Reviewerly/exHarmony huggingface dataset:
Decoherence of the two-mode squeezed vacuum state in a diffusion process

We have studied the case in which one mode of the light field in the two-mode squeezed vacuum state evolves in a diffusion channel. By virtue of thermo-entangled state representation and the technique of integration within an ordered product, the evolution formula of the field density operator is given. Its non-classical properties, such as squeezing effect, antibunching effect, the violation of Cauchy–Schwartze inequality and the entanglement property between two modes, are studied. The influences of the squeezing parameter and the dissipation time on the non-classical properties are discussed. The results obtained by the numerical method show that its non-classical properties are all weakened with the dissipation. On the other hand, its squeezing effect and the entanglement property between two modes are strengthened, but its antibunching effect and the violation of Cauchy–Schwartze inequality are weakened with the increase of the squeezing parameter.
Analysis of digital histopathology images for breast cancer diagnosis

Significant development of mammogram-based computer-aided diagnosis using image processing techniques has been reported by different research articles. Huge opportunity still persists to develop a fully automatic system to detect abnormalities and predict future risk factors. Digital histopathology analyzes breast biopsy slide image for diagnosis of breast tumors.
Performance Issues And Gains Of Caching The Pathfinding Data

Using non-cached methods for finding the shortest path between nodes is the most common case when using pathfinding systems. That approach generates a couple of issues. Foremost, it has a significant impact on processing resources as calculations must be done over again for each iteration, even for the repeating events. That’s not a big concern if pathfinding is invoked a reasonable number of times or the nodes involved are always different, but if pathfinding occurs many times on the same nodes, then the caching of once calculated path becomes an acceptable course of action. This paper has explored one of such caching algorithms, FAST-N algorithm and compared it with standard non-cached pathfinding. Doing so, it outlined margins of justifiable use of such systems. On a small number of pathfinding requests or simple node structure, because of increase in memory usage and rather hefty initial calculation processing requirements, it has been concluded that non-cached system makes more sense than cached one. On the other hand, when confronted with a large number of pathfinding requests and more complex node structure, caching can generate significant benefits concerning processing power and speed.
Temporal high-resolution evaluation algorithm for sinusoidally phase modulated interference signals

In this paper, we present a practical approach for phase analysis of sinusoidally phase shifted interference signals, which are generally used to detect optical path length changes in one arm of the interferometer based on an algorithm introduced by de Groot. We describe the original algorithm from our point of view and try to emphasize the limitations and some details that need to be known for practical implementation. We introduce methods for how to overcome these limitations, and in addition, we provide an extension of the algorithm to a temporal high-resolution mode, which provides a possibility to calculate a phase value for each sampled point of an interference signal and opens new applications for the existing measurement devices without any hardware changes. Simulated and experimental results verify our extensions.
Symmetrical Bell state preparation and measurement without a third party

We present a linear optical Bell state preparation and measurement schemes without photon-photon interaction at an optical element. Unlike the standard schemes, it can be symmetrically divided into two parties without a third party. © 2019 The Author(s)
A Dual-Branch CNN Structure for Deformable Object Detection

Object detectors based on CNN are now able to achieve satisfactory accuracy, but their ability to deal with some targets with geometric deformation or occlusion is often poor. This is largely due to the fixed geometric structure of the convolution kernel and the single inflexible network structure. In our work, we use dual branch parallel processing to extract the different features of the target area to coordinate the prediction. To further enhance the performance of the network, this study rebuilds the feature extraction module. Finally, our detector learns to adapt to a variety of different shapes and sizes. The proposed method achieves up to 81.76% mAP on the Pascal VOC2007 dataset and 79.6% mAP on the Pascal VOC2012 dataset.
From Genetic Evolution to Engineering Optimization

This chapter focuses on one of the main members of the evolutionary algorithms class called genetic algorithms (GAs). GAs mimic the process of sexual reproduction and survival of the fittest in nature. The process begins by coding the design parameters into a chromosome-like structure to form an individual member. Next, a prespecified number of individual members are generated with different genetic contents to form a population. For each member of the population, a measure of its fitness or success is calculated. The members “most fit” in the population are then allowed to mate and reproduce, to have children. The children create a new and better generation of population, and the process repeats until an optimum solution or solutions are produced. This chapter illustrates how GAs are used to solve the following two problems: electronic circuit design problem and music composition.
BLI AND LCI IMPROVE POLYP DETECTION RATE AND DELINEATION ACCURACY FOR DEEP LEARNING NETWORKS

