Reviewerly/exHarmony huggingface dataset:
Studies on a time-energy entangled photon pair source and superconducting nanowire single-photon detectors for increased quantum system efficiency

Time-energy entangled photon pairs are created by a system consisting of a 1064 nm pump diode laser that is fiber coupled to a high generation rate photon pair source. The source is a dual element periodically poled Magnesium Oxide doped Lithium Niobate (MgO:LN) waveguide that upconverts 1064 nm photons to single 532 nm photons in the first stage. In the second stage, the green photons are down converted to time-energy entangled photon pairs at 794 nm and 1614 nm. The output photon pairs are guided by fiber to sorting optics where they are separated and sent into high-efficiency photon detectors. In particular, the 1614 nm photons are detected by a superconducting nanowire with efficiency near 85% and dead time less than 30 ns. Detector output electrical signals are sent to a time tagger with bin resolution as narrow as 25 ps for coincidence counting. The ultimate goal of this setup is to demonstrate a single-source, high efficiency, high data rate, low noise, quantum communication system to enable Earth-space quantum networks. Test results that characterize the time-energy entangled photon pair creation rates of our source will be presented, via measures of accidental and true coincidence rates versus pump current. To reduce noise (accidentals) as much as possible, and for better understanding of our overall quantum system path-efficiency, studies of fluorescence caused by our pump’s 1064 nm and 532 nm photons will be investigated and discussed. Finally, characteristic measurements of our superconducting nanowire detector, such as dead time and detection efficiency versus electrical bias, will be offered. Please verify that (1) all pages are present, (2) all figures are correct, (3) all fonts and special characters are correct, and (4) all text and figures fit within the red margin lines shown on this review document. Complete formatting information is available at http://SPIE.org/manuscripts Return to the Manage Active Submissions page at http://spie.org/submissions/tasks.aspx and approve or disapprove this submission. Your manuscript will not be published without this approval. Please contact author_help@spie.org with any questions or concerns.
Statistical analyses of volcanic gas data

본 연구는 화산의 분화 시기를 예측하기 위해 일련의 통계기법을 토대로 화산가스 자료를 해석하는 방법을 소개한다. 사용된 통계기법은 ‘자료변형’, ‘인자선택’, ‘시계열분석’의 세 단계로 구분된다. 1단계에서 측정된 자료는 선형 보간(linear interpolation)에 의해 등시계열(evenly spaced time series)로 변형되고, 이후 무차원 계열(dimensionless series)로 바뀐다. 2단계에서 다수의 자료들은 요인분석(factor analysis)을 비롯해 상관분석(correlation analysis)과 교차상관분석(cross correlation analysis)을 바탕으로 소수 자료로 축소해 추후 분석에 사용된다. 3단계에서 선택된 자료는 Classical Decomposition with Moving Averages (CDMA) 및 주파수 분석(spectra analyses)에 의해 우연성분(periodic component), 주기성분(periodic component), 추세성분(trend component)으로 분리할 수 있다. 마지막으로 추출된 추세성분을 사용해 화산분화 예측에 대해 토의했다. 분화징후를 지시하는 신호가 종종 비화산적 배경 신호와 혼재함을 고려할 때, 본 연구에서 제시된 통계분석은 화산가스 자료를 해석하는 데 있어 체계적이고 유용한 방법을 제공할 수 있다.
Mining human activity and smartphone position from motion sensors

The wide use of motion sensors in today's smartphones has enabled a range of innovative applications which these sensors are not originally designed for. Human activity recognition and smartphone position detection are two of them. In this paper, we present a system for the joint recognition of human activity and smartphone position. Our study shows that the coordinate transformation approach applied to motion data makes our system robust to smartphone orientation variation. Contrary to popular belief, the simple neural network does provide the accuracy comparable to the deep learning models in our problem. In addition, it suggests that the motion sensor sampling rate is another key factor to the recognition problem.
Calibrating Perez Model Coefficients Using Subset Simulation

Abstract The Perez sky diffuse model has been validated in many locations, however, studies have shown that the precision of estimation can be improved through localization of the model coefficients. This paper studies the effect of tuning Perez irradiance coefficients based on local information for estimating the incident solar radiation on tilted surfaces. The added value of a calibrated Perez model is highlighted by evaluating the heating and cooling energy needs of an office building. Calibration is performed by using Subset Simulation, i.e. a sampling technique based on Markov Chain Monte Carlo. The versatility of the Subset Simulation technique allows for handling multivariant calibration problems and is therefore suitable for finetuning Perez irradiance coefficients. Measurements of global horizontal, direct normal and diffuse horizontal solar irradiation, as well as the global solar irradiation on 90°, 30° and 15° tilted surfaces form the basis of the calibration. It is found that in the studied location, the default Perez model overestimates the incident solar radiation on vertical surfaces facing south. The calibrated coefficients are then embedded in the source code of EnergyPlus. Simulations with a reference office show that the effect of calibrating Perez coefficients can be significant, because it leads to approximately 12% difference in predicted annual cooling energy use and 9% difference in peak cooling loads, respectively.
Alibaba Pictures: An Illustration of Changing Business Cultures and Film Finance

