OpenRaiser/Intern-Atlas huggingface dataset:
An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network
Venue: arXiv cs.LG 2019
Year: 2019

Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This paper proposes a method to construct classifiers from multi-resolution hierarchical granular representations (MRHGRC) using hyperbox fuzzy sets. The proposed approach forms a series of granular inferences hierarchically through many levels of abstraction. An attractive characteristic of our classifier is that it can maintain relatively high accuracy at a low degree of granularity based on reusing the knowledge learned from lower levels of abstraction. In addition, our approach can reduce the data size significantly as well as handling the uncertainty and incompleteness associated with data in real-world applications. The construction process of the classifier consists of two phases. The first phase is to formulate the model at the greatest level of granularity, while the later stage aims to reduce the complexity of the constructed model and deduce it from data at higher abstraction levels. Experimental outcomes conducted comprehensively on both synthetic and real datasets indicated the efficiency of our method in terms of training time and predictive performance in comparison to other types of fuzzy min-max neural networks and common machine learning algorithms.
Research on fuzzy PID Shared control method of small brain-controlled uav
Venue: arXiv cs.SY 2019
Year: 2019

Brain-controlled unmanned aerial vehicle (uav) is a uav that can analyze human brain electrical signals through BCI to obtain flight commands. The research of brain-controlled uav can promote the integration of brain-computer and has a broad application prospect. At present, BCI still has some problems, such as limited recognition accuracy, limited recognition time and small number of recognition commands in the acquisition of control commands by analyzing eeg signals. Therefore, the control performance of the quadrotor which is controlled only by brain is not ideal. Based on the concept of Shared control, this paper designs an assistant controller using fuzzy PID control, and realizes the cooperative control between automatic control and brain control. By evaluating the current flight status and setting the switching rate, the switching mechanism of automatic control and brain control can be decided to improve the system control performance. Finally, a rectangular trajectory tracking control experiment of the same height is designed for small quadrotor to verify the algorithm.
Vision-to-Language Tasks Based on Attributes and Attention Mechanism
Venue: arXiv cs.CV 2019
Year: 2019

Vision-to-language tasks aim to integrate computer vision and natural language processing together, which has attracted the attention of many researchers. For typical approaches, they encode image into feature representations and decode it into natural language sentences. While they neglect high-level semantic concepts and subtle relationships between image regions and natural language elements. To make full use of these information, this paper attempt to exploit the text guided attention and semantic-guided attention (SA) to find the more correlated spatial information and reduce the semantic gap between vision and language. Our method includes two level attention networks. One is the text-guided attention network which is used to select the text-related regions. The other is SA network which is used to highlight the concept-related regions and the region-related concepts. At last, all these information are incorporated to generate captions or answers. Practically, image captioning and visual question answering experiments have been carried out, and the experimental results have shown the excellent performance of the proposed approach.
Automatic Realistic Music Video Generation from Segments of Youtube Videos
Venue: arXiv cs.MM 2019
Year: 2019

A Music Video (MV) is a video aiming at visually illustrating or extending the meaning of its background music. This paper proposes a novel method to automatically generate, from an input music track, a music video made of segments of Youtube music videos which would fit this music. The system analyzes the input music to find its genre (pop, rock, ...) and finds segmented MVs with the same genre in the database. Then, a K-Means clustering is done to group video segments by color histogram, meaning segments of MVs having the same global distribution of colors. A few clusters are randomly selected, then are assembled around music boundaries, which are moments where a significant change in the music occurs (for instance, transitioning from verse to chorus). This way, when the music changes, the video color mood changes as well. This work aims at generating high-quality realistic MVs, which could be mistaken for man-made MVs. By asking users to identify, in a batch of music videos containing professional MVs, amateur-made MVs and generated MVs by our algorithm, we show that our algorithm gives satisfying results, as 45% of generated videos are mistaken for professional MVs and 21.6% are mistaken for amateur-made MVs. More information can be found in the project website: http://ml.cs.tsinghua.edu.cn/~sarah/
Categorization of Program Regions for Agile Compilation using Machine Learning and Hardware Support
Venue: arXiv cs.PL 2019
Year: 2019