Studies have suggested that polyp detection rates can be improved by using other modalities than white-light imaging (WLI) such as linked-color imaging (LCI) from Fujifilm. Our aim is to evaluate the influence of the modality on polyp detection rate and delineation accuracy of an artificial intelligence (AI) system.
A Novel Modeling Method for Acoustic Model in Deep Neural Network by Introducing Language Vector

Much attention has been paid on Multi-language or cross-language speech recognition domain. A kind of speech recognition system with one language and different accents is also an application in the above domain. In this paper, a new concept called language vector is proposed and then is applied in the learning of acoustic model in deep neural network. The proposed method introduces language vector by conditional learning and multi-task learning and dramatically improves the performance of the English speech recognition system aimed at British accent and Chinglish accent.
Authority-Based Ranking in Propaganda Summary Generation Considering Values

The era of big data provides a favorable condition for the study of news report and public opinions. By using keyword search, language processing, data mining and information techniques, websites, blogs, forums can be further analyzed for public opinion monitor and precisely guidance. Thus, in this paper, we propose a novel ranking methodology for object or propaganda summary generation, i.e., ValueRank, which incorporates tuples values in authority flow and facilitating the approximation of importance for arbitrary databases. According to ObjectRank and other PageRank fashioned techniques that consider solely authority flow through relationships and therefore their effectiveness is limited on bibliographic databases. Experimental evaluation on DBLP and Northwind databases verified the effectiveness and efficiency of our proposed approach.
Editors’ introduction: Special issue on non-classical modal and predicate logics

There was a time when modal logic—any modal logic—was ‘non-classical’. In the early 1950s, someone as influential as Quine was still arguing that quantifying into modal contexts is incoherent (see, e.g. (4)). Today, modal logic is not only widely accepted but also a thriving, crowded research topic, having become a tool applicable in a vast array of areas. The notion that using modal operators at all is non-classical (which, in the mid-20th century, meant the same thing as deviant) now seems almost quaint. To be ‘non-classical’ in modal logic today means taking a much larger step beyond the narrow confines of two-valued classical logic than it did when Quine wrote. Meanwhile, research into logics that would still today be thought of as non-classical—logics with consequence relations that yield different theories (usually sub-theories) than classical consequence—is well over a century old, but much of that work has been confined to the propositional level. Some non-classical logics are essentially first order, like free logic, but there is still a common sense that, as one popular textbook puts it, ‘the major interest in non-classical logics is usually at the propositional level’ (3, p. xvii). While it is true that there is much to be learned by studying propositional connectives and exploring their non-classical possibilities (as the magisterial The Connectives by Humberstone testifies (2)), most applications of logic in mathematics, computer science, linguistics and beyond involves quantification and, indeed, higher-order quantification. For non-classical logics to reach the level of acceptance that standard modal logic has will involve further exploration of languages and theories beyond the propositional level.
Development of a therapy decision-supporting system for laryngeal cancer based on Bayesian networks

The increasing complexity of cancer diagnostics and more personalized treatment options, also in head and neck oncology, require new techniques of patient information processing and systems to support the decision-making process in the Head and neck tumor board (HN-TB). For this purpose, a digital patient model of larynx cancer (LC) based on Bayes ' networks (BN) was developed.
Policy Networks: A Framework for Scalable Integration of Multiple Decision-Making Models

Policy networks are graphical models that integrate decision-making models. They allow for multiple Markov decision processes (MDPs) that describe distinct focused aspects of a domain to work in harmony to solve a large-scale problem. This paper defines policy networks and shows how they are able to naturally generalize many previous models, such as options and constrained MDPs.
The Multimodal Correction Detection Problem

In order for socially aware agents to be truly useful, they should have abilities associated with human intelligence, such as the ability to detect their own mistakes from user reactions. This is an instance of implicit feedback. In this work we address the problem of detecting an agent's mistakes by identifying when the user tries to correct the agent. We refer to this problem as the Correction Detection task. We use a multimodal approach, using both the voice (acoustics and non-verbal sounds) as well as the transcript of the user's spoken commands.
SESOS: A Verifiable Searchable Outsourcing Scheme for Ordered Structured Data in Cloud Computing

