OpenRaiser/Intern-Atlas huggingface dataset:
DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations
Venue: arXiv cs.CV 2020
Year: 2020

This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes; and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure corresponding to the natural variation within a given population or a given subject. These two cases are developed experimentally for individual digits and handwriting signatures, respectively, achieving in both cases remarkable results. Also, we provide experimental results for the task of on-line signature verification showing the high potential of DeepWriteSYN to improve significantly one-shot learning scenarios. To the best of our knowledge, this is the first synthesis approach capable of generating realistic on-line handwriting in the short term (including handwritten signatures) via deep learning. This can be very useful as a module toward long-term realistic handwriting generation either completely synthetic or as natural variation of given handwriting samples.
Exploring Level Blending across Platformers via Paths and Affordances
Venue: arXiv cs.LG 2020
Year: 2020

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.
JXES: JSON Support for the XES Event Log Standard
Venue: arXiv cs.OH 2020
Year: 2020

Process mining assumes the existence of an event log where each event refers to a case, an activity, and a point in time. XES is an XML based IEEE approved standard format for event logs supported by most of the process mining tools. JSON (JavaScript Object Notation) is a lightweight data interchange format. In this paper, we present JXES, the JSON standard for the event logs and also provide implementation in ProM for importing and exporting event logs in JSON format using 4 different parsers. The evaluation results show notable performance differences between the different parsers (Simple JSON, Jackson, GSON, Jsoninter).
P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions
Venue: arXiv cs.CV 2020
Year: 2020

Learning deep neural network (DNN) classifier with noisy labels is a challenging task because the DNN can easily over-fit on these noisy labels due to its high capability. In this paper, we present a very simple but effective training paradigm called P-DIFF, which can train DNN classifiers but obviously alleviate the adverse impact of noisy labels. Our proposed probability difference distribution implicitly reflects the probability of a training sample to be clean, then this probability is employed to re-weight the corresponding sample during the training process. P-DIFF can also achieve good performance even without prior knowledge on the noise rate of training samples. Experiments on benchmark datasets also demonstrate that P-DIFF is superior to the state-of-the-art sample selection methods.
Moment-based Spectrum Sensing Under Generalized Noise Channels
Venue: arXiv cs.IT 2020
Year: 2020

A new spectrum sensing detector is proposed and analytically studied, when it operates under generalized noise channels. Particularly, the McLeish distribution is used to model the underlying noise, which is suitable for both non-Gaussian (impulsive) as well as classical Gaussian noise modeling. The introduced detector adopts a moment-based approach, whereas it is not required to know the transmit signal and channel fading statistics (i.e., blind detection). Important performance metrics are presented in closed forms, such as the false-alarm probability, detection probability and decision threshold. Analytical and simulation results are cross-compared validating the accuracy of the proposed approach. Finally, it is demonstrated that the proposed approach outperforms the conventional energy detector in the practical case of noise uncertainty, yet introducing a comparable computational complexity.
A study of vowel nasalization using instantaneous spectra
Venue: arXiv cs.SD 2020
Year: 2020

