Reviewerly/exHarmony huggingface dataset:
Learned in Speech Recognition: Contextual Acoustic Word Embeddings

End-to-end acoustic-to-word speech recognition models have recently gained popularity because they are easy to train, scale well to large amounts of training data, and do not require a lexicon. In addition, word models may also be easier to integrate with downstream tasks such as spoken language understanding, because inference (search) is much simplified compared to phoneme, character or any other sort of sub-word units. In this paper, we describe methods to construct contextual acoustic word embeddings directly from a supervised sequence-to-sequence acoustic-to-word speech recognition model using the learned attention distribution. On a suite of 16 standard sentence evaluation tasks, our embeddings show competitive performance against a word2vec model trained on the speech transcriptions. In addition, we evaluate these embeddings on a spoken language understanding task, and observe that our embeddings match the performance of text-based embeddings in a pipeline of first performing speech recognition and then constructing word embeddings from transcriptions.
Learned Neural Iterative Decoding for Lossy Image Compression Systems

For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques. Specifically, we propose a recurrent neural network approach for nonlinear, iterative decoding. Our decoder, which works with any encoder, employs self-connected memory units that make use of causal and non-causal spatial context information to progressively reduce reconstruction error over a fixed number of steps. We experiment with variants of our estimator and find that iterative refinement consistently creates lower distortion images of higher perceptual quality compared to other approaches. Specifically, on the Kodak Lossless True Color Image Suite, we observe as much as a 0.871 decibel (dB) gain over JPEG, a 1.095 dB gain over JPEG 2000, and a 0.971 dB gain over a competitive neural model.
A SAR Image Despeckling Method Using Multi-Scale Nonlocal Low-Rank Model

Speckle noise is an inherent nature of synthetic aperture radar (SAR) images, which degrades the quality of the images and makes the interpretation of SAR images difficult. In this letter, we propose a despeckling method by simultaneously exploring low-rank prior and multi-scale prior of SAR images. Especially, we propose a low-rank minimization model by considering a data fidelity term derived from the Fisher-Tippett distribution and a weighted nuclear norm regularization term. Furthermore, we explore the multi-scale prior by selecting similar patches from different scales of the SAR image. The resulting optimization problem is solved by the alternating direction method of multipliers (ADMM). Experiments conducted on both simulated and real SAR images demonstrate that the proposed method can provide promising despeckling results in terms of speckle reduction and texture and edge details preservation.
Physics-based simulation ontology: an ontology to support modelling and reuse of data for physics-based simulation

The current work presents an ontology developed for physics-based simulation in engineering design, called Physics-based Simulation Ontology (PSO). The purpose of the ontology is to assist in modelling the physical phenomenon of interest in a veridical manner, while capturing the necessary and reusable information for physics-based simulation solvers. The development involved extending an existing upper ontology, Basic Formal Ontology (BFO), to define lower-level terms of PSO. PSO has two parts – PSO-Physics, which consists of terms and relations used to model physical phenomena based on the perspective of classical mechanics involving partial differential equations, and PSO-Sim, which consists of terms used to represent the information artefacts that are about the physical phenomena modelled with PSO-Physics. The former terms are used to model the physical phenomenon of interest independent of solver-specific interpretations, which can be reused across different solvers, while the latter terms are used to instantiate solver-specific input data. A case study involving two simulation solvers was conducted to demonstrate this capability of PSO. Discussion around the benefits and limitations of using BFO for the current work is also provided, which should be valuable for any future work that extends an existing upper ontology to develop ontologies for engineering applications.
TFDASH: A Fairness, Stability, and Efficiency Aware Rate Control Approach for Multiple Clients Over DASH

Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet and adopted in the industry. It, however, does not impose any adaptation logic for selecting the quality of video segments requested by clients and suffers from lackluster performance with respect to a number of desirable properties: efficiency, stability, and fairness when multiple players compete for a bottleneck link. In this paper, we propose a throughput-friendly DASH rate control scheme for video streaming with multiple clients over DASH to well balance the tradeoffs among efficiency, stability, and fairness. The core idea behind guaranteeing fairness and high efficiency (bandwidth utilization) is to avoid OFF periods during the downloading process for all clients, i.e., the bandwidth is in perfect-subscription or over-subscription with bandwidth utilization approach to 100%. We also propose a dual-threshold buffer model to solve the instability problem caused by the above idea. As a result, by integrating these novel components, we also propose a probability-driven rate adaption logic taking into account several key factors that most influence visual quality, including buffer occupancy, video playback quality, video bit-rate switching frequency and amplitude, to guarantee high-quality video streaming. Our experiments evidently demonstrate the superior performance of the proposed method.
Platform Economy and Techno-Regulation—Experimenting with Reputation and Nudge

In the cloud-based society, where the vast majority of social, economic and personal interactions is mediated by information communication technology (ICT), technology is no longer simply a subject of regulation but is becoming an integral part of the regulatory process. Techno-regulation, the “intentional influencing of individuals’ behavior by building norms into technological devices,” is inspiring new ways to support legal safeguards through hardware and software tools, technical solutions allowing the creation of legal relations, hampering breaches of law and even promoting norm compliance. This paper touches on these issues by focusing on Digital Labor Platforms, one of the most relevant phenomena in the gig economy. We present a research project exploring innovative techno-regulatory solutions to protect gig economy workers. The idea is to integrate, in the same strategy, legal principles, regulatory objectives and software solutions. Our attention focuses on two results of our activity—a techno-regulatory model relying on reputational mechanisms to affect the behavior of digital labor market operators and GigAdvisor, a cross-platform experimental application implementing the model.
Security Challenges of the 3GPP 5G Service Based Architecture

3GPP is currently finalizing the first publication of 5G specifications in their Release 15. Several new features and concepts are introduced to the radio interface and the core network in order to enhance the softwarization and virtualization of functional entities in the mobile core network. One of the driving new concepts is the service-based architecture (SBA). Service concepts and technologies are already well established in the area of Internet-based platforms and applications. With the 3GPP 5G SBA, flexible and on-demand service composition - even across organizational boundaries - provides a new level of flexibility and business opportunities to the mobile industry. However, this type of service orientation comes with new security implications that have not been present to their full extent in previous mobile network generations. This article introduces the foundational security concepts and technologies of SBA in 3GPP 5G, focusing mainly on non-roaming scenarios, and discusses security challenges arising from these architectural changes.
Comparative Study of Heart Disease Diagnosis Using Top Ten Data Mining Classification Algorithms

Data mining has been used for many purposes, especially for prediction system. In healthcare, data mining algorithms often used in disease diagnosis. Meanwhile, heart disease is known as a primary cause of death over the years. Many studies have been performed in heart disease diagnosis using data mining methods. There are some popular data mining algorithms that can be used in heart disease diagnosis, for example, k-Nearest Neighbor, CART, and AdaBoost. The algorithms are used to analyze a sample of cardiovascular patients data and predict the heart disease type that they suffer. Some parameters are taken from the patient, including EKG morphology, blood pressure, and information about the existence of chest pain, shortness of breath, palpitation, and cold sweat. In this study, medical records data are collected from Harapan Kita Hospital and utilized as a dataset sample in this research. Top ten data mining classification algorithms are used in diagnosing heart disease from Harapan Kita Hospital data and examining their performance by checking the accuracy and speed.
Improving RTS Game AI by Supervised Policy Learning, Tactical Search, and Deep Reinforcement Learning

