Reviewerly/exHarmony huggingface dataset:
Improved JPS Algorithm Using New Jump Point for Path Planning of Mobile Robot

In view of the discussion of robot collision in complex environment due to control or positioning error and robot size, this paper presents a jump point search algorithm with safe distance(SD-JPS) for path planning. Based on the fast jump point search method, a new definition of the jump point and a node domain matrix are proposed to improve the JPS algorithm. The SD-JPS algorithm can select the any size of the node domain matrix to obtain the safe distance between the robot and the obstacle, and improve the flexibility of the robot movement. In addition, the different effects of SD-JPS and A* algorithm on the number of search nodes, planed routes and planning time are also studied. Simulation results show that the improved JPS algorithm can obtain different safety distances and plan the optimal path with shorter time.
PixelVAE++: Improved PixelVAE with Discrete Prior

Constructing powerful generative models for natural images is a challenging task. PixelCNN models capture details and local information in images very well but have limited receptive field. Variational autoencoders with a factorial decoder can capture global information easily, but they often fail to reconstruct details faithfully. PixelVAE combines the best features of the two models and constructs a generative model that is able to learn local and global structures. Here we introduce PixelVAE++, a VAE with three types of latent variables and a PixelCNN++ for the decoder. We introduce a novel architecture that reuses a part of the decoder as an encoder. We achieve the state of the art performance on binary data sets such as MNIST and Omniglot and achieve the state of the art performance on CIFAR-10 among latent variable models while keeping the latent variables informative.
Double Quantization for Communication-Efficient Distributed Optimization

Modern distributed training of machine learning models often suffers from high communication overhead for synchronizing stochastic gradients and model parameters. In this paper, to reduce the communication complexity, we propose \emph{double quantization}, a general scheme for quantizing both model parameters and gradients. Three communication-efficient algorithms are proposed based on this general scheme. Specifically, (i) we propose a low-precision algorithm AsyLPG with asynchronous parallelism, (ii) we explore integrating gradient sparsification with double quantization and develop Sparse-AsyLPG, (iii) we show that double quantization can be accelerated by the momentum technique and design accelerated AsyLPG. We establish rigorous performance guarantees for the algorithms, and conduct experiments on a multi-server test-bed with real-world datasets to demonstrate that our algorithms can effectively save transmitted bits without performance degradation, and significantly outperform existing methods with either model parameter or gradient quantization.
MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations

Dialog act prediction is an essential language comprehension task for both dialog system building and discourse analysis. Previous dialog act schemes, such as SWBD-DAMSL, are designed for human-human conversations, in which conversation partners have perfect language understanding ability. In this paper, we design a dialog act annotation scheme, MIDAS (Machine Interaction Dialog Act Scheme), targeted on open-domain human-machine conversations. MIDAS is designed to assist machines which have limited ability to understand their human partners. MIDAS has a hierarchical structure and supports multi-label annotations. We collected and annotated a large open-domain human-machine spoken conversation dataset (consists of 24K utterances). To show the applicability of the scheme, we leverage transfer learning methods to train a multi-label dialog act prediction model and reach an F1 score of 0.79.
Leukemia Epidemiology in Karbala province of Iraq

Objective: Investigate epidemiology of leukemia in Karbala province of Iraq, compare and identify possible changes with other populations. Methods: This was retrospective descriptive study for more than 400 leukemia patients in Karbala province from November 2011 to May 2018 with evaluation of age, gender distribution, types distribution and frequency distribution types of leukemia according to age and gender.Result: About 402 patients with leukemia were retrospectively enrolled in this study at median age of 30 years. Males accounted for a higher proportion of leukemia patients, 58.2 % compared to 41.8% females, with a male to female ratio of nearly 1.4:1. Acute lymphoblastic leukemia (ALL) was the most prevalent in the study group, contributing 41% (median age 10 years), followed by chronic myeloid leukemia (CML) 24.1% (median age 42 years), acute myeloid leukemia (AML) 19.2% (median age 36 years) and chronic lymphocytic leukemia(CLL) of less frequent type which contributed only 15.7% (median age 60 years). Conclusion: This is the first statistical study of leukemia in Karbala. It can be used as basic information to investigate epidemiological characteristics, to evaluate progress in recent years and to develop future leukemia strategies. More statistical leukemia analyses in Iraq are needed.&#x0D;
A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images