Nasalization of vowels is a phenomenon where oral and nasal tracts participate simultaneously for the production of speech. Acoustic coupling of oral and nasal tracts results in a complex production system, which is subjected to a continuous changes owing to glottal activity. Identification of the duration of nasalization in vowels, and the extent of coupling of oral and nasal tracts, is a challenging task. The present research focuses on the changes in instantaneous vocal tract system response to study the effects of co--articulatory load of nasals on vowels. The dominant resonance frequency (DRF) contour derived from the spectra illustrates the contribution of oral and nasal resonances during nasalization. The extent of coupling determines the dominance of these resonances during glottal open and closed phases. A higher extent leads to more decay of energy for the oral resonances, and hence the vowel spectra exhibits a dominant nasal resonance. A comparison of the proposed parameters is made with the previously suggested measures. Several examples of segments of vowels in the context of nasal consonants for English language for both male and female speakers of English are used to illustrate different aspects of the proposed analysis method.
High correlated variables creator machine: Prediction of the compressive strength of concrete
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we introduce a novel hybrid model for predicting the compressive strength of concrete using ultrasonic pulse velocity (UPV) and rebound number (RN). First, 516 data from 8 studies of UPV and rebound hammer (RH) tests was collected. Then, high correlated variables creator machine (HVCM) is used to create the new variables that have a better correlation with the output and improve the prediction models. Three single models, including a step-by-step regression (SBSR), gene expression programming (GEP) and an adaptive neuro-fuzzy inference system (ANFIS) as well as three hybrid models, i.e. HCVCM-SBSR, HCVCM-GEP and HCVCM-ANFIS, were employed to predict the compressive strength of concrete. The statistical parameters and error terms such as coefficient of determination, root mean square error (RMSE), normalized mean square error (NMSE), fractional bias, the maximum positive and negative errors, and mean absolute percentage error (MAPE), were computed to evaluate and compare the models. The results show that HCVCM-ANFIS can predict the compressive strength of concrete better than all other models. HCVCM improves the accuracy of ANFIS by 5% in the coefficient of determination, 10% in RMSE, 3% in NMSE, 20% in MAPE, and 7% in the maximum negative error.
A Study of Human Gaze Behavior During Visual Crowd Counting
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we describe our study on how humans allocate their attention during visual crowd counting. Using an eye tracker, we collect gaze behavior of human participants who are tasked with counting the number of people in crowd images. Analyzing the collected gaze behavior of ten human participants on thirty crowd images, we observe some common approaches for visual counting. For an image of a small crowd, the approach is to enumerate over all people or groups of people in the crowd, and this explains the high level of similarity between the fixation density maps of different human participants. For an image of a large crowd, our participants tend to focus on one section of the image, count the number of people in that section, and then extrapolate to the other sections. In terms of count accuracy, our human participants are not as good at the counting task, compared to the performance of the current state-of-the-art computer algorithms. Interestingly, there is a tendency to under count the number of people in all crowd images. Gaze behavior data and images can be downloaded from https://www3.cs.stonybrook.edu/~minhhoai/projects/crowd_counting_gaze/.
Statically Verified Refinements for Multiparty Protocols
Venue: arXiv cs.PL 2020
Year: 2020

With distributed computing becoming ubiquitous in the modern era, safe distributed programming is an open challenge. To address this, multiparty session types (MPST) provide a typing discipline for message-passing concurrency, guaranteeing communication safety properties such as deadlock freedom. While originally MPST focus on the communication aspects, and employ a simple typing system for communication payloads, communication protocols in the real world usually contain constraints on the payload. We introduce refined multiparty session types (RMPST), an extension of MPST, that express data dependent protocols via refinement types on the data types. We provide an implementation of RMPST, in a toolchain called Session*, using Scribble, a multiparty protocol description toolchain, and targeting F*, a verification-oriented functional programming language. Users can describe a protocol in Scribble and implement the endpoints in F* using refinement-typed APIs generated from the protocol. The F* compiler can then statically verify the refinements. Moreover, we use a novel approach of callback-styled API generation, providing static linearity guarantees with the inversion of control. We evaluate our approach with real world examples and show that it has little overhead compared to a naïve implementation, while guaranteeing safety properties from the underlying theory.
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing
Venue: arXiv cs.NI 2020
Year: 2020

The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational resources (processor usage) are allocated to stochastic arrivals of network slice requests. Each request arrives with priority (weight), throughput, computational resource, and latency (deadline) requirements, and if feasible, it is served with available communication and computational resources allocated over its requested duration. As each decision of resource allocation makes some of the resources temporarily unavailable for future, the myopic solution that can optimize only the current resource allocation becomes ineffective for network slicing. Therefore, a Q-learning solution is presented to maximize the network utility in terms of the total weight of granted network slicing requests over a time horizon subject to communication and computational constraints. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions. While reinforcement learning sustains scalable performance as the number of served users increases, it can also be effectively used to assign resources to network slices when 5G needs to share the spectrum with incumbent users that may dynamically occupy some of the frequency-time blocks.
Knowledge-Based Legal Document Assembly
Venue: arXiv cs.SE 2020
Year: 2020

