Reviewerly/exHarmony huggingface dataset:
Effects of Case-marking on the Anticipatory Processing of Korean Sentences

The goal of this study was to explore the effect of the case-marking information from pre-verbal arguments on the anticipatory processing of Korean sentences. More specifically, it was examined whether the case-markers can be used to predict an upcoming argument even before it is introduced into the string. In our eye-tracking experiment using the visual-world paradigm, 24 adult native speakers of Korean showed significantly more anticipatory eye-movements to the potential referent of a Theme object as soon as hearing the sequence of a nominative-marked NP and a dative-marked NP, as compared to when the second NP is accusative-marked. These results confirm the predictive mechanism of the parsing system and the case effect on the prediction in Korean: that is, guided by the case-marking information which is available earlier in the input, the parser can predict a forthcoming argument and thus activate a structural representation of the currently processed sentence. In this way, a verb-final sentence can be interpreted incrementally and predictively as well at each moment of processing.
Paradigm of Post-quantum Cryptography and Crypto-agility: Strategy Approach of Quantum-safe Techniques

General security protocols use cryptographic schemes which are not prepared against quantum attacks. Asymmetric public key cryptographic schemes based on well researched methods are defined as broken, from quantum computer perspective. In contrast, symmetric cryptographic schemes and there methods are considered quantum-safe. A new effective strategy is necessary with focusing on post-quantum cryptography for evaluating and researching new quantum-safe cryptographic principles and crypto-agility to update vulnerable security protocols.
New quantumness domains through generalized squeezed states

Current definitions of both squeezing operator and squeezed vacuum state are critically examined on the grounds of consistency with the underlying su(1,1) algebraic structure. Accordingly, the generalized coherent states for su(1,1) in its Schwinger two-photon realization are proposed as squeezed states. The physical implication of this assumption is that two additional degrees of freedom become available for the control of quantum optical systems. The resulting physical predictions are evaluated in terms of quadrature squeezing and photon statistics, while the application to a Mach–Zehnder interferometer is discussed to show the emergence of nonclassical regions, characterized by negative values of Mandel's parameter, which cannot be anticipated by the current formulation, and then outline future possible use in quantum technologies.
Optimal phase estimation with photon-number difference measurement using twin-Fock states of light*

Quantum phase measurement with multiphoton twin-Fock states has been shown to be optimal for detecting equal numbers of photons at the output ports of a Mach–Zehnder interferometer (i.e., the so-called single-fringe detection), since the phase sensitivity can saturate the quantum Cramér–Rao lower bound at certain values of phase shift. Here we report a further step to achieve a global phase estimation at the Heisenberg limit by detecting the particle-number difference (i.e., the <?CDATA ${\hat{J}}_{z}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mover accent="true"> <mml:mi>J</mml:mi> <mml:mo stretchy="false">^</mml:mo> </mml:mover> <mml:mi>z</mml:mi> </mml:msub> </mml:mrow> </mml:math> measurement). We show the role of experimental imperfections on the ultimate estimation precision with the six-photon twin-Fock state of light. Our results show that both the precision and the sensing region of the <?CDATA ${\hat{J}}_{z}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mover accent="true"> <mml:mi>J</mml:mi> <mml:mo stretchy="false">^</mml:mo> </mml:mover> <mml:mi>z</mml:mi> </mml:msub> </mml:mrow> </mml:math> measurement are better than those of the single-fringe detection, due to combined contributions of the measurement outcomes. We numerically simulate the phase estimation protocol using an asymptotically unbiased maximum likelihood estimator.
Design of Point-and-Click User Interfaces for Proof Assistants

In interactive theorem proving, human users interact with proof assistants to construct and verify formal proofs. The most popular proof assistants today all have user interfaces that are largely text-based. This leads to a steep learning curve for new users of these tools. In this paper, we propose a framework for designing user interfaces for proof assistants based on pointing and clicking. While a main goal of the design is ease of learning for new users, we intend for the design to be suitable for real verification tasks. The design is also extensible, allowing custom proof methods and search functionality to be added in a convenient way. We implement our ideas in a web interface, with backend provided by holpy, a new system for interactive theorem proving implemented in Python. The resulting user interface is tested on theorems in logic, sets, functions, Peano arithmetic, and lists, demonstrating its applicability in a wide range of areas.
On Hard Exploration for Reinforcement Learning: A Case Study in Pommerman

