Reviewerly/exHarmony huggingface dataset:
Monogamy of finite-dimensional entanglement induced by coherence

One fundamental feature of quantum entanglement is the monogamy which shows that the entanglement of two systems limits the ability of either system to entangle with a third one. Such an understanding is only well described in the qubit systems, but it remains an open question for high-dimensional quantum systems. Here the relative entropy of entanglement and the negativity are respectively used to quantify the entanglement. Based on the resource theory of coherence, we found that the monogamy inequalities in arbitrarily finite-dimensional systems can be successfully established for the entanglement induced by quantum coherence. Moreover, the similar inequalities are also constructed for the distribution of quantum coherence.
Measuring Performance of N-Gram and Jaccard-Similarity Metrics in Document Plagiarism Application

String-based similarity metrics were mainly used to lexically measure the similarity between words based on the string sequences and character compositions. This research aimed to build an application that can identify the similarity between documents. The program employed two lexical-based algorithms, N-gram and Jaccard, to check the documents similarity. The author focused on analysing the algorithms' performance based on accuracy, sensitivity, and efficiency metric. Datasets used in this research were the final thesis documents in Indonesian and English language. Experiment results revealed that Jaccard algorithm has a better performance in term of accuracy and sensitivity compared to N-gram. Notwithstanding its superior performance, Jaccard had a longer running time than N-gram to process documents. Furthermore, the results also pointed out that the cross-language documents were indeed affecting the degree of similarity checking.
Application of Fuzzy Inference Systems in Archaeology

In this research, applications of fuzzy mathematics and fuzzy inference systems in archaeology are explained. Then, a fuzzy inference system is designed for one of the archaeological indexes called Gender Determination. Rules of such a fuzzy inference system are obtained by using archaeological and osteological concepts. Based on the designed system, one can evaluate the gender of uncovered bones in archaeological studies.
Recognizing Pornographic Images using Deep Convolutional Neural Networks

In this paper, we propose to use deep convolutional neural network (CNN) architectures, namely the deep residual networks (ResNet), the GoogLeNet, the AlexNet, and the AlexNet architectures, for pornographic image dataset. Also, the local descriptors, namely the local binary patterns (LBP), the histogram of oriented gradients, and the scale invariant feature transform (SIFT) combined with a support vector machine (SVM), a multilayer perceptron (MLP), or a K-nearest neighbor (KNN) techniques are proposed. Additionally, a bag of visual words (BOW) and the BOW using extracted HOG features (HOG-BOW) are compared. To classify the pornographic images, we compare the CNN architectures to well-known local descriptor techniques combined with the SVM, the MLP, and the MLP methods. Experimental results indicate that the ResNet architecture yields higher accuracies than all other approaches.
Video retargeting based on SH equalisation and seam carving

This study presents an efficient algorithm for video retargeting in the saliency histogram (SH) domain. It divides video frames into mesh quads based on the SH and reforms mesh grids at a quad level to fit the dimensions of a screen. Specifically, the first frame of a scene is retargeted by using the SH equalisation-seam carving (SHE-SC) algorithm, and the rest of the frames are classified into two types based on the content difference between consecutive frames, and then retargeted using different schemes. Experimental results show that the proposed algorithm is preferable to other existing algorithms.
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers

Modeling the errors of a speech recognizer can help simulate errorful recognized speech data from plain text, which has proven useful for tasks like discriminative language modeling, improving robustness of NLP systems, where limited or even no audio data is available at train time. Previous work typically considered replicating behavior of GMM-HMM based systems, but the behavior of more modern posterior-based neural network acoustic models is not the same and requires adjustments to the error prediction model. In this work, we extend a prior phonetic confusion based model for predicting speech recognition errors in two ways: first, we introduce a sampling-based paradigm that better simulates the behavior of a posterior-based acoustic model. Second, we investigate replacing the confusion matrix with a sequence-to-sequence model in order to introduce context dependency into the prediction. We evaluate the error predictors in two ways: first by predicting the errors made by a Switchboard ASR system on unseen data (Fisher), and then using that same predictor to estimate the behavior of an unrelated cloud-based ASR system on a novel task. Sampling greatly improves predictive accuracy within a 100-guess paradigm, while the sequence model performs similarly to the confusion matrix.
An Approach to Apply Reinforcement Learning for a VNF Scaling Problem

