Reviewerly/exHarmony huggingface dataset:
T-Sample: A Dual Reservoir-Based Sampling Method for Characterizing Large Graph Streams

Reservoir sampling is widely employed to characterize connectivity of large graph streams by producing edge samples. However, existing reservoir-based sampling methods mainly characterize large graph streams by a measure of counting triangles but perform poorly in accuracy when used to analyze the topological characteristics reflected by node degrees because they produce disconnected edge samples, making them ineffective in many applications that require both types of connectivity estimation simultaneously in real time. This paper proposes a new method, called triangle-induced reservoir sampling, or T-Sample, to produce connected edge samples. While every edge in a graph stream is still processed only once by T-Sample, a dual sampling mechanism performing both uniform sampling and non-uniform sampling is carefully designed with a base reservoir and an incremental reservoir. Specifically, the uniform sampling can be used to count triangles by employing the existing algorithms while the non-uniform sampling ensures that the edge samples are connected. Experimental results driven by real datasets show that T-Sample can obtain much more accurate estimations on the distributions of node degrees than the existing reservoir-based sampling methods.
Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning

A large body of research is currently investigating on the connection between machine learning and game theory. In this work, game theory notions are injected into a preference learning framework. Specifically, a preference learning problem is seen as a two-players zero-sum game. An algorithm is proposed to incrementally include new useful features into the hypothesis. This can be particularly important when dealing with a very large number of potential features like, for instance, in relational learning and rule extraction. A game theoretical analysis is used to demonstrate the convergence of the algorithm. Furthermore, leveraging on the natural analogy between features and rules, the resulting models can be easily interpreted by humans. An extensive set of experiments on classification tasks shows the effectiveness of the proposed method in terms of interpretability and feature selection quality, with accuracy at the state-of-the-art.
Oversampling for Nonlinear Waveshaping: Choosing the Right Filters

An assessment of filters for classic oversampled audio waveshaping schemes is carried out in this paper, pursuing aliasing reduction.For this purpose, the quality measure of the Aweighted noise-to-mask ratio is computed for test tones covering the frequency range from 27.5 Hz to 4.186 kHz, sampled at 44.1 kHz, and processed at eight-times oversampling.All filters are designed to have their passband contained within a ±1 dB range and to display a minimum stopband attenuation value of 40 dB.Waveshaping of sinusoids via hard clipping is investigated: the spectral enrichment due to the discontinuities introduced by its nonlinear transfer function maximizes aliasing distortion.The obtained results suggest that linear interpolation equalized with a high shelving filter is a sufficiently good method for upsampling.Concerning decimation, the interpolated FIR, elliptic, and cascaded integrator-comb filters all improve the results with respect to the trivial case.Regarding performance, the cascaded integrator-comb filter is the only tested decimation filter that achieves perceptually sufficient aliasing suppression for the entire frequency range when combined with the linear interpolator.
Accurate Eating Detection on a Daily Wearable Necklace (demo)

While there are many research proposals for wearable Automatic Dietary Monitoring (ADM) systems that detect eating of a user, it is difficult to notice real-world users wearing such devices in public. We propose a new wearable ADM system that could be used daily by real-world users. It is designed in a form of necklace, providing natural and firm contact of sensor on user's skin to accurately capture eating activities. At our preliminary experiments, our wearable ADM system detected eating of a user with 86.1% accuracy.
A Column Styled Composable Schema Matcher for Semantic Data-Types

Schema matching exists as a long-standing challenge in many database related applications, such as data integration, where two databases with different schema have to be integrated. With the evolvement from database to big data, the schema matching has been enriched with various purposes and application contexts, ranging from data integration, to service integration, to semantic data clouding, until more recent exploratory data analysis over big data. These enriched contexts increase the demand for schema matching between semantic data-types, such as XML, RDF etc. The existing integration approaches have not dealt with the challenges of defining a relation between XML and other semantic data-types. To address these challenges, this paper studies the problem of schema mapping from XML to RDF in two folds. Firstly, testify the validity of single matcher in a column based manner for the semantic data types. Secondly, testify the validity of a highly configurable framework that utilizes hierarchical classification in order to construct a composable pipeline. We propose and implement a Reconfigurable pipeline for Semi-Automatic Schema Matching (REPSASM), which aims to solve the customizability of the matching problem by providing an environment in which a user can create, configure and experiment with their own schema-matching procedure. The experiments performed within this work show that the configurability and hierarchical classification improves the matching result, and it proposes an algorithm to automatically optimize such a hierarchy pipeline.
Axiomatic construction of quantum Langevin equations

