Reviewerly/exHarmony huggingface dataset:
Enhanced Secret Hiding Mechanism Based on Genetic Algorithm

Many industrial production processes, transactions, and multimedia communications are generated in daily life. Digital watermarking technology plays a vital role in the field of multimedia information security. Information security has got many remarkable achievements. However, a contradiction between the robustness of the watermark and the quality of the cover multimedia has not been paid attention by scholars. This paper proposes an algorithm to improve the cover image quality of QIM-based watermarking algorithm based on genetic algorithm. The experimental results demonstrate that the proposed algorithm is effective.
Analyses of Subject Competencies of the Units Related to Core Concept Relation in Middle School Technology-Home Economics Textbooks based on 2015 Revised Curriculum

본 연구의 목적은 교과역량 ‘실천적문제해결능력’과 ‘관계형성능력’의 분석준거를 설정하고 2015 개정 중학교 기술 · 가정교과서의 핵심개념 ‘관계’ 관련 단원을 분석준거에 따라 분석하는 데 있다. 12종 출판사의 중학교 ‘기술 · 가정 2’ 교과서의 핵심개념 ‘관계’ 관련 단원에서 활동과제를 중심으로 개발된 분석 준거에 따라 분석한 결과는 다음과 같다. 첫째, 교과서에 구현된 교과역량이 출판사별로 차이가 많았다. ‘실천적문제해결능력’은 A교과서, E교과서와 G교과서가 높게 나타났고 반면 D교과서의 빈도가 가장 낮았다. ‘관계형성능력’은 K교과서, A교과서가 높게 나타났고 가장 낮은 빈도를 보인 것은 J교과서였다. 둘째, ‘실천적문제해결능력’과 ‘관계형성능력’의 역량별 하위요소 중심으로 12종 교과서 전체의 활동과제들을 살펴보면, 우선 ‘실천적문제해결능력’의 가치판단이 가장 높은 빈도를 보였고 이어서 논리적 사고, 실천적 추론, 의사결정 등으로 나타났다. ‘관계형성능력’에서는 가족관계와 공동체의식이 가장 높은 빈도로 나타났고 의사소통, 갈등관리, 타인존중 등의 순으로 나타났지만, 환경 및 생태의식은 구현되지 않았다. 이와 같이 2015 개정 중학교 기술 · 가정 12종 교과서의 핵심개념 ‘관계’ 관련 단원은 교육과정의 세부목표에서 제시된 두 교과역량을 대체로 구현하고 있다.The purposes of this study were to develop analysis criteria and analyze the two subject competencies of units dealing with the core concept Relation of the middle school Technology-Home Economics textbook in accordance with the 2015 revised curriculum: practical-problem-solving-ability and relationship ability . The subject of analysis of the current study was the activity assignment of Chapter 1 related to the core concept Relation , from the twelve middle school ‘Technology-Home Economics II’ textbooks. The results of this study were as follows: First, some textbooks showed differences in highlighting or downplaying the components of certain subject competencies. The analysis results for practical-problem-solving-ability in textbook A, E, and G were shown in the high frequency, while in textbook D in the lowest frequency. The analysis results for relationship ability in textbook K and A were shown in the high frequency, while in textbook J in the lowest frequency. Second, Value Judgement was the most frequent component of practical-problem-solving-ability followed by Logical Thinking, Practical Reasoning, and Decision-Making. Family Relation and Sense of Community was the component of relationship ability most frequently dealt with followed by Communication, Conflict Management, and Respecting Others, however Environmental and Ecological Awareness was not shown at all. The two subject competencies were mostly reflected in the Technology-Home Economics textbooks from all the twelve publishers.
Analysing a Quality-of-Life Survey by Using a Coclustering Model for Ordinal Data and Some Dynamic Implications

Summary The data set that motivated this work is a psychological survey on women affected by a breast tumour. Patients replied at different stages of their treatment to questionnaires with answers on an ordinal scale. The questions relate to aspects of their life referred to as ‘dimensions’. To assist psychologists in analysing the results, it is useful to highlight the structure of the data set. The clustering method achieves this by creating groups of individuals that are depicted by a representative of the group. From a psychological position, it is also useful to observe how questions may be clustered. The simultaneous clustering of both patients and questions is called ‘coclustering’. However, placing questions in the same group when they are not related to the same dimension does not make sense from a psychological perspective. Therefore, constrained coclustering was performed to prevent questions of different dimensions from being placed in the same column cluster. The evolution of coclusters over time was then investigated. The method uses a constrained latent block model embedding a probability distribution for ordinal data. Parameter estimation relies on a stochastic expectation–maximization algorithm associated with a Gibbs sampler, and the integrated completed likelihood–Bayesian information criterion is used to select the number of coclusters.
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets

There are many problems in machine learning and data mining which are equivalent to selecting a non-redundant, high quality set of objects. Recommender systems, feature selection, and data summarization are among many applications of this. In this paper, we consider this problem as an optimization problem that seeks to maximize the sum of a sum-sum diversity function and a non-negative monotone submodular function. The diversity function addresses the redundancy, and the submodular function controls the predictive quality. We consider the problem in big data settings (in other words, distributed and streaming settings) where the data cannot be stored on a single machine or the process time is too high for a single machine. We show that a greedy algorithm achieves a constant factor approximation of the optimal solution in these settings. Moreover, we formulate the multi-label feature selection problem as such an optimization problem. This formulation combined with our algorithm leads to the first distributed multi-label feature selection method. We compare the performance of this method with centralized multi-label feature selection methods in the literature, and we show that its performance is comparable or in some cases is even better than current centralized multi-label feature selection methods.
Emoticons in Social Media

This chapter investigates the usage of Japanese emoticons (kaomoji, 顔文字) in posts and comments of 1,125 Japanese Facebook users, with a focus on generation and gender differences. The analysis of 2,454 kaomoji revealed that they were more frequently used in comments than in posts and more often added to shorter sentences than to longer ones. Women under the age of 30 had the highest proportion of kaomoji use, while men over the age of 60 the lowest. Japanese men over the age of 60 used a more diverse range of kaomoji than women (highest type-token ratio). In contrast to Western emoticons, kaomoji examined in this study did not function as markers of irony, unless combined with different notations of laughter: 笑, わら, or w. This usage was only found in the data of users under the age of 30, irrespective of gender.
Embodied Vision-and-Language Navigation with Dynamic Convolutional Filters

In Vision-and-Language Navigation (VLN), an embodied agent needs to reach a target destination with the only guidance of a natural language instruction. To explore the environment and progress towards the target location, the agent must perform a series of low-level actions, such as rotate, before stepping ahead. In this paper, we propose to exploit dynamic convolutional filters to encode the visual information and the lingual description in an efficient way. Differently from some previous works that abstract from the agent perspective and use high-level navigation spaces, we design a policy which decodes the information provided by dynamic convolution into a series of low-level, agent friendly actions. Results show that our model exploiting dynamic filters performs better than other architectures with traditional convolution, being the new state of the art for embodied VLN in the low-level action space. Additionally, we attempt to categorize recent work on VLN depending on their architectural choices and distinguish two main groups: we call them low-level actions and high-level actions models. To the best of our knowledge, we are the first to propose this analysis and categorization for VLN.
Language networks and semantic networks

How does the human brain work? How can we make sentences and make sentences? These are the questions dealt with already many scientists and several companies developing artificial intelligence. This article presents a study on language networks. At the beginning is the research of the works that have already dealt with this issue. In the next part, the author deals with the application of language networks and semantic networks.
Lower bounds for linear decision lists.

We demonstrate a lower bound technique for linear decision lists, which are decision lists where the queries are arbitrary linear threshold functions. We use this technique to prove an explicit lower bound by showing that any linear decision list computing the function $MAJ \circ XOR$ requires size $2^{0.18 n}$. This completely answers an open question of Tur{a}n and Vatan [FoCM'97]. We also show that the spectral classes $PL_1, PL_\infty$, and the polynomial threshold function classes $\widehat{PT}_1, PT_1$, are incomparable to linear decision lists.
Cubical Syntax for Reflection-Free Extensional Equality

We contribute XTT, a cubical reconstruction of Observational Type Theory [Altenkirch et al., 2007] which extends Martin-Lof's intensional type theory with a dependent equality type that enjoys function extensionality and a judgmental version of the unicity of identity proofs principle (UIP): any two elements of the same equality type are judgmentally equal. Moreover, we conjecture that the typing relation can be decided in a practical way. In this paper, we establish an algebraic canonicity theorem using a novel extension of the logical families or categorical gluing argument inspired by Coquand and Shulman [Coquand, 2018; Shulman, 2015]: every closed element of boolean type is derivably equal to either true or false.
"Why Did You Do That?" - Explaining Black Box Models with Inductive Synthesis.