How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the recently-proposed multi-agent benchmark of Pommerman. This domain is very challenging for RL — past work has shown that model-free RL algorithms fail to achieve significant learning without artificially reducing the environment’s complexity. In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman. While model-free random exploration is typically futile, we develop a model-based automatic reasoning module that can be used for safer exploration by pruning actions that will surely lead the agent to death. We empirically demonstrate that this module can significantly improve learning.
Connecting Data for Digital Libraries: The Library, the Dictionary and the Corpus

The paper presents two experiments related to enhancing the content of a digital library with data from external repositories. The concept involves three related resources: a digital library of Middle Polish prints where items are stored in image form, the same items in textual form in a linguistically annotated corpus, and a dictionary of Middle Polish. The first experiment demonstrates how the results of automated OCR obtained with open source tools can be replaced with transcribed content from the corpus, enabling the user to search within individual prints. The second experiment links the print content with the electronic dictionary, filtering relevant entries with the dictionary of modern Polish to eliminate redundant results. Interconnecting all relevant resources in a digital library-centered platform creates new possibilities both for researchers involved in development of these resources as well as for scholars studying the Polish language of the 17th and 18th centuries.
Nuclear Magnetic Resonance Model of an Entangled Sensor under Noise

Entangled sensors have been attracting much attention recently because they can achieve higher sensitivity than those of classical sensors. To exploit entanglement as a resource, it is important to understand the effect of noise, because the entangled state is highly sensitive to noise. In this paper, we present a Nuclear Magnetic Resonance (NMR) model ofan entangled sensor in a controlled environment; one can implement the entangled sensor under various noisy environments. In particular, we experimentally investigate the performance of the entangled sensor under time-inhomogeneous noisy environments, where the entangled sensor has the potential to surpass classical sensors. Our “entangled sensor” consists of a multi-spin molecule solved in isotropic liquid, and we can perform, or simulate, quantum sensing by using NMR techniques.
BME-UW at SRST-2019: Surface realization with Interpreted Regular Tree Grammars

The Surface Realization Shared Task involves mapping Universal Dependency graphs to raw text, i.e. restoring word order and inflection from a graph of typed, directed dependencies between lemmas. Interpreted Regular Tree Grammars (IRTGs) encode the correspondence between generations in multiple algebras, and have previously been used for semantic parsing from raw text. Our system induces an IRTG for simultaneously building pairs of surface forms and UD graphs in the SRST training data, then prunes this grammar for each UD graph in the test data for efficient parsing and generation of the surface ordering of lemmas. For the inflection step we use a standard sequence-to-sequence model with a biLSTM encoder and an LSTM decoder with attention. Both components of our system are available on GitHub under an MIT license.
Enhancing Attributed Network Embedding via Similarity Measure

Network embedding aims to represent network structural and attributed information with low-dimensional vectors, which has been demonstrated to be beneficial for many network analysis tasks, such as link prediction, node classification and visualization. However, nodes in networks are commonly associated with rich contents, which are facilitated to characterize the properties of nodes. Most existing attributed network embedding algorithms tend to learn attribute representations separated from structure representations, which require a subsequent processing of combination. Besides, these traditional approaches ignore the potential high-order proximity introduced by attributes. Motivated by this, we investigate how structures and attributes can be captured simultaneously and introduce similarity measure to preserve high-order proximity in an attributed network. In this paper, we propose a novel attributed network embedding framework, Similarity Enhancing Attributed Network Embedding (SEANE), which jointly preserves structural and attributed information, and adopts similarity measure to enhance the node embedding. We evaluate our proposed framework by using four real-world datasets on link prediction, node classification and nearest nodes searching. The experimental results demonstrate the outperformance of SEANE on link prediction and node classification tasks.
Mobile anti-phishing: Approaches and challenges

As technology is constantly evolving, modern conveniences such as mobile devices have changed the way that society performs its daily routines and functions as a whole. Because the evolution of technology has made it almost a requirement for citizens to possess mobile device, this increase in ownership of mobile devices has also brought security issues such as phishing. A phisher intends to obtain information that will aid the hacker by appearing to the unsuspecting owner as a trustworthy entity. The goal of phishing is to fool the owners of mobile devices into giving the attacker sensitive information, such as usernames, passwords, as well as credit and debit card information. In this paper, we provide a taxonomy of mobile anti-phishing techniques on mobile devices and mitigation techniques that are available for use on mobile devices. Our goal is to provide an informative model to identify current solutions in reducing phishing attacks on mobile devices and outline limitations and future challenges.
Technical Analysis and Cryptocurrencies

