Reviewerly/exHarmony huggingface dataset:
Handling Unforeseen Failures Using Argumentation-Based Learning

General Purpose Service Robots operate in different environments of a dynamic nature. Even the robot's programmer cannot predict what kind of failure conditions a robot may confront in its lifetime. Therefore, general purpose service robots need to efficiently handle unforeseen failure conditions. This requires the capability of handling unforeseen failures while the robot is performing a task. Existing research typically offers special-purpose solutions depending on what has been foreseen at the design time. In this research, we propose a general purpose argumentation-based architecture which is able to autonomously recover from unforeseen failures. We compare the proposed method with existing incremental online learning methods in the literature. The results show that the proposed argumentation-based learning approach is capable of learning complex scenarios faster with a lower number of observations. Moreover, the final precision of the proposed method is higher than other methods.
A Bayesian Attention Neural Network Layer for Speaker Recognition

Neural network based attention modeling has found utility in areas such as visual analysis, speech recognition and more recently speaker recognition. Attention represents a gating (or weighting) function on information and governs how the corresponding statistics are accumulated. In the context of speaker recognition, attention can be incorporated as a frame weighted mean of an information stream. These weights can be made to sum to one (the standard approach) or be calculated in other ways. If the weights can be made to represent event observation probabilities, we can extend the approach to be within a Bayesian framework. More specifically, we combine prior information with the frame weighted statistics to produce an adapted or posterior estimate of the mean. We evaluate the proposed method on NIST data.
Performance Analysis of Raptor Code for Reconciliation in Continuous Variable Quantum Key Distribution

In information theory and communication, quantum communication and technologies have brought about huge changes. It has been proved that secret key communication over a quantum channel is possible using quantum key distribution and different types of codes has been used to research if they could yield the desired results for information reconciliation in continuous variable quantum key distribution. Raptor codes are the first rateless codes with linear time encoding and decoding with vast application in information communication. Its versatile use and behavior inspired to analysis of their performance for information reconciliation. In this paper, we use a special pre-coding scheme based on the Digital Video Broadcasting-Satellite Standard (DVB-S2) and a special code length relationship between the intermediate symbols and the encoded symbols. Simulation results show that this raptor codes' frame error rate performance can be manipulated easily over different ranges of signal to noise ratio.
Teach an All-rounder with Experts in Different Domains

In many automatic speech recognition (ASR) tasks, an ideal model has to be applicable over multiple domains. In this paper, we propose to teach an all-rounder with experts in different domains. Concretely, we build a multi-domain acoustic model by applying the teacher-student training framework. First, for each domain, a teacher model (domain-dependent model) is trained by fine-tuning a multi-condition model with domain-specific subset. Then all these teacher models are used to teach one single student model simultaneously. We perform experiments on two predefined domain setups. One is domains with different speaking styles, the other is near-field, far-field and far-field with noise. Moreover, two types of models are examined: deep feedforward sequential memory network (DFSMN) and long short term memory (LSTM). Experimental results show that the model trained with this framework outperforms not only multi-condition model but also domain-dependent model. Specially, our training method provides up to 10.4% relative character error rate improvement over baseline model (multi-condition model).
Hadoop Performance Evaluation in Software Defined Data Center Networks

The wide spread of Big Data applications and services raised the need of implementing dedicated frameworks for the efficient management of data storage and access. Complex data center architectures have been defined to support such applications, where the network connections are often the bottleneck for data access and retrieval. In this paper we analyze a data center architecture based on a Fat-Tree topology running Hadoop as the framework for data management. The network is based on the software defined networking paradigm allowing the routing protocol to be switched between the spanning tree, the shortest path bridging and the equal cost multipath. The results show that performance of Hadoop are strongly influenced by the choice of the network protocols in dependence of the traffic load. The performance evaluation is based on a dedicated novel simulation framework named MaxHadoop.
Eliciting Public Display Awareness and Engagement: An Experimental Study and Semantic Strategy

As displays move from the desktop and mobile world to situated urban and public spaces, these mixed use interfaces will continue to pose both opportunities and challenges for architects and product designers alike. In particular, the ability to visually communicate interface modality on a platform that supports both public and private interactions. To address this challenge starts with eliciting user attention and awareness with the goal of creating direct user engagement with the screen. Although other researchers have explored the topic of attention and public displays, our interdisciplinary team leveraged a far more fundamental product semantic strategy. Using the linguistic concept of ‘complementation’ we conducted a field study (n = 32) implementing both implicit and explicit screen based prompts. The results from the quantitative experiment show a foundational semantic approach to “coupled signals” that can be applied to a large range of design applications.
Nonclassical properties of coherent state orthogonalization via Hermite polynomial excited operation

