to work 16–24 h a day are maybe in the majority. Under the influence of prolonged over-exertion, high repetition or bad posture probably make caregivers in the risk of waist injuries. In this article, a LTC monitoring system for caregivers based on wireless sensing is designed. Through the change of waist bending rate, the system can remind caregivers to keep in a correct posture. In addition to this, caregivers can also track their own personal visualization chart via a mobile app at any time.
Character-Aware Low-Resource Neural Machine Translation with Weight Sharing and Pre-training

Neural Machine Translation (NMT) has recently achieved the state-of-the-art in many machine translation tasks, but one of the challenges that NMT faces is the lack of parallel corpora, especially for low-resource language pairs. And the result is that the performance of NMT is much less effective for low-resource languages. To address this specific problem, in this paper, we describe a novel NMT model that is based on encoder-decoder architecture and relies on character-level inputs. Our proposed model employs Convolutional Neural Networks (CNN) and highway networks over character inputs, whose outputs are given to an encoder-decoder neural machine translation network. Besides, we also present two other approaches to improve the performance of the low-resource NMT system much further. First, we use language modeling implemented by denoising autoencoding to pre-train and initialize the full model. Second, we share the weights of the front few layers of two encoders between two languages to strengthen the encoding ability of the model. We demonstrate our model on two low-resource language pairs. On the IWSLT2015 English-Vietnamese translation task, our proposed model obtains improvements up to 2.5 BLEU points compared to the baseline. We also outperform the baseline approach more than 3 BLEU points on the CWMT2018 Chinese-Mongolian translation task.
Threat Modeling for Breaking of CAPTCHA System

The online websites are accessed by millions of people and the information present on it holds value. To secure them from attacker, one such mechanism is “Completely Automated Public Turing Test to keep the Computers and Humans Apart”. They are used to ensure that internet user’s activity is performed by humans only and not the bots. CAPTCHAs are solved by people every day to prevent Denial of Service attack and online spam attack. But unfortunately, it is now possible to break them by using Machine Learning. This paper presents, the Vulnerabilities related to Text-based CAPTCHA System, compromised system using Machine Learning and proposed Algorithm. A Threat Modeling was performed on the website using a Text-based CAPTCHA System in order to discover various Attack Vectors with the help of a Tool and performs detailed analysis on affected areas. Lastly, a solution is provided to the website service provider to overcome the exsisting system flaws and also to make them even more strong and secure.
Hyper-Entanglement in Time and Frequency

Hyper-entanglement, i.e. entanglement in more than one degree of freedom, enables a multiplicative increase in Hilbert space size. Such systems can be treated as multi-partite even though the number of state particles is not increased, making them highly attractive for applications in high-capacity quantum communications and information processing [1]. Until now, such states have been realized only using combinations of fully independent degrees of freedom, described by commuting operators, such as polarization and optical paths. Time and frequency, in turn, are linked and described by non-commuting operators. Here, using two discrete forms of energy-time entanglement we demonstrate that time and frequency can be used for genuine multi-partite hyper-entangled states [2]. This is achieved by increasing the time-frequency product to far exceed the Heisenberg uncertainty limit, effectively making the time and frequency degrees independent.
The Moving Target Recognition and Tracking Using RGB-D Data with the Mobile Robot

This paper investigated the problem of moving target recognition and tracking in unknown environments for autonomous mobile robots. The mobile robot is equipped with the Kinect sensor to acquire the appearance and depth information of the environment for target recognition and motion control. As for the visual target recognition, the depth feature is fused with the color feature on the basis of the Kernel Correlation Filtering algorithm (KCF), and the Kalman filtering algorithm based on the target motion state is integrated with the KCF, to cope with such situations as the target occlusion in tracking process. The movement of the robot can be controlled according to the results of visual target recognition and the standard of keeping the target in the central area of the mobile robot’s vision. Finally, controlling the mobile robot to track the moving target is realized. The experimental results on Kobuki robot platform prove the effectiveness of the presented method.
Navigation Assistance Using an Umbrella

Internet of Things (IOT) is one of the fastest growing technologies which allows versatile creation of worldly applications with devices that communicate via computer based systems. It has made way for exceptional innovations which has made our lives assisted and easy. One such example is smart umbrella, which not only acts a shield from rain but also consists of various other features such as alerts of weather and virtual assistance for the elderly. This paper proposes an umbrella which acts as a navigation assistant to pedestrians to reach their respective destinations.
A Framework for Distributed Representations of Domain Embedding

In many information retrieval and natural language processing tasks, it is common to use pertained word embedding to alleviate training difficulty. This motivate us to learn low-dimension representations for all domains on the Internet. There are hundreds of millions of domains on the Internet, majority of the domains attribute to only one or two specific field. Representing these property of domains in a low-dimensional and interpretable space attribute to many tasks, such as domain recommendation, domain classification, web search and et al. In this paper, we propose a novel algorithm named domain embedding, an unsupervised model which learns a fixed length representation for each domain. Experimental results show the superior performance of the proposed method.
Probabilistic Plan Recognition Under Temporal Logic Tasks

In this paper, we consider the plan recognition problem in the real-time strategy game. A probabilistic plan recognition algorithm is proposed to predict the future goals and identify the temporal logic tasks of the non-cooperative agent based on the observations. In order to model the temporal logic tasks, the plan library is composed of the Finite Transition System and Nondeterministic Büchi Automation. Specially, we provide a unified framework to combine the plan recognition and the planning, and propose the probability calculation algorithm to calculate the posterior probability distribution of the goals and tasks. Finally, we verify the effectiveness of the proposed algorithm by the compared simulations.
Data for development: The case for Africa

ABSTRACT This study presents the results of a systematic review of data use practices by African countries to support the United Nations Sustainable Development Goals (UN SDGs) #2 – agriculture. Data use practices and existing data infrastructure are reviewed through the lens of the four big data value‐chain ‐ data generation, data acquisition, data storage, and data analysis. The findings show that – 1) national statistical offices are the primary institutions responsible for data collection and analysis at country level; 2) survey is the primary means for data generation and acquisition; c) the capability to transform data into actionable insight and knowledge is severely limited; 4) data is heavily used for reporting purposes.