Reviewerly/exHarmony huggingface dataset:
User Preference Adaptive Fitness of Interactive Genetic Algorithm Based Ceramic Disk Pattern Generation Method

Pattern design in real life is a complex optimization challenge that objective function can not express explicitly, and can be classified as an implicit target optimization problem.Owing to the fact that the objective function in implicit target optimization problem can not be completely structured, the traditional intelligent decision optimization method is inapplicable.This paper proposes an interactive genetic algorithm based on user preference adaptive fitness, in which users actively participate in optimization and take their subjective evaluation value as the adaptive fitness of evolutionary individuals.On the basis of adjusting the tone coordination degree of ceramic disc pattern, the improved method gives the calculation of user interest degree and style similarity, and completes the establishment of user preference model.The individual similarity calculation of the algorithm integrates the user preference information, making the calculation result more objective.In the experiment, the grain genes of ceramic disc pattern can be divided into two types: main body and border, both of which can be encoded by 16-bit binary code strings.And the weight values of style, tone collocation and structure were set respectively, and the ceramic disc patterns of modern fashion and fresh styles were obtained.In addition, the algorithm performance comparison is also completed.The experimental results show that with the progress of evolution, the midpoint and average width of the fitness of the population decrease, and the population quality is improved.Our algorithm can generate ceramic disc patterns of different styles, and the algorithm can obtain more satisfactory solutions in less evolutionary generations, which speeds up the algorithm's convergence speed and shows better evolutionary optimization ability.
No-reference quality assessment for image-based assessment of economically important tropical woods

Image Quality Assessment (IQA) is essential for the accuracy of systems for automatic recognition of tree species for wood samples. In this study, a No-Reference IQA (NR-IQA), wood NR-IQA (WNR-IQA) metric was proposed to assess the quality of wood images. Support Vector Regression (SVR) was trained using Generalized Gaussian Distribution (GGD) and Asymmetric Generalized Gaussian Distribution (AGGD) features, which were measured for wood images. Meanwhile, the Mean Opinion Score (MOS) was obtained from the subjective evaluation. This was followed by a comparison between the proposed IQA metric, WNR-IQA, and three established NR-IQA metrics, namely Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), deepIQA, Deep Bilinear Convolutional Neural Networks (DB-CNN), and five Full Reference-IQA (FR-IQA) metrics known as MSSIM, SSIM, FSIM, IWSSIM, and GMSD. The proposed WNR-IQA metric, BRISQUE, deepIQA, DB-CNN, and FR-IQAs were then compared with MOS values to evaluate the performance of the automatic IQA metrics. As a result, the WNR-IQA metric exhibited a higher performance compared to BRISQUE, deepIQA, DB-CNN, and FR-IQA metrics. Highest quality images may not be routinely available due to logistic factors, such as dust, poor illumination, and hot environment present in the timber industry. Moreover, motion blur could occur due to the relative motion between the camera and the wood slice. Therefore, the advantage of WNR-IQA could be seen from its independency from a "perfect" reference image for the image quality evaluation.
The development of a program for characterizing forensic handwriting examiners’ expertise–signature examination pilot study

Criticisms levelled at forensic handwriting examina tion expertise have focused on the clear lack of validation evidence offered to substantiate the claims of its practitioners. In general, expertise can be thought of as a skill that is more developed in the specialist than in the lay person. This paper out lines the shift in the process for delineating, and in time articulating, the nature of the expertise claimed within the Australian and New Zealand government and police document examination communities. A pilot study is presented where we com pared the opinions regarding the authorship of one hundred and fifty questioned signatures between seven government trained document examiners and eight lay persons. It was found that the government trained document examiners were statistically better at accurately determining the authorship of questioned signatures than were the lay group. Purchase Article - $10
Radiation-induced interaction potential of two qubits strongly coupled with a quantized electromagnetic field

We investigate the interaction of two two-level qubits with a single-mode quantum field in a cavity without the rotating wave approximation and considering that qubits can be located at an arbitrary distance from each other. We demonstrate that there exists a radiation-induced interaction potential between atoms. We studied the properties of the system numerically and in addition constructed a simple analytical approximation. It is shown that the observable characteristics are substantially dependent on the distance between the qubits in the strong-coupling regime. This allows one to perform the quantum control of the qubits, which can be exploited for the recording and transmission of quantum information.
Laplacian state transfer in Q-graph

