Reviewerly/exHarmony huggingface dataset:
A Study of Deep Learning for Factoid Question Answering System

End-to-end question answering system has attracted considerable attention in the artificial intelligence research community in recent years. In this paper, we proposed an integrated deep learning model for factoid question answering system. This study uses the Delta Reading Comprehension Dataset (DRCD) to build a model to implement a factoid question answering system and to combine the classification of question and answer to evaluate with exact match (EM) and F1 score. The study determines whether the comparison can increase the proportion of EM and whether the expected answer type can effectively increase the answer accuracy rate. To perfect the transformation, a question-and-answer system that uses the BERT pre-training model is applied to the DRCD dataset together with the expected answer type analysis and comparison. The contribution of this paper is that we proposed a system architecture of factoid question answering (QA) system using BERT with question expected answer type (Q-EAT) and answer type classification (AT) models. Findings confirm that the classification of question and answer can improve the EM ratio. When the question sentence and the answer classification are the same, the prediction accuracy EM of the question answering system will be improved.
P-CRITICAL: a reservoir autoregulation plasticity rule for neuromorphic hardware

Backpropagation algorithms on recurrent artificial neural networks require an unfolding of accumulated states over time. These states must be kept in memory for an undefined period of time which is task-dependent. This paper uses the reservoir computing paradigm where an untrained recurrent neural network layer is used as a preprocessor stage to learn temporal and limited data. These so-called reservoirs require either extensive fine-tuning or neuroplasticity with unsupervised learning rules. We propose a new local plasticity rule named P-CRITICAL designed for automatic reservoir tuning that translates well to Intel's Loihi research chip, a recent neuromorphic processor. We compare our approach on well-known datasets from the machine learning community while using a spiking neuronal architecture. We observe an improved performance on tasks coming from various modalities without the need to tune parameters. Such algorithms could be a key to end-to-end energy-efficient neuromorphic-based machine learning on edge devices.
Covered Vehicle Detection in Autonomous Driving Based on Faster RCNN

Nowadays, Deep learning algorithm is widely used in image object detection. In the field of autonomous driving, the car senses the surrounding environment with the video taken by the camera. Aiming at the front vehicle occlusion problem, in this paper, we migrate Faster RCNN algorithm in deep learning to our own dataset and we get a neural network model with higher robustness. Through the comparison of alternating optimization training method and approximate joint training method, we adopt approximate joint training method to train the Faster RCNN model. Under this training pattern, we improve the size and proportion of anchors constantly. Finally, we conduct extensive experiments and discover that the average test accuracy gets the highest when the basic sizes of the anchors are 64, 128, 256, 512 and the ratios are 0.5, 0.8, 1, 1.2, 1.5, 1.8, 2. And the average precision in test set is 89.06%, which is 3.2% higher than the model under the traditional hyperparameters settings. The best model we train in this paper can get a higher average precision in the test set. It can almost test every car between the overlap cars and therefore the robustness of this model is very high.
UML Consistency Rules

UML models are standard artifacts used by software engineers for designing software. As software is designed, different UML diagram types (e.g., class diagrams and sequence diagrams) are produced by software designers. Since the various UML diagram types describe different aspects of a software system, they are not independent but strongly depend on each other, hence they must be consistent. Inconsistencies cause faults in thefi nal software systems. It is, therefore, paramount that they get detected, analyzed, andfi xed. Consistency rules are a useful tool proposed in the literature to detect inconsistencies. They categorize constraints that help in identifying inconsistencies when violated. This case study aims at collecting and analyzing UML models with OCL consistency rules proposed in the literature and at promoting the development of a reference benchmark that can be reused by the (FM-)research community. We collected 33 UML consistency rules and 206 different UML diagrams contained in 34 open-source UML models presented in the literature. We propose an FM-based encoding of the consistency rules in OCL. This encoding allows analyzing whether the consistency rules are satisfied or violated within the 34 UML models. To assess the proposed benchmark, we analyzed how the UML models, consistency rules, diagram types contained in the benchmark help in assessing the consistency of UML models, and the consistency of diagrams across the different software development phases. Our results show that the considered UML models and consistency rules allowed identifying 2731 inconsistencies and that those inconsistencies refer to different software development phases. We concluded that the considered UML models and consistency rules could be considered as an initial benchmark that can be further extended by the research community.
An Autonomous Human Following Caddie Robot with High-Level Driving Functions