Constructing strong AI systems for video games is difficult due to enormous state and action spaces and the lack of good state evaluation functions and high-level action abstractions. In spite of recent research progress in popular video game genres such as Atari 2600 console games and multiplayer online battle arena (MOBA) games, to this day strong human players can still defeat the best AI systems in adversarial video games. In this paper, we propose to use a deep Convolutional Neural Network (CNN) to select among a limited set of abstract action choices in Real-Time Strategy (RTS) games, and to utilize the remaining computation time for game tree search to improve low-level tactics. The CNN is trained by supervised learning on game states labeled by Puppet Search, a strategic search algorithm that uses action abstractions. Replacing Puppet Search by a CNN frees up time that can be used for improving units' tactical behavior while executing the strategic plan. Experiments in the μRTS game show that the combined algorithm results in higher win-rates than either of its two independent components and other state-of-the-art μRTS agents. We then present a case-study that investigates how deep Reinforcement Learning (RL) can be used in modern video games, such as Total War: Warhammer, to improve tactical multi-agent AI modules. We use popular RL algorithms such as Deep-Q Networks (DQN) and Asynchronous AdvantageActor Critic (A3C), basic network architectures and minimal hyper-parameter tuning to learn complex cooperative behaviors that defeat the highest difficulty built-in AI in mediumscale scenarios.
Deep Convolutional Neural Network with Multi-Task Learning Scheme for Modulations Recognition

One of the main characteristics in cognitive radios is situation awareness. By classifying the modulation schemes used in surrounding transmissions, a secondary user (SU) can identify the existing users in the system and adjust his/her transmission parameters accordingly. In this paper, we propose a multi-task learning (MTL) approach to recognize the modulation scheme used among a specific set of analog and digital modulations. This approach uses a deep convolutional neural network (CNN) to extract the necessary features in order to classify the different modulation schemes. The MTL is used to separately train the modulation classes that normally cause a considerable confusion and therefore improve the overall classification accuracy. Our results on the RadioML dataset show that the suggested architecture achieves higher overall classification accuracy compared to the recently proposed Convolutional, Long Short Term Memory (LSTM), Deep Neural Network (CLDNN). Our classification accuracy of 86.97% at 18 dB SNR outperforms the state-of-the-art with 5% relative improvement.
To Reverse the Gradient or Not: an Empirical Comparison of Adversarial and Multi-task Learning in Speech Recognition

Transcribed datasets typically contain speaker identity for each instance in the data. We investigate two ways to incorporate this information during training: Multi-Task Learning and Adversarial Learning. In multi-task learning, the goal is speaker prediction; we expect a performance improvement with this joint training if the two tasks of speech recognition and speaker recognition share a common set of underlying features. In contrast, adversarial learning is a means to learn representations invariant to the speaker. We then expect better performance if this learnt invariance helps generalizing to new speakers. While the two approaches seem natural in the context of speech recognition, they are incompatible because they correspond to opposite gradients back-propagated to the model. In order to better understand the effect of these approaches in terms of error rates, we compare both strategies in controlled settings. Moreover, we explore the use of additional un-transcribed data in a semi-supervised, adversarial learning manner to improve error rates. Our results show that deep models trained on big datasets already develop invariant representations to speakers without any auxiliary loss. When considering adversarial learning and multi-task learning, the impact on the acoustic model seems minor. However, models trained in a semi-supervised manner can improve error-rates.
Anomaly Detection for an E-commerce Pricing System

Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early detection of anomalies in an automated real-time fashion is an important part of such a pricing system. In this paper, we describe unsupervised and supervised anomaly detection approaches we developed and deployed for a large-scale online pricing system at Walmart. Our system detects anomalies both in batch and real-time streaming settings, and the items flagged are reviewed and actioned based on priority and business impact. We found that having the right architecture design was critical to facilitate model performance at scale, and business impact and speed were important factors influencing model selection, parameter choice, and prioritization in a production environment for a large-scale system. We conducted analyses on the performance of various approaches on a test set using real-world retail data and fully deployed our approach into production. We found that our approach was able to detect the most important anomalies with high precision.
A Trivial Yet Optimal Solution to Vertex Fault Tolerant Spanners