The concept of Network Functions Virtualisation provides the rapid and flexible deployment of new services for telco operators. Horizontal scaling is applied when one or more instances of a virtual network function component or a virtual network function are added or removed in response to the traffic from the users. In this paper, we present how Reinforcement Learning can help scaling the virtual network function components. Our results show that Reinforcement Learning can be used to control the scaling decisions in the Management and Orchestration of Network Function Virtualisation.
Analytic Properties of Downsampling for Bandlimited Signals

In this paper we study downsampling for bandlimited signals. Downsampling in the discrete-time domain corresponds to a removal of samples. For any downsampled signal that was created from a bandlimited signal with finite energy, we can always compute a bandlimited continuous-time signal such that the samples of this signal, taken at Nyquist rate, are equal to the downsampled discrete-time signal. However, as we show, this is no longer true for the space of bounded bandlimited signals that vanish at infinity. We explicitly construct a signal in this space, which after downsampling does not have a bounded bandlimited interpolation. This shows that downsampling in this signal space is an operation that can lead out of the set of discrete-time signals for which we have a one-to-one correspondence with continuous-time signals.
Adaptive Filtering for Event Recognition from Noisy Signal: an Application to Earthquake Detection

Seismic event classification and detection have been important research topics because of their significance and wide applications on hazard assessment and global security. In the real world, seismic data acquisition are always impacted by unavoidable nature factors, which will introduce low-frequency noise to the seismic events of interests. Pre-processing of seismic signal using denoising techniques can be critical to the detection of the seismic events. In our work, we develop an end-to-end framework which can automatically learn the hyper-parameter in the denoising algorithm so that we do not need to manually set the hyper-parameter. Specifically, our network structure consists of two modules, an adaptive filtering module for signal denoising, and a classification module for signal classification. We further develop two mechanisms of the adaptive filtering module, namely, sample-specific mechanism and dataset-specific mechanism. We validate the performance of our detection method using a series of field seismic datasets. The classification results show that our framework can not only remove signal noise effectively but also improve the classification accuracy.
Blind Quality Assessment for 3D-synthesized Images by Measuring Geometric Distortions and Image Complexity

Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direction of video technology development. Depth image-based rendering (DIBR) technologies are employed to synthesize FVV images in the "blind" environment. Therefore, a real-time reliable blind quality assessment metric is urgently required. However, existing stste-of-art quality assessment methods are limited to estimate geometric distortions generated by DIBR. In this research, a novel blind quality metric, measuring Geometric Distortions and Image Complexity (GDIC), is proposed for DIBR-synthesized images. Firstly, a DIBR-synthesized image is decomposed into wavelet subbands by using discrete wavelet transform. Then, we adopt canny operator to capture the edge of wavelet subbands and compute the edge similarity between low-frequency subband and highfrequency subbands. The edge similarity is used to quantify geometric distortions in DIBR-synthesized images. Secondly, a hybrid filter combining the autoregressive and bilateral filter is adopted to compute image complexity. Finally, the overall quality score is calculated by normalizing geometric distortions via image complexity. Experiments show that our proposed GDIC is superior to prevailing image quality assessment metrics, which were intended for natural and DIBR-synthesized images.
Recognition Algorithm for Sleep Postures using a Smart Fabric Pad with Multiple Pressure Sensors

The existing studies suggest the following methods for measuring sleep postures: installing cameras and record the sleep postures then analyze the postures or measuring the change of the posture by attaching the sensor to the user's body. However, the installation of high-cost devices such as a camera or direct attachment of sensors on the subject's body present issues relating to the cost and convenience. As a solution, this paper develops a recognition algorithm for analyzing sleep postures using a smart pad with embedded fabric type pressure sensors. This algorithm is applied using a fabric-made smart pad with multiple pressure sensors. The sensors detect the distribution of the body pressure on the pad during sleeping, and the collected body pressure distribution data determines the sleep postures of the users. Further, this smart fabric pad does not require any additional analytical devices. This pad allows the users to monitor own sleep postures continuously. With the analyzed sleep posture data, the user can recognize one's typical sleep postures. In order to verify the effectiveness of the algorithm, this paper conducts an experiment to validate using the sleep posture data defined as nine categories. As a result, the algorithm had an average of 91.4% accuracy rate.
Visual Relationship Recognition via Language and Position Guided Attention

