OpenRaiser/Intern-Atlas huggingface dataset:
Towards Monitoring Parkinson's Disease Following Drug Treatment: CGP Classification of rs-MRI Data
Venue: arXiv cs.NE 2019
Year: 2019

Background and Objective: It is commonly accepted that accurate monitoring of neurodegenerative diseases is crucial for effective disease management and delivery of medication and treatment. This research develops automatic clinical monitoring techniques for PD, following treatment, using the novel application of EAs. Specifically, the research question addressed was: Can accurate monitoring of PD be achieved using EAs on rs-fMRI data for patients prescribed Modafinil (typically prescribed for PD patients to relieve physical fatigue)? Methods: This research develops novel clinical monitoring tools using data from a controlled experiment where participants were administered Modafinil versus placebo, examining the novel application of EAs to both map and predict the functional connectivity in participants using rs-fMRI data. Specifically, CGP was used to classify DCM analysis and timeseries data. Results were validated with two other commonly used classification methods (ANN and SVM) and via k-fold cross-validation. Results: Findings revealed a maximum accuracy of 74.57% for CGP. Furthermore, CGP provided comparable performance accuracy relative to ANN and SVM. Nevertheless, EAs enable us to decode the classifier, in terms of understanding the data inputs that are used, more easily than in ANN and SVM. Conclusions: These findings underscore the applicability of both DCM analyses for classification and CGP as a novel classification technique for brain imaging data with medical implications for medication monitoring. Furthermore, classification of fMRI data for research typically involves statistical modelling techniques being often hypothesis driven, whereas EAs use data-driven explanatory modelling methods resulting in numerous benefits. DCM analysis is novel for classification and advantageous as it provides information on the causal links between different brain regions.
Neural Arabic Text Diacritization: State of the Art Results and a Novel Approach for Machine Translation
Venue: arXiv cs.CL 2019
Year: 2019

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF) and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models, which require language-dependent post-processing steps, unlike ours. Moreover, we show that diacritics in Arabic can be used to enhance the models of NLP tasks such as Machine Translation (MT) by proposing the Translation over Diacritization (ToD) approach.
The State of NLP Literature: A Diachronic Analysis of the ACL Anthology
Venue: arXiv cs.DL 2019
Year: 2019

The ACL Anthology (AA) is a digital repository of tens of thousands of articles on Natural Language Processing (NLP). This paper examines the literature as a whole to identify broad trends in productivity, focus, and impact. It presents the analyses in a sequence of questions and answers. The goal is to record the state of the AA literature: who and how many of us are publishing? what are we publishing on? where and in what form are we publishing? and what is the impact of our publications? The answers are usually in the form of numbers, graphs, and inter-connected visualizations. Special emphasis is laid on the demographics and inclusiveness of NLP publishing. Notably, we find that only about 30% of first authors are female, and that this percentage has not improved since the year 2000. We also show that, on average, female first authors are cited less than male first authors, even when controlling for experience. We hope that recording citation and participation gaps across demographic groups will encourage more inclusiveness and fairness in research.
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Venue: arXiv cs.LG 2019
Year: 2019

Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for clinical diagnostic applications. Structural brain network (e.g. DTI) and functional brain network (e.g. fMRI) are the primary networks of interest. Most existing works in brain network analysis focus on either structural or functional connectivity, which cannot leverage the complementary information from each other. Although multi-view learning methods have been proposed to learn from both networks (or views), these methods aim to reach a consensus among multiple views, and thus distinct intrinsic properties of each view may be ignored. How to jointly learn representations from structural and functional brain networks while preserving their inherent properties is a critical problem. In this paper, we propose a framework of Siamese community-preserving graph convolutional network (SCP-GCN) to learn the structural and functional joint embedding of brain networks. Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity. Moreover, we propose to preserve the community structure of brain networks in the graph convolutions by considering the intra-community and inter-community properties in the learning process. Furthermore, we use Siamese architecture which models the pair-wise similarity learning to guide the learning process. To evaluate the proposed approach, we conduct extensive experiments on two real brain network datasets. The experimental results demonstrate the superior performance of the proposed approach in structural and functional joint embedding for neurological disorder analysis, indicating its promising value for clinical applications.
Robo-PlaNet: Learning to Poke in a Day
Venue: arXiv cs.LG 2019
Year: 2019

Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observations. This architecture is useful for learning tasks in which either the agent does not have access to meaningful states (like position/velocity of robotic joints) or where the observed states significantly deviate from the physical state of the agent (which is commonly the case in low-cost robots in the form of backlash or noisy joint readings). PlaNet, by design, interleaves phases of training the dynamics model with phases of collecting more data on the target environment, leading to long training times. In this work, we introduce Robo-PlaNet, an asynchronous version of PlaNet. This algorithm consistently reaches higher performance in the same amount of time, which we demonstrate in both a simulated and a real robotic experiment.
Worst Cases Policy Gradients
Venue: arXiv cs.LG 2019
Year: 2019

Recent advances in deep reinforcement learning have demonstrated the capability of learning complex control policies from many types of environments. When learning policies for safety-critical applications, it is essential to be sensitive to risks and avoid catastrophic events. Towards this goal, we propose an actor-critic framework that models the uncertainty of the future and simultaneously learns a policy based on that uncertainty model. Specifically, given a distribution of the future return for any state and action, we optimize policies for varying levels of conditional Value-at-Risk. The learned policy can map the same state to different actions depending on the propensity for risk. We demonstrate the effectiveness of our approach in the domain of driving simulations, where we learn maneuvers in two scenarios. Our learned controller can dynamically select actions along a continuous axis, where safe and conservative behaviors are found at one end while riskier behaviors are found at the other. Finally, when testing with very different simulation parameters, our risk-averse policies generalize significantly better compared to other reinforcement learning approaches.
Performance Comparison of MPICH and MPI4py on Raspberry Pi-3B Beowulf Cluster
Venue: arXiv cs.DC 2019
Year: 2019

Moore's Law is running out. Instead of making powerful computer by increasing number of transistor now we are moving toward Parallelism. Beowulf cluster means cluster of any Commodity hardware. Our Cluster works exactly similar to current day's supercomputers. The motivation is to create a small sized, cheap device on which students and researchers can get hands on experience. There is a master node, which interacts with user and all other nodes are slave nodes. Load is equally divided among all nodes and they send their results to master. Master combines those results and show the final output to the user. For communication between nodes we have created a network over Ethernet. We are using MPI4py, which a Python based implantation of Message Passing Interface (MPI) and MPICH which also an open source implementation of MPI and allows us to code in C, C++ and Fortran. MPI is a standard for writing codes for such clusters. We have written parallel programs of Monte Carlo's Simulation for finding value of pi and prime number generator in Python and C++ making use of MPI4py and MPICH respectively. We have also written sequential programs applying same algorithms in Python. Then we compared the time it takes to run them on cluster in parallel and in sequential on a computer having 6500 core i7 Intel processor. It is found that making use of parallelism; we were able to outperform an expensive computer which costs much more than our cluster.
Forecasting the effect of heat stress index and climate change on cloud data center energy consumption
Venue: arXiv cs.CY 2019
Year: 2019

In this paper, we estimate the effect of heat stress index (a measure which takes into account rising temperatures as well as humidity) on data center energy consumption. We use forecasting models to predict future energy use by data centers, taking into account rising temperature scenarios. We compare those estimates with baseline forecasted energy consumption (without heat stress index or rising temperature correction) and present the result that there is a sizeable and significant difference in the two forecasts. We show that rising temperatures will cause a negative impact on data center energy consumption, increasing it by about 8 percent, and conclude that data center energy consumption analyses and forecasts must include the effects of heat stress index and rising temperatures and other climate change related effects.
A perspective on multi-agent communication for information fusion
Venue: arXiv cs.MA 2019
Year: 2019