This chapter stands as a transition between the Chinese political-economic and media macro-contexts and the micro-contexts of film production and creative talents. Through the lens of Alibaba Pictures and following Peterson’s framework, I analyse the organisational process and the change in business cultures within the Chinese corporate media world. I argue that this change directly affects the development and programming of content. In addition, Alibaba’s (and Tencent’s) recent embrace of blockchain and smart contract technologies may further transform the creative industries (Zen 2018; Zhao 2018).
High spatial frequency structured light imaging texture analysis using Gabor filtering differentiates tumor from normal tissue subtypes

A recent clinical study involved multi-spatial frequency (0.00, 0.15, 0.61, 1.37 mm<sup>-1</sup> ), multi-spectral (490, 550, 600, 700 nm) structured light imaging (SLI) of freshly excised, bread-loafed breast conserving surgery tumor specimens. The specimens contained regions of interest (ROIs) of confirmed and homogeneous histological categories of breast tumor tissue determined by expert histopathological analysis. ROIs were sampled into ~4x4mm sub-images before conversion into Gabor filter bank feature vectors. Feature vectors were used in binary, benign-malignant classification scenarios using a support vector machine classifier and 8-fold cross validation. Classification was performed first on feature vectors containing only planar (0.00 mm<sup>-1</sup> ) 490 nm monochromatic SLI data. For comparison, a second set of feature vectors from the same sub-images contained the same planar 490 nm illumination data concatenated with high spatial frequency (1.37 mm<sup>-1</sup> ) 490 nm monochromatic SLI data. The classification performance of each filter bank was determined for both sets of feature vectors based on receiver operating characteristic (ROC) area under the curve (AUC) values. Gabor filtering and subsequent classification revealed that surface tissue textures exhibited strong rotational dependence and that high spatial frequency surface tissue features were more diagnostic than low spatial frequency surface tissue features. A Gabor filter bank that included 4 relatively high spatial frequencies (0.70-1.98 mm<sup>-1</sup> ) and 8 rotation angles (0-157.5&deg;, equally spaced) achieved ROC AUC values &lt;0.9 for 9 of 12 binary tissue subtype classifications.
A Secure Scalable Life-Long Learning Based on Multiagent Framework Using Cloud Computing

The major problem on the road to artificial intelligence is the development of lifelong learning systems. They have the ability to learn new concepts incrementally overtime. They are also able to allocate required resources dynamically without human intervention and are able to store data securely. In this work we have extended the incremental classifier and representation learning method known as iCaRL to meet this criterion. The proposed solution is able to learn strong classifiers and a data representation simultaneously. It is able to allocate an optimal scaling plan to meet its resource requirements without human intervention. It securely stores propriety image data by using state of the art interplanetary file system and block chain technology. Finally, it is able to focus on object of interests in an image using attention network. We have shown by experiments on CIFAR-100 and Image net 2012 that it performs better in terms of accuracy than the existing iCaRL system while satisfying criteria of lifelong learning.
LSTM-based Turn-taking Estimation Model using Lexical/Prosodic Contents and Dialog History

A natural conversation involves rapid exchanges of turns while talking. Taking turns at appropriate timing or intervals is a requisite feature for a dialog system as a conversation partner. We propose a Recurrent Neural Network (RNN) based model that takes the current utterance and the dialog history as its input to classify utterances into turn-taking related classes and estimates the turn-taking timing. The dialog history is represented by a sequence of speaker-specified joint embedding of lexical and prosodic contents. To this end, we trained a neural network to embed the lexical and the prosodic contents into a joint embedding space. To learn meaningful embedding spaces, the prosodic feature sequence from each single utterance is mapped into a fixed-dimensional space using RNN and combined with utterance lexical embedding. These joint embeddings are then shifted to different parts of embedding spaces according to the speakers. Finally, the speaker-specified joint embeddings are used as the input of our proposed model. We tested this model on a spontaneous conversation dataset and confirmed that it outperformed conventional models that use lexical/prosodic features and dialog history without speaker information.
Image Segmentation with Minimum Cross Entropy

Image segmentation is used to classify the pixels in different regions according to their intensity level. Several segmentation techniques have been proposed, and some of them use complex operators. An interesting method to choose the best thresholds is the Minimum Cross Entropy (MCET); which provides excellent results for bi–level thresholding. Nevertheless, the extension of the segmentation problem into multiple thresholds increases significantly the computational effort required to find optimal threshold values. Each new threshold adds complexity to the formulation of the problem. Evolutionary algorithms use heuristics to optimize criteria over a finite number of iterations. The correct selection of an Evolutionary algorithm to minimize the MCET directly impacts the performance of the method. Current approaches take a large number of iterations to converge and a high rate of MCET function evaluations. This chapter presents the use of evolutionary algorithms for multilevel thresholding using the MCET.
Superconducting nanowire single-photon detectors at the infrared spectrum range: detection efficiency and timing jitter

