Reviewerly/exHarmony huggingface dataset:
A Feature-Rich Vietnamese Named Entity Recognition Model

In this paper, we present a feature-based named entity recognition (NER) model that achieves the start-of-the-art accuracy for Vietnamese language. We combine word, word-shape features, PoS, chunk, Brown-cluster-based features, and word-embedding-based features in the Conditional Random Fields (CRF) model. We also explore the effects of word segmentation, PoS tagging, and chunking results of many popular Vietnamese NLP toolkits on the accuracy of the proposed feature-based NER model. Up to now, our work is the first work that systematically performs an extrinsic evaluation of basic Vietnamese NLP toolkits on the downstream NER task. Experimental results show that while automatically-generated word segmentation is useful, PoS and chunking information generated by Vietnamese NLP tools does not show their benefits for the proposed feature-based NER model.
Engineering statistical transmutation of identical quantum particles

A fundamental pillar of quantum mechanics concerns indistinguishable quantum particles. In three dimensions they may be classified into fermions or bosons, having, respectively, antisymmetric or symmetric wave functions under particle exchange. One of numerous manifestations of this quantum statistics is the tendency of fermions (bosons) to anti-bunch (bunch). In a two-particle scattering experiment with two possible outgoing channels, the probability of the two particles to arrive each at a different terminal is enhanced (with respect to classical particles) for fermions, and reduced for bosons. Here we show that by entangling the particles with an external degree of freedom, we can engineer quantum statistical transmutation, e.g. causing fermions to bunch. Our analysis may have consequences on the observed fractional statistics of anyons, including non-Abelian statistics, with serious implications on quantum computing operations in the presence of external degrees of freedom.
Checking the quality of approximation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="mml84" display="inline" overflow="scroll" altimg="si2.gif"><mml:mi>p</mml:mi></mml:math>-values in statistical tests for random number generators by using a three-level test

Statistical tests of pseudorandom number generators (PRNGs) are applicable to any type of random number generators and are indispensable for evaluation. While several practical packages for statistical tests of randomness exist, they may suffer from a lack of reliability: for some tests, the amount of approximation error can be deemed significant. Reducing this error by finding a better approximation is necessary, but it generally requires an enormous amount of effort. In this paper, we introduce an experimental method for revealing defects in statistical tests by using a three-level test proposed by Okutomi and Nakamura. In particular, we investigate the NIST test suite and the test batteries in TestU01, which are widely used statistical packages. Furthermore, we show the efficiency of several modifications for some tests.
SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays

Details-on-demand is a crucial feature in the visual information-seeking process but is often only implemented in highly constrained settings. The most common solution, hover queries (i.e., tooltips), are fast and expressive but are usually limited to single mark (e.g., a bar in a bar chart). `Queries' to retrieve details for more complex sets of objects (e.g., comparisons between pairs of elements, averages across multiple items, trend lines, etc.) are difficult for end-users to invoke explicitly. Further, the output of these queries require complex annotations and overlays which need to be displayed and dismissed on demand to avoid clutter. In this work we introduce SmartCues, a library to support details-on-demand through dynamically computed overlays. For end-users, SmartCues provides multitouch interactions to construct complex queries for a variety of details. For designers, SmartCues offers an interaction library that can be used out-of-the-box, and can be extended for new charts and detail types. We demonstrate how SmartCues can be implemented across a wide array of visualization types and, through a lab study, show that end users can effectively use SmartCues.
Lower Bounds on the Redundancy of Huffman Codes With Known and Unknown Probabilities

In this paper, we provide a method to obtain tight lower bounds on the minimum redundancy achievable by a Huffman code when the probability distribution underlying an alphabet is only partially known. In particular, we address the case where the occurrence probabilities are unknown for some of the symbols in an alphabet. Bounds can be obtained for alphabets of a given size, for alphabets of up to a given size, and alphabets of arbitrary size. The method operates on a computer algebra system, yielding closed-form numbers for all results. Finally, we show the potential of the proposed method to shed some light on the structure of the minimum redundancy achievable by the Huffman code.
Parameter inference and model comparison using theoretical predictions from noisy simulations

When inferring unknown parameters or comparing different models, data must be compared to underlying theory. Even if a model has no closed-form solution to derive summary statistics, it is often still possible to simulate mock data in order to generate theoretical predictions. For realistic simulations of noisy data, this is identical to drawing realizations of the data from a likelihood distribution. Though the estimated summary statistic from simulated data vectors may be unbiased, the estimator has variance which should be accounted for. We show how to correct the likelihood in the presence of an estimated summary statistic by marginalizing over the true summary statistic in the framework of a Bayesian hierarchical model. For Gaussian likelihoods where the covariance must also be estimated from simulations, we present an alteration to the Sellentin-Heavens corrected likelihood. We show that excluding the proposed correction leads to an incorrect estimate of the Bayesian evidence with JLA data. The correction is highly relevant for cosmological inference that relies on simulated data for theory (e.g. weak lensing peak statistics and simulated power spectra) and can reduce the number of simulations required.
Optimal choice of state tomography quorum formed by projection operators

