Reviewerly/exHarmony huggingface dataset:
CLEO

In this paper, we propose CLEO, which is a machine learning approach to equal-cost multipath routing (ECMP) schemes to distribute and balance traffic. ECMP-based traffic load-balancing is widely practiced by datacenters, but hash collision resulting from skewed ECMP hashing makes it difficult to achieve the desired throughputs over paths. Various solutions have been proposed to overcome the performance degradation caused by hash collision, but most of these solutions require modifying packet headers or replacing switches. To solve this problem, CLEO builds a neural-network model that characterizes the ECMP scheme of a switch. The proof-of-concept evaluation shows that CLEO improves the root mean square error fourfold between the desired and real path throughputs.
OK Computer: mort analogique, renaissance numérique et le tournant électronique de Radiohead

OK Computer marks an important volta or fin-de-siècle in Radiohead’s oeuvre. It is clear that the thematic leitmotif running throughout the album, influencing everything from Yorke’s lyrics, the sonic dynamism and experimentation on show, to the album’s artwork, is, in a word, denouement. This paper will explore the theme of denouement in relation to the album’s sonic, thematic, and aesthetic qualities. It will argue that the album’s title refers to the acquiescence, acceptance, and transition from more traditional analogue methods of making, recording, and distributing music adopted by the band up to and including The Bends (1995), and an inauguration of the band’s conscientious pursuit of radical digital experimentation to follow in Kid A (2000). In order to provide as holistic an analysis of the theme of denouement as possible, this paper’s analysis will be predicated on questions, concepts, and ideas including but not limited to the following: the leitmotif of denouement present in the thematic influences on the album, such as the influence of the track “Exit Music (For a Film)” commissioned by director Baz Luhrmann on the rest of the album; the influence of radical analogue experimentation culled from Miles Davis’ avant-garde jazz fusion album Bitches Brew (1970); the theme of resignation, interstice, liminality, and transference/-formation in relation to the “computer syndrome” as discussed in Michael Heffernan’s “Fin de Siècle, Fin du Monde?” (2000). The goal of this paper is to explore how OK Computer simultaneously represents Radiohead’s analogue death, digital rebirth, and embracing of the computer.
Design and Construction of a 1/15th Scale Wave Tank Model of the Azura Commercial Wave Energy Converter

Abstract The design of a 1/15th geometrically scaled wave tank model of the Azura™ commercial-scale wave energy device is presented. The objectives of the wave tank tests, conducted at the University of Maine Harlod Alfond Wind/Wave Ocean Engineering Lab (W2), included verification of the Azura’s energy capture in irregular waves, evaluation of performance in survival wave conditions, and testing of two advanced control algorithms. Due to the difficulty in properly Froude Scaling a hydraulic system, the model used a direct-drive rotary motor/generator power takeoff (PTO), with the dynamics of the hydraulic PTO included via a hardware-in-the-loop simulation. This PTO implementation led to additional design requirements being imposed on the model drivetrain. In addition to the model PTO design, the instrumentation design, structural design, and test plans are presented. The resulting model and PTO achieved a high level of controllability, and accurately emulated the dynamics of the hydraulic PTO of the full-scale Azura prototype.
Relation between Titles and Keywords in Japanese Academic Papers using Quantitative Analysis and Machine Learning

In this study, we analyzed keywords from different academic papers using data from more than 300 papers. Using the concept of quantitative surveys and machine learning, we conducted various analyses on the keywords in different papers. The findings obtained from these surveys and analyses are assumed to lend themselves to the automatic assignment of keywords for papers. In this study, the number of keywords included in a paper is quantitatively expressed using the covering rate and density of keywords. The results confirm that paper titles are likely to include keywords. The performed keyword analyses predict words that can be used as keywords via machine learning. The proposed method has an accuracy range 0.6–0.8. In addition, by analyzing the features used in machine learning, we can obtain the characteristics of the words that are mentioned as keywords in papers.
Decentralised Digital Ecosystems and Competition Law: A Conceptual Framework

In an environment where populism has the potential to drive regulation and where daily Internet use means dealing with a very few, very big, businesses, the concept of decentralisation is attractive. We are now at a stage where technology-powered decentralisation has the potential to have sweeping effects on the economy and on personal lives. It might be even expected that decentralisation does not raise serious competition law issues. This paper analyses digital ledger technology as a tool to implement decentralisation. It provides an overview of the current blockchain landscape. It also offers a framework of analysis by first clarifying the concept of decentralisation. It analyses decentralised ecosystems by adopting a systems perspective. In doing so, it draws the important distinction between blockchain and distributed ledger technology as centralised systems and those which actually offer decentralisation. The paper uses the broad conceptual framework that it develops to discuss a selected number of potential competition policy aspects related to blockchain ecosystems.
How Data Anonymisation Techniques influence Disease Triage in Digital Health