This paper reviews some recent research progress in superconducting nanowire single-photon detectors (SNSPDs) at the infrared spectrum range, with particular emphasis on detection efficiency and timing jitter. For detection efficiency, we present fractal SNSPDs with reduced polarization sensitivity; for timing jitter, we present two mechanisms of device timing jitter – vortex-crossing-induced timing jitter and spatial-inhomogeneity-induced timing jitter.
Novel Scene Recognition Using TrainDetector

Our ability to process the image keeps improving day by day, since the introduction of deep learning. Lastly, this contributed to the advance of object recognition through a Convolutional neural network and Place recognition, which is our concern in this paper. Through this research, it was observed a complexity in the extraction of the correct and relevant features for scene recognition. To address this issue, we extracted at the pixel level several subareas which contain more color intensity than other parts, and we went through each image once to build the feature representation of it. We also noticed that several available models based on Convolution Neural Network requires a Graphics Processing Units (GPU) for their implementation and are difficult to train. We propose in this paper, a novel Scene Recognition method using Single-Shot-Detector (SSD), Multi-modal Local-Receptive-Field (MM-LRF) and Extreme-Learning-Machine (ELM) that we named TrainDetector. It outperforms the state-of-the-art techniques when we apply it to three well-known scene recognition Datasets.
An Evaluation Approach for a Physically-Based Sticky Lip Model †

Physically-based mouth models operate on the principle that a better mouth animation will be produced by simulating physically accurate behaviour of the mouth. In the development of these models, it is useful to have an evaluation approach which can be used to judge the effectiveness of a model and draw comparisons against other models and real-life mouth behaviour. This article presents a set of metrics which can be used to describe the motion of the lips, as well as a process for measuring these from video of real or simulated mouths, implemented using Python and OpenCV. As an example, the process is used to evaluate a physically-based mouth model focusing on recreating the stickiness effect of saliva between the lips. The metrics highlight the changes in behaviour due to the addition of stickiness between the lips in the synthetic mouth model and show quantitatively improved behaviour in relation to real mouth movements. The article concludes that the presented metrics provide a useful approach for evaluation of mouth animation models that incorporate sticky lip effects.
Science Fiction Prototypes Illustrating Future See-Through Digital Structures in Mobile Work Machines

In the context of mobile work machines, efficient and safe operation is often significantly impeded by the lack of visibility due to obstacles such as the engine cover, moving booms and safety features obstructing the view. At the moment, the work machine industry is putting substantial effort into trying to solve the problem; however, it seems that the current maturity of relevant technologies is preventing solutions from entering the market in the immediate future. Therefore, this paper takes a stance that leans firmly into the future and describes the potential of see-through applications by operator-driven Science Fiction Prototypes. The research has included field studies with expert machine operators in tractor, harvester, mining, and cargo handling contexts. The main contribution of the paper is the introduced visual Science Fiction Prototypes and a brief consideration of the potential technologies that are presented as design outcomes of research.
Using Scene Context to Improve Action Recognition

Recently action recognition has been used for a variety of applications such as surveillance, smart homes, and in-home elder monitoring. Such applications usually focus on recognizing human actions without taking into account the different scenarios where the action occurs. In this paper, we propose a two-stream architecture that considers not only the movements to identify the action, but also the context scene where the action is performed. Experiments show that the scene context may improve the recognition of certain actions. Our proposed architecture is tested against baselines and the standard two-stream network.
Margarita Alonso-Ramos (Ed.), <i>Spanish Learner Corpus Research: Current Trends and Future Perspectives</i>

Abstract This article reviews Spanish Learner Corpus Research: Current Trends and Future Perspectives 9789027210753
Micro-expressions recognition using center symmetric local mapped pattern

Local feature description is widely used in micro-expressions (ME) recognition. However, contemporary low-level handcrafted feature is insufficient in representing ME due to its insignificant and subtle motion which results in low recognition rate. This paper presents a novel handcrafted feature to represent ME based on intensity-level difference mapping, namely Center-Symmetric Local Mapped Pattern (CS-LMP). Due to its capability in capturing subtle pixel changes, CS-LMP is proposed to retrieve ME subtle motions which results in better accuracy. In this paper, CS-LMP features are extracted from ME public datasets and the results are compared to other state-of-the-art approaches where the classifications are performed using support vector machine and K-nearest neighbours. The results show that our approach produces prominent results as high as 79.59% compared to competing approaches.
GPU-based Concurrent Multi-UAVs Path Flow Analysis using K-MST Algorithm

