OpenRaiser/Intern-Atlas huggingface dataset:
Inception-Residual Block based Neural Network for Thermal Image Denoising
Venue: arXiv cs.CV 2018
Year: 2018

Thermal cameras show noisy images due to their limited thermal resolution, especially for the scenes of a low temperature difference. In order to deal with a noise problem, this paper proposes a novel neural network architecture with repeatable denoising inception-residual blocks(DnIRB) for noise learning. Each DnIRB has two sub-blocks with difference receptive fields and one shortcut connection to prevent a vanishing gradient problem. The proposed approach is tested for thermal images. The experimental results indicate that the proposed approach shows the best SQNR performance and reasonable processing time compared with state-of-the-art denoising methods.
Improving risk management by using smart containers for real-time traceability
Venue: arXiv cs.CY 2018
Year: 2018

This research proposes implications of application functions by using the chain traceability data acquired from the Smart Object attached with Extended Real-time Data (SO-ERD: e.g. smart container, smart pallet, etc.) to improve risk management at the level of the logistics chain. Recent applications using traceability data and major issues in traceability systems have been explored by an academic literature. Information is classified by the usage of current traceability data for supporting risk detection and decisions in operational, tactical, and strategical levels. It is found that real-time data has been a significant impact on the usage for the transportation activity in all decision levels such the function of food quality control and collaborative planning among partners. However, there are some uncertainties in the aggregation of event-based traceability data captured by various partners which are preventing the adoption of data usage for the chain. Under the environment of Industry 4.0 and the Internet of Things (IoT), the SO-ERD enables independent data tracing through the chain in real-time. Its data has potential to overcome current issues and improve the supply chain risk management. Therefore, Implications of risk management are proposed with the usage of SO-ERD data based on the literature review which reveals current concerns of decision functions in the supply chain. The implications can be an impact to the domain needs.
What is the appropriate length of the publication period over which to assess research performance?
Venue: arXiv cs.DL 2018
Year: 2018

National research assessment exercises are conducted in different nations over varying periods. The choice of the publication period to be observed has to address often contrasting needs: it has to ensure the reliability of the results issuing from the evaluation, but also reach the achievement of frequent assessments. In this work we attempt to identify which is the most appropriate or optimal publication period to be observed. For this, we analyze the variation of individual researchers' productivity rankings with the length of the publication period within the period 2003-2008, by the over 30,000 Italian university scientists in the hard sciences. First we analyze the variation in rankings referring to pairs of contiguous and overlapping publication periods, and show that the variations reduce markedly with periods above three years. Then we will show the strong randomness of performance rankings over publication periods under three years. We conclude that the choice of a three year publication period would seem reliable, particularly for physics, chemistry, biology and medicine.
Performance assessment of the deep learning technologies in grading glaucoma severity
Venue: arXiv cs.CV 2018
Year: 2018

Objective: To validate and compare the performance of eight available deep learning architectures in grading the severity of glaucoma based on color fundus images. Materials and Methods: We retrospectively collected a dataset of 5978 fundus images and their glaucoma severities were annotated by the consensus of two experienced ophthalmologists. We preprocessed the images to generate global and local regions of interest (ROIs), namely the global field-of-view images and the local disc region images. We then divided the generated images into three independent sub-groups for training, validation, and testing purposes. With the datasets, eight convolutional neural networks (CNNs) (i.e., VGG16, VGG19, ResNet, DenseNet, InceptionV3, InceptionResNet, Xception, and NASNetMobile) were trained separately to grade glaucoma severity, and validated quantitatively using the area under the receiver operating characteristic (ROC) curve and the quadratic kappa score. Results: The CNNs, except VGG16 and VGG19, achieved average kappa scores of 80.36% and 78.22% when trained from scratch on global and local ROIs, and 85.29% and 82.72% when fine-tuned using the pre-trained weights, respectively. VGG16 and VGG19 achieved reasonable accuracy when trained from scratch, but they failed when using pre-trained weights for global and local ROIs. Among these CNNs, the DenseNet had the highest classification accuracy (i.e., 75.50%) based on pre-trained weights when using global ROIs, as compared to 65.50% when using local ROIs. Conclusion: The experiments demonstrated the feasibility of the deep learning technology in grading glaucoma severity. In particular, global field-of-view images contain relatively richer information that may be critical for glaucoma assessment, suggesting that we should use the entire field-of-view of a fundus image for training a deep learning network.
Revisiting the scaling of citations for research assessment
Venue: arXiv cs.DL 2018
Year: 2018

