Reviewerly/exHarmony huggingface dataset:
EXPERIMENTAL STUDY OF THE INFLUENCE OF REGISTER FORMALITY ON THE PREFERRED REALIZATION OF THE COMPLEMENT CLAUSE IN RUSSIAN

In Russian, sentential complements (that-clauses) are optionally preceded by the demonstrative to ‘that’ (sometimes called ‘correlative’), with the required case marking or preposition selected by the verb. Several factors have been suggested in the literature as influencing the choice of the construction with to, including the matrix predicate as well as information structure. Th is work investigates the influence of register formality on the preferred realization of the complement. It is hypothesized that the construction with to, being the more explicit variant, is associated with more formal registers. Th is hypothesis has been previously tested on the basis of corpus data and received limited support. Here it is tested in a (two-alternative) forced-choice experiment where the register (news vs. spoken) is manipulated within items and subjects. 18 alternating predicates were tested, with two matched pairs of sentences with the contrasted registers constructed for each predicate. In addition, the experiment tested the influence of lexical biases, as measured by the ‘collostructional analysis’ or by relative frequency of the alternating constructions in the corpus. Th e results showed a reliable effect of lexical biases, as well as a much weaker but significant effect of formality suggesting that in the news register the construction with to is preferred more oft en compared to the spoken register, in line with the experimental hypothesis, whereas register did not interact with the lexical bias. Interestingly, a substantial amount of variation in the results was due to lexical biases as well as individual speaker preferences for one or the other construction.
Continuous-variable polarization mode entanglement in a V-type micromaser

The micromaser is an archetype experimental setting where a beam of excited two-level atoms is injected into a high-finesse cavity. It has played a pivotal role as a testbed for predictions of quantum optics. We consider a generalized micromaser setting consisting of a high-quality cavity pumped by a beam of three-level atoms. The atoms are assumed to be prepared to carry quantum coherence between their excited state doublet. Our objective is to produce quantum entanglement between the right-handed circular (RHC) and left-handed circular (LHC) polarized photons in the cavity, exploiting the quantum coherence in the pump atoms. For that aim, we derive the generalized micromaser master equation for our system. We find that the dynamics of the micromaser field driven by the pump beam is equivalent to two non-interacting RHC and LHC photonic systems sharing a common non-equilibrium environment. The effect of the shared bath is to mediate an incoherent interaction between the otherwise non-interacting cavity photons, which emerges only if the atoms carry quantum coherence. We take into account cavity losses as a source of quantum decoherence and characterize the quantum entanglement between the LHC and RHC polarized photons in terms of logarithmic negativity, Hillery–Zubairy and spin squeezing criterion, calculated using the dynamical solution of the master equation. We show that, in the same parameter regime, one of the criteria shows entanglement while for the other never detects entanglement. Our results reveal that LHC and RHC polarized photons can be entangled in the transient regime according to the logarithmic negativity criterion.
The anatomy of a vulnerability database: A systematic mapping study

Software vulnerabilities play a major role, as there are multiple risks associated, including loss and manipulation of private data. The software engineering research community has been contributing to the body of knowledge by proposing several empirical studies on vulnerabilities and automated techniques to detect and remove them from source code. The reliability and generalizability of the findings heavily depend on the quality of the information mineable from publicly available datasets of vulnerabilities as well as on the availability and suitability of those databases. In this paper, we seek to understand the anatomy of the currently available vulnerability databases through a systematic mapping study where we analyze (1) what are the popular vulnerability databases adopted; (2) what are the goals for adoption; (3) what are the other sources of information adopted; (4) what are the methods and techniques; (5) which tools are proposed. An improved understanding of these aspects might not only allow researchers to take informed decisions on the databases to consider when doing research but also practitioners to establish reliable sources of information to inform their security policies and standards.
A Formal Proof of the Strong Normalization Theorem for System T in Agda

We present a framework for the formal meta-theory of lambda calculi in first-order syntax, with two sorts of names, one to represent both free and bound variables, and the other for constants, and by using Stoughton's multiple substitutions. On top of the framework we formalize Girard's proof of the Strong Normalization Theorem for both the simply-typed lambda calculus and System T. As to the latter, we also present a simplification of the original proof. The whole development has been machine-checked using the Agda system.
LayoutQT—Layout Quadrant Tags to embed visual features for document analysis

