Reviewerly/exHarmony huggingface dataset:
Quantum Computer: Hello, Music!

Quantum computing is emerging as a promising technology, which is built on the principles of subatomic physics. By the time of writing, fully fledged practical quantum computers are not widely available. But research and development are advancing rapidly. Various software simulators are already available. And a few companies have already started to provide access to quantum hardware via the cloud. These initiatives have enabled experiments with quantum computing to tackle some realistic problems in science; e.g., in chemistry and cryptography. In spite of continuing progress in developing increasingly more sophisticated hardware and software, research in quantum computing has been focusing primarily on developing scientific applications. Up till now there has been virtually no research activity aimed at widening the range of applications of this technology beyond science and engineering. In particular applications for the entertainment industry and creative economies. This article introduces a new field of research, which is referred to as Quantum Computer Music. This research is aimed at the development of quantum computing tools and approaches to creating, performing, listening to and distributing music. The article begins with a brief historical background. Then, it introduces the notion of algorithmic music and presents two quantum computer music systems: a singing voice synthesiser and a musical sequencer based on quantum walk. A primer on quantum computing is also given. The chapter ends with a concluding discussion and advice for further work to develop this new exciting area of research.
IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis

In this work, we investigate the problem of revealing the functionality of a black-box agent. Notably, we are interested in the interpretable and formal description of the behavior of such an agent. Ideally, this description would take the form of a program written in a high-level language. This task is also known as reverse engineering and plays a pivotal role in software engineering, computer security, but also most recently in interpretability. In contrast to prior work, we do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. We approach this problem by iteratively refining a candidate set using a generative neural program synthesis approach until we arrive at a functionally equivalent program. We assess the performance of our approach on the Karel dataset. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding an approximately functional equivalent program in 78% of cases -- even exceeding prior work that had privileged information on the black-box.
Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search

Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying questions have been studied recently but the accurate utilization of user responses to clarifying questions has been relatively less explored. In this paper, we enrich the representations learned by Transformer networks using a novel attention mechanism from external information sources that weights each term in the conversation. We evaluate this Guided Transformer model in a conversational search scenario that includes clarifying questions. In our experiments, we use two separate external sources, including the top retrieved documents and a set of different possible clarifying questions for the query. We implement the proposed representation learning model for two downstream tasks in conversational search; document retrieval and next clarifying question selection. Our experiments use a public dataset for search clarification and demonstrate significant improvements compared to competitive baselines.
Ventral-Dorsal Neural Networks: Object Detection via Selective Attention

Deep Convolutional Neural Networks (CNNs) have been repeatedly proven to perform well on image classification tasks. Object detection methods, however, are still in need of significant improvements. In this paper, we propose a new framework called Ventral-Dorsal Networks (VDNets) which is inspired by the structure of the human visual system. Roughly, the visual input signal is analyzed along two separate neural streams, one in the temporal lobe and the other in the parietal lobe. The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal. The ventral pathway from primary visual cortex, entering the temporal lobe, is dominated by "what" information, while the dorsal pathway, into the parietal lobe, is dominated by "where" information. Inspired by this structure, we propose the integration of a "Ventral Network" and a "Dorsal Network", which are complementary. Information about object identity can guide localization, and location information can guide attention to relevant image regions, improving object recognition. This new dual network framework sharpens the focus of object detection. Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches on PASCAL VOC 2007 by 8% (mAP) and PASCAL VOC 2012 by 3% (mAP). Moreover, a comparison of techniques on Yearbook images displays substantial qualitative and quantitative benefits of VDNet.
Towards 1ULP evaluation of Daubechies Wavelets

We present algorithms to numerically evaluate Daubechies wavelets and scaling functions to high relative accuracy. These algorithms refine the suggestion of Daubechies and Lagarias to evaluate functions defined by two-scale difference equations using splines; carefully choosing amongst a family of rapidly convergent interpolators which effectively capture all the smoothness present in the function and whose error term admits a small asymptotic constant. We are also able to efficiently compute derivatives, though with a smoothness-induced reduction in accuracy. An implementation is provided in the Boost Software Library.
Data Processing for Optimizing Naturalness of Vietnamese Text-to-speech System