Over the past decade, national research evaluation exercises, traditionally conducted using the peer review method, have begun opening to bibliometric indicators. The citations received by a publication are assumed as proxy for its quality, but they require standardization prior to use in comparative evaluation of organizations or individual scientists: the citation data must be standardized, due to the varying citation behavior across research fields. The objective of this paper is to compare the effectiveness of the different methods of normalizing citations, in order to provide useful indications to research assessment practitioners. Simulating a typical national research assessment exercise, he analysis is conducted for all subject categories in the hard sciences and is based on the Thomson Reuters Science Citation Index-Expanded. Comparisons show that the citations average is the most effective scaling parameter, when the average is based only on the publications actually cited.
Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we propose an approach to detect incipient slip, i.e. predict slip, by using a high-resolution vision-based tactile sensor, GelSlim. The sensor dynamically captures the tactile imprints of the contact object and their changes with a soft gel pad. The method assumes the object is mostly rigid and treats the motion of object's imprint on sensor surface as a 2D rigid-body motion. We use the deviation of the true motion field from that of a 2D planar rigid transformation as a measure of slip. The output is a dense slip field which we use to detect when small areas of the contact patch start to slip (incipient slip). The method can detect both translational and rotational incipient slip without any prior knowledge of the object at 24 Hz. We test the method on 10 objects 240 times and achieve 86.25% detection accuracy. We further show how the slip feedback can be used to monitor the gripping force to avoid slip with a closed-loop bottle-cap screwing and unscrewing experiment with incipient slip detection feedback. The method was demonstrated to be useful for the robot to apply proper gripping force and stop screwing at the right point before breaking objects. The method can be applied to many manipulation tasks in both structured and unstructured environments.
An individual-level assessment of the relationship between spin-off activities and research performance in universities
Venue: arXiv cs.DL 2018
Year: 2018

One of the most problematic aspects in the creation of spin-offs by university personnel concerns the relationship between entrepreneurial activity and research activity by researcher-entrepreneurs. The literature has expressed varying and opposing views as to the nature of the relationship but very little has been produced to empirically legitimate one position or another. The present work proposes to address this shortcoming by exploring the relationship existing between academic spin-off generation and the research performance of enterprise founders. The study investigates whether, and to what extent, scientific performance by academic entrepreneurs is different than that of their colleagues, and if the involvement in entrepreneurial activity has an influence on the individual's research activity. The research questions are answered by considering all spin-offs generated by Italian universities over the period 2001-2008 and evaluating, through a bibliometric approach, the scientific performance of founders relative to that of their colleagues who carry out research in the same field. The data show better scientific performance by the researcher-entrepreneurs than that of their colleagues and in addition, although there are some variations across fields, the creation of a spin-off does not seem, on average, to have negative effects on the scientific performance of the founders.
On Single-Channel Speech Enhancement and On Non-Linear Modulation-Domain Kalman Filtering
Venue: arXiv cs.SD 2018
Year: 2018

This report focuses on algorithms that perform single-channel speech enhancement. The author of this report uses modulation-domain Kalman filtering algorithms for speech enhancement, i.e. noise suppression and dereverberation, in [1], [2], [3], [4] and [5]. Modulation-domain Kalman filtering can be applied for both noise and late reverberation suppression and in [2], [1], [3] and [4], various model-based speech enhancement algorithms that perform modulation-domain Kalman filtering are designed, implemented and tested. The model-based enhancement algorithm in [2] estimates and tracks the speech phase. The short-time-Fourier-transform-based enhancement algorithm in [5] uses the active speech level estimator presented in [6]. This report describes how different algorithms perform speech enhancement and the algorithms discussed in this report are addressed to researchers interested in monaural speech enhancement. The algorithms are composed of different processing blocks and techniques [7]; understanding the implementation choices made during the system design is important because this provides insights that can assist the development of new algorithms. Index Terms - Speech enhancement, dereverberation, denoising, Kalman filter, minimum mean squared error estimation.
Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm. In this paper, we propose a new online PnP algorithm for Fourier ptychographic microscopy (FPM) based on the fast iterative shrinkage/threshold algorithm (FISTA). Specifically, the proposed algorithm uses only a subset of measurements, which makes it scalable to a large set of measurements. We validate the algorithm by showing that it can lead to significant performance gains on both simulated and experimental data.
Testing Halfspaces over Rotation-Invariant Distributions
Venue: arXiv cs.DS 2018
Year: 2018