In the digital health area, there is a growing trend towards data-driven disease diagnostics and prescription triage. Discussions with health industry partners have revealed that distributed high-dimensional data repositories are not only helpful to medical & drug research but also for algorithms that support day-to-day medical diagnostics such as detecting Atrial fibrillation (AFib). Yet, recent privacy legislation in Europe requires that such data repositories be anonymised to protect against personal information exposure. Existing anonymisation algorithms work on the premise of transforming data to remove outliers that can result in re-identifications of individual records. While on the one hand this protects against data exposure, on the other hand anonymisation inadvertently results in base rate neglect. In the medical diagnostics context, base rate neglect can lead to false diagnostics and prescription triage, which is undesirable. In this paper, we study the impact of different anonymisation techniques on real-world disease diagnostics, and how they potentially influence decision making based on a real-world case as well as a semi-synthetic health data set. We demonstrate that the best results countervailing base rate neglect and ensuring data anonymity are obtained through the composition of several selected but dynamic per-row assigned anonymisation approaches incorporating attribute compartmentation.
Auto-Precision Scaling for Distributed Deep Learning

It has been reported that the communication cost for synchronizing gradients can be a bottleneck, which limits the scalability of distributed deep learning. Using low-precision gradients is a promising technique for reducing the bandwidth requirement. In this work, we propose Auto Precision Scaling (APS), an algorithm that can improve the accuracy when we communicate gradients by low-precision floating-point values. APS can improve the accuracy for all precisions with a trivial communication cost. Our experimental results show that for many applications, APS can train state-of-the-art models by 8-bit gradients with no or only a tiny accuracy loss (<0.05%). Furthermore, we can avoid any accuracy loss by designing a hybrid-precision technique. Finally, we propose a performance model to evaluate the proposed method. Our experimental results show that APS can get a significant speedup over state-of-the-art methods. To make it available to researchers and developers, we design and implement CPD (Customized-Precision Deep Learning) system, which can simulate the training process using an arbitrary low-precision customized floating-point format. We integrate CPD into PyTorch and make it open-source.
Co-Designing an Immersive and Interactive Alcohol Resistance Training Tool Using 360-Degree Video

The presented design case gives an example of co-designing an interactive 360-degree simulation for alcohol prevention, where the user attends a party and creates her own experience through a number of choices. Overall, the application consists of 125 different movie sequences. The 360-degree simulation is experienced through VR glasses to provide an immersive and intense experience. As an example, the user can choose whether to drink alcohol or not, what type of alcohol and how often. The user is also faced with non-alcohol related choices such as the opportunity to help an intoxicated man, starting a fight or flirting with other guests. The alcohol intake among adolescents in Denmark is among the highest in Europe. Lately it has been found that peer pressure is the main reason why adolescents start drinking. The goal of the application is to give Danish adolescents (aged 15 to 17) a first-hand virtual experience with alcohol and some tools for recognizing and handling peer pressure, which they can use later in real life. The stakeholders in the co-design process consisted of 10 students (aged 18-28) studying film making and game design at Askov Hojskole, Denmark, their teachers, alcohol experts from social services and researchers with expertise within health promotion, social marketing, virtual reality (VR), interaction design and game development. Additionally, 35 students from Askov Efterskole (aged 15-17) participated as actors and extras. This article describes the iterative design process of the application, containing exploration of key concepts, concepts design, prototype design and usability testing.
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP

While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-)supervised training. Thus, for TX-Ray, we modify the established computer vision explainability principle of 'visualizing preferred inputs of neurons' to make it usable transfer analysis and NLP. This allows one to analyze, track and quantify how self- or supervised NLP models first build knowledge abstractions in pretraining (1), and then transfer these abstractions to a new domain (2), or adapt them during supervised fine-tuning (3). TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. We find that, similar to Lottery Ticket based pruning, TX-Ray based pruning can improve test set generalization and that it can reveal how early stages of self-supervision automatically learn linguistic abstractions like parts-of-speech.
DIFAR: Deep Image Formation and Retouching

We present a novel neural network architecture for the image signalprocessing (ISP) pipeline. In a camera system, the ISP is a critical componentthat forms a high quality RGB image from RAW camera sensor data. Typical ISPpipelines sequentially apply a complex set of traditional image processingmodules, such as demosaicing, denoising, tone mapping, etc. We introduce a newdeep network that replaces all these modules, dubbed Deep Image Formation AndRetouching (DIFAR). DIFAR introduces a multi-scale context-aware pixel-levelblock for local denoising/demosaicing operations and a retouching block forglobal refinement of image colour, luminance and saturation. DIFAR can also betrained for RGB to RGB image enhancement. DIFAR is parameter-efficient andoutperforms recently proposed deep learning approaches in both objective andperceptual metrics, setting new state-of-the-art performance on multipledatasets including Samsung S7 and MIT-Adobe 5k.
DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions.