A minimal set of measurement operators for quantum state tomography has in the nondegenerate case ideally eigenbases which are mutually unbiased. This is different for the degenerate case. Here we consider the situation where the measurement operators are projections on individual pure quantum states. This corresponds to maximal degeneracy. We present numerically optimized sets of projectors and find that they significantly outperform those which are taken from a set of mutually unbiased bases.
Power- and Time-Aware Deep Learning Inference for Mobile Embedded Devices

Deep learning is a state-of-the-art approach that provides highly accurate inference for many cyber-physical systems (CPS) such as autonomous cars and robots.Deep learning inference often needs to be performed locally on mobile and embedded devices, rather than in the cloud, to address concerns such as latency, power consumption, and limited bandwidth.However, existing approaches have focused on delivering ''best-effort'' performance to resource-constrained mobile embedded devices, resulting in unpredictable performance under highly variable environments of CPS.In this paper, we propose a novel deep learning inference runtime, called DeepRT, that supports multiple QoS objectives simultaneously against unpredictable workloads.In DeepRT, the multiple inputs/multiple outputs (MIMO) modeling and control methodology is proposed as a primary tool to support multiple QoS goals including the inference latency and power consumption.DeepRT's MIMO controller coordinates multiple computing resources, such as CPUs and GPUs, by capturing their close interactions and effects on multiple QoS objectives.We demonstrate the viability of DeepRT's QoS management architecture by implementing a prototype of DeepRT.The evaluation results demonstrate that, compared with baseline approaches, DeepRT can support the desired inference latency as well as power consumption for various deep learning models in a highly robust manner.
Unfolding a quantum master equation into a system of real-valued equations: Computationally effective expansion over the basis of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>SU</mml:mi><mml:mo>(</mml:mo><mml:mi>N</mml:mi><mml:mo>)</mml:mo></mml:math> generators

Dynamics of an open $N$-state quantum system is often modeled with a Markovian master equation describing the evolution of the system density operator. By using generators of $\text{SU}(N)$ group as a basis, the density operator can be transformed into a real-valued ``coherence-vector.'' A generator of the dissipative evolution, so-called ``Lindbladian,'' can be expanded over the same basis and recast in the form of a real matrix. Together, these expansions result is a nonhomogeneous system of ${N}^{2}\ensuremath{-}1$ real-valued linear ordinary differential equations. Now one can, e.g., implement standard high-performance algorithms to integrate the system of equations forward in time while being sure in exact preservation of the trace (norm) and Hermiticity of the density operator. However, when performed in a straightforward way, the expansion turns to be an operation of the time complexity $O({N}^{10})$. The complexity can be reduced when the number of dissipative operators is independent of $N$, which is often the case for physically meaningful models. Here we present an algorithm to transform quantum master equation into a system of real-valued differential equations and propagate it forward in time. By using a specific scalable model, we evaluate computational efficiency of the algorithm and demonstrate that it is possible to handle the model system with $N={10}^{3}$ states on a single node of a computer cluster.
Structure of minimum error discrimination for linearly independent states

In this paper we study the Minimum Error Discrimination problem (MED) for ensembles of linearly independent (LI) states. We define a bijective map from the set of those ensembles to itself and we show that the Pretty Good Measurement (PGM) and the optimal measurement for the MED are related by the map. In particular, the fixed points of the map are those ensembles for which the PGM is the optimal measurement. Also, we simplify the optimality conditions for the measurement of an ensemble of LI states.
Deep cartoon colorizer: An automatic approach for colorization of vintage cartoons

Although there exist several approaches to the automatic colorization of the natural scene images and movies the problem of cartoon colorization has barely been considered. Therefore, this paper proposes a fully automatic pipeline to create a plausible colorization of vintage cartoons which is visually appealing to a human observer. Particularly, the Deep Cartoon Colorizer is proposed. The method incorporates an encoder–decoder convolutional neural network which is trained to map colors onto consecutive cartoon frames. The method was trained with 4944 images and tested on 34 vintage Disney cartoons including both the decolorized and the originally monochrome movies. In total 265388 cartoon frames were assessed including 246591 decolorized frames and 18797 originally monochrome ones. The resulting automatic colorizations were evaluated both quantitatively (by means of popular image perceptual quality measures) and qualitatively (by performing a human perception test). The resulting median values of the image quality measures range from 0.66 for Visual Information Fidelity and 0.85 for HaarPSI, through 0.94 for Structural Similarity Index, to 0.97 for Universal Image Quality Index which is a very good result. The subjective scores assigned by the human raters’ on a ten-degree scale are on average equal to 6.11 and 6.63 for decolorized and monochrome frames respectively. This result also confirms that the desired effect of plausible colorization was obtained by the introduced approach.
Quantum Zeno effect generalized