A compiler processes the code written in a high level language and produces machine executable code. The compiler writers often face the challenge of keeping the compilation times reasonable. That is because aggressive optimization passes which potentially will give rise to high performance are often expensive in terms of running time and memory footprint. Consequently the compiler designers arrive at a compromise where they either simplify the optimization algorithm which may decrease the performance of the produced code, or they will restrict the optimization to the subset of the overall input program in which case large parts of the input application will go un-optimized. The problem we address in this paper is that of keeping the compilation times reasonable, and at the same time optimizing the input program to the fullest extent possible. Consequently, the performance of the produced code will match the performance when all the aggressive optimization passes are applied over the entire input program.
G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data
Venue: arXiv cs.LG 2019
Year: 2019

Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications. First, GANs may generate more labeled training data, which may help improve classification accuracy. Second, in scenarios where real data cannot be released outside certain premises for privacy and/or security reasons, using GAN- synthetic data to conduct training is a plausible alternative. This paper proposes a generalization bound to guarantee the generalization capability of a classifier learning from GAN-synthetic data. This generalization bound helps developers gauge the generalization gap between learning from synthetic data and testing on real data, and can therefore provide the clues to improve the generalization capability.
A new definition of the distortion matrix for an audio-to-score alignment system
Venue: arXiv cs.SD 2019
Year: 2019

In this paper we present a new definition of the distortion matrix for a score following framework based on DTW. The proposal consists of arranging the score information in a sequence of note combinations and learning a spectral pattern for each combination using instrument models. Then, the distortion matrix is computed using these spectral patterns and a novel decomposition of the input signal.
Learning the Non-linearity in Convolutional Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

We propose the introduction of nonlinear operation into the feature generation process in convolutional neural networks. This nonlinearity can be implemented in various ways. First we discuss the use of nonlinearities in the process of data augmentation to increase the robustness of the neural networks recognition capacity. To this end, we randomly disturb the input data set by applying exponents within a certain numerical range to individual data points of the input space. Second we propose nonlinear convolutional neural networks where we apply the exponential operation to each element of the receptive field. To this end, we define an additional weight matrix of the same dimension as the standard kernel weight matrix. The weights of this matrix then constitute the exponents of the corresponding components of the receptive field. In the basic setting, we keep the weight parameters fixed during training by defining suitable parameters. Alternatively, we make the exponential weight parameters end-to-end trainable using a suitable parameterization. The network architecture is applied to time series analysis data set showing a considerable increase in the classification performance compared to baseline networks.
Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities
Venue: arXiv cs.LG 2019
Year: 2019

We study model-based reinforcement learning in an unknown finite communicating Markov decision process. We propose a simple algorithm that leverages a variance based confidence interval. We show that the proposed algorithm, UCRL-V, achieves the optimal regret $\tilde{\mathcal{O}}(\sqrt{DSAT})$ up to logarithmic factors, and so our work closes a gap with the lower bound without additional assumptions on the MDP. We perform experiments in a variety of environments that validates the theoretical bounds as well as prove UCRL-V to be better than the state-of-the-art algorithms.
On the Containment Problem for Unambiguous Single-Register Automata with Guessing
Venue: arXiv cs.FL 2019
Year: 2019

Register automata extend classical finite automata with a finite set of registers that can store data from an infinite data domain for later equality comparisons with data from an input data word. While the registers in the original model of register automata, introduced in 1994 by Kaminski and Francez, can only store data occurring in the data word processed so far, we study here the more expressive class of register automata with guessing, where registers can nondeterministically take any value from the infinite data domain, even if this data does not occur in the input data word. It is well known that the containment problem, i.e., the problem of deciding for two given register automata with guessing A and B, whether the language L(A) accepted by A is contained in the language L(B) accepted by B, is undecidable, even if B only uses a single register. We prove that the problem is decidable if B is unambiguous and uses a single register.
Complexity Analysis of Approaching Clinical Psychiatry with Predictive Analytics and Neural Networks
Venue: arXiv cs.CY 2019
Year: 2019

As the emerging field of predictive analytics in psychiatry generated and continues to generate massive interest overtime with its major promises to positively change and revolutionize clinical psychiatry, health care and medical professionals are greatly looking forward to its integration and application into psychiatry. However, by directly applying predictive analytics to the practice of psychiatry, this could cause detrimental damage to those that use predictive analytics through creating or worsening existing medical issues. In both cases, medical ethics issues arise, and need to be addressed. This paper will use literature to provide descriptions of selected stages in the treatment of mental disorders and phases in a predictive analytics project, approach mental disorder diagnoses using predictive models that rely on neural networks, analyze the complexities in clinical psychiatry, neural networks and predictive analytics, and conclude with emphasizing and elaborating on limitations and medical ethics issues of applying neural networks and predictive analytics to clinical psychiatry.
Oriented coloring of graphs with low maximum degree
Venue: arXiv cs.DM 2019
Year: 2019