Nowadays, mobile robot platforms are utilized in various fields not only for transportation but also for other diverse services such as industrial, medical and, sports, etc. Mobile robots are also an emerging application as sports field robots, where they can help serve players or even play the games. In this paper, a novel caddie robot which can autonomously follow the golfer as well as provide useful information such as golf course navigation system and weather updates, is introduced. The locomotion of the caddie robot is designed with two modes: autonomous human following mode and manual driving mode. The transition between each mode can be achieved manually or by an algorithm based on the velocity, heading angle, and inclination of the ground surface. Moreover, the transition to manual mode is activated after a caddie robot has recognized the human intention input by hand. In addition, the advanced control algorithm along with a trajectory generator for the caddie robot are developed taking into consideration the locomotion modes. Experimental results show that the proposed strategies to drive various operating modes are efficient and the robot is verified to be utilized in the golf course.
Defect Detection of Axle Box Cover Device Fixing Bolts in Metro Based on Convolutional Neural Network

Due to the heavy load and dark, humid working environment of the metro, its components are prone to failure, which affects the safety of operation. In this paper, a set of fault detection algorithms based on the combination of Faster R-CNN and one-class convolutional neural network (OC-CNN) is designed for the axle box cover device fixing bolts (ABCDFBs) of metros. First, the ABCDFBs are located by Faster R-CNN algorithm, and then a classification model trained only with positive samples of bolts is used to determine whether the bolt image is normal or not. This algorithm solves the problem of insufficient negative samples in the actual scene, and has good robustness to complex environmental conditions such as dust, water stains, and trial-varying light. Based on the images of the axle box cover device collected by the high-precision linear array camera on the scene, experiments show that the algorithm performs well in speed and accuracy.
Mixing Up Real Samples and Adversarial Samples for Semi-Supervised Learning

Consistency regularization methods have shown great success in semi-supervised learning tasks. Most existing methods focus on either the local neighborhood or in-between neighborhood of training samples to enforce the consistency constraint. In this paper, we propose a novel generalized framework called Adversarial Mixup (AdvMixup), which unifies the local and in-between neighborhood approaches by defining a virtual data distribution along the paths between the training samples and adversarial samples. Experimental results on both synthetic data and benchmark datasets exhibit that our AdvMixup can achieve better performance and robustness than state-of-the-art methods for semi-supervised learning.
Fusing LiDAR and Color Imagery for Object Detection using Convolutional Neural Networks

The goal of this paper is answer to this question: how much fusing LiDAR and color images can improve the performance of a convolutional neural network (CNN)-based detector? To this end, we trained state-of-the-art CNN-based detectors using different configurations of color images and their associated LiDAR data, in conjunction and independently. Moreover, we investigate the effect of sparse and dense LiDAR data on the detection accuracy. For this purpose, we estimate a dense depth image from spare LiDAR data using a recent self-supervised depth completion technique [1] that requires only sequences of color and sparse depth images, without the need for dense depth labels. Then, we compared two detectors when are trained on sparse or dense LiDAR data. The obtained results on the KITTI dataset show that fusing dense LiDAR and color images is an efficient solution for future object detectors.
Sick of Scents: Investigating Non-invasive Olfactory Motion Sickness Mitigation in Automated Driving

While automated vehicles are supposed to become places for purposes beyond transportation, motion sickness is still a largely unsolved issue that may be critical for this transformation. Due to its previously shown positive impact on the gastric and central nervous system, we hypothesize that olfaction (in particular the scents of lavender and ginger) may be able to reduce motion sickness symptoms in a non-invasive manner. We investigate the effects of these scents on the driver-passenger in chauffeured drives in a test track study with a reading-span non-driving related task. Evaluation of self-rated (Simulator Sickness Questionnaire, UX Curves) and physiologically measured motion sickness (Electrogastrography, Electrocardiography), and observations are presented and discussed. Results indicate that the issued scents were detrimental to the well-being of participants in the comparisons between post-task (baseline, scented) and pre-test measurements, with symptoms in the lavender-scented group being perceived as slightly less harsh than in the ginger-scented group.
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In recent years, in many application fields, extracting information from data in the form of functions is of most interest rather than investigating traditional multivariate vectors. Often these functions have complex spatial dependences that need to be accounted for using appropriate statistical analysis. Spatial Functional Statistics presents a fruitful analytics framework for this analysis. The definition of a distance measure between spatially dependent functional data is critical for many functional data analysis tasks such as clustering and classification. For this reason, and based on the specific characteristics of functional data, several distance measures have been proposed in the last few years. In this work we develop a weighted L2 distance for spatially dependent functional data, with an optimized weight function. Assuming a penalized basis representation for the functional data, we consider weight functions depending also on the spatial location in two different situations: a classical georeferenced spatial structure and a connected network one. The performance of the proposed distances are compared using standard metrics applied to both real and simulated data analysis.
K-Anonymous Privacy Preserving Scheme Based on Bilinear Pairings over Medical Data

