Reviewerly/exHarmony huggingface dataset:
Topic-Based Algorithm for Multilabel Learning With Missing Labels

In multilabel learning (MLL), each instance can be assigned by several concepts simultaneously from a class dictionary. Usually, labels in the class dictionary have semantic correlations and semantic hierarchy. Instances can be categorized into different topics. Each topic has its own label candidates, and some topics have overlapped label candidates. In this paper, we propose a novel MLL method to deal with missing labels. The proposed algorithm can recover the label matrix according to local, topic-wise, and global semantic properties. Specifically, in the global level, label consistency, label-wise semantic correlations, and semantic hierarchy are exploited; in the local level, label importance and instance-wise semantic correlations in each topic are extracted; and in the topic level, label importance similarities and instance-wise semantic similarities between topics are mined. The experimental results on five image data sets in different applications demonstrate the effectiveness of the proposed approach.
Querying Attributed DL-Lite Ontologies Using Provenance Semirings

Attributed description logic is a recently proposed formalism, targeted for graph-based representation formats, which enriches description logic concepts and roles with finite sets of attribute-value pairs, called annotations. One of the most important uses of annotations is to record provenance information. In this work, we first investigate the complexity of satisfiability and query answering for attributed DL-LiteR ontologies. We then propose a new semantics, based on provenance semirings, for integrating provenance information with query answering. Finally, we establish complexity results for satisfiability and query answering under this semantics.
Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection

As classical planning is known to be computationally hard, no single planner is expected to work well across many planning domains. One solution to this problem is to use online portfolio planners that select a planner for a given task. These portfolios perform a classification task, a well-known and wellresearched task in the field of machine learning. The classification is usually performed using a representation of planning tasks with a collection of hand-crafted statistical features. Recent techniques in machine learning that are based on automatic extraction of features have not been employed yet due to the lack of suitable representations of planning tasks.In this work, we alleviate this barrier. We suggest representing planning tasks by images, allowing to exploit arguably one of the most commonly used and best developed techniques in deep learning. We explore some of the questions that inevitably rise when applying such a technique, and present various ways of building practically useful online portfoliobased planners. An evidence of the usefulness of our proposed technique is a planner that won the cost-optimal track of the International Planning Competition 2018.
Learning Competitive and Discriminative Reconstructions for Anomaly Detection

Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier. Unfortunately, a good threshold is vital for the performance and it is really hard to find an optimal one. In this paper, we take the discriminative information implied in unlabeled data into consideration and propose a new method for anomaly detection that can learn the labels of unlabelled data directly. Our proposed method has an end-to-end architecture with one encoder and two decoders that are trained to model inliers and outliers’ data distributions in a competitive way. This architecture works in a discriminative manner without suffering from overfitting, and the training algorithm of our model is adopted from SGD, thus it is efficient and scalable even for large-scale datasets. Empirical studies on 7 datasets including KDD99, MNIST, Caltech-256, and ImageNet etc. show that our model outperforms the state-of-the-art methods.
Pipe radius estimation using Kinect range cameras

In the heavy construction industry, pipe spool assembly is geometrically complex and suboptimal fabrication processes inevitably lead to fabrication errors and costly rework. In an attempt to mitigate fabrication risks and improve product quality, computer aided tools are being developed to provide an additional layer of control. In this paper, the data acquisition capabilities of two low-cost range cameras are investigated for the eventual purpose of pipe fitting process monitoring in a smart fabrication facility environment. Range images of various pipes are systematically taken at varying distances from the sensor. By using the proposed radius estimation algorithm, the utility of the data for accurate geometrical pipe feature detection is evaluated by a radius feature metric. Results show that the algorithm reliably extracts radius information from point clouds representing piping. In conjunction with the algorithm, the low-cost range camera hardware was able to characterize pipes, of radius ranging from 2.41 cm to 8.78 cm at a distance from the sensor ranging from 0.5 m to 3.75 m, with an average error of 18% for Kinect 1 and 10% for Kinect 2.
Interactive Heuristic D* Path Planning Solution Based on PSO for Two-Link Robotic Arm in Dynamic Environment

This paper is devoted to find an intelligent and safe path for two-link robotic arm in dynamic environment.This paper focuses on computational part of motion planning in completely changing dynamic environment at every motion sample domains, since the local minima and sharp edges are the most common problems in all path planning algorithms.In addition, finding a path solution in a dynamic environment represents a challenge for the robotics researchers, so in this paper, a proposed mixing approach was suggested to overcome all these obstructions.The proposed approach methodology for obtaining robot interactive path planning solution in known dynamic environment utilizes the use of modified heuristic D-star (D*) algorithm based on the full free Cartesian space analysis at each motion sample with the Particle Swarm Optimization (PSO) technique.Also, a modification on the D* algorithm has been done to match the dynamic environment requirements by adding stop and return backward cases which is not included in the original D* algorithm theory.The resultant interactive path solution was computed by taking into consideration the time and position changes of the moving obstacles.Furthermore, to insure the enhancement of the final path length optimality, the PSO technique was used.The simulation results are given to show the effectiveness of the proposed method.
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era

Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields. The aim of the paper is to make modern inference methods (such as variational inference or gradient-based sampling) available for Gaussian models with banded precision. We show that this can efficiently be achieved by equipping an automatic differentiation framework, such as TensorFlow or PyTorch, with some linear algebra operators dedicated to banded matrices. This paper studies the algorithmic aspects of the required operators, details their reverse-mode derivatives, and show that their complexity is linear in the number of observations.
Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.
Defending against Whitebox Adversarial Attacks via Randomized Discretization

Adversarial perturbations dramatically decrease the accuracy of state-of-the-art image classifiers. In this paper, we propose and analyze a simple and computationally efficient defense strategy: inject random Gaussian noise, discretize each pixel, and then feed the result into any pre-trained classifier. Theoretically, we show that our randomized discretization strategy reduces the KL divergence between original and adversarial inputs, leading to a lower bound on the classification accuracy of any classifier against any (potentially whitebox) $L_\infty$-bounded adversarial attack. Empirically, we evaluate our defense on adversarial examples generated by a strong iterative PGD attack. On ImageNet, our defense is more robust than adversarially-trained networks and the winning defenses of the NIPS 2017 Adversarial Attacks & Defenses competition.
Communication without Interception: Defense against Deep-Learning-based Modulation Detection

We consider a communication scenario, in which an intruder, employing a deepneural network (DNN), tries to determine the modulation scheme of theintercepted signal. Our aim is to minimize the accuracy of the intruder, whileguaranteeing that the intended receiver can still recover the underlyingmessage with the highest reliability. This is achieved by constellationperturbation at the encoder, similarly to adversarial attacks against DNN-basedclassifiers. In the latter perturbation is limited to be imperceptible to ahuman observer, while in our case perturbation is constrained so that themessage can still be reliably decoded by the legitimate receiver which isoblivious to the perturbation. Simulation results demonstrate the viability ofour approach to make wireless communication secure against DNN-based intruderswith minimal sacrifice in the communication performance.
An Attentive Spatio-Temporal Neural Model for Successive Point of Interest Recommendation

In a successive Point of Interest (POI) recommendation problem, analyzing user behaviors and contextual check-in information in past POI visits are essential in predicting, thus recommending, where they would likely want to visit next. Although several works, especially the Matrix Factorization and/or Markov chain based methods, are proposed to solve this problem, they have strong independence and conditioning assumptions. In this paper, we propose a deep Long Short Term Memory recurrent neural network model with a memory/attention mechanism, for the successive Point-of-Interest recommendation problem, that captures both the sequential, and temporal/spatial characteristics into its learned representations. Experimental results on two popular Location-Based Social Networks illustrate significant improvements of our method over the state-of-the-art methods. Our method is also robust to overfitting compared with popular methods for the recommendation tasks.
IoT for the Failure of Climate-Change Mitigation and Adaptation and IIoT as a Future Solution

Day after day the world stuck more and more in wars, pollution and so many other risk that threaten the environment.With a population of more than 7.3 billion, the planet suffers from continuous damage from human activity.As a result of these human distortions, climate change is one of the most fatal challenges that face the world.Climate Change won't be stopped or slowed by a single action, but with the help of too many small contributions from different fields, it will have an impressive impact.Changing to electricity generation, manufacturing, and transportation generate most headlines, but the technology field can also play a critical role.The Internet of Things (IoT) in particular, has the potential to reduce greenhouse emissions and help slow the rise of global temperatures.IoT includes more than super brilliant new gadgets and smart widgets.It also influences the Earth's condition, from its available resources to its climate.In this paper we are showing that technology itself could be the tool will save the world if we take advantage of it.Environmental monitoring is a broad application for the Internet of Things (IoT).It involves everything from watching levels of ozone in a meat packing facility to watching national forests for smoke.These solutions are the first step toward creating a numerous connected infrastructures to support innovative services, better flexibility and efficiency.We also make a spot on Industrial Internet of Things (IIoT) and its challenges as the future is for it.
SWALP : Stochastic Weight Averaging in Low-Precision Training

Low precision operations can provide scalability, memory savings, portability, and energy efficiency. This paper proposes SWALP, an approach to low precision training that averages low-precision SGD iterates with a modified learning rate schedule. SWALP is easy to implement and can match the performance of full-precision SGD even with all numbers quantized down to 8 bits, including the gradient accumulators. Additionally, we show that SWALP converges arbitrarily close to the optimal solution for quadratic objectives, and to a noise ball asymptotically smaller than low precision SGD in strongly convex settings.
A Study of Incorrect Paraphrases in Crowdsourced User Utterances

Mohammad-Ali Yaghoub-Zadeh-Fard, Boualem Benatallah, Moshe Chai Barukh, Shayan Zamanirad. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Automatic Detection of Renal Abnormalities by Off-the-shelf CNN Features