Collaborative decision making in multi-agent systems typically requires a predefined communication protocol among agents. Usually, agent-level observations are locally processed and information is exchanged using the predefined protocol, enabling the team to perform more efficiently than each agent operating in isolation. In this work, we consider the situation where agents, with complementary sensing modalities must co-operate to achieve a common goal/task by learning an efficient communication protocol. We frame the problem within an actor-critic scheme, where the agents learn optimal policies in a centralized fashion, while taking action in a distributed manner. We provide an interpretation of the emergent communication between the agents. We observe that the information exchanged is not just an encoding of the raw sensor data but is, rather, a specific set of directive actions that depend on the overall task. Simulation results demonstrate the interpretability of the learnt communication in a variety of tasks.
Subjective Sentiment Analysis for Arabic Newswire Comments
Venue: arXiv cs.CL 2019
Year: 2019

This paper presents an approach based on supervised machine learning methods to discriminate between positive, negative and neutral Arabic reviews in online newswire. The corpus is labeled for subjectivity and sentiment analysis (SSA) at the sentence-level. The model uses both count and TF-IDF representations and apply six machine learning algorithms; Multinomial Naive Bayes, Support Vector Machines (SVM), Random Forest, Logistic Regression, Multi-layer perceptron and k-nearest neighbors using uni-grams, bi-grams features. With the goal of extracting users sentiment from written text. Experimental results showed that n-gram features could substantially improve performance; and showed that the Multinomial Naive Bayes approach is the most accurate in predicting topic polarity. Best results were achieved using count vectors trained by combination of word-based uni-grams and bi-grams with an overall accuracy of 85.57% over two classes and 65.64% over three classes.
Human Driver Behavior Prediction based on UrbanFlow
Venue: arXiv cs.RO 2019
Year: 2019

How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers' behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in urban intersection scenarios. Meanwhile, in order to collect human drivers' driving behavior data in the urban scenario, we describe a system called UrbanFlow which includes the whole procedure from raw bird's-eye view data collection via drone to the final processed trajectories. The system is mainly intended for urban scenarios but can be extended to be used for any traffic scenarios.
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Quantization is an effective method for reducing memory footprint and inference time of Neural Networks, e.g., for efficient inference in the cloud, especially at the edge. However, ultra low precision quantization could lead to significant degradation in model generalization. A promising method to address this is to perform mixed-precision quantization, where more sensitive layers are kept at higher precision. However, the search space for a mixed-precision quantization is exponential in the number of layers. Recent work has proposed HAWQ, a novel Hessian based framework, with the aim of reducing this exponential search space by using second-order information. While promising, this prior work has three major limitations: (i) HAWQV1 only uses the top Hessian eigenvalue as a measure of sensitivity and do not consider the rest of the Hessian spectrum; (ii) HAWQV1 approach only provides relative sensitivity of different layers and therefore requires a manual selection of the mixed-precision setting; and (iii) HAWQV1 does not consider mixed-precision activation quantization. Here, we present HAWQV2 which addresses these shortcomings. For (i), we perform a theoretical analysis showing that a better sensitivity metric is to compute the average of all of the Hessian eigenvalues. For (ii), we develop a Pareto frontier based method for selecting the exact bit precision of different layers without any manual selection. For (iii), we extend the Hessian analysis to mixed-precision activation quantization. We have found this to be very beneficial for object detection. We show that HAWQV2 achieves new state-of-the-art results for a wide range of tasks.
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
Venue: arXiv cs.CL 2019
Year: 2019

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.
Constructing a Data Visualization Recommender System
Venue: arXiv cs.HC 2019
Year: 2019

Choosing a suitable visualization for data is a difficult task. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. In this study, we first define a step-by-step guide on how to build a data visualization recommender system. We then use this guide to create a model for a data visualization recommender system for non-experts that aims to resolve the issues of current solutions. The result is a question-based model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristics-driven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclusively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented guide can be used as a manual for anyone building a data visualization recommender system. The resulting model can be applied in the development of new data visualization software or as part of a learning tool.
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
Venue: arXiv cs.IR 2019
Year: 2019