Duffy et al. [C. Duffy, G. MacGillivray, and É. Sopena, Oriented colourings of graphs with maximum degree three and four, Discrete Mathematics, 342(4), p. 959--974, 2019] recently considered the oriented chromatic number of connected oriented graphs with maximum degree $3$ and $4$, proving it is at most $9$ and $69$, respectively. In this paper, we improve these results by showing that the oriented chromatic number of non-necessarily connected oriented graphs with maximum degree $3$ (resp. $4$) is at most $9$ (resp. $26$). The bound of $26$ actually follows from a general result which determines properties of a target graph to be universal for graphs of bounded maximum degree. This generalization also allows us to get the upper bound of $90$ (resp. $306$, $1322$) for the oriented chromatic number of graphs with maximum degree $5$ (resp. $6$, $7$).
ORangE: Operational Range Estimation for Mobile Robot Exploration on a Single Discharge Cycle
Venue: arXiv cs.RO 2019
Year: 2019

This paper presents an approach for estimating the operational range for mobile robot exploration on a single battery discharge. Deploying robots in the wild usually requires uninterrupted energy sources to maintain the robot's mobility throughout the entire mission. However, for most endeavors into the unknown environments, recharging is usually not an option, due to the lack of pre-installed recharging stations or other mission constraints. In these cases, the ability to model the on-board energy consumption and estimate the operational range is crucial to prevent running out of battery in the wild. To this end, this work describes our recent findings that quantitatively break down the robot's on-board energy consumption and predict the operational range to guarantee safe mission completion on a single battery discharge cycle. Two range estimators with different levels of generality and model fidelity are presented, whose performances were validated on physical robot platforms in both indoor and outdoor environments. Model performance metrics are also presented as benchmarks.
Archive Assisted Archival Fixity Verification Framework
Venue: arXiv cs.DL 2019
Year: 2019

The number of public and private web archives has increased, and we implicitly trust content delivered by these archives. Fixity is checked to ensure an archived resource has remained unaltered since the time it was captured. Some web archives do not allow users to access fixity information and, more importantly, even if fixity information is available, it is provided by the same archive from which the archived resources are requested. In this research, we propose two approaches, namely Atomic and Block, to establish and check fixity of archived resources. In the Atomic approach, the fixity information of each archived web page is stored in a JSON file (or a manifest), and published in a well-known web location (an Archival Fixity server) before it is disseminated to several on-demand web archives. In the Block approach, we first batch together fixity information of multiple archived pages in a single binary-searchable file (or a block) before it is published and disseminated to archives. In both approaches, the fixity information is not obtained directly from archives. Instead, we compute the fixity information (e.g., hash values) based on the playback of archived resources. One advantage of the Atomic approach is the ability to verify fixity of archived pages even with the absence of the Archival Fixity server. The Block approach requires pushing fewer resources into archives, and it performs fixity verification faster than the Atomic approach. On average, it takes about 1.25X, 4X, and 36X longer to disseminate a manifest to perma.cc, archive.org, and webcitation.org, respectively, than archive.is, while it takes 3.5X longer to disseminate a block to archive.org than perma.cc. The Block approach performs 4.46X faster than the Atomic approach on verifying the fixity of archived pages.
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning
Venue: arXiv cs.LG 2019
Year: 2019

Reinforcement learning has become one of the best approach to train a computer game emulator capable of human level performance. In a reinforcement learning approach, an optimal value function is learned across a set of actions, or decisions, that leads to a set of states giving different rewards, with the objective to maximize the overall reward. A policy assigns to each state-action pairs an expected return. We call an optimal policy a policy for which the value function is optimal. QLBS, Q-Learner in the Black-Scholes(-Merton) Worlds, applies the reinforcement learning concepts, and noticeably, the popular Q-learning algorithm, to the financial stochastic model of Black, Scholes and Merton. It is, however, specifically optimized for the geometric Brownian motion and the vanilla options. Its range of application is, therefore, limited to vanilla option pricing within financial markets. We propose MQLV, Modified Q-Learner for the Vasicek model, a new reinforcement learning approach that determines the optimal policy of money management based on the aggregated financial transactions of the clients. It unlocks new frontiers to establish personalized credit card limits or to fulfill bank loan applications, targeting the retail banking industry. MQLV extends the simulation to mean reverting stochastic diffusion processes and it uses a digital function, a Heaviside step function expressed in its discrete form, to estimate the probability of a future event such as a payment default. In our experiments, we first show the similarities between a set of historical financial transactions and Vasicek generated transactions and, then, we underline the potential of MQLV on generated Monte Carlo simulations. Finally, MQLV is the first Q-learning Vasicek-based methodology addressing transparent decision making processes in retail banking.
Automatically Dismantling Online Dating Fraud
Venue: arXiv cs.CR 2019
Year: 2019