While cloud computing is growing at a remarkable speed, privacy issues are far from being solved. One way to diminish privacy concerns is to store data on the cloud in encrypted form. However, encryption often hinders useful computation cloud services. A theoretical approach is to employ the so-called fully homomorphic encryption, yet the overhead is so high that it is not considered a viable solution for practical purposes. The next best thing is to craft special-purpose cryptosystems which support the set of operations required to be addressed by cloud services. In this paper, we put forward one such cryptosystem, which supports efficient search over structured data types, such as timestamps or network addresses, which are comprised of several segments with well-known values. The new cryptosystem, called SESOS, provides the ability to execute LIKE queries, along with the search for exact matches, as well as comparison. In addition, the extended version, called XSESOS, allows for verifying the integrity of ciphertexts. At its heart, SESOS combines any order-preserving encryption (OPE) scheme with a novel encryption scheme called Multi-map Perfectly Secure Cryptosystem(MuPS). We prove that MuPS is perfectly secure, and hence SESOS enjoys the same security properties of the underlying OPE scheme. The overhead of executing equality and comparison operations is negligible. The performance of LIKE queries is significantly improved by up to 1370X and the performance of result decryption improved by 520X compared to existing solutions on a database with merely 100K records (the improvement is even more significant in larger databases).
Multi-variable quantum key distribution based on optical bistability

We demonstrate a novel quantum key distribution scheme whose implementation utilizes multi-spectral bistability states to encode information. The transition between states is triggered optically. This makes it an all-optical system for ease of implementation in optical networks. We shall present an QKD architecture based on this scheme, and discuss its security and robustness against attacks.
Sentiment Analysis of 2019 Presidential Candidates on Kompas.com through Topic-driven Crawlers

Indonesian society is now transforming into the digital age. Web technology as a medium of information in various fields including social, business, education, government, etc., has been popularly used. This situation encourages various parties to be able to utilize the data available on it. One of the uses is sentiment analysis through web mining. Stakeholders certainly want to know whether opinion on their organization is positive, neutral, or negative. Sentiment analysis can be used to measure impressions or attitudes expressed in written language automatically. Unfortunately, the web is only designed for human readability, not for computer analysis. Besides not having a standard structure, web documents are also mixed with tag characters (html tags, java scripts, images, ads, etc.) that need to be eliminated before the data mining process. This study tried to construct a basic topic-driven web crawler as a model to harvest data of formal news on the web. Google search engine is used for topic search. The output of the crawler in the form of unstructured data (html document) is filtered so that the targeted text can be cleaned. This purified text is processed using the Naive Bayes algorithm to determine the polarity of its sentiments. The topic or case study used is the news of the University of Muhammadiyah Malang on the site kompas.com, one of Indonesia's online news media. The experiement showed a page with the requested topic on the kompas.com site can be taken and the sentiment of the extracted text can be analyzed.
Rationalizing local planning processes for DRR-CCA and EMR

Disaster risk reduction, climate change adaptation, and ecosystems management and restoration (DRR-CCA-EMR) require local government units (LGUs) to be at the forefront of the planning process. However, LGUs' ability to undertake many of these plans and mainstream them locally has been hampered by their limited capacities and understanding in its use and their ability to access and process the information needed for these plans. This practitioner or experience paper discusses the challenges of rationalizing the automation, access and use of DRR-CCA-EMR information in eGovernment in order to develop more integrated plans that engage stakeholders at the local level.
Celebrating a Century of Nonlinearity Across the Geosciences

Nonlinear concepts have evolved and become increasingly applicable to a wide range of geoscience inquiries, thus setting the stage for exciting new advances during AGU’s next 100 years.
The Iterative Method for Solving Non-Linear Equations

In this paper, we have combined the ideas of the False Position (FP) and Artificial Bee Colony (ABC) algorithms to find a fast and novel method for solving nonlinear equations. Additionally, to illustrate the efficiency of the proposed method, several benchmark functions are solved and compared with other methods such as ABC, PSO and GA.
Histogram Based Shape and Textural Characteristics for Facial Emotion Recognition

Emotion recognition has many applications in relation between human and machine. A facial emotion recognition framework for 6 basic emotions of happiness, sadness, disgust, surprise, anger and fear is proposed in this paper. The proposed framework utilizes the histogram estimate of shape and textural characteristics of face image. Instead of direct processing on the original gray levels of face image which may have not significant information about facial expression, the processing is done on transformed images containing informative features. The shape features are extracted by morphological operators by reconstruction and the texture ones are acquired by computing the gray-level co-occurrence matrix (GLCM), and applying Gabor filters. The use of whole face image may provide non-informative and redundant information. So, the proposed emotion recognition method just uses the most important components of face such as eyes, nose and mouth. After textural and shape feature extraction, the histogram function is applied to the shape and texture features containing emotional states of face. The simple and powerful nearest neighbor classifier is used for classification of fused histogram features. The experiments show the good performance of the proposed framework compared to some state-of-the-art facial expression methods such as local linear embedding (LLE), Isomap, Morphmap and local directional pattern (LDP).
To Study the Impact of CD-ROMs and Internet on the Student's Academic Work: A Case Study of Amity University, Jaipur