The relative position of text blocks plays a crucial role in document understanding. However, the task of embedding layout information in the representation of a page instance is not trivial. Computer Vision and Natural Language Processing techniques have been advancing in extracting content from document images considering layout features. We propose a set of Layout Quadrant Tags (LayoutQT) as a new way of encoding layout information in textual embedding. We show that this enables a standard NLP pipeline to be significantly enhanced without requiring expensive mid or high-level multimodal fusion. Given that our focus is on developing a low computational cost solution, we focused our experiments on the AWD-LSTM neural network. We evaluated our method for page stream segmentation and document classification tasks on two datasets, Tobacco800 and RVL-CDIP. In the former, our method improved the F1 score from 97.9% to 99.1% and in the latter the F1 score went from 80.4% to 83.6%. Similar levels of performance improvement were also obtained when we applied LayoutQT with BERT.
ReLo: a Dynamic Logic to Reason About Reo Circuits

Critical systems require high reliability and are present in many domains. They are systems in which failure may result in financial damage or even loss of lives. Standard techniques of software engineering are not enough to ensure the absence of unacceptable failures and/or that critical requirements are fulfilled. Reo is a component-based modelling language that aims to provide a framework to build software based on existing pieces of software, which has been used in a wide variety of domains. Its formal semantics provides grounds to certify that systems based on Reo models satisfy specific requirements (i.e., absence of deadlocks). Current logical approaches for reasoning over Reo require the conversion of formal semantics into a logical framework. ReLo is a dynamic logic that naturally subsumes Reo's semantics. It provides a means to reason over Reo circuits. This work extends ReLo by introducing the iteration operator, and soundness and completeness proofs for its axiomatization.
Chaotic golden ratio guided local search for big data optimization

Biological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition-based optimization problems, named as Big Data optimization problem (Big-Opt), introduced at the Congress on Evolutionary Computing Big Data Competition (CEC’2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.
The Development of the Korean Child Protection System

Abstract This chapter explicates the development, cultural influences, unique challenges, and policy strategies of the child protection system in Korea. The Korean government fronted its critical challenge of enforcing an indigenous Korean child protection philosophy and institutions. Traditionally, child-rearing is a task for individuals instead of government intervention. The chapter expounds on the CPS system of social allocations, social provisions, delivery, and finance dimensions. The delivery and financing of services proved to be a significant policy issue. The chapter also emphasizes how the definition of child-rearing and child maltreatment are heavily influenced by cultural traditions and beliefs. Family preservation has been the ultimate goal of Korean child protection services.
Burst error-correcting quantum stabilizer codes designed from idempotents

Abstract Certain classical codes can be viewed isomorphically as ideals of group algebras, while studying their algebraic structures help extracting the code properties. Research has shown that this was remarkably efficient in the case when the code generators are idempotents. In quantum error correction, the theory of stabilizer formalism requires classical self-orthogonal additive codes over the finite field GF (4), which, via the lens of group algebras, are essentially $$F_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> -submodules over GF (4). Therefore, this paper provides a classification on idempotents in commutative group algebra GF (4) G , followed by a criterion that allows idempotents to generate stabilizer subgroups. Later, the construction of quantum stabilizer codes is done in the case when G is a cyclic group $$C_n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>C</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:math> , for $$n=2^m-1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>m</mml:mi> </mml:msup> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> and $$n=2^m+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>m</mml:mi> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . Quantum bounds on their burst error minimum distance are subsequently determined.
Quantum meet-in-the-middle attack on Feistel construction

Inspired by Hosoyamada and Sasaki (in: International conference on security and cryptography for networks, pp 386–403. Springer, 2018), we propose a new quantum meet-in-the-middle (QMITM) attack on r-round ( $$r \ge 7$$ ) Feistel construction to reduce the time complexity, which is based on Guo et al. (Des Codes Cryptogr 80(3):587–618, 2016) classical meet-in-the-middle (MITM) attack. In our attack, we adjust the size of truncated differentials to balance the complexities between constructing the tables and querying firstly and introduce a quantum claw finding algorithm to solve the collision search problem in classical MITM attack. The total time complexities of our attack are only $$O({2^{2n/3}} \cdot n)$$ , $$O({2^{19n/24}} \cdot n)$$ and $$O({2^{(r - 5)n/4}} \cdot n)$$ , when $$r = 7$$ , $$r = 8$$ and $$r > 8$$ , lower than classical and quantum attacks. Moreover, our attack belongs to Q1 model and is more practical than other quantum attacks.
Dilemma and Solution of Intellectual Property Protection of Biological Genetic Resources