By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box model ...
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an ambient Euclidean space. In a number of applications, a priori known spatial constraints can shrink the ambient space into a considerably smaller manifold. Additionally, in these applications the Euclidean geometry might induce a suboptimal similarity measure, which could be improved by choosing a different metric. Euclidean models ignore such information and assign probability mass to data points that can never appear as data, and vastly different likelihoods to points that are similar under the desired metric.We propose the wrapped Gaussian process latent variable model (WGPLVM), that extends Gaussian process latent variable models to take values strictly on a given Riemannian manifold, making the model blind to impossible data points. This allows non-linear, probabilistic inference of low-dimensional Riemannian submanifolds from data. Our evaluation on diverse datasets show that we improve performance on several tasks, including encoding, visualization and uncertainty quantification.
Unsupervised Tuning of Filter Parameters Without Ground-Truth Applied to Aerial Robots

Autonomous robots heavily rely on well-tuned state estimation filters for successful control. This letter presents a novel automatic tuning strategy for learning filter parameters by minimizing the innovation, i.e., the discrepancy between expected and received signals from all sensors. The optimization process only requires the inputs and outputs of the filter without ground truth. Experiments were conducted with the Crazyflie quadrotor, and all parameters of the extended Kalman filter are well tuned after one 10-s manual flight. The proposed method has multiple advantages, of which we demonstrate two experimentally. First, the learned parameters are suitable for each individual drone, even if their particular sensors deviate from the standard, e.g., by being noisier. Second, this manner of self-tuning allows one to effortlessly expand filters when new sensors or better drone models become available. The learned parameters result in a better state estimation performance than the standard Crazyflie parameters.
Robustness self-testing of states and measurements in the prepare-and-measure scenario with 3 → 1 random access code*

Recently, Tavakoli et al . proposed a self-testing scheme in the prepare-and-measure scenario, showing that self-testing is not necessarily based on entanglement and violation of a Bell inequality [ Phys. Rev. A 98 062307 (2018) ]. They realized the self-testing of preparations and measurements in an <?CDATA $N\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> ( <?CDATA $N\ge 2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>2</mml:mn> </mml:math> ) random access code (RAC), and provided robustness bounds in a <?CDATA $2\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2</mml:mn> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> RAC. Since all <?CDATA $N\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>N</mml:mi> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> RACs with shared randomness are combinations of <?CDATA $2\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2</mml:mn> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> and <?CDATA $3\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>3</mml:mn> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> RACs, the <?CDATA $3\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>3</mml:mn> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> RAC is just as important as the <?CDATA $2\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>2</mml:mn> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> RAC. In this paper, we find a set of preparations and measurements in the <?CDATA $3\to 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>3</mml:mn> <mml:mo>→</mml:mo> <mml:mn>1</mml:mn> </mml:math> RAC, and use them to complete the robustness self-testing analysis in the prepare-and-measure scenario. The method is robust to small but inevitable experimental errors.
Apparent Age Estimation with Relational Networks

Apparent age estimation is a newly proposed and under-studied problem of predicting the age that someone looks rather than their actual age. It has applications in many areas within the beauty industry[3]. Methods based on convolutional neural networks (CNNs) have proved to be state-of-the-art on the few datasets used to benchmark this task. However, such CNNs typically collapse spatial information via a Global Average Pooling operation. They do not perform any explicit treatment of spatial relationships of the higher-level features which emerge in the later stages of the network and which may correspond to facial parts or blemishes that are characteristic of age. In this paper, we consider a newly proposed CNN module called relational networks that explicitly capture spatial relationships. We hypothesize that we can estimate age better by learning such relationships in the final set of CNN feature maps where spatial information is still retained. Experiments were conducted on both ChaLearn LAP 2015 and 2016 datasets [6], [7] showing that on average, there is a 3.53% improvement on Mean Absolute Error and 3.31% improvement on -error when compared to the baseline. A test was also calculated to show that the improvement is statistically significant.
High dimensional quantum logic gates and quantum information processing

Quantum information science has extensive applications in various research fields, such as information, physics and computer science. It is well known that the quantum properties of a system exhibit many unique advantages in the security of information transmission and processing, computation, and the improvement of channel capacity. Quantum information includes several research fields, for example, quantum communication, quantum computing and so on. In the past 30 years, the development of quantum communication, quantum computing and quantum information processing has made great progress in both theory and experiments. As a fundamental resource for quantum information processing, entanglement is a key element for many applications, such as quantum key distribution, quantum teleportation, quantum dense coding, quantum secure direct communication and quantum metrology. Because of the weak interaction with the environment and the degrees of freedom, entangled photons are the excellent candidates as the quantum-information carrier. Several remarkable experiments have been performed with the photonic entanglement. However, it is necessary to share a pair of entangled photons between the communicating parties in advance in these quantum information processes. Furthermore, the application of hyperentanglement, the entanglement of photon pairs simultaneously existing in more than one degree of freedom, has been widely studied for the reason that it can improve the channel capacity of quantum communications and implement hyperparallel computing, such as the quantum error-correcting code, quantum repeater and deterministic entanglement purification. Moreover, the quantum logic gates play an essential role in quantum information processing which attracts much attention on the designing of the quantum logic gates. Depending on the coherent dynamics of the cavity quantum electrodynamical system, deterministic quantum gate operations between the quantum systems can be realized. Here in this review, by considering high-dimensional quantum systems, several high-dimensional quantum protocols are presented. They can greatly improve the capacity of quantum channel and noise immunity, which exhibits potential applications in quantum information processing. This review describes the development of quantum computing and quantum information processing using hyperentanglement. And the entangled photons in single degree of freedom and multi-degrees of freedom are used to illustrate the two-dimensional and high-dimensional quantum information processing schemes. Finally, the latest progress and further applications of quantum computing and quantum information processing are introduced.
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds

Real world data often exhibit low-dimensional geometric structures, and can be viewed as samples near a low-dimensional manifold. This paper studies nonparametric regression of Holder functions on low-dimensional manifolds using deep ReLU networks. Suppose $n$ training data are sampled from a Holder function in $\mathcal{H}^{s,\alpha}$ supported on a $d$-dimensional Riemannian manifold isometrically embedded in $\mathbb{R}^D$, with sub-gaussian noise. A deep ReLU network architecture is designed to estimate the underlying function from the training data. The mean squared error of the empirical estimator is proved to converge in the order of $n^{-\frac{2(s+\alpha)}{2(s+\alpha) + d}}\log^3 n$. This result shows that deep ReLU networks give rise to a fast convergence rate depending on the data intrinsic dimension $d$, which is usually much smaller than the ambient dimension $D$. It therefore demonstrates the adaptivity of deep ReLU networks to low-dimensional geometric structures of data, and partially explains the power of deep ReLU networks in tackling high-dimensional data with low-dimensional geometric structures.
Big Data Interoperability in e-Health Systems

Health sector has evolved into a major source of digital data. The data generated is distributed on various servers at different place. Interoperability is a major concern whenever the electronic data is shared among peers. The main goal of an interoperable solution is to provide an understanding of data both syntactically and semantically. The research reviews the current work in data interoperability, its need and existing solutions in health domain. It also highlights the current issues for not achieving a fully interoperable system in healthcare and proposed Architecture for handling existing issues for secure Patient data retrieval and sharing across boundaries. This work will be a knowledge source for various professional and researchers working with Electronic Health Record of patients.
Comparative Analysis of Evolutionary Algorithms Based on Swarm Intelligence for QoS Optimization of Cloud Services

As the technology of cloud computing becomes mature, the service idea of IT resources has been promoted rapidly. The various available cloud services have different quality of service (QoS). Different users have different QoS requirements for services. Finding the best QoS strategy which a user is satisfied with is a multi-criteria NP-hard problem. The completed theory and applied research have proved that the swarm intelligence method can effectively solve most multiobjective optimization problems. Therefore, a Pareto optimal solution for QoS strategy can be found by an evolutionary algorithm based on swarm intelligence. However, so far, only few solutions based on these approaches have been proposed and there exists no comparative study published to date. This motivated us to perform an analysis of state of the art evolutionary algorithm based on swarm intelligence. By a large number of contrast experiments under different circumstances, three QoS evolutionary algorithms based on swarm intelligence are analyzed and compared.
Adversarial Neural Pruning.

Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challenging to deploy them in real-world safety-critical applications. In this paper, we conjecture that the leading cause of adversarial vulnerability is the distortion in the latent feature space, and provide methods to suppress them effectively. Explicitly, we define \emph{vulnerability} for each latent feature and then propose a new loss for adversarial learning, \emph{Vulnerability Suppression (VS)} loss, that aims to minimize the feature-level vulnerability during training. We further propose a Bayesian framework to prune features with high vulnerability to reduce both vulnerability and loss on adversarial samples. We validate our \emph{Adversarial Neural Pruning with Vulnerability Suppression (ANP-VS)} method on multiple benchmark datasets, on which it not only obtains state-of-the-art adversarial robustness but also improves the performance on clean examples, using only a fraction of the parameters used by the full network. Further qualitative analysis suggests that the improvements come from the suppression of feature-level vulnerability.
On a biseparability criterion of bipartite qudit state

In this paper, we propose a new approach to tackle the separability problem for bipartite qudit mixed-states. This is based on the Majorana representation which allows to represent a N -spinors (qudit) as a symmetric state of N spin-1/2. We also discuss how we can exploit such representation and the notion of the biseparability of multipartite qubit states in the sense to establish new criteria of the separability problem based on the PPT and concurrence.