Abstract End-to-end text-to-speech (TTS) systems has proved its great success in the presence of a large amount of high-quality training data recorded in anechoic room with high-quality microphone. Another approach is to use available source of found data like radio broadcast news. We aim to optimize the naturalness of TTS system on the found data using a novel data processing method. The data processing method includes 1) utterance selection and 2) prosodic punctuation insertion to prepare training data which can optimize the naturalness of TTS systems. We showed that using the processing data method, an end-to-end TTS achieved a mean opinion score (MOS) of 4.1 compared to 4.3 of natural speech. We showed that the punctuation insertion contributed the most to the result. To facilitate the research and development of TTS systems, we distributed the processed data of one speaker at https://forms.gle/6Hk5YkqgDxAaC2BU6.
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig

Super Resolution is the problem of recovering a high-resolution image from a single or multiple low-resolution images of the same scene. It is an ill-posed problem since high frequency visual details of the scene are completely lost in low-resolution images. To overcome this, many machine learning approaches have been proposed aiming at training a model to recover the lost details in the new scenes. Such approaches include the recent successful effort in utilizing deep learning techniques to solve super resolution problem. As proven, data itself plays a significant role in the machine learning process especially deep learning approaches which are data hungry. Therefore, to solve the problem, the process of gathering data and its formation could be equally as vital as the machine learning technique used. Herein, we are proposing a new data acquisition technique for gathering real image data set which could be used as an input for super resolution, noise cancellation and quality enhancement techniques. We use a beam-splitter to capture the same scene by a low resolution camera and a high resolution camera. Since we also release the raw images, this large-scale dataset could be used for other tasks such as ISP generation. Unlike current small-scale dataset used for these tasks, our proposed dataset includes 11,421 pairs of low-resolution high-resolution images of diverse scenes. To our knowledge this is the most complete dataset for super resolution, ISP and image quality enhancement. The benchmarking result shows how the new dataset can be successfully used to significantly improve the quality of real-world image super resolution.
Challenges of Bridging the Gap between Mass People and Welfare Organizations in Bangladesh

Computing for the development of marginalized communities is a big deal of challenges for researchers. Different social organizations are working to develop the conditions of a specialized marginalized community namely Street Children, one of the most underprivileged communities in Bangladesh. However, lack of proper engagement among different social welfare organizations, donors, and the mass community limits the goal of the development of street children. Developing a virtual organization hub can eliminate communication gap as well as the information gap by involving people of all communities. However, some human imposed stigmas may often limit the rate of success of potential virtual computing solutions intended for organizations working with the marginalized communities, which we also face in our case. After a partial successful deployment, the design itself needs to be self comprehensive and trustworthy in order to overcome the stigmas that demand a reasonable amount of time. Moreover, after a wide scalable deployment, it is yet to be investigated whether the design of our computational solution can attain the goal for the facilitation of the organizations so that those organizations can become more effective for the development of street children than before.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks. The primary goal of the package is to make community detection, node and whole graph embedding available to a wide audience of machine learning researchers and practitioners. We designed Karate Club with an emphasis on a consistent application interface, scalability, ease of use, sensible out of the box model behaviour, standardized dataset ingestion, and output generation. This paper discusses the design principles behind this framework with practical examples. We show Karate Club's efficiency with respect to learning performance on a wide range of real world clustering problems, classification tasks and support evidence with regards to its competitive speed.
Captioning Images Taken by People Who Are Blind

While an important problem in the vision community is to design algorithms that can automatically caption images, few publicly-available datasets for algorithm development directly address the interests of real users. Observing that people who are blind have relied on (human-based) image captioning services to learn about images they take for nearly a decade, we introduce the first image captioning dataset to represent this real use case. This new dataset, which we call VizWiz-Captions, consists of over 39,000 images originating from people who are blind that are each paired with five captions. We analyze this dataset to (1) characterize the typical captions, (2) characterize the diversity of content found in the images, and (3) compare its content to that found in eight popular vision datasets. We also analyze modern image captioning algorithms to identify what makes this new dataset challenging for the vision community. We publicly-share the dataset with captioning challenge instructions at https://vizwiz.org
Implementing Dynamic Answer Set Programming