Sequential recommendation task aims to predict user preference over items in the future given user historical behaviors. The order of user behaviors implies that there are resourceful sequential patterns embedded in the behavior history which reveal the underlying dynamics of user interests. Various sequential recommendation methods are proposed to model the dynamic user behaviors. However, most of the models only consider the user's own behaviors and dynamics, while ignoring the collaborative relations among users and items, i.e., similar tastes of users or analogous properties of items. Without modeling collaborative relations, those methods suffer from the lack of recommendation diversity and thus may have worse performance. Worse still, most existing methods only consider the user-side sequence and ignore the temporal dynamics on the item side. To tackle the problems of the current sequential recommendation models, we propose Sequential Collaborative Recommender (SCoRe) which effectively mines high-order collaborative information using cross-neighbor relation modeling and, additionally utilizes both user-side and item-side historical sequences to better capture user and item dynamics. Experiments on three real-world yet large-scale datasets demonstrate the superiority of the proposed model over strong baselines.
Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles
Venue: arXiv cs.CL 2019
Year: 2019

Text style transfer is usually performed using attributes that can take a handful of discrete values (e.g., positive to negative reviews). In this work, we introduce an architecture that can leverage pre-trained consistent continuous distributed style representations and use them to transfer to an attribute unseen during training, without requiring any re-tuning of the style transfer model. We demonstrate the method by training an architecture to transfer text conveying one sentiment to another sentiment, using a fine-grained set of over 20 sentiment labels rather than the binary positive/negative often used in style transfer. Our experiments show that this model can then rewrite text to match a target sentiment that was unseen during training.
Un systeme de lemmatisation pour les applications de TALN
Venue: arXiv cs.CL 2019
Year: 2019

This paper presents a method of stemming for the Arabian texts based on the linguistic techniques of the natural language processing. This method leans on the notion of scheme (one of the strong points of the morphology of the Arabian language). The advantage of this approach is that it doesn't use a dictionary of inflexions but a smart dynamic recognition of the different words of the language.
A Dynamic Modelling Framework for Human Hand Gesture Task Recognition
Venue: arXiv cs.LG 2019
Year: 2019

Gesture recognition and hand motion tracking are important tasks in advanced gesture based interaction systems. In this paper, we propose to apply a sliding windows filtering approach to sample the incoming streams of data from data gloves and a decision tree model to recognize the gestures in real time for a manual grafting operation of a vegetable seedling propagation facility. The sequence of these recognized gestures defines the tasks that are taking place, which helps to evaluate individuals' performances and to identify any bottlenecks in real time. In this work, two pairs of data gloves are utilized, which reports the location of the fingers, hands, and wrists wirelessly (i.e., via Bluetooth). To evaluate the performance of the proposed framework, a preliminary experiment was conducted in multiple lab settings of tomato grafting operations, where multiple subjects wear the data gloves while performing different tasks. Our results show an accuracy of 91% on average, in terms of gesture recognition in real time by employing our proposed framework.
Gemini: A Functional Programming Language for Hardware Description
Venue: arXiv cs.PL 2019
Year: 2019

This paper presents Gemini, a functional programming language for hardware description that provides features such as parametric polymorphism, recursive datatypes, higher-order functions, and type inference for higher expressivity compared to modern hardware description languages. Gemini demonstrates the theory and implementation of novel type-theoretical concepts through its unique type system consisting of multiple atomic kinds and dependent types, which allows the language to model both software and hardware constructs safely and perform type inference through multi-staged compilation. The primary technical results of this paper include formalizations of the Gemini grammar, typing rules, and evaluation rules, a proof of safety of Gemini's type system, and a prototype implementation of the compiler's semantic analysis phase.
Can Neural Image Captioning be Controlled via Forced Attention?
Venue: arXiv cs.CL 2019
Year: 2019

Learned dynamic weighting of the conditioning signal (attention) has been shown to improve neural language generation in a variety of settings. The weights applied when generating a particular output sequence have also been viewed as providing a potentially explanatory insight into the internal workings of the generator. In this paper, we reverse the direction of this connection and ask whether through the control of the attention of the model we can control its output. Specifically, we take a standard neural image captioning model that uses attention, and fix the attention to pre-determined areas in the image. We evaluate whether the resulting output is more likely to mention the class of the object in that area than the normally generated caption. We introduce three effective methods to control the attention and find that these are producing expected results in up to 28.56% of the cases.