This paper carries out a comprehensive examination of technical trading rules in cryptocurrency markets, using data from two Bitcoin markets and three other popular cryptocurrencies. We employ almost 15,000 technical trading rules from the main five classes of technical trading rules and find significant predictability and profitability for each class of technical trading rule in each cryptocurrency. We find that the breakeven transaction costs are substantially higher than those typically found in cryptocurrency markets. To safeguard against data-snooping, we implement a number of multiple hypothesis procedures which confirms our findings that technical trading rules do offer significant predictive power and profitability to investors. We also show that the technical trading rules offer substantially higher risk-adjusted returns than the simple buy-and-hold strategy, showing protection against lengthy and severe drawdowns associated with cryptocurrency markets. However there is no predictability for Bitcoin in the out-of-sample period, although predictability remains in other cryptocurrency markets.
Data Diversification: An Elegant Strategy For Neural Machine Translation.

We introduce Data Diversification: a simple strategy to boost neural machinetranslation (NMT) performance. It diversifies the training data by using thepredictions of multiple forward and backward models and then merging them withthe original dataset on which the final NMT model is trained. Our method isapplicable to all NMT models. It does not require extra monolingual data likeback-translation, nor does it add more computations and parameters likeensembles of models. In the experiments, our method achieves state-of-the-artBLEU score of 30.7 & 43.7 in the WMT'14 English-German & English-French tasks.It also substantially improves on 8 other translation tasks: 4 IWSLT tasks(English-German and English-French) and 4 low-resource translation tasks(English-Nepali and English-Sinhala). We demonstrate that our method is moreeffective than knowledge distillation and dual learning, it exhibits strongcorrelation with ensembles of models, and it trades perplexity off for betterBLEU score. We have released our source code athttps://github.com/nxphi47/data_diversification
Performance Enhancement of Quantum Annealing under the Lechner–Hauke–Zoller Scheme by Non-linear Driving of the Constraint Term

We analyze the performance of quantum annealing as formulated by Lechner, Hauke, and Zoller (LHZ), by which a Hamiltonian with all-to-all two-body interactions is reduced to a corresponding Hamiltonian with local many-body interactions. Mean-field analyses show that problematic first-order quantum phase transitions that exist in the original LHZ formulation can be avoided, and thus an exponential speedup is achieved, if we drive the coefficient of the many-body term, which represents the constraint, non-linearly as a function of time. This result applies not only to a simple ferromagnetic model but also to the spin glass problem if a parameter in the spin glass model is chosen appropriately.
Benchmarking regression methods for function approximation in reinforcement learning: heat pump control

Motivated by the increasing interest in the application of machine learning techniques for power system control and demand response applications, this paper presents a benchmark of regression methods (extremely randomized trees (extra-trees), multi-layer perceptron (MLP), extreme gradient boosting, light gradient boosting machines, support vector regression (SVR) and extreme learning machines (ELMs)) available for function approximation in reinforcement learning (RL) techniques. In addition, we use Bayesian optimization to optimally select the hyperparameters of the regression algorithms. As a case study, the control of a heat pump (HP) in a grid-connected single-user microgrid powered by a photovoltaic (PV) installation is considered. The operation of the HP is controlled using fitted Q-iteration (FQI), a batch RL algorithm, with objective of maximizing PV self-consumption and minimizing electricity cost. Simulation results show that extra-trees are a suitable choice for application of FQI in real world applications where the importance on accuracy and computation times are equally weighted. Simulation results also show that a bag of ELMs performs better than (i) a single ELM with a 25.8% increment in PV self-consumption costing a factor of 3.8 increase in the computational time (ii) a MLP with a 5.4% increase in PV self-consumption and with a computation time of 25% that of the MLP. With SVR, a 32% decrease in PV self-consumption with a computation time increased by a factor 50 compared to extra-trees was obtained.
Learning to Gesticulate by Observation Using a Deep Generative Approach