We introduce an implementation of an extension of Answer Set Programming (ASP) with language constructs from dynamic (and temporal) logic that provides an expressive computational framework for modeling dynamic applications. Starting from logical foundations, provided by dynamic and temporal equilibrium logics over finite linear traces, we develop a translation of dynamic formulas into temporal logic programs. This provides us with a normal form result establishing the strong equivalence of formulas in different logics. Our translation relies on the introduction of auxiliary atoms to guarantee polynomial space complexity and to provide an embedding that is doomed to be impossible over the same language. Finally, the reduction of dynamic formulas to temporal logic programs allows us to extend ASP with both approaches in a uniform way and to implement both extensions via temporal ASP solvers such as telingo
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version

Graph convolutional networks (GCNs) are a widely used method for graph representation learning. We investigate the power of GCNs, as a function of their number of layers, to distinguish between different random graph models on the basis of the embeddings of their sample graphs. In particular, the graph models that we consider arise from graphons, which are the most general possible parameterizations of infinite exchangeable graph models and which are the central objects of study in the theory of dense graph limits. We exhibit an infinite class of graphons that are well-separated in terms of cut distance and are indistinguishable by a GCN with nonlinear activation functions coming from a certain broad class if its depth is at least logarithmic in the size of the sample graph. These results theoretically match empirical observations of several prior works. Finally, we show a converse result that for pairs of graphons satisfying a degree profile separation property, a very simple GCN architecture suffices for distinguishability. To prove our results, we exploit a connection to random walks on graphs.
Assessing Inference Methods

We analyze different types of simulations that applied researchers may use to assess their inference methods. We show that different types of simulations vary in many dimensions when considered as inference assessments. Moreover, we show that natural ways of running simulations may lead to misleading conclusions, and we propose alternatives. We then provide evidence that even some simple assessments can detect problems in many different settings. Alternative assessments that potentially better approximate the true data generating process may detect problems that simpler assessments would not detect. However, they are not uniformly dominant in this dimension, and may imply some costs.
Regression with Uncertainty Quantification in Large Scale Complex Data

While several methods for predicting uncertainty on deep networks have been recently proposed, they do not readily translate to large and complex datasets. In this paper we utilize a simplified form of the Mixture Density Networks (MDNs) to produce a one-shot approach to quantify uncertainty in regression problems. We show that our uncertainty bounds are on-par or better than other reported existing methods. When applied to standard regression benchmark datasets, we show an improvement in predictive log-likelihood and root-mean-square-error when compared to existing state-of-the-art methods. We also demonstrate this method's efficacy on stochastic, highly volatile time-series data where stock prices are predicted for the next time interval. The resulting uncertainty graph summarizes significant anomalies in the stock price chart. Furthermore, we apply this method to the task of age estimation from the challenging IMDb-Wiki dataset of half a million face images. We successfully predict the uncertainties associated with the prediction and empirically analyze the underlying causes of the uncertainties. This uncertainty quantification can be used to pre-process low quality datasets and further enable learning.
Integrating Motion into Vision Models for Better Visual Prediction

We demonstrate an improved vision system that learns a model of its environment using a self-supervised, predictive learning method. The system includes a pan-tilt camera, a foveated visual input, a saccading reflex to servo the foveated region to areas high prediction error, input frame transformation synced to the camera motion, and a recursive, hierachical machine learning technique based on the Predictive Vision Model. In earlier work, which did not integrate camera motion into the vision model, prediction was impaired and camera movement suffered from undesired feedback effects. Here we detail the integration of camera motion into the predictive learning system and show improved visual prediction and saccadic behavior. From these experiences, we speculate on the integration of additional sensory and motor systems into self-supervised, predictive learning models.
Decomposition of Pauli groups via weak central products

