OpenRaiser/Intern-Atlas huggingface dataset:
The MIDI Degradation Toolkit: Symbolic Music Augmentation and Correction
Venue: arXiv cs.SD 2020
Year: 2020

In this paper, we introduce the MIDI Degradation Toolkit (MDTK), containing functions which take as input a musical excerpt (a set of notes with pitch, onset time, and duration), and return a "degraded" version of that excerpt with some error (or errors) introduced. Using the toolkit, we create the Altered and Corrupted MIDI Excerpts dataset version 1.0 (ACME v1.0), and propose four tasks of increasing difficulty to detect, classify, locate, and correct the degradations. We hypothesize that models trained for these tasks can be useful in (for example) improving automatic music transcription performance if applied as a post-processing step. To that end, MDTK includes a script that measures the distribution of different types of errors in a transcription, and creates a degraded dataset with similar properties. MDTK's degradations can also be applied dynamically to a dataset during training (with or without the above script), generating novel degraded excerpts each epoch. MDTK could also be used to test the robustness of any system designed to take MIDI (or similar) data as input (e.g. systems designed for voice separation, metrical alignment, or chord detection) to such transcription errors or otherwise noisy data. The toolkit and dataset are both publicly available online, and we encourage contribution and feedback from the community.
Realization of p_valued Deutsch quantum gates
Venue: arXiv cs.ET 2020
Year: 2020

In this report reversible Toffoli and quantum Deutsch gates are extended to the p_valued domain. Their structural parameters are determined and their behavior is proven. Both conjunctive and disjunctive control strategies with positive and mixed polarities are introduced for the first time in a p_valued domain. The design is based on elementary Muthukrishnan_Stroud quantum gates, hence the realizability of the extended gates in the context of ion traps should be possible.
Multi-Agent Systems based on Contextual Defeasible Logic considering Focus
Venue: arXiv cs.AI 2020
Year: 2020

In this paper, we extend previous work on distributed reasoning using Contextual Defeasible Logic (CDL), which enables decentralised distributed reasoning based on a distributed knowledge base, such that the knowledge from different knowledge bases may conflict with each other. However, there are many use case scenarios that are not possible to represent in this model. One kind of such scenarios are the ones that require that agents share and reason with relevant knowledge when issuing a query to others. Another kind of scenarios are those in which the bindings among the agents (defined by means of mapping rules) are not static, such as in knowledge-intensive and dynamic environments. This work presents a multi-agent model based on CDL that not only allows agents to reason with their local knowledge bases and mapping rules, but also allows agents to reason about relevant knowledge (focus) -- which are not known by the agents a priori -- in the context of a specific query. We present a use case scenario, some formalisations of the model proposed, and an initial implementation based on the BDI (Belief-Desire-Intention) agent model.
Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
Venue: arXiv cs.NE 2020
Year: 2020

A decomposition-based multi-objective evolutionary algorithm with a differential evolution variation operator (MOEA/D-DE) shows high performance on challenging multi-objective problems (MOPs). The DE mutation consists of three key components: a mutation strategy, an index selection method for parent individuals, and a bound-handling method. However, the configuration of the DE mutation operator that should be used for MOEA/D-DE has not been thoroughly investigated in the literature. This configuration choice confuses researchers and users of MOEA/D-DE. To address this issue, we present a review of the existing configurations of the DE mutation operator in MOEA/D-DE and systematically examine the influence of each component on the performance of MOEA/D-DE. Our review reveals that the configuration of the DE mutation operator differs depending on the source code of MOEA/D-DE. In our analysis, a total of 30 configurations (three index selection methods, two mutation strategies, and five bound handling methods) are investigated on 16 MOPs with up to five objectives. Results show that each component significantly affects the performance of MOEA/D-DE. We also present the most suitable configuration of the DE mutation operator, which maximizes the effectiveness of MOEA/D-DE.
Action Units Recognition by Pairwise Deep Architecture
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we propose a new automatic Action Units (AUs) recognition method used in a competition, Affective Behavior Analysis in-the-wild (ABAW). Our method tackles a problem of AUs label inconsistency among subjects by using pairwise deep architecture. While the baseline score is 0.31, our method achieved 0.67 in validation dataset of the competition.
Open-Set Hypothesis Transfer with Semantic Consistency
Venue: arXiv cs.CV 2020
Year: 2020