The chronic kidney disease (CKD) and end-stage renal disease are the leading causes of kidney (renal) failure which may be fatal. Thus, it is important to diagnose the kidney diseases at an earlier stage before it cause fatality. This paper proposes an algorithm for automatic kidney abnormalities detection and classification at an early stage. Kidney abnormalities detection involves two stages: feature extraction and detection. The input images used in this work are kidney ultrasound (US) images which are classified into three categories such as normal kidney, kidney with cyst and kidney with stone. The pre-trained convolutional neural networks (CNN) is used to extract features from the kidney US images then, the extracted features from CNN are fed into the support vector machine (SVM) classifier for classifying the kidney abnormalities. The training and testing phase of CNN requires large number of labelled images. Due to unavailability of larger datasets in the kidney US images, off-the-shelf CNN features are used which perform well for smaller datasets. Performance evaluation is done for classification and 91.8% of the accuracy is obtained. Further, the sensitivity and specificity achieved for the SVM classifier in detecting normal kidney images is 93.75% and 92.5%, respectively.
AUTOMATIC DETECTION AND RECOGNITION OF 3D MANUAL GESTURES FOR HUMAN-MACHINE INTERACTION

Abstract. In this paper, we propose an approach to detect and recognize 3D one-handed gestures for human-machine interaction. The logical structure of the modules of the system for recording a gestural database is described. The logical structure of the database of 3D gestures is presented. Examples of frames showing gestures in the format of Full High Definition, in the map depth mode and in the infrared illustrated. Models of a deep convolutional network for detecting faces and hand shapes are described. The results of automatic detection of the area with the face and the shape of the hand are given. Identified the distinctive features of the gesture at a certain point in time. The process of recognizing 3D one-handed gestures is described. Due to its versatility, this method can be used in tasks of biometrics, computer vision, machine learning, automatic systems of face recognition, sign languages.
Fluorescence Image Histology Pattern Transformation Using Image Style Transfer

Confocal laser endomicroscopy (CLE) allow on-the-fly in vivo intraoperative imaging in a discreet field of view, especially for brain tumors, rather than extracting tissue for examination ex vivo with conventional light microscopy. Fluorescein sodium-driven CLE imaging is more interactive, rapid, and portable than conventional hematoxylin and eosin (H&E)-staining. However, it has several limitations: CLE images may be contaminated with artifacts (motion, red blood cells, noise), and neuropathologists are mainly trained on colorful stained histology slides like H&E while the CLE images are gray. To improve the diagnostic quality of CLE, we used a micrograph of an H&E slide from a glioma tumor biopsy and image style transfer, a neural network method for integrating the content and style of two images. This was done through minimizing the deviation of the target image from both the content (CLE) and style (H&E) images. The style transferred images were assessed and compared to conventional H&E histology by neurosurgeons and a neuropathologist who then validated the quality enhancement in 100 pairs of original and transformed images. Average reviewers’ score on test images showed 84 out of 100 transformed images had fewer artifacts and more noticeable critical structures compared to their original CLE form. By providing images that are more interpretable than the original CLE images and more rapidly acquired than H&E slides, the style transfer method allows a real-time, cellular-level tissue examination using CLE technology that closely resembles the conventional appearance of H&E staining and may yield better diagnostic recognition than original CLE grayscale images.
Prediction Model of User Physical Activity using Data Characteristics-based Long Short-term Memory Recurrent Neural Networks

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices.Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably.However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models.The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location.Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information.Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances.In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs).To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model.The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute
On the Optimality of Sparse Model-Based Planning for Markov Decision Processes.

This work considers the sample complexity of obtaining an $\epsilon$-optimal policy in a discounted Markov Decision Process (MDP), given only access to a generative model. In this model, the learner accesses the underlying transition model via a sampling oracle that provides a sample of the next state, when given any state-action pair as input. In this work, we study the effectiveness of the most natural approach to model-based planning: we build the maximum likelihood estimate of the transition model in the from observations and then find an optimal policy in this empirical MDP. We ask arguably the most basic and unresolved question in model-based planning: is the naive plug-in approach, non-asymptotically, minimax optimal in the quality of the policy it finds, given a fixed sample size? With access to a generative model, we resolve this question in the strongest possible sense: our main result shows that \emph{any} high accuracy solution in the model constructed with $N$ samples, provides an $\epsilon$-optimal policy in the true underlying MDP. In comparison, all prior (non-asymptotically) minimax optimal results use model-free approaches, such as the Variance Reduced Q-value iteration algorithm (Sidford et al 2018), while the best known model-based results (e.g. Azar et al 2013) require larger sample sample sizes in their dependence on the planning horizon or the state space. Notably, we show that the model-based approach allows the use of \emph{any} efficient planning algorithm in the empirical MDP, which simplifies the algorithm design as this approach does not tie the algorithm to the sampling procedure. The core of our analysis is a novel absorbing MDP construction to address the statistical dependency issues that arise in the analysis of model-based planning approaches, a construction which may be helpful more generally.
A Blockchain Platform in Connected Medical-Device Environments: Trustworthy technology to guard against cyberthreats

For consumer-oriented medical devices, the issues of rising cybersecurity threats and the need of end users to maintain control lead to a search for effective solutions. This article presents a blockchain framework for consumer-oriented medical devices aiming at added value, including immutability, auditability, and accountability in processes such as user-device binding, registration, maintenance, and alerting. The proposed solution implements a set of smart contracts (SC) to deliver more secure services in smart-home environments.