Recent years have witnessed the advent of technologies such as the Internet of Things, cloud computing, and big data. Also, the analysis and research of data have attracted more and more attention from researchers. For example, the analysis of medical data can help government agencies do right decisions in public health services, or assist medical research institutes in conducting medical research. But it is followed by the privacy preserving of patient medical data in a cloud environment. Our paper proposes a problem-solving scheme that is a k-anonymous privacy preserving scheme based on bilinear pairings (KPSBP) which combines k-anonymity and secure searchable encryption to ensure the patient data privacy is not compromised and the medical data is well shared.
Data acquisition of timed-up and go test with older adults: accelerometer, magnetometer, electrocardiography and electroencephalography sensors’ data

We present a dataset related to the acquisition of different sensors data during the performance of the Timed-Up and Go test with the mobile device positioned in a waistband for the acquisition of accelerometer and magnetometer data, and a BITalino device positioned in a chest band for the acquisition of Electrocardiography and Electroencephalography for further processing. The data acquired from the BITalino device is acquired simultaneously by a Bluetooth connection with the same mobile application. The data was acquired in five institutions, including Centro Comunitário das Lameiras, Lar Nossa Senhora de Fátima, Centro Comunitário das Minas da Panasqueira, Lar da Misericórdia da Santa Casa da Misericórdia do Fundão, and Lar da Aldeia de Joanes da Santa Casa da Misericórdia do Fundão from Fundão and Covilhã municipalities (Portugal). This article describes the data acquired from a several subjects from the different institutions for the acquisition of accelerometer and magnetometer data, where each person performed the Timed-Up and Go test three times, where each output from the sensors was acquired with a sampling rate of 100 Hz. Related to the data acquired by the sensors connected to the BITalino device, 31 persons performed the different experiments related to the Timed-Up and Go Test. Following the data acquired from Electroencephalography and Electrocardiography sensors, only the data acquired from 14 individuals was considered valid. The data acquired by a BITalino device has a sampling rate of 100 Hz. These data can be reused for testing machine learning methods for the evaluation of the performance of the Timed-Up and Go test with older adults.
A demonstration of willump

Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss critical opportunities to leverage the statistical nature of ML. In this demo, we present Willump, an optimizer for ML inference that introduces statistically-motivated optimizations targeting ML applications whose performance bottleneck is feature computation. Willump automatically cascades feature computation for classification queries: Willump classifies most data inputs using only high-value, low-cost features selected by a cost model, improving query performance by up to 5 x without statistically significant accuracy loss. In this demo, we use interactive and easily-downloadable Jupyter notebooks to show VLDB attendees which applications Willump can speed up, how to use Willump, and how Willump produces such large performance gains.
Movement Tracking by Optical Flow Assisted Inertial Navigation

Robust and accurate six degree-of-freedom tracking on portable devices remains a challenging problem, especially on small hand-held devices such as smartphones. For improved robustness and accuracy, complementary movement information from an IMU and a camera is often fused. Conventional visual-inertial methods fuse information from IMUs with a sparse cloud of feature points tracked by the device camera. We consider a visually dense approach, where the IMU data is fused with the dense optical flow field estimated from the camera data. Learning-based methods applied to the full image frames can leverage visual cues and global consistency of the flow field to improve the flow estimates. We show how a learning-based optical flow model can be combined with conventional inertial navigation, and how ideas from probabilistic deep learning can aid the robustness of the measurement updates. The practical applicability is demonstrated on real-world data acquired by an iPad in a challenging low-texture environment.
Design of Collaborative Transportation System via Multiple Mobile Manipulators