From fully connected neural networks to convolutional neural networks, the learned parameters within a neural network have been primarily relegated to the linear parameters (e.g., convolutional filters). The non-linear functions (e.g., activation functions) have largely remained, with few exceptions in recent years, parameter-less, static throughout training, and seen limited variation in design. Largely ignored by the deep learning community, radial basis function (RBF) networks provide an interesting mechanism for learning more complex non-linear activation functions in addition to the linear parameters in a network. However, the interest in RBF networks has waned over time due to the difficulty of integrating RBFs into more complex deep neural network architectures in a tractable and stable manner. In this work, we present a novel approach that enables end-to-end learning of deep RBF networks with fully learnable activation basis functions in an automatic and tractable manner. We demonstrate that our approach for enabling the use of learnable activation basis functions in deep neural networks, which we will refer to as DeepLABNet, is an effective tool for automated activation function learning within complex network architectures.
Domain Randomization for Detection and Position Estimation of Multiples of a Single Object With Applications to Localizing Bolts on Structures

Abstract Periodic replacement of fasteners such as bolts are an integral part of many structures (e.g., airplanes, cars, ships) and require periodic maintenance that may involve either their tightening or replacement. Current manual practices are time consuming and costly especially due to the large number of bolts. Thus, an automated method that is able to visually detect and localize bolt positions would be highly beneficial. In this paper, we demonstrate the use of deep neural network using domain randomization for detecting and localizing multiple bolts on a workpiece. In contrast to previous deep learning approaches that require training on real images, the use of domain randomization allows for all training to be done in simulation. The key idea here is to create a wide variety of computer generated synthetic images by varying the texture, color, camera position and orientation, distractor objects, and noise, and train the neural network on these images such that the neural network is robust to scene variability and hence provides accurate results when deployed on real images. Using domain randomization, we train two neural networks, a faster regional convolutional neural network for detecting the bolt and predicting a bounding box, and a regression convolutional neural network for estimating the x- and y-position of the bolt relative to the coordinates fixed to the workpiece. Our results indicate that in the best case we are able to detect bolts with 85% accuracy and are able to predict the position of 75% of bolts within 1.27 cm. The novelty of this work is in the use of domain randomization to detect and localize: (1) multiples of a single object, and (2) small sized objects (0.6 cm × 2.5 cm).
Cooperative Transport of a Payload With Offset CG Using Multiple UAVs

Abstract Cooperative transportation by multiple Unmanned Aerial Vehicles (UAVs) has been a topic of interest amongst robotics researchers since a decade. Researchers have developed different control schemes to address some of the issues related to cooperative transport. However, most of the existing control strategies assume a stationary center of gravity (CG) coinciding with the geometric center of the payload. In real world applications such as package delivery or human transport, position of CG, in general, would not be at geometric center, or would not be even known a – priori. This paper proposes a Proportional, Integral and Derivative (PID) controller to address the issue of control when the CG is offset during transportation of a common payload using multiple UAVs. The proposed PID scheme is centralized in nature in that it provides the same control action to all UAVs. Using extensive numerical simulations, the paper shows that such a scheme is shown to work effectively irrespective of the location of CG on the payload. The control scheme is also independent of the payload geometry and number of UAVs employed for cooperative transport. The work presented in this paper provides the groundwork to develop better control strategies to solve the problem of multi-UAV cooperative transport with a – priori unknown CG.
A DIGITAL IMAGE IN THE PRACTICE OF CYTOLOGY: A PILOT STUDY

Cytological study is a highly specialized type of laboratory analysis of the cellular composition of biological material and is to assess the morphological characteristics of cellular elements. The modern development of digital technologies is increasingly forming the interest of specialists to such a section as telepathology (digital pathology), which is a process of virtual microscopy with the transformation of classical cytological preparations into digital. Most morphologists currently use some forms of digital imaging, such as static images obtained by optical cameras mounted under a microscope. The development of more high quality image and resolution in the digital pathology promotes the use of telepathology, including telecitology in their daily work for training specialists, counselling of medications, monitoring the quality of diagnosis.
MODEL INFERENCE FOR ORDINARY DIFFERENTIAL EQUATIONS BY PARAMETRIC POLYNOMIAL KERNEL REGRESSION

