OpenRaiser/Intern-Atlas huggingface dataset:
MIDI-Sheet Music Alignment Using Bootleg Score Synthesis
Venue: arXiv cs.MM 2020
Year: 2020

MIDI-sheet music alignment is the task of finding correspondences between a MIDI representation of a piece and its corresponding sheet music images. Rather than using optical music recognition to bridge the gap between sheet music and MIDI, we explore an alternative approach: projecting the MIDI data into pixel space and performing alignment in the image domain. Our method converts the MIDI data into a crude representation of the score that only contains rectangular floating notehead blobs, a process we call bootleg score synthesis. Furthermore, we project sheet music images into the same bootleg space by applying a deep watershed notehead detector and filling in the bounding boxes around each detected notehead. Finally, we align the bootleg representations using a simple variant of dynamic time warping. On a dataset of 68 real scanned piano scores from IMSLP and corresponding MIDI performances, our method achieves a 97.3% accuracy at an error tolerance of one second, outperforming several baseline systems that employ optical music recognition.
Supervised Grapheme-to-Phoneme Conversion of Orthographic Schwas in Hindi and Punjabi
Venue: arXiv cs.CL 2020
Year: 2020

Hindi grapheme-to-phoneme (G2P) conversion is mostly trivial, with one exception: whether a schwa represented in the orthography is pronounced or unpronounced (deleted). Previous work has attempted to predict schwa deletion in a rule-based fashion using prosodic or phonetic analysis. We present the first statistical schwa deletion classifier for Hindi, which relies solely on the orthography as the input and outperforms previous approaches. We trained our model on a newly-compiled pronunciation lexicon extracted from various online dictionaries. Our best Hindi model achieves state of the art performance, and also achieves good performance on a closely related language, Punjabi, without modification.
Alleviating the recommendation bias via rank aggregation
Venue: arXiv cs.SI 2020
Year: 2020

The primary goal of a recommender system is often known as "helping users find relevant items", and a lot of recommendation algorithms are proposed accordingly. However, these accuracy-oriented methods usually suffer the problem of recommendation bias on popular items, which is not welcome to not only users but also item providers. To alleviate the recommendation bias problem, we propose a generic rank aggregation framework for the recommendation results of an existing algorithm, in which the user- and item-oriented ranking results are linearly aggregated together, with a parameter controlling the weight of the latter ranking process. Experiment results of a typical algorithm on two real-world data sets show that, this framework is effective to improve the recommendation fairness of any existing accuracy-oriented algorithms, while avoiding significant accuracy loss.
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server. However, recent studies have shown that even sharing local parameter updates from a client to the federated server may be susceptible to gradient leakage attacks and intrude the client privacy regarding its training data. In this paper, we present a principled framework for evaluating and comparing different forms of client privacy leakage attacks. We first provide formal and experimental analysis to show how adversaries can reconstruct the private local training data by simply analyzing the shared parameter update from local training (e.g., local gradient or weight update vector). We then analyze how different hyperparameter configurations in federated learning and different settings of the attack algorithm may impact on both attack effectiveness and attack cost. Our framework also measures, evaluates, and analyzes the effectiveness of client privacy leakage attacks under different gradient compression ratios when using communication efficient FL protocols. Our experiments also include some preliminary mitigation strategies to highlight the importance of providing a systematic attack evaluation framework towards an in-depth understanding of the various forms of client privacy leakage threats in federated learning and developing theoretical foundations for attack mitigation.
Anger makes fake news viral online
Venue: arXiv cs.SI 2020
Year: 2020

Fake news that manipulates political elections, strikes financial systems, and even incites riots is more viral than real news online, resulting in unstable societies and buffeted democracy. The easier contagion of fake news online can be causally explained by the greater anger it carries. The same results in Twitter and Weibo indicate that this mechanism is independent of the platform. Moreover, mutations in emotions like increasing anger will progressively speed up the information spread. Specifically, increasing the occupation of anger by 0.1 and reducing that of joy by 0.1 will produce nearly 6 more retweets in the Weibo dataset. Offline questionnaires reveal that anger leads to more incentivized audiences in terms of anxiety management and information sharing and accordingly makes fake news more contagious than real news online. Cures such as tagging anger in social media could be implemented to slow or prevent the contagion of fake news at the source.
Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics
Venue: arXiv cs.CV 2020
Year: 2020

Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multi-frame photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community.
Where is the context? -- A critique of recent dialogue datasets
Venue: arXiv cs.CL 2020
Year: 2020

Recent dialogue datasets like MultiWOZ 2.1 and Taskmaster-1 constitute some of the most challenging tasks for present-day dialogue models and, therefore, are widely used for system evaluation. We identify several issues with the above-mentioned datasets, such as history independence, strong knowledge base dependence, and ambiguous system responses. Finally, we outline key desiderata for future datasets that we believe would be more suitable for the construction of conversational artificial intelligence.
Bayesian Optimization with Output-Weighted Optimal Sampling
Venue: arXiv cs.LG 2020
Year: 2020

In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result but often involves inaccurate and cumbersome entropy estimations. We approach the problem from the perspective of importance-sampling theory, and advocate the use of the likelihood ratio to guide the search algorithm towards regions of the input space where the objective function to be minimized assumes abnormally small values. The likelihood ratio acts as a sampling weight and can be computed at each iteration without severely deteriorating the overall efficiency of the algorithm. In particular, it can be approximated in a way that makes the approach tractable in high dimensions. The "likelihood-weighted" acquisition functions introduced in this work are found to outperform their unweighted counterparts in a number of applications.
Self-Supervised Representation Learning on Document Images
Venue: arXiv cs.CV 2020
Year: 2020

This work analyses the impact of self-supervised pre-training on document images in the context of document image classification. While previous approaches explore the effect of self-supervision on natural images, we show that patch-based pre-training performs poorly on document images because of their different structural properties and poor intra-sample semantic information. We propose two context-aware alternatives to improve performance on the Tobacco-3482 image classification task. We also propose a novel method for self-supervision, which makes use of the inherent multi-modality of documents (image and text), which performs better than other popular self-supervised methods, including supervised ImageNet pre-training, on document image classification scenarios with a limited amount of data.
Contextualised Graph Attention for Improved Relation Extraction
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents a contextualized graph attention network that combines edge features and multiple sub-graphs for improving relation extraction. A novel method is proposed to use multiple sub-graphs to learn rich node representations in graph-based networks. To this end multiple sub-graphs are obtained from a single dependency tree. Two types of edge features are proposed, which are effectively combined with GAT and GCN models to apply for relation extraction. The proposed model achieves state-of-the-art performance on Semeval 2010 Task 8 dataset, achieving an F1-score of 86.3.
Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction
Venue: arXiv cs.CL 2020
Year: 2020

Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in different languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem.
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection
Venue: arXiv cs.CL 2020
Year: 2020

Universal Dependencies is an open community effort to create cross-linguistically consistent treebank annotation for many languages within a dependency-based lexicalist framework. The annotation consists in a linguistically motivated word segmentation; a morphological layer comprising lemmas, universal part-of-speech tags, and standardized morphological features; and a syntactic layer focusing on syntactic relations between predicates, arguments and modifiers. In this paper, we describe version 2 of the guidelines (UD v2), discuss the major changes from UD v1 to UD v2, and give an overview of the currently available treebanks for 90 languages.
Hidden variables simulating quantum contextuality increasingly violate the Holevo bound
Venue: arXiv cs.LO 2020
Year: 2020

In this paper from 2011 we approach some questions about quantum contextuality with tools from formal logic. In particular, we consider an experiment associated with the Peres-Mermin square. The language of all possible sequences of outcomes of the experiment is classified in the Chomsky hierarchy and seen to be a regular language. Next, we make the rather evident observation that a finite set of hidden finite valued variables can never account for indeterminism in an ideally isolated repeatable experiment. We see that, when the language of possible outcomes of the experiment is regular, as is the case with the Peres-Mermin square, the amount of binary-valued hidden variables needed to de-randomize the model for all sequences of experiments up to length n grows as bad as it could be: linearly in n. We introduce a very abstract model of machine that simulates nature in a particular sense. A lower-bound on the number of memory states of such machines is proved if they were to simulate the experiment that corresponds to the Peres-Mermin square. Moreover, the proof of this lower bound is seen to scale to a certain generalization of the Peres- Mermin square. For this scaled experiment it is seen that the Holevo bound is violated and that the degree of violation increases uniformly.
Exponentially Huge Natural Deduction proofs are Redundant: Preliminary results on $M_\supset$
Venue: arXiv cs.LO 2020
Year: 2020