This paper provides a new idea in research field of collaborative transportation using multiple mobile manipulators, where the force constraint problem, caused by force sensors in traditional control method, between robotic arm and moving objects is transformed into position constraint problem of position sensors in flexible grasping point that placed on lifting mechanism. And the lifting mechanism is grasped flexibility by each mobile manipulator by a robotic arm with six degree of freedom. The hardware designing processes of mobile manipulator and lifting mechanism is provided in details firstly. Then the designing of software, including monitoring configuration software in upper computer (MRCC) that running on the host PC and the lower computer software that running on core control board of each mobile manipulator, is given. Finally, the application of collaborative transportation system is described, which includes two different parts: verification in debugging mode and designment of control block in automatic mode, and the automatic mode is an open interface that it makes possible for researchers to design their own collaborative control law.
Embedding-Based Entity Alignment Using Relation Structural Similarity

Entity alignment aims to find entities in different knowledge graphs that semantically represent the same real-world entity. Recently, embedding-based entity alignment methods, which represent knowledge graphs as low-dimensional embeddings and perform entity alignments by measuring the similarity between entity embeddings, have achieved promising results. Most of these methods mainly focus on improving the knowledge graph embedding model or leveraging attributes to obtain more semantic information. However, the structural similarity between the two relations (considering all entities attached on the two relations) in different KGs has not been utilized in the existing methods. In this paper, we propose a novel embedding-based entity alignment method that takes the advantages of relation structural similarity. Specifically, our method first jointly learns the embeddings of two knowledge graphs in a uniform vector space, using the entity pairs regarding to the seed alignments (the alignments already known) that each shares the same embedding. Then, it iteratively computes the structural similarity between the relations in different knowledge graphs according to the seed alignments and the alignments with high reliability generated during training, which makes the embeddings of relations with high similarity closer to each other. Experimental results on five widely used real-world datasets show that the proposed approach significantly outperforms the state-of-the-art embedding-based ones for entity alignment.
Jomdrone: Data Mining Financial Sense in the Property Agency

Drones are like a flying robot that works with the Global Positioning System and sensors in them so that they can do the job it is designed to do. Researches have been done and it shows that drones have indeed made a positive impact on industries. In this research, the study will be done to see the effect of drones in the real estate market. Making work easier for potential tenants, landlord as well as the real estate agents. It can help to save time to travel, cost to travel and yet still help to show the potential tenants the houses at ease. The solution to this would be using drones, which the agent can put a minimum price to the given services. In this research, a mixed-method will be used. This is because, through this method, it will show how can the quality of the services be improved as well as see how likely will the public use drone services.
Communication Efficient Decentralized Track Fusion Using Selective Information Extraction

We consider a decentralized sensor network of multiple nodes with limited communication capability where the cross-correlations between local estimates are unknown. To reduce the bandwidth the individual nodes determine which subset of local information is the most valuable from a global perspective. Three information selection methods (ISM) are derived. The proposed ISM require no other information than the communicated estimates. The simulation evaluation shows that by using the proposed ISM it is possible to determine which subset of local information is globally most valuable such that both reduced bandwidth and high performance are achieved.
An Enhanced Mondrian Anonymization Model based on Self-Organizing Map

In the era of big data, privacy preservation has been the focus for data mining. Mondrian anonymization is a state-of-the-art data anonymization algorithm for relational dataset, widely used in many classical syntactic privacy-preserving data mining methods, like k-anonymity, l-diversity, t-closeness, etc. Mondrian anonymization is named for its multidimensional data partitioning in geometric space to find the best partitions for data anonymization. However, one problem with using Mondrian anonymization is taking too much time and memory for the high-dimensional data. Another problem is that the Mondrian-based privacy preservation may lead to the unstable performance of data mining models. For example, in Mondrian-based k-anonymity, the accuracy results of data mining may drop dramatically with the growth of k value. For solving these problems, in this paper we propose an enhanced Mondrian anonymization model based on Self-Organizing Map (SOM-Mondrian). With the help of SOM, multidimensional data are converted from a high dimensional space into two-dimensional space; at the same time, preserving their topological properties of the input space. The resulting two-dimensional data are then used by Mondrian algorithm to find the best partitions for data anonymization. To our best knowledge, we are the first to propose SOM-based method for Mondrian anonymization. Experimental results show that, after applying our proposed method, the processing time of Mondrian anonymization decreases significantly from 12.11 seconds to 0.16 seconds; besides, the accuracy of data mining applications increases, about 2% higher than the results under the standard Mondrian anonymization, and also shows steadier and more robust (the degree of variation is reduced by 75%) to the varying k value.