Unsupervised open-set domain adaptation (UODA) is a realistic problem where unlabeled target data contain unknown classes. Prior methods rely on the coexistence of both source and target domain data to perform domain alignment, which greatly limits their applications when source domain data are restricted due to privacy concerns. This paper addresses the challenging hypothesis transfer setting for UODA, where data from source domain are no longer available during adaptation on target domain. We introduce a method that focuses on the semantic consistency under transformation of target data, which is rarely appreciated by previous domain adaptation methods. Specifically, our model first discovers confident predictions and performs classification with pseudo-labels. Then we enforce the model to output consistent and definite predictions on semantically similar inputs. As a result, unlabeled data can be classified into discriminative classes coincided with either source classes or unknown classes. Experimental results show that our model outperforms state-of-the-art methods on UODA benchmarks.
Phonemer at WNUT-2020 Task 2: Sequence Classification Using COVID Twitter BERT and Bagging Ensemble Technique based on Plurality Voting
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents the approach that we employed to tackle the EMNLP WNUT-2020 Shared Task 2 : Identification of informative COVID-19 English Tweets. The task is to develop a system that automatically identifies whether an English Tweet related to the novel coronavirus (COVID-19) is informative or not. We solve the task in three stages. The first stage involves pre-processing the dataset by filtering only relevant information. This is followed by experimenting with multiple deep learning models like CNNs, RNNs and Transformer based models. In the last stage, we propose an ensemble of the best model trained on different subsets of the provided dataset. Our final approach achieved an F1-score of 0.9037 and we were ranked sixth overall with F1-score as the evaluation criteria.
A Survey on Explainability in Machine Reading Comprehension
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents a systematic review of benchmarks and approaches for explainability in Machine Reading Comprehension (MRC). We present how the representation and inference challenges evolved and the steps which were taken to tackle these challenges. We also present the evaluation methodologies to assess the performance of explainable systems. In addition, we identify persisting open research questions and highlight critical directions for future work.
X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation
Venue: arXiv cs.CV 2020
Year: 2020

We suggest to represent an X-Field -a set of 2D images taken across different view, time or illumination conditions, i.e., video, light field, reflectance fields or combinations thereof-by learning a neural network (NN) to map their view, time or light coordinates to 2D images. Executing this NN at new coordinates results in joint view, time or light interpolation. The key idea to make this workable is a NN that already knows the "basic tricks" of graphics (lighting, 3D projection, occlusion) in a hard-coded and differentiable form. The NN represents the input to that rendering as an implicit map, that for any view, time, or light coordinate and for any pixel can quantify how it will move if view, time or light coordinates change (Jacobian of pixel position with respect to view, time, illumination, etc.). Our X-Field representation is trained for one scene within minutes, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination.
Performance of Intelligent Reconfigurable Surface-Based Wireless Communications Using QAM Signaling
Venue: arXiv cs.IT 2020
Year: 2020

Intelligent reconfigurable surface (IRS) is being seen as a promising technology for 6G wireless networks. The IRS can reconfigure the wireless propagation environment, which results in significant performance improvement of wireless communications. In this paper, we analyze the performance of bandwidth-efficient quadrature amplitude modulation (QAM) techniques for IRS-assisted wireless communications over Rayleigh fading channels. New closed-form expressions of the generic average symbol error rate (ASER) for rectangular QAM, square QAM and cross QAM schemes are derived. Moreover, simplified expressions of the ASER for low signal-to-noise-ratio (SNR) and high SNR regions are also presented, which are useful to provide insights analytically. We comprehensively analyze the impact of modulation parameters and the number of IRS elements employed. We also verify our theoretical results through simulations. Our results demonstrate that employing IRS significantly enhances the ASER performance in comparison to additive white Gaussian noise channel at a low SNR regime. Thus, IRS-assisted wireless communications can be a promising candidate for various low powered communication applications such as internet-of-things (IoT).
Understanding the Role of Adversarial Regularization in Supervised Learning
Venue: arXiv cs.LG 2020
Year: 2020