A phenomenological construction of quantum Langevin equations, based on the physical criteria of (i) the canonical equal-time commutators, (ii) the Kubo formula, (iii) the virial theorem and (iv) the quantum fluctuation–dissipation theorem is presented. The case of a single harmonic oscillator coupled to a large external bath is analysed in detail. This allows to distinguish a Markovian semi-classical approach, due to Bedeaux and Mazur, from a non-Markovian full quantum approach, due to to Ford, Kac and Mazur. The quantum-fluctuation–dissipation theorem is seen to be incompatible with a Markovian dynamics. Possible applications to the quantum spherical model are discussed.
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder

We introduce the Scratchpad Mechanism, a novel addition to the sequence-to-sequence (seq2seq) neural network architecture and demonstrate its effectiveness in improving the overall fluency of seq2seq models for natural language generation tasks.By enabling the decoder at each time step to write to all of the encoder output layers, Scratchpad can employ the encoder as a "scratchpad" memory to keep track of what has been generated so far and thereby guide future generation.We evaluate Scratchpad in the context of three well-studied natural language generation tasks -Machine Translation, Question Generation, and Text Summarization -and obtain stateof-the-art or comparable performance on standard datasets for each task.Qualitative assessments in the form of human judgements (question generation), attention visualization (MT), and sample output (summarization) provide further evidence of the ability of Scratchpad to generate fluent and expressive output.
A fully automated, faster noise rejection approach to increasing the analytical capability of chemical imaging for digital histopathology

Chemical hyperspectral imaging (HSI) data is naturally high dimensional and large. There are thus inherent manual trade-offs in acquisition time, and the quality of data. Minimum Noise Fraction (MNF) developed by Green et al. [1] has been extensively studied as a method for noise removal in HSI data. It too, however entails a manual speed-accuracy trade-off, namely the process of manually selecting the relevant bands in the MNF space. This process currently takes roughly around a month’s time for acquiring and pre-processing an entire TMA with acceptable signal to noise ratio. We present three approaches termed ‘Fast MNF’, ‘Approx MNF’ and ‘Rand MNF’ where the computational time of the algorithm is reduced, as well as the entire process of band selection is fully automated. This automated approach is shown to perform at the same level of accuracy as MNF with now large speedup factors, resulting in the same task to be accomplished in hours. The different approximations produced by the three algorithms, show the reconstruction accuracy vs storage (50×) and runtime speed (60×) trade-off. We apply the approach for automating the denoising of different tissue histology samples, in which the accuracy of classification (differentiating between the different histologic and pathologic classes) strongly depends on the SNR (signal to noise ratio) of recovered data. Therefore, we also compare the effect of the proposed denoising algorithms on classification accuracy. Since denoising HSI data is done unsupervised, we also use a metric that assesses the quality of denoising in the image domain between the noisy and denoised image in the absence of ground truth.
Corpus-based Check-up for Thesaurus

In this paper we discuss the usefulness of applying a checking procedure to existing thesauri.The procedure is based on the analysis of discrepancies of corpus-based and thesaurus-based word similarities.We applied the procedure to more than 30 thousand words of the Russian wordnet and found some serious errors in word sense description, including inaccurate relationships and missing senses of ambiguous words.
SemiTagRec: A Semi-supervised Learning Based Tag Recommendation Approach for Docker Repositories

