OpenRaiser/Intern-Atlas huggingface dataset:
Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks
Venue: arXiv cs.SD 2020
Year: 2020

In recent decades, neural network based methods have significantly improved the performace of speech enhancement. Most of them estimate time-frequency (T-F) representation of target speech directly or indirectly, then resynthesize waveform using the estimated T-F representation. In this work, we proposed the temporal convolutional recurrent network (TCRN), an end-to-end model that directly map noisy waveform to clean waveform. The TCRN, which is combined convolution and recurrent neural network, is able to efficiently and effectively leverage short-term ang long-term information. Futuremore, we present the architecture that repeatedly downsample and upsample speech during forward propagation. We show that our model is able to improve the performance of model, compared with existing convolutional recurrent networks. Futuremore, We present several key techniques to stabilize the training process. The experimental results show that our model consistently outperforms existing speech enhancement approaches, in terms of speech intelligibility and quality.
Polar decreasing monomial-Cartesian codes
Venue: arXiv cs.IT 2020
Year: 2020

We prove that families of polar codes with multiple kernels over certain symmetric channels can be viewed as polar decreasing monomial-Cartesian codes, offering a unified treatment for such codes, over any finite field. We define decreasing monomial-Cartesian codes as the evaluation of a set of monomials closed under divisibility over a Cartesian product. Polar decreasing monomial-Cartesian codes are decreasing monomial-Cartesian codes whose sets of monomials are closed respect a partial order inspired by the recent work of Bardet, Dragoi, Otmani, and Tillich ["Algebraic properties of polar codes from a new polynomial formalism," 2016 IEEE International Symposium on Information Theory (ISIT)]. Extending the main theorem of Mori and Tanaka ["Source and Channel Polarization Over Finite Fields and Reed-Solomon Matrices," in IEEE Transactions on Information Theory, vol. 60, no. 5, pp. 2720--2736, May 2014], we prove that any sequence of invertible matrices over an arbitrary field satisfying certain conditions polarizes any symmetric over the field channel. In addition, we prove that the dual of a decreasing monomial-Cartesian code is monomially equivalent to a decreasing monomial-Cartesian code. Defining the minimal generating set for a set of monomials, we use it to describe the length, dimension and minimum distance of a decreasing monomial-Cartesian code.
Optimum multiplexer design in quantum-dot cellular automata
Venue: arXiv cs.ET 2020
Year: 2020

Quantum-dot Cellular Automata (QCA) is one of the most important computing technologies for the future and will be the alternative candidate for current CMOS technology. QCA is attracting a lot of researchers due to many features such as high speed, small size, and low power consumption. QCA has two main building blocks (majority gate and inverter) used for design any Boolean function. QCA also has an inherent capability that used to design many important gates such as XOR and Multiplexer in optimal form without following any Boolean function. This paper presents a novel design 2:1 QCA-Multiplexer in two forms. The proposed design is very simple, highly efficient and can be used to produce many logical functions. The proposed design output comes from the inherent capabilities of quantum technology. New 4:1 QCA-Multiplexer has been built using the proposed structure. The output waveforms showed the wonderful performance of the proposed design in terms of the number of cells, area, and latency.
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation
Venue: arXiv cs.AI 2020
Year: 2020

Deep learning models trained using massive amounts of data tend to capture one view of the data and its associated mapping. Different deep learning models built on the same training data may capture different views of the data based on the underlying techniques used. For explaining the decisions arrived by blackbox deep learning models, we argue that it is essential to reproduce that model's view of the training data faithfully. This faithful reproduction can then be used for explanation generation. We investigate two methods for data view extraction: hill-climbing approach and a GAN-driven approach. We then use this synthesized data for creating shadow models for explanation generation: Decision-Tree model and Formal Concept Analysis based model. We evaluate these approaches on a Blackbox model trained on public datasets and show its usefulness in explanation generation.
An Automated Testing Framework For Smart TV apps Based on Model Separation
Venue: arXiv cs.SE 2020
Year: 2020

Smart TV application (app) is a new technological software app that can deal with smart TV devices to add more functionality and features. Despite its importance nowadays, far too little attention has been paid to present a systematic approach to test this kind of app so far. In this paper, we present a systematic model-based testing approach for smart TV app. We used our new notion of model separation to use sub-models based on the user preference instead of the exhaustive testing to generate the test cases. Based on the constructed model, we generated a set of test cases to assess the selected paths to the chosen destination in the app. We also defined new mutation operators for smart TV app to assess our testing approach. The evaluation results showed that our approach can generate more comprehensive models of smart TV apps with less time as compared to manual exploratory testing. The results also showed that our approach can generate effective test cases in term of fault detection.
Uncertainty Weighted Causal Graphs
Venue: arXiv cs.AI 2020
Year: 2020