The goal of the system presented in this paper is to develop a natural talking gesture generation behavior for a humanoid robot, by feeding a Generative Adversarial Network (GAN) with human talking gestures recorded by a Kinect. A direct kinematic approach is used to translate from human poses to robot joint positions. The provided videos show that the robot is able to use a wide variety of gestures, offering a non-dreary, natural expression level.
The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. In this paper, we propose a new representation learning method, named Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared with state-of-the-art CNN architectures, we achieve better results on several commonly used public benchmarks.
Card-Based Secure Ranking Computations

Consider a group of people who want to know the “rich list” among them, namely the ranking in terms of their total assets, without revealing any information about the actual value of their assets. This can be achieved by a “secure ranking computation,” which was first considered by Jiang and Gong (CT-RSA 2006); they constructed a secure ranking computation protocol based on a public-key cryptosystem. In this paper, instead of using a public-key cryptosystem, we use a deck of physical cards to provide secure ranking computation protocols. Therefore, our card-based protocols do not rely on computers, and they are simple and easy for humans to implement. Specifically, we design four protocols considering tradeoffs between the number of cards and the number of shuffles required to execute protocols. We also present a guide to choose an appropriate protocol according to the number of people participating in the protocol and the size of the input range.
Choi states, symmetry-based quantum gate teleportation, and stored-program quantum computing

The stored-program architecture is canonical in classical computing, while its power needs more investigations for the quantum case. We study quantum information processing with stored quantum program states, i.e., using qubits instead of bits to encode quantum operations. We develop a stored-program model based on Choi states, following from channel-state duality, and a symmetry-based generalization of deterministic gate teleportation, which can be employed to compose Choi states. Our model enriches the family of universal models for quantum computing, in particular the measurement-based models, and can also be employed for tasks including quantum simulation and communication.
Cross-Media Semantic Correlation Learning Based on Deep Hash Network and Semantic Expansion for Social Network Cross-Media Search

Cross-media search from large-scale social network big data has become increasingly valuable in our daily life because it can support querying different data modalities. Deep hash networks have shown high potential in achieving efficient and effective cross-media search performance. However, due to the fact that social network data often exhibit text sparsity, diversity, and noise characteristics, the search performance of existing methods often degrades when dealing with this data. In order to address this problem, this article proposes a novel end-to-end cross-media semantic correlation learning model based on a deep hash network and semantic expansion for social network cross-media search (DHNS). The approach combines deep network feature learning and hash-code quantization learning for multimodal data into a unified optimization architecture, which successfully preserves both intramedia similarity and intermedia correlation, by minimizing both cross-media correlation loss and binary hash quantization loss. In addition, our approach realizes semantic relationship expansion by constructing the image-word relation graph and mining the potential semantic relationship between images and words, and obtaining the semantic embedding based on both internal graph deep walk and an external knowledge base. Experimental results demonstrate that DHNS yields better cross-media search performance on standard benchmarks.
Quantum illumination assistant with error-correcting codes

Abstract We scheme how to enhance the detection ability of quantum target recognition without using entanglement resources. Based on the commonly used error-correcting codes and corresponding decoding method, our scheme gives lower error probability and higher signal-to-noise ratio (SNR) in comparison with the conventional entanglement protocols. In addition, we further investigate the interplay between the SNR and the detection efficiency in quantum target recognition. Results show that, they behave a completely reverse trend when increasing the auxiliary dimension. This is an important limiting factor when optimizing the detection process. Under the existing experimental conditions, our protocol has stronger ability to resist environmental noise when keeping a certain SNR and detection efficiency. Our scheme provides a potential platform for further research and implementation of quantum target recognition.
Object Detection in Convolution Neural Networks Using Iterative Refinements

One of the major problems in computer vision is the object detection through image classification.In recent years, the convolutional neural network (CNN) has been extensively applied for image classification.This paper combines iterative refinement and joint score function into a strategy to accurately localize the detected objects.First, a unified model with fast approximation was proposed to correct the position and range of region proposals, which are often incorrect in conventional linear methods.Focusing on data, the model can acquire knowledge without any cost, and suit different CNN architectures for various datasets.Next, a joint score function was introduced to process the number of candidate regions in the images.The joint score function deals with the relative position of the occluded object, and depends on the image data and output loss.Experimental results show that the proposed strategy achieved a 3.6% higher mean precision than the contrastive method.The research greatly promotes the object detection accuracy in computer vision.