Docker has been the mainstream technology for providing reusable software artifacts by packaging applications, dependencies, and execution environments into images. Developers can easily build and deploy their applications using Docker. Currently, a large number of reusable Docker repositories are in the online open source communities, especially Docker Hub and Docker Store. Effectively reusing these artifacts requires a well understanding of them, and semantic tags provide this way. However, the communities do not support tags well, and little training data is available. This paper addresses the problem and proposes a semi-supervised learning based tag recommendation approach, SemiTagRec, for Docker repositories. SemiTagRec contains four components. (1) Predictor calculates the probabilities of assigning tags to Docker repositories. (2) Extender introduces in new tags as the candidates based on tag correlation analysis. (3) Evaluator measures the candidate tags. (4) Integrator combines the results of predictor and evaluator, and then takes the tags with high scores as the final result. SemiTagRec uses the newly tagged repositories together with the original ones as training data for the next round of training. In this iterative manner, SemiTagRec trains the predictor with the cumulative labeled data set and the extended tag vocabulary to achieve high accuracy of tag recommendation. Finally, we conducted some experiments and evaluated SemiTagRec by comparing it with other related works. Experimental results show that SemiTagRec outperforms the other approaches in terms of Recall@5 and Recall@10.
Quantum fast-forwarding: Markov chains and graph property testing

We introduce a new tool for quantum algorithms called quantum fast-forwarding (QFF). The tool uses quantum walks as a means to quadratically fast-forward a reversible Markov chain. More specifically, with $P$ the Markov chain transition matrix and $D = \sqrt{P\circ P^T}$ its discriminant matrix ($D=P$ if $P$ is symmetric), we construct a quantum walk algorithm that for any quantum state $|v\rangle$ and integer $t$ returns a quantum state $\epsilon$-close to the state $D^t|v\rangle/\|D^t|v\rangle\|$. The algorithm uses $O\Big(\|D^t|v\rangle\|^{-1}\sqrt{t\log(\epsilon\|D^t|v\rangle\|)^{-1}}\Big)$ expected quantum walk steps and $O(\|D^t|v\rangle\|^{-1})$ expected reflections around $|v\rangle$. This shows that quantum walks can accelerate the transient dynamics of Markov chains, complementing the line of results that proves the acceleration of their limit behavior. We show that this tool leads to speedups on random walk algorithms in a very natural way. Specifically we consider random walk algorithms for testing the graph expansion and clusterability, and show that we can quadratically improve the dependency of the classical property testers on the random walk runtime. Moreover, our quantum algorithm exponentially improves the space complexity of the classical tester to logarithmic. As a subroutine of independent interest, we use QFF for determining whether a given pair of nodes lies in the same cluster or in separate clusters. This solves a robust version of $s$-$t$ connectivity, relevant in a learning context for classifying objects among a set of examples. The different algorithms crucially rely on the quantum speedup of the transient behavior of random walks.
Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks

The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for learning and memory and short-term changes for adaptation and synaptic computation. By studying the performance of reservoir computing (RC) models in a memory task, we revealed that short-term synaptic plasticity is fundamentally important for long-term synaptic changes in neural networks. Specifically, short-term depression (STD) greatly expands the operational range of a neural network in which it can accommodate long-term synaptic changes while maintaining system performance. This is achieved by dynamically adjusting neural networks close to a critical state. The effects of STD can be further strengthened by synaptic weight heterogeneity, resulting in networks that can tolerate very large, long-term changes in synaptic weights. Our results highlight a potential mechanism used by the brain to organize plasticity at different time scales, thereby maintaining optimal information processing while allowing internal structural changes necessary for learning and memory.
Information Security in Libraries

Libraries in the United States handle sensitive patron information, including personally identifiable information and circulation records. With libraries providing services to millions of patrons across the U.S., it is important that they understand the importance of patron privacy and how to protect it. This study investigates how knowledge transferred within an online cybersecurity education affects library employee information security practices. The results of this study suggest that knowledge transfer does have a positive effect on library employee information security and risk management practices.
Spatiotemporal Feature Extraction for Pedestrian Re-identification

Video-based person re-identification (ReID) is a problem of person retrieval that aims to match the same person in two different videos, which has gradually entered the arena of public security. The system generally involve three important parts: feature extraction, feature aggregation and loss function. Pedestrian feature extraction and aggregation are critical steps in this field. Most of the previous studies concentrate on designing various feature extractors. However, these extractors cannot effectively extract spatiotemporal information. In this paper, several spatiotemporal convolution blocks were proposed to optimize the feature extraction model of person Re-identification. Firstly, 2D convolution and 3D convolution are simultaneously used on video volume to extract spatiotemporal feature. Secondly, non-local block is embedded into ResNet3D-50 to capture long-range dependencies. As a result, the proposed model could learn the inner link of pedestrian action in a video. Experimental results on MARS dataset show that our model has achieved significant progress compared to state-of-the-art methods.
Dataset Creation for Ranking Constructive News Comments