This paper proposes a knowledge-based legal document assembly method that uses a machine-readable representation of knowledge of legal professionals. This knowledgebase has two components - the formal knowledge of legal norms represented as a rule-base and the tacit knowledge represented by a document template. A document assembly system is developed as a proof of concept. It collects input data in the form of an interactive interview, performs legal reasoning over input data, and generates the output document. The system also creates an argument graph as an explanation of the reasoning process providing the user with an interpretation of how the input data and the rule-base influence the content of the output document. The system also semantically marks up data in the output document, facilitating its further processing and providing support to the interoperability of information systems in the legal domain.
The Role of Individual User Differences in Interpretable and Explainable Machine Learning Systems
Venue: arXiv cs.CY 2020
Year: 2020

There is increased interest in assisting non-expert audiences to effectively interact with machine learning (ML) tools and understand the complex output such systems produce. Here, we describe user experiments designed to study how individual skills and personality traits predict interpretability, explainability, and knowledge discovery from ML generated model output. Our work relies on Fuzzy Trace Theory, a leading theory of how humans process numerical stimuli, to examine how different end users will interpret the output they receive while interacting with the ML system. While our sample was small, we found that interpretability -- being able to make sense of system output -- and explainability -- understanding how that output was generated -- were distinct aspects of user experience. Additionally, subjects were more able to interpret model output if they possessed individual traits that promote metacognitive monitoring and editing, associated with more detailed, verbatim, processing of ML output. Finally, subjects who are more familiar with ML systems felt better supported by them and more able to discover new patterns in data; however, this did not necessarily translate to meaningful insights. Our work motivates the design of systems that explicitly take users' mental representations into account during the design process to more effectively support end user requirements.
WDRN : A Wavelet Decomposed RelightNet for Image Relighting
Venue: arXiv cs.CV 2020
Year: 2020

The task of recalibrating the illumination settings in an image to a target configuration is known as relighting. Relighting techniques have potential applications in digital photography, gaming industry and in augmented reality. In this paper, we address the one-to-one relighting problem where an image at a target illumination settings is predicted given an input image with specific illumination conditions. To this end, we propose a wavelet decomposed RelightNet called WDRN which is a novel encoder-decoder network employing wavelet based decomposition followed by convolution layers under a muti-resolution framework. We also propose a novel loss function called gray loss that ensures efficient learning of gradient in illumination along different directions of the ground truth image giving rise to visually superior relit images. The proposed solution won the first position in the relighting challenge event in advances in image manipulation (AIM) 2020 workshop which proves its effectiveness measured in terms of a Mean Perceptual Score which in turn is measured using SSIM and a Learned Perceptual Image Patch Similarity score.
Spatio-Temporal Top-k Similarity Search for Trajectories in Graphs
Venue: arXiv cs.DS 2020
Year: 2020

We study the problem of finding the $k$ most similar trajectories to a given query trajectory. Our work is inspired by the work of Grossi et al. [6] that considers trajectories as walks in a graph. Each visited vertex is accompanied by a time-interval. Grossi et al. define a similarity function that captures temporal and spatial aspects. We improve this similarity function to derive a new spatio-temporal distance function for which we can show that a specific type of triangle inequality is satisfied. This distance function is the basis for our index structures, which can be constructed efficiently, need only linear memory, and can quickly answer queries for the top-$k$ most similar trajectories. Our evaluation on real-world and synthetic data sets shows that our algorithms outperform the baselines with respect to indexing time by several orders of magnitude while achieving similar or better query time and quality of results.
On the use of local structural properties for improving the efficiency of hierarchical community detection methods
Venue: arXiv cs.LG 2020
Year: 2020