The Q-graph of a graph G is defined to be the graph obtained from G by inserting a new vertex into each edge of G, and joining by edges those pairs of new vertices which lie on adjacent edges of G. In this paper, we investigate the existence of Laplacian perfect state transfer and Laplacian pretty good state transfer in Q-graphs of r-regular graphs for r ≥ 2. We prove that there is no Laplacian perfect state transfer in the Q-graph of an r-regular graph, if r+1 is a prime number. In contrast, we give sufficient conditions for the Q-graph of an r-regular graph, where r+1 is a prime number, to have Laplacian pretty good state transfer.
Renormalized Mutual Information for Artificial Scientific Discovery

We derive a well-defined renormalized version of mutual information that allows to estimate the dependence between continuous random variables in the important case when one is deterministically dependent on the other. This is the situation relevant for feature extraction, where the goal is to produce a low-dimensional effective description of a high-dimensional system. Our approach enables the discovery of collective variables in physical systems, thus adding to the toolbox of artificial scientific discovery, while also aiding the analysis of information flow in artificial neural networks.
Finding the Duplicate Questions in Stack Overflow using Word Embeddings

Searching query in the web applications may or may not yield anticipated results, constrained by the questions asked. User may not feel comfortable by seeing a bunch of questions which may not be relevant to his question. Henceforth we device a technique by finding the similar queries or searches to an entered query. For this purpose, Word Embeddings are used on questions asked in Stack Overflow. We use Cosine Similarity to find the similarity between two sentences as every word in the sentence is given Embedding i.e., a vector containing of numbers. We rank the queries based on this similarity. Our goal is to make the best similar question to the query asked to get the best rank.
DOM and dative in (Italo-)Romance

We aim at showing that the superficial identity of DOM internal arguments and of goal dative involves no accidental homophony or syncretism, but rather an underlying identical structure of embedding. Specifically, we conclude that DOM arguments are syntactically oblique (Section 2). We introduce the matter by detailing referential/animacy splits in Italo-Romance microvariation (Section 1.1). We show that in Italian varieties goal arguments can be introduced by prepositions different from a; the same oblique morphology is then associated with animate/definite (DOM) objects (Section 1.2). In Section 5, the existence of both leísta varieties (Ibero-Romance) and loísta varieties (in Italo- and Ibero- Romance) in clitic doubling provide further evidence in favour of a common treatment for goal and DOM datives.
Model selection versus traditional hypothesis testing in circular statistics: a simulation study

Many studies in biology involve data measured on a circular scale. Such data require different statistical treatment from those measured on linear scales. The most common statistical exploration of circular data involves testing the null hypothesis that the data show no aggregation and are instead uniformly distributed over the whole circle. The most common means of performing this type of investigation is with a Rayleigh test. An alternative might be to compare the fit of the uniform distribution model to alternative models. Such model-fitting approaches have become a standard technique with linear data, and their greater application to circular data has been recently advocated. Here we present simulation data that demonstrate that such model-based inference can offer very similar performance to the best traditional tests, but only if adjustment is made in order to control type I error rate.
Minimal informationally complete measurements for probability representation of quantum dynamics

Abstract In the present work, we suggest an approach for describing dynamics of finite-dimensional quantum systems in terms of pseudostochastic maps acting on probability distributions, which are obtained via minimal informationally complete quantum measurements. The suggested method for probability representation of quantum dynamics preserves the tensor product structure, which makes it favourable for the analysis of multi-qubit systems. A key advantage of the suggested approach is that minimal informationally complete positive operator-valued measures (MIC-POVMs) are easier to construct in comparison with their symmetric versions (SIC-POVMs). We establish a correspondence between the standard quantum-mechanical formalism and the MIC-POVM-based probability formalism. Within the latter approach, we derive equations for the unitary von-Neumann evolution and the Markovian dissipative evolution, which is governed by the Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) generator. We apply the MIC-POVM-based probability representation to the digital quantum computing model. In particular, for the case of spin-1/2 evolution, we demonstrate identifying a transition of a dissipative quantum dynamics to a completely classical-like stochastic dynamics. One of the most important findings is that the MIC-POVM-based probability representation gives more strict requirements for revealing the non-classical character of dissipative quantum dynamics in comparison with the SIC-POVM-based approach. Our results give a physical interpretation of quantum computations and pave a way for exploring the resources of noisy intermediate-scale quantum devices.
An Analysis of Model-Based Heuristic Search Techniques for StarCraft Combat Scenarios