We give a short and easy upper bound on the worst-case size of fault tolerant spanners, which improves on all prior work and is fully optimal at least in the setting of vertex faults.
Artificial Intelligence &amp; Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley

This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within a review of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, and how these fears connect to the idea of an audio uncanny valley. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonization made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and broader assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) The current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”.
STFT Spectral Loss for Training a Neural Speech Waveform Model

This paper proposes a new loss using short-time Fourier transform (STFT) spectra for the aim of training a high-performance neural speech waveform model that predicts raw continuous speech waveform samples directly. Not only amplitude spectra but also phase spectra obtained from generated speech waveforms are used to calculate the proposed loss. We also mathematically show that training of the waveform model on the basis of the proposed loss can be interpreted as maximum likelihood training that assumes the amplitude and phase spectra of generated speech waveforms following Gaussian and von Mises distributions, respectively. Furthermore, this paper presents a simple network architecture as the speech waveform model, which is composed of uni-directional long short-term memories (LSTMs) and an auto-regressive structure. Experimental results showed that the proposed neural model synthesized high-quality speech waveforms.
Generation of high dynamic range illumination from a single image for the enhancement of undesirably illuminated images

This paper presents an algorithm that enhances undesirably illuminated images by generating and fusing multi-level illuminations from a single image. The input image is first decomposed into illumination and reflectance components by using an edge-preserving smoothing filter. Then the reflectance component is scaled up to improve the image details in bright areas. The illumination component is scaled up and down to generate several illumination images that correspond to certain camera exposure values different from the original. The virtual multi-exposure illuminations are blended into an enhanced illumination, where we also propose a method to generate appropriate weight maps for the tone fusion. Finally, an enhanced image is obtained by multiplying the equalized illumination and enhanced reflectance. Experiments show that the proposed algorithm produces visually pleasing output and also yields comparable objective results to the conventional enhancement methods, while requiring modest computational loads.
Unsupervised Intuitive Physics from Visual Observations

While learning models of intuitive physics is an active area of research, current approaches fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test time. Some approaches sidestep these requirements by building models on top of handcrafted physical simulators. In both cases, however, methods cannot learn automatically new physical environments and their laws as humans do. In this work, we successfully demonstrate, for the first time, learning unsupervised predictors of physical states, such as the position of objects in an environment, directly from raw visual observations and without relying on simulators. We do so in two steps: (i) we learn to track dynamically-salient objects in videos using causality and equivariance, two non-generative unsupervised learning principles that do not require manual or external supervision. (ii) we demonstrate that the extracted positions are sufficient to successfully train visual motion predictors that can take the underlying environment into account. We validate our predictors on synthetic datasets; then, we introduce a new dataset, Roll4Real, consisting of real objects rolling on complex terrains (pool table, elliptical bowl, and random height-field). We show that it is possible to learn reliable object trajectory extrapolators from raw videos alone, without any external supervision and with no more prior knowledge than the choice of a convolutional neural network architecture.
Forgetting auxiliary atoms in forks

In this work we tackle the problem of checking strong equivalence of logic programs that may contain local auxiliary atoms, to be removed from their stable models and to be forbidden in any external context. We call this property projective strong equivalence (PSE). It has been recently proved that not any logic program containing auxiliary atoms can be reformulated, under PSE, as another logic program or formula without them – this is known as strongly persistent forgetting. In this paper, we introduce a conservative extension of Equilibrium Logic and its monotonic basis, the logic of Here-and-There, in which we deal with a new connective ‘|’ we call fork. We provide a semantic characterisation of PSE for forks and use it to show that, in this extension, it is always possible to forget auxiliary atoms under strong persistence. We further define when the obtained fork is representable as a regular formula.
Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that outperforms general purpose numerical solvers by a large margin and compares favourably to current state-of-the-art. Further, our approach is able to handle the large problem instances commonly occurring in structure from motion settings and it is trivially parallelizable. Experiments are presented for a number of different instances of both synthetic and real-world data.