In this work, we theoretically construct an orthogonal state of a coherent state where the orthogonalizer is based on the Hermite polynomial excited operation with Hm(x) being a Hermite polynomial of order m and creation operator , called the Hermite-excite-orthogonalized coherent state (HEOCS). Then, in comparison with the Hermite polynomial excited coherent state (HPECS), we numerically investigate their different nonclassical properties by examining several measurable features, such as photon number distribution, sub-Poisson statistics, the antibunching effect, the quadrature squeezing effect, and the negative Wigner function. The results demonstrate that both HEOCS and HPECS are non-Gaussian quantum states with highly nonclassical characteristics by improving and modulating the order of the Hermite polynomial and coherent amplitude, which indicates that performing the Hermite-excited operation on given Gaussian states is an effective approach to generating non-Gaussian states.
Nonlinear State Estimation Using Particle Filters on the Stiefel Manifold

Many problems in statistical signal processing involve tracking the state of a dynamic system that evolves on a Stiefel manifold. To this aim, we introduce in this paper a novel particle filter algorithm that approximates the optimal importance function on the Stiefel manifold and is capable of handling nonlinear observation functions. To sample from the required importance function, we develop adaptations of previous MCMC algorithms. We verify via numerical simulations that, in a scenario with a strongly nonlinear observation model, the new proposed method outperforms existing algorithms that use the prior importance function at the cost, however, of increased computational complexity.
Adaptive Brightness Learning for Active Object Recognition

State-of-the-art object detection methods based on deep learning achieved promising performances in recent years. However, the performances are limited by the passive nature of the traditional object recognition framework in ignoring the relationship between imaging configuration and recognition performance as well as the importance of recognition performance feedback for improving image quality. To address the above limitations, an active object recognition method based on reinforcement learning is proposed in this paper by taking adaptive brightness adjustment as an example. Progressive brightness adjustment strategy is learned by maximizing recognition performance on reference high-quality training samples. With the help of active object recognition and brightness adjustment strategy, low-quality images can be converted into high-quality images, and overall performances are improved without retraining the detector.
Research on Safety Early Warning Management of Coal Mining Face Based on Expert System

Coal mining face is the place where coal mine accidents occur frequently.In this paper, the security early warning expert system is buliled based on the analysis method of system engineering and the investigation of coal mining face in a large number of coal mines. The classification, acquisition mechanism and representation of knowledge are described, and the system structure, reasoning mechanism and the establishment of knowledge base are also introduced.
A Partial Coherent Interferometry for measuring the thickness of a dynamic liquid sheet

This work presents a non-invasive technique, Partial Coherent Interferometry, for measuring the thickness distribution of a dynamic liquid sheet. It utilizes the linear relationship between the degree of coherence and the optical path difference to determine the thickness of the sheet. In an interferometer using a partial coherent laser, the interference patterns before and after the liquid sheet insertion are digitally recorded. The relative thickness distribution is determined by applying a phase unwrapping process to the recorded interference pattern. By measuring the change of the degree of coherence introduced by the sheet, the absolute thicknesses at the reference points are determined. By matching the relative thickness to the absolute thicknesses at different points, the absolute thickness distribution is retrieved. To verify the reliability of this method, a glass sample and a static ethanol film, both with nominal thicknesses (152.4 µm), are measured first. The results, a nearly uniform thickness of 148.8 ± 1.8 µm for glass and a varying thickness distribution around 154.6 µm for static ethanol film, show the capability of this method to measure a two-dimensional thickness distribution of a thin liquid sheet. Then a dynamic impinging sheet formed by the two ethanol jets with Reynolds number 763 and Weber number 143 is measured by Partial Coherent Interferometry. The thickness distribution ranges from 26 to 38 µm with an uncertainty of 2.1 µm.
Research on co-propagation of QKD and classical communication by reducing the classical optical power

We investigate the crosstalk noise, especially the spontaneous Raman scattering, in the optical fiber of a co-propagation system between quantum key distribution (QKD) and classical communications. Although many methods have been proposed, such as increasing the wavelength spacing and narrowband filtering technique, to suppress Raman scattering noise, these methods greatly affect the performance of QKD. One way to solve the obstacle restricting the coexistence is to decrease the classical signal power. Based on the high gain of the gated avalanche photodiode and pulse position modulation, we demonstrate that the co-propagation system works effectively with only a small effect on long-haul fibers, which has great significance for the practical widespread commercialization of QKD.
Cyber Intelligence Networks: A Typology