Causality has traditionally been a scientific way to generate knowledge by relating causes to effects. From an imaginery point of view, causal graphs are a helpful tool for representing and infering new causal information. In previous works, we have generated automatically causal graphs associated to a given concept by analyzing sets of documents and extracting and representing the found causal information in that visual way. The retrieved information shows that causality is frequently imperfect rather than exact, feature gathered by the graph. In this work we will attempt to go a step further modelling the uncertainty in the graph through probabilistic improving the management of the imprecision in the quoted graph.
METTEOR: Robust Multi-Traffic Topology Engineering for Commercial Data Center Networks
Venue: arXiv cs.NI 2020
Year: 2020

Numerous optical circuit switched data center networks have been proposed over the past decade for higher capacity, though commercial adoption of these architectures have been minimal so far. One major challenge commonly facing these architectures is the difficulty of handling bursty traffic with optical circuit switches (OCS) with high switching latency. Prior works generally rely on fast-switching OCS prototypes to better react to traffic changes via frequent reconfigurations. This approach, unfortunately, adds further complexity to the control plane. We propose METTEOR, an easily deployable solution for optical circuit switched data centers, that is designed for the current capabilities of commercial OCSs. Using multiple predicted traffic matrices, METTEOR designs data center topologies that are less sensitive to traffic changes, thus eliminating the need of frequently reconfiguring OCSs upon traffic changes. Results based on extensive evaluations using production traces show that METTEOR increases the percentage of direct-hop traffic by about 80% over a fat tree at comparable cost, and by about 30% over a uniform mesh, at comparable maximum link utilizations. Compared to ideal solutions that reconfigure OCSs on every traffic matrix, METTEOR achieves close-to-optimal bandwidth utilization even with biweekly reconfiguration. This drastically lowers the controller and management complexity needed to perform METTEOR in commercial settings.
Assessment of Amazon Comprehend Medical: Medication Information Extraction
Venue: arXiv cs.CL 2020
Year: 2020

In November 27, 2018, Amazon Web Services (AWS) released Amazon Comprehend Medical (ACM), a deep learning based system that automatically extracts clinical concepts (which include anatomy, medical conditions, protected health information (PH)I, test names, treatment names, and medical procedures, and medications) from clinical text notes. Uptake and trust in any new data product relies on independent validation across benchmark datasets and tools to establish and confirm expected quality of results. This work focuses on the medication extraction task, and particularly, ACM was evaluated using the official test sets from the 2009 i2b2 Medication Extraction Challenge and 2018 n2c2 Track 2: Adverse Drug Events and Medication Extraction in EHRs. Overall, ACM achieved F-scores of 0.768 and 0.828. These scores ranked the lowest when compared to the three best systems in the respective challenges. To further establish the generalizability of its medication extraction performance, a set of random internal clinical text notes from NYU Langone Medical Center were also included in this work. And in this corpus, ACM garnered an F-score of 0.753.
DeepLocNet: Deep Observation Classification and Ranging Bias Regression for Radio Positioning Systems
Venue: arXiv cs.RO 2020
Year: 2020

WiFi technology has been used pervasively in fine-grained indoor localization, gesture recognition, and adaptive communication. Achieving better performance in these tasks generally boils down to differentiating Line-Of-Sight (LOS) from Non-Line-Of-Sight (NLOS) signal propagation reliably which generally requires expensive/specialized hardware due to the complex nature of indoor environments. Hence, the development of low-cost accurate positioning systems that exploit available infrastructure is not entirely solved. In this paper, we develop a framework for indoor localization and tracking of ubiquitous mobile devices such as smartphones using on-board sensors. We present a novel deep LOS/NLOS classifier which uses the Received Signal Strength Indicator (RSSI), and can classify the input signal with an accuracy of 85\%. The proposed algorithm can globally localize and track a smartphone (or robot) with a priori unknown location, and with a semi-accurate prior map (error within 0.8 m) of the WiFi Access Points (AP). Through simultaneously solving for the trajectory and the map of access points, we recover a trajectory of the device and corrected locations for the access points. Experimental evaluations of the framework show that localization accuracy is increased by using the trained deep network; furthermore, the system becomes robust to any error in the map of APs.
How to measure musical preference on Facebook? Evidence from a mixed-method data collection
Venue: arXiv cs.SI 2020
Year: 2020