Visual relationship recognition, as a challenging task used to distinguish the interactions between object pairs, has received much attention recently. Considering the fact that most visual relationships are semantic concepts defined by human beings, there are many human knowledge, or priors, hidden in them, which haven't been fully exploited by existing methods. In this work, we propose a novel visual relationship recognition model using language and position guided attention: language and position information are exploited and vectored firstly, and then both of them are used to guide the generation of attention maps. With the guided attention, the hidden human knowledge can be made better use to enhance the selection of spatial and channel features. Experiments on VRD [2] and VGR [1] show that, with language and position guided attention module, our proposed model achieves state-of-the-art performance.
Efficient FPGA Implementation of Modular Arithmetic for Elliptic Curve Cryptography

High throughput and low resource are the crucial design parameters of elliptic curve cryptographic (ECC) processor in many applications. The efficiency of ECC processor mainly depends on modular arithmetic operations such as modular addition, subtraction and multiplication. In this brief, hardware architectures of modular arithmetic over the prime field are presented. The novelty of this work is to execute modular addition and modular subtraction in a combined architecture. In addition, multiplication and modular operations are executed in a separate module instead of doing modular multiplication in a single operation which has significantly improved circuit latency and area optimization. The proposed architectures have been synthesized on Virtex-5 field-programmable gate array (FPGA) technology and achieved 0.575μs and 2.04μs computational time, as well as 4% and 17% of available slice-LUTs for 256-bit, combined modular addition and subtraction and modular multiplication respectively. The design offers almost 50-80% area minimization and reduces nearly 10-90% of the required time in comparison with the related designs. To our best knowledge, our architectures provide a better throughput/area performance on FPGA than the recent related work which is very impressive for the design of Ecc processor.
A Graded Approach to Network Forensics with Privacy Concerns

Anomaly detection in recent or historic traffic traces is a typical approach in applying network forensics to analyze previous security incidents in networks, as well as for real-time network monitoring for detecting intrusions or other security incidents without known signatures. However, even in the aftermath of a security incident, privacy expectations of legitimate users remain a primary concern. In this paper, we describe our findings regarding the preference of network administrators for releasing data. We then go on to describe a methodology that balances the motivations of preserving maximum privacy for legitimate users and obtaining maximum possible information regarding potentially anomalous behavior. Our methodology is based on a graded approach to progressing from highly anonymized data to further disclosure for targeted traffic streams. In particular, we show that it is possible to obtain significant progress from highly aggregated data that is typically considered essentially valueless for the purpose of anomaly detection. We present the result of these first steps as executed on a real enterprise network, showing how the graded approach can work in practice.
Improving CTC Using Stimulated Learning for Sequence Modeling

Connectionist temporal classification (CTC) is a sequence-level loss that has been successfully applied to train recurrent neural network (RNN) models for automatic speech recognition. However, one major weakness of CTC is the conditional independence assumption that makes it difficult for the model to learn label dependencies. In this paper, we propose stimulated CTC, which uses stimulated learning to help CTC models learn label dependencies implicitly by using an auxiliary RNN to generate the appropriate stimuli. This stimuli comes in the form of an additional stimulation loss term which encourages the model to learn said label dependencies. The auxiliary network is only used during training and the inference model has the same structure as a standard CTC model. The proposed stimulated CTC model achieves about 35 % relative character error rate improvements on a synthetic gesture keyboard recognition task and over 30 % relative word error rate improvements on the Librispeech automatic speech recognition tasks over a baseline model trained with CTC only.
E-CNN: Accurate Spherical Camera Rotation Estimation via Uniformization of Distorted Optical Flow Fields

Spherical cameras, which can acquire all-round information, are effective to estimate rotation for robotic applications. Recently, Convolutional Neural Networks have shown great robustness in solving such regression problems. However they are designed for planar images and cannot deal with the non-uniform distortion present in spherical images, when expressed in the planar equirectangular projection. This can lower the accuracy of motion estimation. In this research, we propose an Equirectangular-Convolutional Neural Network (E-CNN) to solve this issue. This novel network regresses 3D spherical camera rotation by uniformizing distorted optical flow patterns in the equirectangular projection. We experimentally show that this results in consistently lower error as opposed to learning from the distorted optical flow.
Single Image Interpolation Exploiting Semi-local Similarity

This paper explores the modeling and exploitation of semi-local similarity in natural images to address the ill-posed nature of image interpolation. Our approach distinguishes itself from prior approaches by direct and careful use of semi-local similar patches to interpolate each individual patch. Our work uses a simple, parallelizable algorithm without the need to solve complicated optimization problems. Experimental results demonstrate that our interpolated images achieve significantly higher objective and subjective quality compared with those from state-of-the-art algorithms.
Deep Graph Regularized Learning for Binary Classification