In this research, we identify different types of cyber intelligence networks and specify their unique characteristics. While existing research discusses intelligence cooperation in general terms or analyzes single case studies, we provide insight in the diversity of cyber intelligence networks in existence. Based on a qualitative study of cyber intelligence networks in the Netherlands, we identify four distinct types: centralized networks, business networks, operational networks, and local networks. These networks differ in terms of their history, activities, governance structure, communication frequency, goal consensus, member commitment, and perceived results. Based on the typology, we claim that the network type influences the challenges and outcomes of cyber intelligence cooperation. Next, we argue for an expanded focus on private sector engagement in these networks. Lastly, we advocate for incentivizing bottom-up cyber intelligence cooperation.
Short-answer grading using textual entailment

Given a question, a reference answer, and the answer given by the student, the aim of the automatic short answer grading task is to assign a grade to the student’s answer. We use for this a large number of matching rules relying on recognizing entailment relation between dependency structures of th e two answers. Comparison of the grades generated by our method with those given by human judges on a computer science dataset shows a quite promising maximum correlation of 0.627.
Unsupervised Feature Ranking and Selection Based on Autoencoders

Feature selection is one of the most important and widely-used dimension reduction techniques due to its efficiency and intractability of the results. In this paper, we propose a simple but efficient unsupervised feature ranking and selection method by exploiting the geometry of the original feature space using AutoEncoders. Average reconstruction error of training samples by ignoring features, one at time, and the contribution of feature in the latent space (bottleneck of the auto-encoder) are proposed as two useful measures for ranking the features. The proposed method is evaluated for three different tasks: (1) feature selection, (2) discovering image interest points, and (3) extracting important blocks of an images Result on standard benchmarks confirm that the performance of our method is better than state-of-the-art methods.
Comparison of Data Augmentation and Adaptation Strategies for Code-switched Automatic Speech Recognition

Code-switching occurs when the speaker alternates between two or more languages or dialects. It is a pervasive phenomenon in most Indic spoken languages. Code-switching poses a challenge in language modeling as it complicates the orthographic realization of text, and generally, there is a shortage of code-switched data. In this paper, we investigate data augmentation and adaptation strategies for language modeling. Using Bengali and English as an example, we study augmenting the code-switched transcripts with separate transliterated Bengali and English corpora. We present results on two speech recognition tasks, namely, voice search and dictation. We show improvements on both tasks with Maximum Entropy (MaxEnt) and Long Short-Term Memory (LSTM) language models (LMs). We also explore different adaptation strategies for MaxEnt LM and LSTM LM, demonstrating that the transliteration-based data-augmented LSTM LM matches the adapted MaxEnt LM which is trained on more Bengali-English data.
A Windowed Digraph Fourier Transform

We propose a methodology to carry out vertex-frequency analyses of graph signals, with the goal of unveiling the signal's frequency occupancy over a localized region in the network. To this end, we first introduce localized graph signals in the vertex domain, by defining windows that are localized around each node by construction. Recent directed graph Fourier transform (DGFT) advances facilitate the frequency analysis of said localized signals, to reveal the signal's energy distribution in a way akin to a spectrogram in the vertex-frequency plane. We then learn a set of windows by applying gradient descent method to an optimization problem governed by penalty parameters in the spectral domain. We also argue about the tradeoff between the resolution in the vertex and frequency domains based on the said parameters. We evaluate the performance of the proposed windowed GFT approach through numerical experiments on synthetic and real-world graphs.
Security-enhanced three-factor remote user authentication scheme based on Chebyshev chaotic maps

With the wide deployment of new computing paradigms, such as cloud computing and edge computing, the people can access services provided by remote servers more conveniently via the Internet. To preserve the security of those messages transmitted over the public channel, remote user authentication protocols are popularly implemented in various information systems. Recently, Park et al. pointed that Cao and Ge’s three-factor authentication scheme suffers from offline identity guessing attack and server impersonation attack. They also proposed a new scheme after presenting the corresponding cryptanalysis. However, we found that Park et al.’s scheme is vulnerable to offline password guessing attack, which is the most serious threat against this kind of authentication scheme. In addition, their scheme cannot provide complete correctness due to the misuse of bio-hashing and also fails to achieve user untraceability and perfect forward secrecy. To conquer these security pitfalls, we put forward a password, smart card, and biometrics-based three-factor remote user authentication scheme using the extended Chebyshev chaotic maps. The security analysis indicates that the proposed scheme can withstand various well-known attacks including offline guessing attack, impersonation attack, and so on. The performance evaluation shows that the proposed scheme provides stronger security guarantee at the cost of acceptable computation overhead. Thus, the proposed scheme is more desirable for securing communication in mobile networks.
Reflections of L1 reading comprehension skills in university academic grades for an undergraduate translation programme