We present an algorithm for testing halfspaces over arbitrary, unknown rotation-invariant distributions. Using $\tilde O(\sqrt{n}ε^{-7})$ random examples of an unknown function $f$, the algorithm determines with high probability whether $f$ is of the form $f(x) = sign(\sum_i w_ix_i-t)$ or is $ε$-far from all such functions. This sample size is significantly smaller than the well-known requirement of $Ω(n)$ samples for learning halfspaces, and known lower bounds imply that our sample size is optimal (in its dependence on $n$) up to logarithmic factors. The algorithm is distribution-free in the sense that it requires no knowledge of the distribution aside from the promise of rotation invariance. To prove the correctness of this algorithm we present a theorem relating the distance between a function and a halfspace to the distance between their centers of mass, that applies to arbitrary distributions.
Clustered Monotone Transforms for Rating Factorization
Venue: arXiv cs.IR 2018
Year: 2018

Exploiting low-rank structure of the user-item rating matrix has been the crux of many recommendation engines. However, existing recommendation engines force raters with heterogeneous behavior profiles to map their intrinsic rating scales to a common rating scale (e.g. 1-5). This non-linear transformation of the rating scale shatters the low-rank structure of the rating matrix, therefore resulting in a poor fit and consequentially, poor recommendations. In this paper, we propose Clustered Monotone Transforms for Rating Factorization (CMTRF), a novel approach to perform regression up to unknown monotonic transforms over unknown population segments. Essentially, for recommendation systems, the technique searches for monotonic transformations of the rating scales resulting in a better fit. This is combined with an underlying matrix factorization regression model that couples the user-wise ratings to exploit shared low dimensional structure. The rating scale transformations can be generated for each user, for a cluster of users, or for all the users at once, forming the basis of three simple and efficient algorithms proposed in this paper, all of which alternate between transformation of the rating scales and matrix factorization regression. Despite the non-convexity, CMTRF is theoretically shown to recover a unique solution under mild conditions. Experimental results on two synthetic and seven real-world datasets show that CMTRF outperforms other state-of-the-art baselines.
Modeling Melodic Feature Dependency with Modularized Variational Auto-Encoder
Venue: arXiv cs.AI 2018
Year: 2018

Automatic melody generation has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melodies has turned out to be highly challenging. This paper introduces 1) a new variant of variational autoencoder (VAE), where the model structure is designed in a modularized manner in order to model polyphonic and dynamic music with domain knowledge, and 2) a hierarchical encoding/decoding strategy, which explicitly models the dependency between melodic features. The proposed framework is capable of generating distinct melodies that sounds natural, and the experiments for evaluating generated music clips show that the proposed model outperforms the baselines in human evaluation.
Dial2Desc: End-to-end Dialogue Description Generation
Venue: arXiv cs.CL 2018
Year: 2018

We first propose a new task named Dialogue Description (Dial2Desc). Unlike other existing dialogue summarization tasks such as meeting summarization, we do not maintain the natural flow of a conversation but describe an object or an action of what people are talking about. The Dial2Desc system takes a dialogue text as input, then outputs a concise description of the object or the action involved in this conversation. After reading this short description, one can quickly extract the main topic of a conversation and build a clear picture in his mind, without reading or listening to the whole conversation. Based on the existing dialogue dataset, we build a new dataset, which has more than one hundred thousand dialogue-description pairs. As a step forward, we demonstrate that one can get more accurate and descriptive results using a new neural attentive model that exploits the interaction between utterances from different speakers, compared with other baselines.
Neural Music Synthesis for Flexible Timbre Control
Venue: arXiv cs.SD 2018
Year: 2018

The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process --- creating audio samples from a score and instrument information --- is modeled using generative neural networks. This paper describes a neural music synthesis model with flexible timbre controls, which consists of a recurrent neural network conditioned on a learned instrument embedding followed by a WaveNet vocoder. The learned embedding space successfully captures the diverse variations in timbres within a large dataset and enables timbre control and morphing by interpolating between instruments in the embedding space. The synthesis quality is evaluated both numerically and perceptually, and an interactive web demo is presented.
Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Images
Venue: arXiv cs.CR 2018
Year: 2018

A block scrambling-based encryption scheme is presented to enhance the security of Encryption-then-Compression (EtC) systems with JPEG compression, which allow us to securely transmit images through an untrusted channel provider, such as social network service providers. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the conventional scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images even when the original image has three color channels. These features enhance security against various attacks such as jigsaw puzzle solver and brute-force attacks. In an experiment, the security against jigsaw puzzle solver attacks is evaluated. Encrypted images were uploaded to and then downloaded from Facebook and Twitter, and the results demonstrated that the proposed scheme is effective for EtC systems.
SARN: Relational Reasoning through Sequential Attention
Venue: arXiv cs.LG 2018
Year: 2018