Despite numerous attempts sought to provide empirical evidence of adversarial regularization outperforming sole supervision, the theoretical understanding of such phenomena remains elusive. In this study, we aim to resolve whether adversarial regularization indeed performs better than sole supervision at a fundamental level. To bring this insight into fruition, we study vanishing gradient issue, asymptotic iteration complexity, gradient flow and provable convergence in the context of sole supervision and adversarial regularization. The key ingredient is a theoretical justification supported by empirical evidence of adversarial acceleration in gradient descent. In addition, motivated by a recently introduced unit-wise capacity based generalization bound, we analyze the generalization error in adversarial framework. Guided by our observation, we cast doubts on the ability of this measure to explain generalization. We therefore leave as open questions to explore new measures that can explain generalization behavior in adversarial learning. Furthermore, we observe an intriguing phenomenon in the neural embedded vector space while contrasting adversarial learning with sole supervision.
On Store Languages and Applications
Venue: arXiv cs.FL 2020
Year: 2020

The store language of a machine of some arbitrary type is the set of all store configurations (state plus store contents but not the input) that can appear in an accepting computation. New algorithms and characterizations of store languages are obtained, such as the result that any nondeterministic pushdown automaton augmented with reversal-bounded counters, where the pushdown can "flip" its contents up to a bounded number of times, can be accepted by a machine with only reversal-bounded counters. Then, connections are made between store languages and several model checking and reachability problems, such as accepting the set of all predecessor and successor configurations from a given set of configurations, and determining whether there are at least one, or infinitely many, common configurations between accepting computations of two machines. These are explored for a variety of different machine models often containing multiple parallel data stores. Many of the machine models studied can accept the set of predecessor configurations (of a regular set of configurations), the set of successor configurations, and the set of common configurations between two machines, with a machine model that is simpler than itself, with a decidable emptiness, infiniteness, and disjointness property. Store languages are key to showing these properties.
Dynamic Facial Asset and Rig Generation from a Single Scan
Venue: arXiv cs.GR 2020
Year: 2020

The creation of high-fidelity computer-generated (CG) characters used in film and gaming requires intensive manual labor and a comprehensive set of facial assets to be captured with complex hardware, resulting in high cost and long production cycles. In order to simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial assets, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic and physically-based textures, as well as secondary facial components (e.g., teeth and eyeballs). Built upon a facial database consisting of pore-level details, with over $4,000$ scans of varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of the full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with cutting-edge industry pipelines for facial animation and rendering. We demonstrate that our framework is robust and effective by inferring on a wide range of novel subjects, and illustrate compelling rendering results while animating faces with generated customized physically-based dynamic textures.
DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization
Venue: arXiv cs.CV 2020
Year: 2020

Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc. Image retrieval for localization is an efficient and effective solution to the problem. In this paper, we propose a novel multi-task architecture to fuse the geometric and semantic information into the multi-scale latent embedding representation for visual place recognition. To use the high-quality ground truths without any human effort, the effective multi-scale feature discriminator is proposed for adversarial training to achieve the domain adaptation from synthetic virtual KITTI dataset to real-world KITTI dataset. The proposed approach is validated on the Extended CMU-Seasons dataset and Oxford RobotCar dataset through a series of crucial comparison experiments, where our performance outperforms state-of-the-art baselines for retrieval-based localization and large-scale place recognition under the challenging environment.
Stability for Two-class Multiserver-job Systems
Venue: arXiv cs.PF 2020
Year: 2020

Multiserver-job systems, where jobs require concurrent service at many servers, occur widely in practice. Much is known in the dropping setting, where jobs are immediately discarded if they require more servers than are currently available. However, very little is known in the more practical setting where jobs queue instead. In this paper, we derive a closed-form analytical expression for the stability region of a two-class (non-dropping) multiserver-job system where each class of jobs requires a distinct number of servers and requires a distinct exponential distribution of service time, and jobs are served in first-come-first-served (FCFS) order. This is the first result of any kind for an FCFS multiserver-job system where the classes have distinct service distributions. Our work is based on a technique that leverages the idea of a "saturated" system, in which an unlimited number of jobs are always available. Our analytical formula provides insight into the behavior of FCFS multiserver-job systems, highlighting the huge wastage (idle servers while jobs are in the queue) that can occur, as well as the nonmonotonic effects of the service rates on wastage.
Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans
Venue: arXiv cs.CV 2020
Year: 2020