Unmanned Aerial Vehicles have widespread applications in the fields of remote sensing, reconnaissance, product delivery, and search & rescue. With the advent of new technologies the manufacturing cost of typical UAVs have gone down whereas on the contrary the robustness and safety factors have improved drastically. Hence multiple UAV systems are being planned for coordinating specific large tasks such as search & rescue and delivering commercial products door to door. Many factors affect the optimized operation of these multiple flying machines in a given designated geographical area. The most crucial being the challenge of chartering an optimal flight path for each UAV based on distance, terrain and fuel costs. Subsequently, cumulative flying time costs are calculated and a final scenario is presented highlighting the optimal usage of multiple UAVs working in unison. The prime focus is on finding localized shortest path using Prim's algorithm and then clustering these localized paths into smaller sub-graphs optimally. Multicore processors are leveraged to provide accelerated computations as the paths change dynamically with respect to time.
A family of system Hamiltonian in quantum adiabatic search type problem

In this paper [J. Sun, S. Lu and F. Liu, Open Syst. Info. Dyn.23(3) (2016) 1650016], we have studied a general class of adiabatic evolutions in quantum adiabatic search type problem, and found another limitation on it, besides those reported in our early work. In this paper, we extend this study by considering adding an extra driving scheme to the system Hamiltonian of this type of quantum adiabatic evolution. It will be shown that, like the previous study in [J. Sun, S. Lu and F. Liu, Open Syst. Info. Dyn.23(3) (2016) 1650016], the limitation above can also possibly penetrate into the search type quantum-adiabatic computation even by this more general family of system Hamiltonian, which may shed light on what we should have in mind when solving practical problems by means of quantum adiabatic evolution.
Research on the Development of China’s Financial IC Card Chips

Focusing on the financial IC chip technology that is urgently needed in China and the information security of IC card chips which is related to the security of the national financial system, this paper summarizes and researches into the development of China's financial IC card industry, the mainstream module, design standards and specifications, and major technical routes of card chips. In addition, it also centers round the security protection of financial IC card chips. Based on the analysis and introduction of mainstream international and national commercial cipher algorithm it studies the attacking modes and principles of power and electromagnetic analysis attacks and side-channel attack technologies faced by China's IC card chips, and finally summarizes main protection technologies against various attacks.
Generating Linked Data Repositories Using UML Artifacts

The usability of diagrams and models is increasing day by day, because of this we experience problem in searching and accessing from large size repositories of diagrams and models of software systems. This research might be helpful to search and access the diagrams and models in bigger repositories. For this purpose, this research developed linked data repositories which contain UML (Unified Modeling Language) artifacts, these artifacts are being organized with using UML class model. In particular, UML is being broadly applied to data modeling in many application domains, and generating linked data repositories from the UML class model is becoming a challenging task in the context of semantic web. This paper proposes an approach, in which we will build a construction tool by joining the characteristics of RDF (Resource Description Framework) and UML. Firstly we will formally define design artifacts and linked data repositories. After that we will propose a construction tool in which we will extract UML artifacts, these UML class model further transforms into the corresponding RDFs. The generated RDF linked data then will be verified by using W3C RDF, this is a validating service used to generate and verify the RDF triples and graphs. Finally, the proposed construction tool will be implemented with few experiments and research is validated using W3C RDF validating service. The proposed approach aims to give such a design that may facilitate the users to customize linked data repositories so that diagrams and models could be examined from large size data.
Application of Genetic Algorithms in Project Declaration

With the development of information technology, the arrival of era of big data, the scale of scientific research has been expanded continuously in universities, and the level of scientific research has been improved continuously. Nowadays, new demands are placed on scientific research management. In the scientific research management platform, the Genetic Algorithms has been applied well. Especially, the Genetic Algorithms has a better embodiment in expert detection of the project declaration subsystem. In order to solve the problem that a large amount of manpower is needed in the process of scientific research project review, we mainly analyze the application of Genetic Algorithms and use Genetic Algorithms to solve the problem of project management in scientific research management.
Changing of Participants’ Attitudes in Argument-based Negotiation

We are modelling argument-based negotiation where the initiator is convincing the partner to do an action. The initiator is using a partner model which evaluates the hypothetical attitudes of the partner related to the action under consideration. The partner when reasoning operates with an actual model – the actual attitudes which still are hidden from the initiator. Both models are changing during negotiation as influenced by the presented arguments. The choice of an argument by a negotiation participant depends, on one hand, on the attitudes related to the action, and on the other hand, on the result of reasoning based on these attitudes. The paper studies how the participants are changing their attitudes during a dialogue. A human-human dialogue illustrates the results of the analysis of a small dialogue corpus. A limited version of the model is implemented on the computer.