Reviewerly/exHarmony huggingface dataset:
Development and Implementation of Spline-based Path Planning Algorithm in ROS/Gazebo Environment

Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment.&#x0D; In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria.&#x0D; This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point.&#x0D; To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms.&#x0D; The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.
Deep Learning Algorithms for Predicting Breast Cancer Based on Tumor Cells

This paper is the comparison of classification algorithms for breast cancer based on tumor cell. We focus on using deep learning algorithms to classify types of breast cancer with several of activation function: Tanh, Rectifier, Maxout and Exprectifier. And comparison with different machine learning methonds such as, Naïve Bayes(NB), Decision tree(DT), Support Vector Machine(SVM), Vote (DT+NB+SVM), Random Forest(RF) and AdaBoost. Experimental data were downloaded from breast cancer Wisconsin dataset and using machine learning tool rapidminer. Using ten-fold cross-validation. We found that the high accuracy of 96.99% with deep learning by Exprectifier activation function.
The RBO dataset of articulated objects and interactions

We present a dataset with models of 14 articulated objects commonly found in human environments and with RGB-D video sequences and wrenches recorded of human interactions with them. The 358 interaction sequences total 67 minutes of human manipulation under varying experimental conditions (type of interaction, lighting, perspective, and background). Each interaction with an object is annotated with the ground-truth poses of its rigid parts and the kinematic state obtained by a motion-capture system. For a subset of 78 sequences (25 minutes), we also measured the interaction wrenches. The object models contain textured three-dimensional triangle meshes of each link and their motion constraints. We provide Python scripts to download and visualize the data. The data are available at https://turbo.github.io/articulated-objects/ and hosted at https://zenodo.org/record/1036660/ .
End-to-End Trained CNN Encoder-Decoder Networks for Image Steganography

All the existing image steganography methods use manually crafted features to hide binary payloads into cover images. This leads to small payload capacity and image distortion. Here we propose a convolutional neural network based encoder-decoder architecture for embedding of images as payload. To this end, we make following three major contributions: (i) we propose a deep learning based generic encoder-decoder architecture for image steganography; (ii) we introduce a new loss function that ensures joint end-to-end training of encoder-decoder networks; (iii) we perform extensive empirical evaluation of proposed architecture on a range of challenging publicly available datasets (MNIST, CIFAR10, PASCAL-VOC12, ImageNet, LFW) and report state-of-the-art payload capacity at high PSNR and SSIM values.
OCR-D

Various research projects were concerned with the development and adaptation of methods for OCR specifically for historical printed documents (cf. METAe [20], IMPACT [1], eMOP [9]). However, these initiatives have ended before the wide adoption of deep neural networks and, despite the various project's achievements, there remains a lack of OCR software that is a) comprehensive with regard to the challenges presented by the wide variety of historical documents and b) available as ready-to-use Free Software. The OCR-D project aims to rectify that.
GPCNeT

Network congestion is one of the biggest problems facing HPC systems today, affecting system throughput, performance, user experience, and reproducibility. Congestion manifests as run-to-run variability due to contention for shared resources (e.g., filesystems) or routes between compute endpoints. Despite its significance, current network benchmarks fail to proxy the real-world network utilization seen on congested systems. We propose a new open-source benchmark suite called the Global Performance and Congestion Network Tests (GPCNeT) to advance the state of the practice in this area. The guiding principles used in designing GPCNeT are described and the methodology employed to maximize its utility is presented. The capabilities of GPCNeT are evaluated by analyzing results from several world's largest HPC systems, including an evaluation of congestion management on a next-generation network. The results show that systems of all technologies and scales are susceptible to congestion and this work motivates the need for congestion control in next-generation networks.
Training binary neural networks with real-to-binary convolutions