More and more digital data is available for social science analysis. This provides new ways of measuring several concepts. But when we start using new data sources, we have to understand how the new data source could be processed and how it could be analysed effectively. It is especially for Facebook data since there is no established gold standard analysis-framework. However, researchers have in-depth knowledge on how to measure different concepts using survey data. Thus, cross-referencing Facebook data with survey data is a reasonable way to support Facebook data analysis at different decision points. In this paper, we present how music preference could be measured by Facebook data and how survey data could support the selection of main indicators. Based on our results, we provide some general suggestions for Facebook data processing and indicator operationalization.
Phylogenetic signal in phonotactics
Venue: arXiv cs.CL 2020
Year: 2020

Phylogenetic methods have broad potential in linguistics beyond tree inference. Here, we show how a phylogenetic approach opens the possibility of gaining historical insights from entirely new kinds of linguistic data--in this instance, statistical phonotactics. We extract phonotactic data from 111 Pama-Nyungan vocabularies and apply tests for phylogenetic signal, quantifying the degree to which the data reflect phylogenetic history. We test three datasets: (1) binary variables recording the presence or absence of biphones (two-segment sequences) in a lexicon (2) frequencies of transitions between segments, and (3) frequencies of transitions between natural sound classes. Australian languages have been characterized as having a high degree of phonotactic homogeneity. Nevertheless, we detect phylogenetic signal in all datasets. Phylogenetic signal is greater in finer-grained frequency data than in binary data, and greatest in natural-class-based data. These results demonstrate the viability of employing a new source of readily extractable data in historical and comparative linguistics.
Widening and Squeezing: Towards Accurate and Efficient QNNs
Venue: arXiv cs.CV 2020
Year: 2020

Quantization neural networks (QNNs) are very attractive to the industry because their extremely cheap calculation and storage overhead, but their performance is still worse than that of networks with full-precision parameters. Most of existing methods aim to enhance performance of QNNs especially binary neural networks by exploiting more effective training techniques. However, we find the representation capability of quantization features is far weaker than full-precision features by experiments. We address this problem by projecting features in original full-precision networks to high-dimensional quantization features. Simultaneously, redundant quantization features will be eliminated in order to avoid unrestricted growth of dimensions for some datasets. Then, a compact quantization neural network but with sufficient representation ability will be established. Experimental results on benchmark datasets demonstrate that the proposed method is able to establish QNNs with much less parameters and calculations but almost the same performance as that of full-precision baseline models, e.g. $29.9\%$ top-1 error of binary ResNet-18 on the ImageNet ILSVRC 2012 dataset.
Estimation of Regional Economic Development Indicator from Transportation Network Analytics
Venue: arXiv cs.SI 2020
Year: 2020

With the booming economy in China, many researches have pointed out that the improvement of regional transportation infrastructure among other factors had an important effect on economic growth. Utilizing a large-scale dataset which includes 3.5 billion entry and exit records of vehicles along highways generated from toll collection systems, we attempt to establish the relevance of mid-distance land transport patterns to regional economic status through transportation network analyses. We apply standard measurements of complex networks to analyze the highway transportation networks. A set of traffic flow features are computed and correlated to the regional economic development indicator. The multi-linear regression models explain about 89% to 96% of the variation of cities' GDP across three provinces in China. We then fit gravity models using annual traffic volumes of cars, buses, and freight trucks between pairs of cities for each province separately as well as for the whole dataset. We find the temporal changes of distance-decay effects on spatial interactions between cities in transportation networks, which link to the economic development patterns of each province. We conclude that transportation big data reveal the status of regional economic development and contain valuable information of human mobility, production linkages, and logistics for regional management and planning. Our research offers insights into the investigation of regional economic development status using highway transportation big data.
Exponential discretization of weights of neural network connections in pre-trained neural networks
Venue: arXiv cs.NE 2020
Year: 2020

To reduce random access memory (RAM) requirements and to increase speed of recognition algorithms we consider a weight discretization problem for trained neural networks. We show that an exponential discretization is preferable to a linear discretization since it allows one to achieve the same accuracy when the number of bits is 1 or 2 less. The quality of the neural network VGG-16 is already satisfactory (top5 accuracy 69%) in the case of 3 bit exponential discretization. The ResNet50 neural network shows top5 accuracy 84% at 4 bits. Other neural networks perform fairly well at 5 bits (top5 accuracies of Xception, Inception-v3, and MobileNet-v2 top5 were 87%, 90%, and 77%, respectively). At less number of bits, the accuracy decreases rapidly.
PEL-BERT: A Joint Model for Protocol Entity Linking
Venue: arXiv cs.CL 2020
Year: 2020

Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently. Nevertheless, the fine-tuned BERT model trained on our protocol corpus still has a weak performance on the Entity Linking (EL) task. In this paper, we propose a model that joints a fine-tuned language model with an RFC Domain Model. Firstly, we design a Protocol Knowledge Base as the guideline for protocol EL. Secondly, we propose a novel model, PEL-BERT, to link named entities in protocols to categories in Protocol Knowledge Base. Finally, we conduct a comprehensive study on the performance of pre-trained language models on descriptive texts and abstract concepts. Experimental results demonstrate that our model achieves state-of-the-art performance in EL on our annotated dataset, outperforming all the baselines.
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study
Venue: arXiv cs.HC 2020
Year: 2020

Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" - a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instance-level explanations.
Efficient Fault Injection based on Dynamic HDL Slicing Technique
Venue: arXiv cs.AR 2020
Year: 2020

This work proposes a fault injection methodology where Hardware Description Language (HDL) code slicing is exploited to prune fault injection locations, thus enabling more efficient campaigns for safety mechanisms evaluation. In particular, the dynamic HDL slicing technique provides for a highly collapsed critical fault list and allows avoiding injections at redundant locations or time-steps. Experimental results show that the proposed methodology integrated into commercial tool flow doubles the simulation speed when comparing to the state-of-the-art industrial-grade EDA tool flows.
Practical MIMO for Visible Light Communication
Venue: arXiv cs.NI 2020
Year: 2020

Visible Light Communication (VLC) is seen as a complementary wireless technology to Radio Frequency (RF). However, VLC is very sensitive to the signal blockage and suffers from shadowing due to the high directionality of the optical channel. Hence there is a big interest in researching novel approaches for VLC like usage of multiple antenna techniques providing spatial diversity which can be exploited as a way to combat signal blockage and fading. We present a complete and low-cost MIMO-VLC transceiver system consisting of COTS components. In particular, we show that COTS 802.11n (WiFi) devices can be used so that the physical and data link layers of RF WiFi are reused for VLC. In addition, this allows us to directly utilize the multiple antenna (spatial) techniques available in 802.11n. Results from our measurement study show that such techniques are highly effective at improving the robustness of VLC links in the presence of obstacles and node mobility. Moreover, we show that multiple antennas can also be used to increase the data rate of VLC by means of spatial multiplexing.
Learning Extremal Representations with Deep Archetypal Analysis
Venue: arXiv cs.CV 2020
Year: 2020

Archetypes are typical population representatives in an extremal sense, where typicality is understood as the most extreme manifestation of a trait or feature. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. However, it might not always be possible to identify meaningful archetypes in a given feature space. Learning an appropriate feature space and identifying suitable archetypes simultaneously addresses this problem. This paper introduces a generative formulation of the linear archetype model, parameterized by neural networks. By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a variational autoencoder, and an optimal representation with respect to the unknown archetypes can be learned end-to-end. The reformulation of linear Archetypal Analysis as deep variational information bottleneck, allows the incorporation of arbitrarily complex side information during training. Furthermore, an alternative prior, based on a modified Dirichlet distribution, is proposed. The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information. A second application illustrates the exploration of the chemical space of small organic molecules. In this experiment, it is demonstrated that exchanging the side information but keeping the same set of molecules, e. g. using as side information the heat capacity of each molecule instead of the band gap energy, will result in the identification of different archetypes. As an application, these learned representations of chemical space might reveal distinct starting points for de novo molecular design.
Improving Generalizability of Fake News Detection Methods using Propensity Score Matching
Venue: arXiv cs.SI 2020
Year: 2020

Recently, due to the booming influence of online social networks, detecting fake news is drawing significant attention from both academic communities and general public. In this paper, we consider the existence of confounding variables in the features of fake news and use Propensity Score Matching (PSM) to select generalizable features in order to reduce the effects of the confounding variables. Experimental results show that the generalizability of fake news method is significantly better by using PSM than using raw frequency to select features. We investigate multiple types of fake news methods (classifiers) such as logistic regression, random forests, and support vector machines. We have consistent observations of performance improvement.