Concerns about poor L1 reading skills in undergraduate students have been expressed in several contexts. These skills form a basis for building robust L2 reading comprehension skills, vital for translators. This study, which is part of a larger organization-based action research study to improve L1 reading levels, aimed to explore how L1 reading comprehension skills were reflected and predicted the overall academic performance of students on a South American undergraduate translator training programme using regression modelling. Seventy-nine students from second, third and fourth year of the programme were recruited for this study. They were asked to take a standardized Spanish reading comprehension test measuring word-level comprehension, sentence-level comprehension and text-level comprehension. Also, participants’ final grades for all completed courses were obtained, as well as students’ scores in a pre-entry English test providing a baseline level L2 knowledge prior to being formally trained on English. The results showed that students’ overall grades were predicted by and correlated with lower order sentence-level reading skills, rather than higher-order text-level skills, which was found to be of some concern inasmuch as the translation process demands reading skills that go well beyond the sentence boundary. Given the impact that evaluation can have on student learning, and that the connection between reading comprehension and academic results on translation programs does not appear to have been considered in the literature previously, the study may highlight a need for other institutions wishing to improve L1 reading skills to similarly examine their summative evaluation practices.
Nonlinear Multi-scale Super-resolution Using Deep Learning

We propose a deep learning architecture capable of performing up to 8× single image super-resolution. Our architecture incorporates an adversarial component from the super-resolution generative adversarial networks (SRGANs) and a multi-scale learning component from the multiple scale super-resolution network (MSSRNet), which only together can recover smaller structures inherent in satellite images. To further enhance our performance, we integrate progressive growing and training to our network. This, aided by feed forwarding connections in the network to move along and enrich information from previous inputs, produces super-resolved images at scaling factors of 2, 4, and 8. To ensure and enhance the stability of GANs, we employ Wasserstein GANs (WGANs) during training. Experimentally, we find that our architecture can recover small objects in satellite images during super-resolution whereas previous methods cannot.
Constructing a Chinese electronic medical record corpus for named entity recognition on resident admit notes

Electronic Medical Records(EMRs) contain much medical information about patients. Medical named entity extracting from EMRs can provide value information to support doctors' decision making. The research on information extraction of Chinese Electronic Medical Records is still behind that has done in English.This paper proposed a practical annotation scheme for medical entity extraction on Resident Admit Notes (RANs), and a model which can automatic extract medical entity. Nine types of clinical entities, four types of clinical relationships were defined in our annotation scheme. An end-to-end deep neural network with convolution neural network and long-short term memory units was applied in our model for the medical named entity recognition(NER).We annotated RANs in three rounds. The overall F-score of annotation consistency was up to 97.73%. And our NER model on the 255 annotated RANs achieved the best F-score of 91.08%.The annotation scheme and the model for NER in this paper are effective to extract medical named entity from RANs and provide the basis for fully excavating the patient's information.
Analysis of the AnyWalker Software Architecture Using the UML Refactoring Tool

Refactoring (restructuring of a system, which keeps its functionality invariant) of the software can be aimed at the optimization of such characteristics as modifiability, maintainability and understandability. Model refactoring can be conducted not only at the code level, but also at the model level. According to the MDA approach models are described using the Unified Modelling Language (UML). UML class diagrams are used to specify software architecture. UML Refactoring is a software tool, which provides UML class diagram analysis and refactoring. This article proposes results of AnyWalker project UML class diagram refactoring using the UML Refactoring tool.
A Human-in-the-Loop Cyber-Physical Approach for Students Performance Assessment

As the number of smart devices that surround us increases, so do the possibilities of leveraging them to create intelligent, socially-aware systems. However, apart from e-health systems, few applications consider humans as active system players. In fact, most applications/systems have the sole objective of providing some service to humans without considering their intents, actions or emotions. In this respect, they can be looked at as open loop. Systems that do consider human feedback are called Human-in-the-Loop Cyber-Physical-Systems (HiTLCPS), and integrate the humans in a feedback loop, with potential to react and change the environment. In this paper, we propose an approach that implements the HITLCPS concept. The developed platform, ISABELA, explores both online social networks (OSN) data as well as a variety of other data, including environment data and data from personal mobile devices, thus combining IoT, social sensors and mobile sensing. In addition to present the system architecture, the paper provides preliminary results obtained during field trials.