We present an algorithm for multi-scale tumor (chimeric cell) detection in high resolution slide scans. The broad range of tumor sizes in our dataset pose a challenge for current Convolutional Neural Networks (CNN) which often fail when image features are very small (8 pixels). Our approach modifies the effective receptive field at different layers in a CNN so that objects with a broad range of varying scales can be detected in a single forward pass. We define rules for computing adaptive prior anchor boxes which we show are solvable under the equal proportion interval principle. Two mechanisms in our CNN architecture alleviate the effects of non-discriminative features prevalent in our data - a foveal detection algorithm that incorporates a cascade residual-inception module and a deconvolution module with additional context information. When integrated into a Single Shot MultiBox Detector (SSD), these additions permit more accurate detection of small-scale objects. The results permit efficient real-time analysis of medical images in pathology and related biomedical research fields.
A Recommender System based on the analysis of personality traits in Telegram social network
Venue: arXiv cs.SI 2020
Year: 2020

Accessing people's personality traits has always been a challenging task. On the other hand, acquiring personality traits based on behavioral data is one of the growing interest of human beings. Numerous researches showed that people spend a large amount of time on social networks and show behaviors that create some personality patterns in cyberspace. One of these social networks that have been widely welcomed in some countries, including Iran, is Telegram. The basis of this research is automatically identifying users' personalities based on their behavior on Telegram. For this purpose, messages from Telegram group users are extracted, and then the personality traits of each member according to the NEO Personality Inventory are identified. For personality analysis, the study is employed three approaches, including; Cosine Similarity, Bayes, and MLP algorithms. Finally, this study provides a recommender system that uses the Cosine similarity algorithm to explore and recommend relevant Telegram channels to members according to the extracted personalities. The results show a 65.42% satisfaction rate for the recommender system based on the proposed personality analysis.
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
Venue: arXiv cs.CV 2020
Year: 2020

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.
Deep Learning for Earth Image Segmentation based on Imperfect Polyline Labels with Annotation Errors
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, deep learning techniques (e.g., U-Net, DeepLab) have achieved tremendous success in image segmentation. The performance of these models heavily relies on high-quality ground truth segment labels. Unfortunately, in many real-world problems, ground truth segment labels often have geometric annotation errors due to manual annotation mistakes, GPS errors, or visually interpreting background imagery at a coarse resolution. Such location errors will significantly impact the training performance of existing deep learning algorithms. Existing research on label errors either models ground truth errors in label semantics (assuming label locations to be correct) or models label location errors with simple square patch shifting. These methods cannot fully incorporate the geometric properties of label location errors. To fill the gap, this paper proposes a generic learning framework based on the EM algorithm to update deep learning model parameters and infer hidden true label locations simultaneously. Evaluations on a real-world hydrological dataset in the streamline refinement application show that the proposed framework outperforms baseline methods in classification accuracy (reducing the number of false positives by 67% and reducing the number of false negatives by 55%).
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval
Venue: arXiv cs.IR 2020
Year: 2020

Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching. In this paper, we study the problem of transferring the deep knowledge of the pre-trained language model (PLM) to Term-based Sparse representations, aiming to improve the representation capacity of bag-of-words(BoW) method for semantic-level matching, while still keeping its advantages. Specifically, we propose a novel framework SparTerm to directly learn sparse text representations in the full vocabulary space. The proposed SparTerm comprises an importance predictor to predict the importance for each term in the vocabulary, and a gating controller to control the term activation. These two modules cooperatively ensure the sparsity and flexibility of the final text representation, which unifies the term-weighting and expansion in the same framework. Evaluated on MSMARCO dataset, SparTerm significantly outperforms traditional sparse methods and achieves state of the art ranking performance among all the PLM-based sparse models.