Community detection is a fundamental problem in the analysis of complex networks. It is the analogue of clustering in network data mining. Within community detection methods, hierarchical algorithms are popular. However, their iterative nature and the need to recompute the structural properties used to split the network (i.e. edge betweenness in Girvan and Newman's algorithm), make them unsuitable for large network data sets. In this paper, we study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection while, at the same time, achieving competitive results in terms of modularity. In particular, we study the potential use of the structural properties commonly used to perform local link prediction, a supervised learning problem where community structure is relevant, as nodes are prone to establish new links with other nodes within their communities. In addition, we check the performance impact of network pruning heuristics as an ancillary tactic to make hierarchical community detection more efficient
The Importance of Pessimism in Fixed-Dataset Policy Optimization
Venue: arXiv cs.AI 2020
Year: 2020

We study worst-case guarantees on the expected return of fixed-dataset policy optimization algorithms. Our core contribution is a unified conceptual and mathematical framework for the study of algorithms in this regime. This analysis reveals that for naive approaches, the possibility of erroneous value overestimation leads to a difficult-to-satisfy requirement: in order to guarantee that we select a policy which is near-optimal, we may need the dataset to be informative of the value of every policy. To avoid this, algorithms can follow the pessimism principle, which states that we should choose the policy which acts optimally in the worst possible world. We show why pessimistic algorithms can achieve good performance even when the dataset is not informative of every policy, and derive families of algorithms which follow this principle. These theoretical findings are validated by experiments on a tabular gridworld, and deep learning experiments on four MinAtar environments.
A Design Space of Vision Science Methods for Visualization Research
Venue: arXiv cs.HC 2020
Year: 2020

A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for understanding how people process visualized data. Alternative methods from vision science offer a rich suite of tools for understanding visualizations, but no curated collection of these methods exists in either perception or visualization research. We introduce a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to ultimately inform visualization design guidelines. This paper provides a shared lexicon for facilitating experimental visualization research. We discuss popular experimental paradigms, adjustment types, response types, and dependent measures used in vision science research, rooting each in visualization examples. We then discuss the advantages and limitations of each technique. Researchers can use this design space to create innovative studies and progress scientific understanding of design choices and evaluations in visualization. We highlight a history of collaborative success between visualization and vision science research and advocate for a deeper relationship between the two fields that can elaborate on and extend the methodological design space for understanding visualization and vision.
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
Venue: arXiv cs.HC 2020
Year: 2020

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains. Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time. Despite recent advances in exploring deep learning models with visual analytics tools, little work has explored the issue of explaining and diagnosing the knowledge transfer process between deep learning models. In this paper, we present a visual analytics framework for the multi-level exploration of the transfer learning processes when training deep neural networks. Our framework establishes a multi-aspect design to explain how the learned knowledge from the existing model is transferred into the new learning task when training deep neural networks. Based on a comprehensive requirement and task analysis, we employ descriptive visualization with performance measures and detailed inspections of model behaviors from the statistical, instance, feature, and model structure levels. We demonstrate our framework through two case studies on image classification by fine-tuning AlexNets to illustrate how analysts can utilize our framework.
Coverage Probability and Ergodic Capacity of Intelligent Reflecting Surface-Enhanced Communication Systems
Venue: arXiv cs.IT 2020
Year: 2020

This paper studies the performance of a single-input single-output (SISO) system enhanced by the assistance of an intelligent reflecting surface (IRS), which is equipped with a finite number of elements under Rayleigh fading channels. From the instantaneous channel capacity, we compute a closed-form expression of the coverage probability as a function of statistical channel information only. A scaling law of the coverage probability and the number of phase shifts is further obtained. The ergodic capacity is derived, then a simple upper bound to simplify matters of utilizing the symbolic functions and can be applied for a long period of time. Numerical results manifest the tightness and effectiveness of our closed-form expressions compared with Monte-Carlo simulations.