With growing interest in data-driven classification, deep learning is now prevalent due to its ability to learn feature mapping functions solely from data. For very small training sets, however, deep learning, even with traditional regularization techniques, often overfits, resulting in sub-par classification performance. In this paper, we propose a novel binary classifier deep learning method, based on an iterative quadratic programming (QP) formulation with a graph Laplacian regularizer (GLR), combining the merits of model-based and data-driven approaches. Specifically, the proposed network employs a convolutional neural network (CNN) to learn deep features, which are used to define edge weights for a graph to pose a convex QP problem. Further, we design a novel loss function to penalize samples at the class boundary during semi-supervised learning. Results demonstrate that, given a small-size training dataset, our network outperforms several state-of-the-art classifiers, including CNN, model-based GLR and dynamic graph CNN classifiers.
From 0 to 10 million annotated words: part-of-speech tagging for Middle High German

By building a part-of-speech (POS) tagger for Middle High German, we investigate strategies for dealing with a low resource, diverse and non-standard language in the domain of natural language processing. We highlight various aspects such as the data quantity needed for training and the influence of data quality on tagger performance. Since the lack of annotated resources poses a problem for training a tagger, we exemplify how existing resources can be adapted fruitfully to serve as additional training data. The resulting POS model achieves a tagging accuracy of about 91% on a diverse test set representing the different genres, time periods and varieties of MHG. In order to verify its general applicability, we evaluate the performance on different genres, authors and varieties of MHG, separately. We explore self-learning techniques which yield the advantage that unannotated data can be utilized to improve tagging performance on specific subcorpora.
Fast and scalable algorithm for product large data on multicore system

Summary The problem of designing efficient parallel algorithms to calculate the product of n numbers when the multipliers are large is a fundamental problem in many applications of computer science such as cryptography. In this work, we present a new parallel algorithm on exclusive read shared memory model. The performance of the introduced algorithm is measured based on three factors, namely, (1) the number of cores, (2) the size of the array, and (3) the size of the multiplier. The experimental study on a multi core system reveals that the introduced algorithm is faster than the best‐known optimal parallel algorithm. The improvement of the proposed algorithm in processing time compared to the best known parallel algorithm is 80 % when the size of the array was 2 20 and the sizes of the multiplier were 1024, 2048, and 4096 bits. Moreover, our algorithm is a highly scalable parallel algorithm compared with the best‐known optimal parallel algorithm.
Fast trajectory tracking of the steady state of open quantum systems

We present a control strategy to speed up the steady-state tracking for open quantum systems governed by a time-dependent Markovian master equation. With this fast trajectory tracking strategy, the quantum state strictly follows the instantaneous steady state of open quantum systems and evolves into the target state without any fidelity loss. A compact expression for the counterdiabatic term is derived in terms of a superoperator formalism in Liouville space. To be specific, we utilize a universal method to transform the counterdiabatic superoperator into the ordinary operator form. It can be seen from the operator form that in addition to the coherent control field, an incoherent control is required for the fast trajectory tracking. The control strategy is successfully applied in two examples: One is a two-level quantum system with a time-dependent Hamiltonian coupling to a heat bath, the other is a two-level system following an isothermal process.
Features of application of e-learning management system Moodle in FSBEI HE "Bryansk state technical university»

Brief information about the educational content management system LCMS Moodle is given. The approach to account administration is shown, the structure of a typical training course that meets the requirements of regulatory documentation is given.
Learning from the Best: A Teacher-student Multilingual Framework for Low-resource Languages

The traditional method of pretraining neural acoustic models in low-resource languages consists of initializing the acoustic model parameters with a large, annotated multilingual corpus and can be a drain on time and resources. In an attempt to reuse TDNN-LSTMs already pre-trained using multilingual training, we have applied Teacher-Student (TS) learning as a method of pretraining to transfer knowledge from a multilingual TDNN-LSTM to a TDNN. The pretraining time is reduced by an order of magnitude with the use of language-specific data during the teacher-student training. Additionally, the TS architecture allows us to leverage untranscribed data, previously untouched during supervised training. The best student TDNN achieves a WER within 1% of the teacher TDNN-LSTM performance and shows consistent improvement in recognition over TDNNs trained using the traditional pipeline over all the evaluation languages. Switching to TDNN from TDNN-LSTM also allows sub-real time decoding.