Real-Time Strategy games have become a popular test-bed for modern AI system due to their real-time computational constraints, complex multi-unit control problems, and imperfect information. One of the most important aspects of any RTS AI system is the efficient control of units in complex combat scenarios, also known as micromanagement. Recently, a model-based heuristic search technique called Portfolio Greedy Search (PGS) has shown promisingpaper we present the first integration of PGS into the StarCraft game engine, and compare its performance to the current state-of-the-art deep reinforcement learning method in several benchmark combat scenarios. We then perform theperformance for providing real-time decision making in RTS combat scenarios, but has so far only been tested in SparCraft: an RTS combat simulator. In this same experiments within the SparCraft simulator in order to investigate any differences between PGS performance in the simulator and in the actual game. Lastly, we investigate how varying parameters of the SparCraft simulator affect the performance of PGS in the StarCraft game engine. We demonstrate that the performance of PGS relies heavily on the accuracy of the underlying model, outperforming other techniques only for scenarios where the SparCraft simulation model more accurately matches the StarCraft game engine.
Universal implementation of arbitrary unitary matrices by femtosecond laser written waveguide lattice

The integrated photonic platform has established itself as a versatile tool for experimental implementation of linear optical quantum computations, that base on unitary transformation of the input states. In this paper we investigate the potential application of the waveguide lattice architecture to realization of arbitrary unitary matrices. We propose a waveguide lattice construction and find its parameters using methods of numerical optimization. Also we showed that a necessary lattice parameters are within the reach femtosecond laser writing technology.
Weight-Dependent Gates for Network Pruning

In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. This paper argues that the pruning decision should depend on the convolutional weights, and thus proposes novel weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary gates to prune or keep the filters automatically. To prune the network under efficiency constraints, a switchable Efficiency Module is constructed to predict the hardware latency or FLOPs of candidate pruned networks. Combined with the proposed Efficiency Module, W-Gates can perform filter pruning in an efficiency-aware manner and achieve a compact network with a better accuracy-efficiency trade-off. We have demonstrated the effectiveness of the proposed method on ResNet34, ResNet50, and MobileNet V2, respectively achieving up to 1.33/1.28/1.1 higher Top-1 accuracy with lower hardware latency on ImageNet. Compared with state-of-the-art methods, W-Gates also achieves superior performance.
Improving Faceted Search Results for Web-based Information Exploration

The World Wide Web (WWW), a fast-growing store, contains a significant portion of human knowledge. However, the sheer scale of the Web, along with the fact that it is decentralized, highly redundant, and largely inaccurate, causes the use of the knowledge is quite cumbersome. The present search engines (SEs) use the query, and the response lookup process is incapable of producing a precise result. Thus, researchers work beyond this paradigm to explore a new class of methods to seek information, which known as an exploratory search (ES). This ES is open-ended, and its faceted search (FS) improves the overall search process. The search engine presented in this study is running in the cloud computing platform environment. Its development is based on the idea of improving visual ES while exploring information on the Web. This notion reflects the process of seeking and combing the vast information by using the coordinated visualization method, apart from minimizing the effort spent in seeking information per query. Finally, we evaluate the proposed prototype against the Internet Movie Database (IMDb) search engine, an online database of information related to films, television programs, home videos, video games, and streaming content online including cast, production crew, and personal biographies, plot summaries, trivia, fan, and critical reviews, and ratings. The results show that the proposed search engine gives more relevant search results as compared with the others.
The Expertise Level

Computers are quickly gaining on us. Artificial systems are now exceeding the performance of human experts in several domains. However, we do not yet have a deep definition of expertise. This paper examines the nature of expertise and presents an abstract knowledge-level and skill-level description of expertise. A new level lying above the Knowledge Level, called the Expertise Level, is introduced to describe the skills of an expert without having to worry about details of the knowledge required. The Model of Expertise is introduced combining the knowledge-level and expertise-level descriptions. Application of the model to the fields of cognitive architectures and human cognitive augmentation is demonstrated and several famous intelligent systems are analyzed with the model.
Reducing Space Search in Combinatorial Optimization Using Machine Learning Tools

A new metaheuristic, called Feature-Guided MNS (FG-MNS) is proposed, combining well-known local search with simple machine learning techniques. In this metaheuristic, a solution is represented by features (mean depth of each route, standard deviation of the length of each route, etc.). The solver uses decision trees to define promising areas in the features space. The search is mainly focused on the promising areas, in order to minimize the exploration time, and to improve the quality of the found solutions. Additional neighborhoods, guided by the features are proposed.
Nonclassicality detection from few Fock-state probabilities