In the current era of information technology, library users are bound to use e-resources, if at all they are to keep abreast of the latest knowledge in their respective field of interest flooded globally. Traditional resources are gradually being replaced by digital resources in the contemporary modern libraries, apparently due to their inherent benefits which the print resources cannot bring. This paper presents and analyses the status of electronic resources facilities and services provided by the Amity University, Jaipur. It also discusses the purpose of using e-resources, benefits, subject coverage status, overall user satisfactions, problems that are faced by user's Amity University, Jaipur, while accessing e-resources and perceived impact of e-resources on users. Finally, it reports the results from questionnaire-based survey of e-resources use and its impact on user's Amity University, Jaipur.
The Effect of Using Multimedia Application in Language Issues to Increase Vocability of Deaf Children Class III SLB B YRTRW Surakarta 2018-2019

This research aims to the effective use of application multimedia sign language to enhancement vocabulary deaf children class III SLB B YRTRW Surakarta in year 2018/2019. This research use pre-experimental design has the form of one group pretest-posttest design.Â A subject in this research such as 7 children deaf class III in SLB B YRTRW Surakarta 2018/2019 that determined use by surfeited sampling. The instrument research use guide oral test with answer the direct question from the question that given by the Â researcher. Data of research analyzed use by Wilcoxon Sign Rank Test.Based on data analysis that was done, the value of Zhitung = -2,366 with asymp.sig.(2-tailed) is 0,018 that under significance level (p &lt; 0,05). That result shows that application multimedia sign language takes effect to increase vocabulary deaf children class III in SLB B YRTRW Surakarta in year 2018/2019.
PENERAPAN STRATEGI WATCHING BEFORE LEARNING PADA PEMBELAJARAN MATEMATIKA PROGRAM PENDIDIKAN KESETARAAN PAKET C PKBM KASIH BUNDO BUKITTINGGI

Package C's Curricular Education Program is a non-formal education channel aimed at children and citizens who are unable to access formal education services and who can not complete their education or drop out at high school / vocational level. Mathematics subjects in the Equivalency Program C Package have the same Content Standards as formal. . The low learning outcomes of learners is also one of the problems encountered during the learning process. One of the solutions is the innovation of Watching Before Learning (WBL) learning. WBL learning innovation is one of the learning strategies that utilize HP technology or computer. Based on the final test of the students who get the value above the KKM more than the initial test. Learning motivation is also increasing as it utilizes the technology used everyday.
Modal wavefront reconstruction to obtain Zernike coefficient with no cross coupling in lateral shearing measurement

Modal cross coupling frequently occurs in modal approaches from wavefront gradient data such as lateral shearing measurement through Zernike circle polynomials, since the gradients of Zernike circle polynomials are not orthogonal. We use a modal approaches incorporating the Gram matrix, using the orthogonality of angular derivative of m≠0 modes with respect to weight function w(ρ) = ρ (polar coordinates), and the orthogonality of radial derivative of m = 0 modes with respect to weight function w(ρ) = ρ(1-ρ2) (polar coordinates). The Gram matrix method needs no auxiliary vector functions. The Zernike coefficients can be obtained with no modal cross coupling. The simulation results are given, which indicate that the modal cross coupling is avoided by using Gram matrix method. This method can be easily extended to annulus, and the coefficients of Zernike annular polynomials with no modal cross coupling can be obtained.
Safe Policy Learning with Constrained Return Variance

It is desirable for a safety-critical application that the agent performs in a reliable and repeatable manner which conventional setting in reinforcement learning (RL) often fails to provide. In this work, we derive a novel algorithm to learn a safe hierarchical policy by constraining the direct estimate of the variance in the return in the Option-Critic framework [1]. We first present the novel theorem of safe control in the policy gradient methods and then extend the derivation to the Option-Critic framework.
Learning an Effective Control Policy for a Robotic Drumstick via Self-Supervision

We train a neural network to control a drumstick fastened to a motor. The network takes a temporally arranged sequence of desired strikes, or a rhythm, as input and outputs a sequence of motor velocities controlling the drumstick's physical movement. We use a new method of training, we call Collaborative Network Training, in which three networks work together to directly minimize a non-differentiable loss function. In this work, the goal is to minimize the difference between the input sequence and the resulting drumstick strikes on a surface produced by the network outputs. The resulting policy learned by the network works in real-time and has a precision of 10 milliseconds.