The quantum Zeno effect, in its original form, uses frequent projective measurements to freeze the evolution of a quantum system that is initially governed by a fixed Hamiltonian. We generalize this effect simultaneously in three directions by allowing open system dynamics, time-dependent evolution equations, and general quantum operations in place of projective measurements. More precisely, we study Markovian master equations with bounded generators whose time dependence is Lipschitz continuous. Under a spectral gap condition on the quantum operation, we show how frequent measurements again freeze the evolution outside an invariant subspace. Inside this space, the evolution is described by a modified master equation.
Regulating Drones in Restricted Spaces

Commercial and end-user drones come equipped with a wide array of sensors. Unregulated use of such drones in public airspaces poses a serious threat to the privacy of citizens. We make the case for restricted spaces for drones, which are geographic areas for which a host can specify its privacy policies. Guest drones must prove to the host that they are in compliance with the host's policies before entering the restricted space. We then make the case for an information-flow control-based policy enforcement framework on drones, and sketch the design of a prototype framework atop the Robot Operating System (ROS).
An Application of Deep Learning in Character Recognition: An Overview

For automated document analysis, OCR (Optical character recognition) is a basic building block. The robust automated document analysis system can have impact over a wider sphere of life. Many of the researchers have been working hard to build OCR systems in various languages with significant degree of accuracy, character recognition rate and minimum error rate. Deep learning is the start of art technique with efficient and accurate result as compared to other techniques. Every language, moreover every script have its own challenges e.g. scripts where characters are well separated are less challenging as compared to cursive scripts where characters are attached with one another. In this chapter, we would take a detailed account of the state of art deep learning techniques for Arabic like script, Latin script and symbolic script.
The state of play – traditional versus behavioural biometrics

In the drive to secure personal information, more and more organisations are introducing biometrics as a way of identifying and verifying their customers. The primary goals are to improve customer experience while reducing fraud. However, developments such as the recent court case in Illinois – where a woman sued her employer for violating privacy laws with a fingerprint-scanning time clock system – underline the fact that traditional biometrics may not always be a suitable option, despite their current prevalence 1 .
Towards a Blockchain Architecture for Cultural Heritage Tokens

Disputes over Cultural Heritage tokens and collections claims among collectors, organized social groups, countries, ethnicities, even civilizations, are but uncommon over time. Universal ontologies such as the CIDOC Conceptual Reference Model (ISO 21127:2014) have emerged as global standards over the past years, to allow for seamless structuring and interchange Cultural Heritage artifacts documentation information, in spite of their actual nature (physical, intangible, digital, etc.). Yet, no objective universal procedure exists to safeguard the originality of the records and the archives; the immunity of essential sensitive data of the documentation tokens (dates, places, owners, etc.) is still questionable. Aiming toward a coherent, effective Blockchain architecture to establish an immune, objective, collective archive of the documented Cultural Heritage tokens, the present proposes an implementation based on a comparative analysis of the prominent Blockchain architectures.
How data protection fits with the algorithmic society via two intellectual property rights – a comparative analysis

Big Data, IoT and AI are the three interrelated elements of the algorithmic society, responsible for an unprecedented flourishing of innovation. Companies working within such an algorithmic society need to protect the information created and stored for entrepreneurial purposes. Thus, their concerns relate to data protection, in particular with regard to trade secrets and the sui generis protection of databases. This paper tries to answer two questions from a EU and US law perspective. First, it asks whether data generated and managed within the frameworks of Big Data, IoT and AI meet the essential requirements to enjoy trade secret protection and the database right, if any. The answer seems to be in the affirmative in most cases. Second, it studies whether trade secrets and the sui generis right are appropriate in a sharing-based paradigm, such as that of Big Data, IoT and AI. The focus on this upstream protection helps to understand the bottlenecks created at the downstream level, which challenge innovation and transparency, as well as consumer protection. In other words, when both exclusive rights (the sui generis protection for databases) and quasi-intellectual property rights (trade secrets) are present, innovation and the circulation of information are not necessarily promoted, and the presence of this double protection may be beneficial to big businesses only. On the other hand, the presence of mere trade secrets does not seem to utterly exclude an encouragement to innovation and the circulation of information and it is therefore more suitable to SMEs and to safeguard the public interest.
Visual perception of one’s own body under vestibular stimulation using biometric self-avatars in virtual reality