We devise a new class of criteria to certify the nonclassicality of photon- and phonon-number statistics. Our criteria extend and strengthen the broadly used Klyshko's criteria, which require knowledge of only a finite set of Fock-state probabilities. This makes the criteria well-suited to experimental implementation in realistic conditions. Moreover, we prove the completeness of our method in some scenarios, showing that, when only two or three Fock-state probabilities are known, it detects all finite distributions incompatible with classical states. In particular, we show that our criteria detect a broad class of noisy Fock states as nonclassical, even when Klyshko's do not. The method is directly applicable to trapped-ion, superconducting circuits, and optical and optomechanical experiments with photon-number resolving detectors. This work represents a significant milestone towards a complete characterisation of the nonclassicality accessible from limited knowledge of the Fock-state probabilities.
LXPER Index: A Curriculum-specific Text Readability Assessment Model for EFL Students in Korea

Automatic readability assessment is one of the most important applications of Natural Language Processing (NLP) in education.Since automatic readability assessment allows the fast selection of appropriate reading material for readers at all levels of proficiency, it can be particularly useful for the English education of English as Foreign Language (EFL) students around the world.However, most readability assessment models are developed for the native readers of English and have low accuracy for texts in non-native English Language Training (ELT) curriculum.We introduce LXPER Index, which is a readability assessment model for non-native EFL readers in the ELT curriculum of Korea.To measure LXPER Index, we use the mixture of 22 features which we prove to be significant in text readability assessment.We also introduce the Text Corpus of the Korean ELT Curriculum (CoKEC-text), which is the first collection of English texts from a non-native country's ELT curriculum with each text's target grade level labeled.In addition, we assembled the Word Corpus of the Korean ELT Curriculum (CoKEC-word), which is a collection of words from the Korean ELT curriculum with word difficulty labels.Our experiments show that our new model, trained with CoKEC-text, significantly improves the accuracy of automatic readability assessment for texts in the Korean ELT curriculum.The methodology used in this research can be applied to other ELT curricula around the world.
Do all roads lead to Rome? Understanding the role of initialization in iterative back-translation

Back-translation provides a simple yet effective approach to exploit monolingual corpora in Neural Machine Translation (NMT). Its iterative variant, where two opposite NMT models are jointly trained by alternately using a synthetic parallel corpus generated by the reverse model, plays a central role in unsupervised machine translation. In order to start producing sound translations and provide a meaningful training signal to each other, existing approaches rely on either a separate machine translation system to warm up the iterative procedure, or some form of pre-training to initialize the weights of the model. In this paper, we analyze the role that such initialization plays in iterative back-translation. Is the behavior of the final system heavily dependent on it? Or does iterative back-translation converge to a similar solution given any reasonable initialization? Through a series of empirical experiments over a diverse set of warmup systems, we show that, although the quality of the initial system does affect final performance, its effect is relatively small, as iterative back-translation has a strong tendency to convergence to a similar solution. As such, the margin of improvement left for the initialization method is narrow, suggesting that future research should focus more on improving the iterative mechanism itself.
Challenges in Face Recognition Using Machine Learning Algorithms: Case of Makeup and Occlusions

This paper studies a Face Recognition problem caused by significant variations in ace images, which in practice can be due to different poses, emotion expressions, hairstyles or makeup. Existing Artificial Neural Networks (ANN) have achieved a high recognition accuracy comparable with or even better than human recognition, However in the presence of significant variations the existing ANN methods are still weak. We introduce a new benchmark data set of face images with variable makeup, hairstyles and occlusions, named BookClub artistic makeup data, and then examine the performance of the ANNs under different conditions. In our experiments, the recognition accuracy has decreased when the test images include unseen types of the makeup and occlusions, happened in a real-world scenario. We show that the makeup and other occlusions can be used not only to disguise a person’s identity from the ANN algorithms, but also to spoof a wrong identification.
Highly reliable authentication based on handwritten passwords using hybrid neural networks with protection of biometric templates from being compromised

Introduction: Biometrics-to-code converters based on neural networks are the ideological basis for a series of GOST R 52633 standards (unparalleled anywhere in the world) and can be used in the development of highly reliable biometric authentication and electronic signature with biometric activation. Purpose: Developing a model of a biometrics-to-code converter for highly reliable biometric authentication by handwritten passwords with high resistance to attacks on knowledge extraction. Results: We demonstrated the vulnerability of neural networks which makes it possible to perform quick directed enumeration of competing examples in order to compromise a biometric pattern and the personal key of its owner. We described a method of effective protection against this attack, and proposed a hybrid model for a biometrics-to-code converter based on a new type of hybrid neural networks, which does not compromise the biometric standard and the user’s key (password), being resistant to such attacks. The high reliability and effectiveness of the proposed model has been experimentally confirmed in handwritten password verification. The reliability indicators for generating a key from a handwritten password were: FRR = 11.5%, FAR = 0.0009% with a key length of 1024 bits (taking into account the presented fakes of a handwritten pattern). Practical relevance: The results can be used in information security applications or electronic document management.