We estimate the size of a labelled tree by comparing the amount of (labelled) nodes with the size of the set of labels. Roughly speaking, a exponentially big labelled tree, is any labelled tree that has an exponential gap between its size, number of nodes, and the size of its labelling set. The number of sub-formulas of any formula is linear on the size of it, and hence any exponentially big proof has a size $a^n$, where $a>1$ and $n$ is the size of its conclusion. In this article, we show that the linearly height labelled trees whose sizes have an exponential gap with the size of their labelling sets posses at least one sub-tree that occurs exponentially many times in them. Natural Deduction proofs and derivations in minimal implicational logic ($M_\supset$) are essentially labelled trees. By the sub-formula principle any normal derivation of a formula $α$ from a set of formulas $Γ=\{γ_1,\ldots,γ_n\}$ in $M_\supset$, establishing $Γ\vdash_{M_\supset}α$, has only sub-formulas of the formulas $α,γ_1,\ldots,γ_n$ occurring in it. By this relationship between labelled trees and derivations in $M_\supset$, we show that any normal proof of a tautology in $M_\supset$ that is exponential on the size of its conclusion has a sub-proof that occurs exponentially many times in it. Thus, any normal and linearly height bounded proof in $M_\supset$ is inherently redundant. Finally, we briefly discuss how this redundancy provides us with a highly efficient compression method for propositional proofs. We also provide some examples that serve to convince us that exponentially big proofs are more frequent than one can imagine.
Embedded Policing and Policy Enforcement based Security in the era of Digital-Physical Convergence for Next-Generation Vehicular Electronics
Venue: arXiv cs.CR 2020
Year: 2020

The emergence of intelligent, connected vehicles, containing complex functionality has potential to greatly benefit society by improving safety, security and efficiency of vehicular transportation. Much of this has been enabled by technological advancements in embedded system architectures, which provided opportunities for vehicle manufacturers to implement intelligent vehicle services and consolidate them within a small number of flexible and integrable domain controllers. Thus allowing for increasingly centralised operations consisting of both new and legacy functionalities. While this era of digital-physical convergence of critical and non-critical vehicle services presents advantages in terms of reducing the cost and electronic footprint of vehicular electronics, it has produced significant security and safety challenges. One approach to this research problem is to introduce fail-over mechanisms that can detect unexpected or malicious behaviours, caused by attack or malfunction, and pro-actively respond to control and minimise physical damage or safety hazards. This paper presents a novel embedded policing and policy enforcement platform architecture and the accompanied security modelling approach for next-generation in-vehicle domain controllers. To demonstrate the proposed approach, a connected vehicle case study is conducted. A realistic attack scenarios have been considered to derive security policies and enforced by the proposed security platform to provide security and safety to domain-specific features.
Speeding-up Logic Design and Refining Hardware EDA Flow by Exploring Chinese Character based Graphical Representation
Venue: arXiv cs.PL 2020
Year: 2020

Electrical design automation (EDA) techniques have deeply influenced the computer hardware design, especially in the field of very large scale Integration (VLSI) circuits. Particularly, the popularity of FPGA, ASIC and SOC applications have been dramatically increased due to the well developed EDA tool chains. Over decades, improving EDA tool in terms of functionality, efficiency, accuracy and intelligence is not only the academic research hot spot, but the industry attempting goal as well. In this paper, a novel perspective is taken to review current mainstream EDA working flow and design methods, aiming to shorten the EDA design periods and simplify the logic design overload significantly. Specifically, three major contributions are devoted. First, a Chinese character based representation system (CCRS), which is used for presenting logical abstract syntax tree, is proposed. Second, the register-transfer-level (RTL) level symbolic description technique for CCRS are introduced to replace traditional text-based programming methods. Finally, the refined EDA design flow based on CCRS is discussed. It is convincing that the graphic non-pure-english based EDA flow could lower the design cost and complexity. As a fundamental trial in this new field, it is confirmative that a lot of following works will make the related EDA development prosperous.
CodNN -- Robust Neural Networks From Coded Classification
Venue: arXiv cs.LG 2020
Year: 2020

Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known that DNNs are highly sensitive to noise, whether adversarial or random. This poses a fundamental challenge for hardware implementations of DNNs, and for their deployment in critical applications such as autonomous driving. In this paper we construct robust DNNs via error correcting codes. By our approach, either the data or internal layers of the DNN are coded with error correcting codes, and successful computation under noise is guaranteed. Since DNNs can be seen as a layered concatenation of classification tasks, our research begins with the core task of classifying noisy coded inputs, and progresses towards robust DNNs. We focus on binary data and linear codes. Our main result is that the prevalent parity code can guarantee robustness for a large family of DNNs, which includes the recently popularized binarized neural networks. Further, we show that the coded classification problem has a deep connection to Fourier analysis of Boolean functions. In contrast to existing solutions in the literature, our results do not rely on altering the training process of the DNN, and provide mathematically rigorous guarantees rather than experimental evidence.
Disjoint principal component analysis by constrained binary particle swarm optimization
Venue: arXiv cs.NE 2020
Year: 2020

In this paper, we propose an alternative method to the disjoint principal component analysis. The method consists of a principal component analysis with constraints, which allows us to determine disjoint components that are linear combinations of disjoint subsets of the original variables. The proposed method is named constrained binary optimization by particle swarm disjoint principal component analysis, since it is based on the particle swarm optimization. The method uses stochastic optimization to find solutions in cases of high computational complexity. The algorithm associated with the method starts generating randomly a particle population which iteratively evolves until attaining a global optimum which is function of the disjoint components. Numerical results are provided to confirm the quality of the solutions attained by the proposed method. Illustrative examples with real data are conducted to show the potential applications of the method.
PredicTor: Predictive Congestion Control for the Tor Network
Venue: arXiv cs.NI 2020
Year: 2020

In the Tor network, anonymity is achieved through a multi-layered architecture, which comes at the cost of a complex network. Scheduling data in this network is a challenging task and the current approach shows to be incapable of avoiding network congestion and allocating fair data rates. We propose PredicTor, a distributed model predictive control approach, to tackle these challenges. PredicTor is designed to schedule incoming and outgoing data rates on individual nodes of the Tor architecture, leading to a scalable approach. We successfully avoid congestion through exchanging information of predicted behavior with adjacent nodes. Furthermore, we formulate PredicTor with a focus on fair allocation of resources, for which we present and proof a novel optimization-based fairness approach. Our proposed controller is evaluated with the popular network simulator ns-3, where we compare it with the current Tor scheduler as well as with another recently proposed enhancement. PredicTor shows significant improvements over the previous approaches, especially with respect to latency.
Cloud Migration Process A Survey Evaluation Framework and Open Challenges
Venue: arXiv cs.SE 2020
Year: 2020

Moving mission-oriented enterprise applications to cloud environments is a major IT strategic task and requires a systematic approach. The foci of this paper are to review and examine existing cloud migration approaches from the process models perspective. To this aim, an evaluation framework is proposed and used to analyse and compare existing approaches for highlighting their features, similarities, and key differences. The survey distills the state of the art in cloud migration research and makes a rich inventory of important activities, recommendations, techniques, and concerns that are commonly involved in the migration process in one place. This enables academia and practitioners in the cloud computing community to get an overarching view of the cloud migration process. Furthermore, the survey identifies a number challenges that have not been yet addressed by existing approaches, developing opportunities for further research endeavors.
Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations
Venue: arXiv cs.SE 2020
Year: 2020

In this paper, we present a tertiary systematic literature review of previous surveys, secondary systematic literature reviews, and systematic mappings. We identify the main observations (what we know) and challenges (what we do not know) on code smells and refactoring. We show that code smells and refactoring have a strong relationship with quality attributes, i.e., with understandability, maintainability, testability, complexity, functionality, and reusability. We argue that code smells and refactoring could be considered as the two faces of a same coin. Besides, we identify how refactoring affects quality attributes, more than code smells. We also discuss the implications of this work for practitioners, researchers, and instructors. We identify 13 open issues that could guide future research work. Thus, we want to highlight the gap between code smells and refactoring in the current state of software-engineering research. We wish that this work could help the software-engineering research community in collaborating on future work on code smells and refactoring.