Online romance scams are a prevalent form of mass-marketing fraud in the West, and yet few studies have addressed the technical or data-driven responses to this problem. In this type of scam, fraudsters craft fake profiles and manually interact with their victims. Because of the characteristics of this type of fraud and of how dating sites operate, traditional detection methods (e.g., those used in spam filtering) are ineffective. In this paper, we present the results of a multi-pronged investigation into the archetype of online dating profiles used in this form of fraud, including their use of demographics, profile descriptions, and images, shedding light on both the strategies deployed by scammers to appeal to victims and the traits of victims themselves. Further, in response to the severe financial and psychological harm caused by dating fraud, we develop a system to detect romance scammers on online dating platforms. Our work presents the first system for automatically detecting this fraud. Our aim is to provide an early detection system to stop romance scammers as they create fraudulent profiles or before they engage with potential victims. Previous research has indicated that the victims of romance scams score highly on scales for idealized romantic beliefs. We combine a range of structured, unstructured, and deep-learned features that capture these beliefs. No prior work has fully analyzed whether these notions of romance introduce traits that could be leveraged to build a detection system. Our ensemble machine-learning approach is robust to the omission of profile details and performs at high accuracy (97\%). The system enables development of automated tools for dating site providers and individual users.
Predicting next shopping stage using Google Analytics data for E-commerce applications
Venue: arXiv cs.IR 2019
Year: 2019

E-commerce web applications are almost ubiquitous in our day to day life, however as useful as they are, most of them have little to no adaptation to user needs, which in turn can cause both lower conversion rates as well as unsatisfied customers. We propose a machine learning system which learns the user behaviour from multiple previous sessions and predicts useful metrics for the current session. In turn, these metrics can be used by the applications to customize and better target the customer, which can mean anything from offering better offers of specific products, targeted notifications or placing smart ads. The data used for the learning algorithm is extracted from Google Analytics Enhanced E-commerce, which is enabled by most e-commerce websites and thus the system can be used by any such merchant. In order to learn the user patterns, only its behaviour features were used, which don't include names, gender or any other personal information that could identify the user. The learning model that was used is a double recurrent neural network which learns both intra-session and inter-session features. The model predicts for each session a probability score for each of the defined target classes.
Towards better substitution-based word sense induction
Venue: arXiv cs.CL 2019
Year: 2019

Word sense induction (WSI) is the task of unsupervised clustering of word usages within a sentence to distinguish senses. Recent work obtain strong results by clustering lexical substitutes derived from pre-trained RNN language models (ELMo). Adapting the method to BERT improves the scores even further. We extend the previous method to support a dynamic rather than a fixed number of clusters as supported by other prominent methods, and propose a method for interpreting the resulting clusters by associating them with their most informative substitutes. We then perform extensive error analysis revealing the remaining sources of errors in the WSI task. Our code is available at https://github.com/asafamr/bertwsi.
Learning Navigation Subroutines from Egocentric Videos
Venue: arXiv cs.RO 2019
Year: 2019

Planning at a higher level of abstraction instead of low level torques improves the sample efficiency in reinforcement learning, and computational efficiency in classical planning. We propose a method to learn such hierarchical abstractions, or subroutines from egocentric video data of experts performing tasks. We learn a self-supervised inverse model on small amounts of random interaction data to pseudo-label the expert egocentric videos with agent actions. Visuomotor subroutines are acquired from these pseudo-labeled videos by learning a latent intent-conditioned policy that predicts the inferred pseudo-actions from the corresponding image observations. We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuomotor subroutines from passive egocentric videos. We demonstrate the utility of our acquired visuomotor subroutines by using them as is for exploration, and as sub-policies in a hierarchical RL framework for reaching point goals and semantic goals. We also demonstrate behavior of our subroutines in the real world, by deploying them on a real robotic platform. Project website: https://ashishkumar1993.github.io/subroutines/.