The intellectual property protection of biological genetic resources has attracted the attention of many countries, but there are still many difficulties to be solved. At present, the main dilemma lies in whether they are the object of intellectual property protection. Based on the classification of biological genetic resources, the invention based on genetic resources or the invention using their processing, as well as the novelty and originality of digital sequence information, etc., will be included in the scope of protection. In the specific institutional structure, we need to establish basic guiding principles, and solve a series of problems, on this basis. The principle of equity should be applied to coordinate the conflict between the owners and the beneficiaries of genetic resources, and the quality of the system should be tested by whether it can promote biodiversity. The subject of their rights should be limited to the state or the community, and the scope of its power should be limited to four aspects, such as “labeling, control, income, and disposal,” and the standardized use of data should be dealt with through the blockchain technology and the laws and regulations of the digital economy.
Optimization of the Regression Ensemble Size

Ensemble learning algorithms such as bagging often generate unnecessarily large models, which consume extra computational resources and may degrade the generalization ability. Pruning can potentially reduce ensemble size as well as improve performance; however, researchers have previously focused more on pruning classifiers rather than regressors. This is because, in general, ensemble pruning is based on two metrics: diversity and accuracy. Many diversity metrics are known for problems dealing with a finite set of classes defined by discrete labels. Therefore, most of the work on ensemble pruning is focused on such problems: classification, clustering, and feature selection. For the regression problem, it is much more difficult to introduce a diversity metric. In fact, the only such metric known to date is a correlation matrix based on regressor predictions. This study seeks to address this gap. First, we introduce the mathematical condition that allows checking whether the regression ensemble includes redundant estimators, i.e., estimators, whose removal improves the ensemble performance. Developing this approach, we propose a new ambiguity-based pruning (AP) algorithm that bases on error-ambiguity decomposition formulated for a regression problem. To check the quality of AP, we compare it with the two methods that directly minimize the error by sequentially including and excluding regressors, as well as with the state-of-art Ordered Aggregation algorithm. Experimental studies confirm that the proposed approach allows reducing the size of the regression ensemble with simultaneous improvement in its performance and surpasses all compared methods.
Recognition of Occluded Goods under Prior Inference Based on Generative Adversarial Network

Aiming at the recognition of intelligent retail dynamic visual container goods, two problems that lead to low recognition accuracy must be addressed; one is the lack of goods features caused by the occlusion of the hand, and the other is the high similarity of goods. Therefore, this study proposes an approach for occluding goods recognition based on a generative adversarial network combined with prior inference to address the two abovementioned problems. With DarkNet53 as the backbone network, semantic segmentation is used to locate the occluded part in the feature extraction network, and simultaneously, the YOLOX decoupling head is used to obtain the detection frame. Subsequently, a generative adversarial network under prior inference is used to restore and expand the features of the occluded parts, and a multi-scale spatial attention and effective channel attention weighted attention mechanism module is proposed to select fine-grained features of goods. Finally, a metric learning method based on von Mises-Fisher distribution is proposed to increase the class spacing of features to achieve the effect of feature distinction, whilst the distinguished features are utilized to recognize goods at a fine-grained level. The experimental data used in this study were all obtained from the self-made smart retail container dataset, which contains a total of 12 types of goods used for recognition and includes four couples of similar goods. Experimental results reveal that the peak signal-to-noise ratio and structural similarity under improved prior inference are 0.7743 and 0.0183 higher than those of the other models, respectively. Compared with other optimal models, mAP improves the recognition accuracy by 1.2% and the recognition accuracy by 2.82%. This study solves two problems: one is the occlusion caused by hands, and the other is the high similarity of goods, thus meeting the requirements of commodity recognition accuracy in the field of intelligent retail and exhibiting good application prospects.
Limit Bipolar Sequences for Patchwork-Based Robust Digital Audio Watermarking