This paper proposes an attention module augmented relational network called SARN(Sequential Attention Relational Network) that can carry out relational reasoning by extracting reference objects and making efficient pairing between objects. SARN greatly reduces the computational and memory requirements of the relational network, which computes all object pairs. It also shows high accuracy on the Sort-of-CLEVR dataset compared to other models, especially on relational questions.
Semi-Finite Length Analysis for Information Theoretic Tasks
Venue: arXiv cs.IT 2018
Year: 2018

We focus on the optimal value for various information-theoretical tasks. There are several studies for the asymptotic expansion for these optimal values up to the order $\sqrt{n}$ or $\log n$. However, these expansions have errors of the order $o(\sqrt{n})$ or $o(\log n)$, which does not goes to zero asymptotically. To resolve this problem, we derive the asymptotic expansion up to the constant order for upper and lower bounds of these optimal values. While the expansions of upper and lower bonds do not match, they clarify the ranges of these optimal values, whose errors go to zero asymptotically.
Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting Method
Venue: arXiv cs.NE 2018
Year: 2018

A newly introduced method called Taylor-based Optimized Recursive Extended Exponential Smoothed Neural Networks Forecasting method is applied and extended in this study to forecast numerical values. Unlike traditional forecasting techniques which forecast only future values, our proposed method provides a new extension to correct the predicted values which is done by forecasting the estimated error. Experimental results demonstrated that the proposed method has a high accuracy both in training and testing data and outperform the state-of-the-art RNN models on Mackey-Glass, NARMA, Lorenz and Henon map datasets.
Sequence-to-sequence Models for Small-Footprint Keyword Spotting
Venue: arXiv cs.SD 2018
Year: 2018

In this paper, we propose a sequence-to-sequence model for keyword spotting (KWS). Compared with other end-to-end architectures for KWS, our model simplifies the pipelines of production-quality KWS system and satisfies the requirement of high accuracy, low-latency, and small-footprint. We also evaluate the performances of different encoder architectures, which include LSTM and GRU. Experiments on the real-world wake-up data show that our approach outperforms the recently proposed attention-based end-to-end model. Specifically speaking, with 73K parameters, our sequence-to-sequence model achieves $\sim$3.05\% false rejection rate (FRR) at 0.1 false alarm (FA) per hour.
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning
Venue: arXiv cs.CV 2018
Year: 2018

Caricature generation is an interesting yet challenging task. The primary goal is to generate plausible caricatures with reasonable exaggerations given face images. Conventional caricature generation approaches mainly use low-level geometric transformations such as image warping to generate exaggerated images, which lack richness and diversity in terms of content and style. The recent progress in generative adversarial networks (GANs) makes it possible to learn an image-to-image transformation from data, so that richer contents and styles can be generated. However, directly applying the GAN-based models to this task leads to unsatisfactory results because there is a large variance in the caricature distribution. Moreover, some models require strictly paired training data which largely limits their usage scenarios. In this paper, we propose CariGAN overcome these problems. Instead of training on paired data, CariGAN learns transformations only from weakly paired images. Specifically, to enforce reasonable exaggeration and facial deformation, facial landmarks are adopted as an additional condition to constrain the generated image. Furthermore, an attention mechanism is introduced to encourage our model to focus on the key facial parts so that more vivid details in these regions can be generated. Finally, a Diversity Loss is proposed to encourage the model to produce diverse results to help alleviate the `mode collapse' problem of the conventional GAN-based models. Extensive experiments on a new large-scale `WebCaricature' dataset show that the proposed CariGAN can generate more plausible caricatures with larger diversity compared with the state-of-the-art models.
Embedding Individual Table Columns for Resilient SQL Chatbots
Venue: arXiv cs.CL 2018
Year: 2018

Most of the world's data is stored in relational databases. Accessing these requires specialized knowledge of the Structured Query Language (SQL), putting them out of the reach of many people. A recent research thread in Natural Language Processing (NLP) aims to alleviate this problem by automatically translating natural language questions into SQL queries. While the proposed solutions are a great start, they lack robustness and do not easily generalize: the methods require high quality descriptions of the database table columns, and the most widely used training dataset, WikiSQL, is heavily biased towards using those descriptions as part of the questions. In this work, we propose solutions to both problems: we entirely eliminate the need for column descriptions, by relying solely on their contents, and we augment the WikiSQL dataset by paraphrasing column names to reduce bias. We show that the accuracy of existing methods drops when trained on our augmented, column-agnostic dataset, and that our own method reaches state of the art accuracy, while relying on column contents only.