For any $m \ge 1$ and odd prime power $\mathtt{q}=\mathtt{p}^m$, for $\mathtt{q}=2$, and for any $n \ge 1$, we show a result of decomposition for Pauli groups $\mathcal{P}_{n,\mathtt{q}}$ in terms of weak central products. This can be used to describe the underlying structure of Pauli groups on $n$ qudits of dimension $\mathtt{q}$ and enables us to identify abelian subgroups of $\mathcal{P}_{n,\mathtt{q}}$. As a consequence of our main results, we show a similar factorisation for the so--called `lifted' Pauli groups, recently introduced by Gottesman and Kuperberg in the context of error-correcting codes in quantum information theory.
Design Considerations for Building Credible Security Testbeds: A Systematic Study of Industrial Control System Use Cases

This paper presents a mapping framework for design factors and implementation process for building credible Industrial Control Systems (ICS) security testbeds. The resilience of ICSs has become a critical concern to operators and governments following widely publicised cyber security events. The inability to apply conventional Information Technology security practice to ICSs further compounds challenges in adequately securing critical systems. To overcome these challenges, and do so without impacting live environments, testbeds for the exploration, development and evaluation of security controls are widely used. However, how a testbed is designed and its attributes, can directly impact not only its viability but also its credibility as a whole. Through a combined systematic and thematic analysis and mapping of ICS security testbed design attributes, this paper suggests that the expertise of human experimenters, design objectives, the implementation approach, architectural coverage, core characteristics, and evaluation methods; are considerations that can help establish or enhance confidence, trustworthiness and acceptance; thus, credibility of ICS security testbeds.
Peterson-Gorenstein-Zierler algorithm for differential convolutional codes

Differential Convolutional Codes with designed Hamming distance are defined, and an algebraic decoding algorithm, inspired by Peterson-Gorenstein-Zierler's algorithm, is designed for them.
On Higher-order Moments in Adam

In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments. While Adam is an adaptive lower-order moment based (of the stochastic gradient) method, we propose an extension namely, HAdam, which uses higher order moments of the stochastic gradient. Our analysis and experiments reveal that certain higher-order moments of the stochastic gradient are able to achieve better performance compared to the vanilla Adam algorithm. We also provide some analysis of HAdam related to odd and even moments to explain some intriguing and seemingly non-intuitive empirical results.
Efficiently Reusing Old Models Across Languages via Transfer Learning

Recent progress in neural machine translation is directed towards larger neural networks trained on an increasing amount of hardware resources. As a result, NMT models are costly to train, both financially, due to the electricity and hardware cost, and environmentally, due to the carbon footprint. It is especially true in transfer learning for its additional cost of training the "parent" model before transferring knowledge and training the desired "child" model. In this paper, we propose a simple method of re-using an already trained model for different language pairs where there is no need for modifications in model architecture. Our approach does not need a separate parent model for each investigated language pair, as it is typical in NMT transfer learning. To show the applicability of our method, we recycle a Transformer model trained by different researchers and use it to seed models for different language pairs. We achieve better translation quality and shorter convergence times than when training from random initialization.
Jointly Learning to Align and Translate with Transformer Models

The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches. However, on the closely related task of word alignment, traditional statistical word alignment models often remain the go-to solution. In this paper, we present an approach to train a Transformer model to produce both accurate translations and alignments. We extract discrete alignments from the attention probabilities learnt during regular neural machine translation model training and leverage them in a multi-task framework to optimize towards translation and alignment objectives. We demonstrate that our approach produces competitive results compared to GIZA++ trained IBM alignment models without sacrificing translation accuracy and outperforms previous attempts on Transformer model based word alignment. Finally, by incorporating IBM model alignments into our multi-task training, we report significantly better alignment accuracies compared to GIZA++ on three publicly available data sets.
Slide Reduction, Revisited---Filling the Gaps in SVP Approximation

We show how to generalize Gama and Nguyen's slide reduction algorithm [STOC '08] for solving the approximate Shortest Vector Problem over lattices (SVP). As a result, we show the fastest provably correct algorithm for $δ$-approximate SVP for all approximation factors $n^{1/2+\varepsilon} \leq δ\leq n^{O(1)}$. This is the range of approximation factors most relevant for cryptography.