Model inference for dynamical systems aims to estimate the future behaviour of a system from observations. Purely model-free statistical methods, such as Artificial Neural Networks, tend to perform poorly for such tasks. They are therefore not well suited to many questions from applications, for example in Bayesian filtering and reliability estimation. This work introduces a parametric polynomial kernel method that can be used for inferring the future behaviour of Ordinary Differential Equation models, including chaotic dynamical systems, from observations. Using numerical integration techniques, parametric representations of Ordinary Differential Equations can be learnt using Backpropagation and Stochastic Gradient Descent. The polynomial technique presented here is based on a nonparametric method, kernel ridge regression. However, the time complexity of nonparametric kernel ridge regression scales cubically with the number of training data points. Our parametric polynomial method avoids this manifestation of the curse of dimensionality, which becomes particularly relevant when working with large time series data sets. Two numerical demonstrations are presented. First, a simple regression test case is used to illustrate the method and to compare the performance with standard Artificial Neural Network techniques. Second, a more substantial test case is the inference of a chaotic spatio-temporal dynamical system, the Lorenz--Emanuel system, from observations. Our method was able to successfully track the future behaviour of the system over time periods much larger than the training data sampling rate. Finally, some limitations of the method are presented, as well as proposed directions for future work to mitigate these limitations.
Application of Fuzzy Logic in University Suggestion System for Vietnamese High School Students

Choosing which university to enter at the time of high school graduation is a life defining event for students, which will greatly affect their future career. If a student chooses a major that does not fit into his or her personality and interests, it may lead to poor results in college or work. High school students can be counseled or receive advice directly from parents and experienced people. However, not all students will be fortunate to receive accurate advice. This paper builds a system that provides complete information for high school students to choose their majors and universities based on a survey questionnaire. A fuzzy logic-based engine is used to suggest majors and appropriate schools for students.
An Integrity Assessment Framework for Multi-modal Vehicle Localization

Integrity monitoring of sources and process associated with localization systems used in autonomous navigation applications is of paramount importance. In this work, we present a novel source-level integrity monitoring method with a focus on generalization, scalability and versatility. A cross-consistency assessment based framework is developed to monitor the integrity of different spatial data sources. The proposed method is applied to several datasets, which demonstrates promising behavior of our integrity markers in identifying and quantifying unreliable data from sources. Several scenarios, which are normal occurrences in challenging driving conditions, are considered in the experiments and the advantages of the proposed integrity monitoring process are highlighted.
Low Distortion Rate Steganographic Data Transmission Model

In steganography, the data is hidden in different ways. The main objective of the studies is to have a minimum distortion of the cover image. The proposed model is based on the transmission of small amounts of data representing large amounts of data. By hiding the representative data in the cover image, large data is carried indirectly with very low image distortion rates. This model should be used for the safe transmission of data of high importance and which have been modified at certain times. Small amounts of data hidden in the cover image were used to analyze large amounts of data on the server side. Thus, the principle of imperceptibility, which is one of the basic building blocks of steganography, has been increased. The results showed that the proposed model is safer in the range of about 10% to 40%.
Face Inpainting with Dynamic Structural Information of Facial Action Units

In recent years, the deep learning based face inpainting methods have achieved some promising results, mainly related to the generative adversarial networks (GAN). Based on a large number of training samples, GAN generates a false true from a known training sample, and cannot use the trained parameters to generate image other than training samples. In addition, most of the previous works did not take into account high-level facial structure information, such as the co-existence of facial action units. In order to better to exploit facial structural knowledge, this paper proposes a method that combines prior knowledge based on high-level dynamic structural information of facial action units and GAN to complete face images. We primarily validate the effectiveness of our approach in facial expression restoration during face inpainting on the two datasets of BP4D and DISFA.
AKCES-GEC Grammatical Error Correction Dataset for Czech

AKCES-GEC is a grammar error correction corpus for Czech generated from a subset of AKCES. It contains train, dev and test files annotated in M2 format. Note that in comparison to CZESL-GEC dataset, this dataset contains separated edits together with their type annotations in M2 format and also has two times more sentences.If you use this dataset, please use following citation:@article{naplava2019wnut, title={Grammatical Error Correction in Low-Resource Scenarios}, author={N{\'a}plava, Jakub and Straka, Milan}, journal={arXiv preprint arXiv:1910.00353}, year={2019} }