Ranking comments on an online news service is a practically important task for the service provider, and thus there have been many studies on this task.However, most of them considered users' positive feedback, such as "Like"-button clicks, as a quality measure.In this paper, we address directly evaluating the quality of comments on the basis of "constructiveness," separately from user feedback.To this end, we create a new dataset including 100K+ Japanese comments with constructiveness scores (C-scores).Our experiments clarify (a) C-scores are not always related to users' positive feedback and (b) the performance of pairwise ranking models tends to be more enhanced by the variation in comments than that in articles.
Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.
Necessary conditions for steerability of two qubits from consideration of local operations

Einstein-Podolsky-Rosen (EPR) steering refers to the ability of one observer to convince a distant observer that they share entanglement by making local measurements. Determining which states allow a demonstration of EPR steering remains an open problem in general. Here, we outline and demonstrate a method of analytically constructing new classes of two-qubit states which are nonsteerable by arbitrary projective measurements, from consideration of local operations performed by the steering party on states known to be nonsteerable.
MCˆ2: Multi-perspective Convolutional Cube for Conversational Machine Reading Comprehension

Conversational machine reading comprehension (CMRC) extends traditional single-turn machine reading comprehension (MRC) by multi-turn interactions, which requires machines to consider the history of conversation.Most of models simply combine previous questions for conversation understanding and only employ recurrent neural networks (RNN) for reasoning.To comprehend context profoundly and efficiently from different perspectives, we propose a novel neural network model, Multi-perspective Convolutional Cube (MC 2 ).We regard each conversation as a cube.1D and 2D convolutions are integrated with RNN in our model.To avoid models previewing the next turn of conversation, we also extend causal convolution partially to 2D.Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves state-of-the-art results.
Historical Text Normalization with Delayed Rewards

Training neural sequence-to-sequence models with simple token-level log-likelihood is now a standard approach to historical text normalization, albeit often outperformed by phrasebased models.Policy gradient training enables direct optimization for exact matches, and while the small datasets in historical text normalization are prohibitive of from-scratch reinforcement learning, we show that policy gradient fine-tuning leads to significant improvements across the board.Policy gradient training, in particular, leads to more accurate normalizations for long or unseen words.
Design and Construction of Line Following Robot using Arduino

Line following robot is an autonomous vehicle which detect black line to move over the white surface or bright surface.In this paper, the line following robot is constructed by using Arduino nano microcontroller as a main component and consists of three infrared (IR) sensors, four simple DC motors, four wheels and a PCB frame of robot chassis.The infrared sensors are used to sense the black line on white surface.When the infrared signal falls on the white surface, it gets reflected and it falls on the black surface, it is not reflected.In this system, four simple DC motors attached with four wheels are used to move the robot car's direction that is left, right and forward.The Arduino nano is used as a controller to control the speed of DC motors from the L2953D driver circuit.
Real-Time Tissue Elastography in Epidermal Cyst: along with Enhanced MRI and Elasticity Features of Peripheral Rim

Kazumi Fujioka* Author Affiliations Department of Dermatology, Fujioka Dermatological Clinic, Japan Received: March 30, 2019 | Published: April 04, 2019 Corresponding author: Kazumi Fujioka, Department of Dermatology, Fujioka Dermatological Clinic, 1-35-10, Tamagawa, Ota-ku, Tokyo, Japan DOI: 10.26717/BJSTR.2019.16.002904
Tighter monogamy and polygamy relations of multiparty quantum entanglement

We explore monogamy and polygamy relations of entanglement in multipartite systems. By using the power of the bipartite entanglement measure, we establish a class of tight monogamy relations of multiparty entanglement with larger lower bounds in comparison to all known entanglement monogamy relations. We also give a class of tight polygamy relations of multiparty entanglement with smaller upper bounds than the existing ones, in terms of the power of the entanglement of assistance. We provide examples in which our new monogamy and polygamy relations are tighter than the previous ones.