Vestibular input is projected to "multisensory (vestibular) cortex" where it converges with input from other sensory modalities. It has been assumed that this multisensory integration enables a continuous perception of state and presence of one's own body. The present study thus asked whether or not vestibular stimulation may impact this perception.We used an immersive virtual reality setup to realistically manipulate the length of extremities of first person biometric avatars. Twenty-two healthy participants had to adjust arms and legs to their correct length from various start lengths before, during, and after vestibular stimulation.Neither unilateral caloric nor galvanic vestibular stimulation had a modulating effect on the perceived size of own extremities.Our results suggest that vestibular stimulation does not directly influence the explicit somatosensory representation of our body. It is possible that in non-brain-damaged, healthy subjects, changes in whole body size perception are principally not mediated by vestibular information. Alternatively, visual feedback and/or memory may dominate multisensory integration and thereby override possibly existing modulations of body perception by vestibular stimulation. The present observations suggest that multisensory integration and not the processing of a single sensory input is the crucial mechanism in generating our body representation in relation to the external world.
Diversifying Reply Suggestions Using a Matching-Conditional Variational Autoencoder

Budhaditya Deb, Peter Bailey, Milad Shokouhi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Dynamic TOPSIS fuzzy cerebellar model articulation controller for magnetic levitation system

This study proposes a fuzzy Cerebellar Model Articulation Controller (CMAC) using a dynamic Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique for dealing with the metallic sphere position control of a magnetic levitation system (MLS). The proposed Dynamic TOPSIS F uzzy CMAC (DTFCMAC) incorporates a multi-criteria decision analysis with a fuzzy structure to decrease the computational load for parameter learning and to enhance the fuzzy reasoning inference for a CMAC. The Shannon entropy index is used to derive the objective weights for the evaluation criterion. By combining entropy weight and TOPSIS, the optimal threshold value for suitable firing nodes is determined automatically and easily. In the proposed method, the dynamic back-propagation algorithm is applied to train the proposed DTFCMAC online. Moreover, to guarantee the convergence of output tracking error for periodic command tracking, analytical methods developed from a discrete-type Lyapunov function are used to determine the optimal learning-rate parameters for the proposed DTFCMAC. The proposed DTFCMAC is applied to the MLS, and its performance is verified through simulations and experiments. Our findings indicate that the proposed DTFCMAC control system achieves stability and desired control performance for the MLS.
A fast nonogram solver that won the TAAI 2017 and ICGA 2018 tournaments

A nonogram is a pen and paper single-player logic game in which players paint each cell of a two-dimensional grid according to clues for specific rows and columns. This paper improves on the method proposed by Wu et al. by adding a freedom parameter which can significantly reduce the total computat ion cost of maximal painting. Combining a well-designed bitboard with BMI instructions in the CPU, we reduce memory loading while accelerating operations. Our Nonogram program, named Requiem, solved all 1000 puzzles in every Nonogram Tournament from 2011 to 2018 faster than all previous medal programs. Furthermore, we beat four other teams to win the TAAI 2017 Tournament, six other teams to win the ICGA 2018 Tournament and three other teams to win the TAAI 2018. To our knowledge, Requiem offers the best puzzle solving performance at the present time.
Systemically modeling the relationship between climate change and wheat aphid abundance

Climate change influences all living beings. Wheat aphids deplete the nutritional value of wheat and affect the production of wheat in changing climate. In this study, we attempt to explain the ecological mechanisms of how climate change affects wheat aphids by simulating the relationship between climate and the abundance of wheat aphids, which will not only aid in improving wheat aphid forecasting and the effectiveness of prevention and treatment, but also help mitigate food crises. Fuzzy cognitive maps (FCM) are an effective tool for portraying complex systems. Using Sitobion avenae and climatological data collected in China, we made use of differential evolution (DE) algorithms to construct FCM models that directly illustrate the effect of climate on wheat aphid abundance. The relationships among climate and wheat aphids at different growth stages (I–III instar larvae, IV instar larvae with wings, IV instar larvae without wings, adult with wings, adult without wings) were established. The analysis results from the FCM models show that temperature positively influences wheat aphids most. Moreover, these models can be used to determine the numerical value of each climate factor and the abundance of wheat aphids quantitatively. Furthermore, the two overall relationship models between climate and wheat aphids were constructed and the experimental results show that natural enemies and highest daily temperature affect wheat aphids most. Natural enemies and highest daily temperature exert negative and positive impacts on wheat aphids respectively. Some interrelationships among wheat aphids at all growth stages and the internal relationships among climate factors were also shown.