Ensuring the robustness of digital audio watermarking under the influence of interference, various transformations and possible attacks is an urgent problem. One of the most used and fairly stable marking methods is the patchwork method. Its robustness is ensured by the use of expanding bipolar numerical sequences in the formation and embedding of a watermark in a digital audio and correlation detection in the detection and extraction of a watermark. An analysis of the patchwork method showed that the absolute values of the ratio of the maximum of the autocorrelation function (ACF) to its minimum for expanding bipolar sequences and extended marker sequences used in traditional digital watermarking approach 2 with high accuracy. This made it possible to formulate criteria for searching for special expanding bipolar sequences, which have improved correlation properties and greater robustness. The article developed a mathematical apparatus for searching and constructing limit-expanding bipolar sequences used in solving the problem of robust digital audio watermarking using the patchwork method. Limit bipolar sequences are defined as sequences whose autocorrelation functions have the maximum possible ratios of maximum to minimum in absolute value. Theorems and corollaries from them are formulated and proved: on the existence of an upper bound on the minimum values of autocorrelation functions of limit bipolar sequences and on the values of the first and second petals of the ACF. On this basis, a rigorous mathematical definition of limit bipolar sequences is given. A method for searching for the complete set of limit bipolar sequences based on rational search and a method for constructing limit bipolar sequences of arbitrary length using generating functions are developed. The results of the computer simulation of the assessment of the values of the absolute value of the ratio of the maximum to the minimum of the autocorrelation and cross-correlation functions of the studied bipolar sequences for blind reception are presented. It is shown that the proposed limit bipolar sequences are characterized by better correlation properties in comparison with the traditionally used bipolar sequences and are more robust.
Trust, Privacy and Security for Smart Cities

The world is currently at the dawn of a new era characterized by a global transformation reshaping how we interact with our surroundings and each other [...]
Two-Stage Generator Network for High-Quality Image Inpainting in Future Internet

Sharpness is an important factor for image inpainting in future Internet, but the massive model parameters involved may produce insufficient edge consistency and reduce image quality. In this paper, we propose a two-stage transformer-based high-resolution image inpainting method to address this issue. This model consists of a coarse and a fine generator network. A self-attention mechanism is introduced to guide the transformation of higher-order semantics across the network layers, accelerate the forward propagation and reduce the computational cost. An adaptive multi-head attention mechanism is applied to the fine network to control the input of the features in order to reduce the redundant computations during training. The pyramid and perception are fused as the loss function of the generator network to improve the efficiency of the model. The comparison with Pennet, GapNet and Partial show the significance of the proposed method in reducing parameter scale and improving the resolution and texture details of the inpainted image.
Quantum 3D FFT in Tomography

The Radon transform constitutes the conventional tool for tomosynthesis, i.e., the composition of cross-sections of an object from its projections. It is actually a version of the Fourier Transform, which is accompanied by the appropriate digital high pass filters for correct distribution of energy among the reconstructed frequency components. The Radon transform and its inverse are employed in their 2D and 3D versions, respectively, and the whole procedure is verified by the a priori known cross-sections to be reconstructed (known fandom). Usually, 3D medical image cubes, which are to be reconstructed, require powerful computational tools since the 2D projections are of high-resolution containing millions of pixels. Although the 3D FFT is very fast, the large number of projections will result in a 3D spectrum of very large dimensions. Inverting this spectrum with the inverse 3D FFT is extremely time consuming. In this work, the implementation of the 2D Radon transform using the 2D Quantum Fourier Transform is analytically presented. Simultaneously, its inverse version is realized by means of the Quantum inverse 3D FFT. For this purpose, a review of the necessary quantum computational units is presented for the implementation of the quantum 3D FFT and simultaneously simple examples of tomosynthesis are given by means of the quantum version of the 2D Radon transform and its inverse 3D counterpart. The whole procedure of the quantum tomosynthesis is analytically described.
Named Entity Recognition Networks Based on Syntactically Constrained Attention

The task of named entity recognition can be transformed into a machine reading comprehension task by associating the query and its context, which contains entity information, with the encoding layer. In this process, the model learns a priori knowledge about the entity, from the query, to achieve good results. However, as the length of the context and query increases, the model struggles with an increasing number of less relevant words, which can distract it from the task. Although attention mechanisms can help the model understand contextual semantic relations, without explicit constraint information, attention may be allocated to less task-relevant words, leading to a bias in the model’s understanding of the context. To address this problem, we propose a new model, the syntactic constraint-based dual-context aggregation network, which uses syntactic information to guide query and context modeling. By incorporating syntactic constraint information into the attention mechanism, the model can better determine the relevance of each word in the context of the task, and selectively focus on the relevant parts of the context. This enhances the model’s ability to read and understand the context, ultimately improving its performance in named entity recognition tasks. Extensive experiments on three datasets, ACE2004, ACE2005, and GENIA, show that this method achieves superior performance when compared to previous methods.