Parasite Detection on thick blood smears is a critical step in Malaria diagnosis. Most of the thick blood smear microscopic images have the following characteristics: high noise, a similar intensity between background and foreground, and the presence of artifacts. This situation makes the detection process becomes complicated. In this paper, we proposed a robust segmentation technique for malaria parasite detection of microscopic images obtained from various endemic places in Indonesia. The proposed method includes pre-processing, blood component segmentation using intensity slicing and morphological operation, blood component classification utilising rule based on properties of parasite candidates, and parasite candidate formation. The performance was evaluated on 30 thick blood smear microscopic images. The experimental results showed that the proposed segmentation method was robust to the different condition of image and histogram. It reduced the misclassification error and relative foreground error by 2.6% and 45.5%, respectively. Properties addition to blood component classification increased the system precision. Average of precision, recall, and F-measure of the proposed method were all 86%. It is proven that the proposed method is appropriate to be used for malaria parasites detection.
Resource Usage Analysis of a Mobile Banking Application using Sensor-and-Touchscreen-Based Continuous Authentication

Mobile banking applications are one of the most sensitive apps for secure authentication. Recently, use of continuous authentication using behavioral metrics are proposed where interactions of users with the mobile devices are tracked by collecting sensor data and touch-screen data. However, this may bring extra overhead in terms of resource consumption considering battery limited devices. In this work, augmentation of a mobile banking application with continuous authentication using behavioral biometrics is presented and its performance is analyzed in terms of resource usage. The banking application is running on Android phones and the presented architecture uses the data generated by the accelerometer, the gyroscope and the magnetometer sensors and the touchscreen usage. We examined the power consumption, CPU usage, I/O usage and the impacts of the sampling rate of each sensor. We also proposed possible performance improvements as future work.
Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.
A field study to collect expert knowledge for the development of AR HUD navigation concepts

A promising advancement of conventional head-up displays in vehicles is the implementation of augmented reality. By projecting the content onto the vehicle's windshield, information can be displayed in a contact analogue way in the real world. Two major challenges for concept developers are to reduce masking caused by augmented reality content and to create concepts that are suitable for the limited field of view. To approach these challenges, we designed two contact analogue navigation concepts and evaluated them in a field study with a prototype car that contained a complete AR HUD testing environment. The subjects were experts in interaction design, AR, HUD and sales. First results of the experts' suggestions for improvements are given in this extended abstract.
Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach

Recently, pretrained language models (e.g., BERT) have achieved great success on many downstream natural language understanding tasks and exhibit a certain level of commonsense reasoning ability. However, their performance on commonsense tasks is still far from that of humans. As a preliminary attempt, we propose a simple yet effective method to teach pretrained models with commonsense reasoning by leveraging the structured knowledge in ConceptNet, the largest commonsense knowledge base (KB). Specifically, the structured knowledge in KB allows us to construct various logical forms, and then generate multiple-choice questions requiring commonsense logical reasoning. Experimental results demonstrate that, when refined on these training examples, the pretrained models consistently improve their performance on tasks that require commonsense reasoning, especially in the few-shot learning setting. Besides, we also perform analysis to understand which logical relations are more relevant to commonsense reasoning.
First attempt to build realistic driving scenes using video-to-video synthesis in OpenDS framework

Existing programmable simulators enable researchers to customize different driving scenarios to conduct in-lab automotive driver simulations. However, software-based simulators for cognitive research generate and maintain their scenes with the support of 3D engines, which may affect users' experiences to a certain degree since they are not sufficiently realistic. Now, a critical issue is the question of how to build scenes into real-world ones. In this paper, we introduce the first step in utilizing video-to-video synthesis, which is a deep learning approach, in OpenDS framework, which is an open-source driving simulator software, to present simulated scenes as realistically as possible. Off-line evaluations demonstrated promising results from our study, and our future work will focus on how to merge them appropriately to build a close-to-reality, real-time driving simulator.
An Improved Autoencoder and Partial Least Squares Regression-Based Extreme Learning Machine Model for Pump Turbine Characteristics

Complete characteristic curves of a pump turbine are fundamental for improving the modeling accuracy of the pump turbine in a pump turbine governing system. In view of the difficulty in modeling the "S" characteristic region of the complete characteristic curves in the pump turbine, a novel Autoencoder and partial least squares regression based extreme learning machine model (AE-PLS-ELM) was proposed to describe the pump turbine characteristics. First, a mathematical model was formulated to describe the flow and moment characteristic curves. The improved Suter transformation was employed to transfer the original curves into WH and WM curves. Second, the ELM-Autoencoder technique and the partial least squares regression (PLSR) method were introduced to the architecture of the original ELM network. The ELM-Autoencoder technique was employed to obtain the initial weights of the Autoencoder based extreme learning machine (AE-ELM) model. The PLS method was exploited to avoid the multicollinearity problem of the Moore-Penrose generalized inverse. Lastly, the effectiveness of the proposed AE-PLS-ELM model has been verified using real data from a pumped storage unit in China. The results demonstrated that the AE-PLS-ELM model can obtain better modeling accuracy and generalization performance than the traditional models and, thus, can be exploited as an effective and sufficient approach for the modeling of pump turbine characteristics.
Federated User Representation Learning

Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly. We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and resource-efficient way to utilize existing neural personalization techniques in the Federated Learning (FL) setting. FURL divides model parameters into federated and private parameters. Private parameters, such as private user embeddings, are trained locally, but unlike federated parameters, they are not transferred to or averaged on the server. We show theoretically that this parameter split does not affect training for most model personalization approaches. Storing user embeddings locally not only preserves user privacy, but also improves memory locality of personalization compared to on-server training. We evaluate FURL on two datasets, demonstrating a significant improvement in model quality with 8% and 51% performance increases, and approximately the same level of performance as centralized training with only 0% and 4% reductions. Furthermore, we show that user embeddings learned in FL and the centralized setting have a very similar structure, indicating that FURL can learn collaboratively through the shared parameters while preserving user privacy.
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.
Specifying concurrent programs in separation logic: morphisms and simulations

In addition to pre- and postconditions, program specifications in recent separation logics for concurrency have employed an algebraic structure of resources —a form of state transition systems—to describe the state-based program invariants that must be preserved, and to record the permissible atomic changes to program state. In this paper we introduce a novel notion of resource morphism , i.e. structure-preserving function on resources, and show how to effectively integrate it into separation logic, using an associated notion of morphism-specific simulation . We apply morphisms and simulations to programs verified under one resource, to compositionally adapt them to operate under another resource, thus facilitating proof reuse.
Multi-Sensor-Fusion Approach for a Data-Science-Oriented Preventive Health Management System: Concept and Development of a Decentralized Data Collection Approach for Heterogeneous Data Sources

Investigations in preventive and occupational medicine are often based on the acquisition of data in the customer's daily routine. This requires convenient measurement solutions including physiological, psychological, physical, and sometimes emotional parameters. In this paper, the introduction of a decentralized multi-sensor-fusion approach for a preventive health-management system is described. The aim is the provision of a flexible mobile data-collection platform, which can be used in many different health-care related applications. Different heterogeneous data sources can be integrated and measured data are prepared and transferred to a superordinated data-science-oriented cloud-solution. The presented novel approach focuses on the integration and fusion of different mobile data sources on a mobile data collection system (mDCS). This includes directly coupled wireless sensor devices, indirectly coupled devices offering the datasets via vendor-specific cloud solutions (as e.g., Fitbit, San Francisco, USA and Nokia, Espoo, Finland) and questionnaires to acquire subjective and objective parameters. The mDCS functions as a user-specific interface adapter and data concentrator decentralized from a data-science-oriented processing cloud. A low-level data fusion in the mDCS includes the synchronization of the data sources, the individual selection of required data sets and the execution of pre-processing procedures. Thus, the mDCS increases the availability of the processing cloud and in consequence also of the higher level data-fusion procedures. The developed system can be easily adapted to changing health-care applications by using different sensor combinations. The complex processing for data analysis can be supported and intervention measures can be provided.
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach

Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems.In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an evergrowing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable.In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data.We do this by leveraging background knowledge from a larger, more highly represented dialogue sourcenamely, the MetaLWOz dataset.We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains.We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient by not requiring any data annotation.
ChocoPy: a programming language for compilers courses

ChocoPy is a programming language designed for teaching an undergraduate course on programming languages and compilers. ChocoPy is a restricted subset of Python 3.6, using static type annotations to enforce compile-time type safety. ChocoPy is fully specified using formal grammar, typing rules, and operational semantics. Valid ChocoPy programs can be executed in a standard Python interpreter, producing results consistent with ChocoPy semantics. A major component of CS164 at UC Berkeley is the project: students develop a full compiler for ChocoPy, targeting RISC-V, in about twelve weeks. In other exercises, students extend the syntax, type system, and formal semantics to support additional features of Python. In this paper, we outline (1) the motivations for creating the ChocoPy project, (2) salient features of the language, (3) the resources provided to students to develop their compiler, (4) some insights gained from teaching two semesters of ChocoPy-based courses by different instructors. Our assignment resources are available for re-use by other instructors and institutions.
Scalable Micro-planned Generation of Discourse from Structured Data

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically use end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. Rather, it relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system utilizes a three-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent, and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain data set curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular data sets covering diverse data types such as knowledge graphs and key-value maps.
Towards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images

We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.