This paper shows how to train binary networks to within a few percent points (~3-5 %) of the full precision counterpart with a negligible increase in the computational cost. In particular, we first show how to build a strong baseline, which already achieves state-of-the-art accuracy, by combining recently proposed advances, and carefully tuning the optimization procedure. Secondly, we show that by attempting to minimize the discrepancy between the output of the binary and the corresponding real-valued convolution additional significant accuracy gains can be obtained. We materialize this idea in two complementary ways: (1) with a loss function, during training, by matching the spatial attention maps computed at the output of the binary and real-valued convolutions, and (2) in data-driven manner, by using the real-valued activations being available during inference prior to the binarization process for re-scaling the activations right after the binary convolution. Finally, we show that, when putting all of our improvements together, the resulting model reduces the gap to its real-valued counterpart to less than 3% and 5% top-1 error on CIFAR-100 and ImageNet, respectively, when using a ResNet-18 architecture.
Aspect-based sentiment analysis of reviews in the domain of higher education

Purpose Student recruitment and retention are important issues for all higher education institutions. Constant monitoring of student satisfaction levels is therefore crucial. Traditionally, students voice their opinions through official surveys organized by the universities. In addition to that, nowadays, social media and review websites such as “Rate my professors” are rich sources of opinions that should not be ignored. Automated mining of students’ opinions can be realized via aspect-based sentiment analysis (ABSA). ABSA s is a sub-discipline of natural language processing (NLP) that focusses on the identification of sentiments (negative, neutral, positive) and aspects (sentiment targets) in a sentence. The purpose of this paper is to introduce a system for ABSA of free text reviews expressed in student opinion surveys in the Serbian language. Sentiment analysis was carried out at the finest level of text granularity – the level of sentence segment (phrase and clause). Design/methodology/approach The presented system relies on NLP techniques, machine learning models, rules and dictionaries. The corpora collected and annotated for system development and evaluation comprise students’ reviews of teaching staff at the Faculty of Technical Sciences, University of Novi Sad, Serbia, and a corpus of publicly available reviews from the Serbian equivalent of the “Rate my professors” website. Findings The research results indicate that positive sentiment can successfully be identified with the F-measure of 0.83, while negative sentiment can be detected with the F-measure of 0.94. While the F-measure for the aspect’s range is between 0.49 and 0.89, depending on their frequency in the corpus. Furthermore, the authors have concluded that the quality of ABSA depends on the source of the reviews (official students’ surveys vs review websites). Practical implications The system for ABSA presented in this paper could improve the quality of service provided by the Serbian higher education institutions through a more effective search and summary of students’ opinions. For example, a particular educational institution could very easily find out which aspects of their service the students are not satisfied with and to which aspects of their service more attention should be directed. Originality/value To the best of the authors’ knowledge, this is the first study of ABSA carried out at the level of sentence segment for the Serbian language. The methodology and findings presented in this paper provide a much-needed bases for further work on sentiment analysis for the Serbian language that is well under-resourced and under-researched in this area.
OcuLock: Exploring Human Visual System for Authentication in Virtual Reality Head-mounted Display

The increasing popularity of virtual reality (VR) in a wide spectrum of applications has generated sensitive personal data such as medical records and credit card information.While protecting such data from unauthorized access is critical, directly applying traditional authentication methods (e.g., PIN) through new VR input modalities such as remote controllers and head navigation would cause security issues.The authentication action can be purposefully observed by attackers to infer the authentication input.Unlike any other mobile devices, VR presents immersive experience via a head-mounted display (HMD) that fully covers users' eye area without public exposure.Leveraging this feature, we explore human visual system (HVS) as a novel biometric authentication tailored for VR platforms.While previous works used eye globe movement (gaze) to authenticate smartphones or PCs, they suffer from a high error rate and low stability since eye gaze is highly dependent on cognitive states.In this paper, we explore the HVS as a whole to consider not just the eye globe movement but also the eyelid, extraocular muscles, cells, and surrounding nerves in the HVS.Exploring HVS biostructure and unique HVS features triggered by immersive VR content can enhance authentication stability.To this end, we present OcuLock, an HVS-based system for reliable and unobservable VR HMD authentication.OcuLock is empowered by an electrooculography (EOG) based HVS sensing framework and a record-comparison driven authentication scheme.Experiments through 70 subjects show that OcuLock is resistant against common types of attacks such as impersonation attack and statistical attack with Equal Error Rates as low as 3.55% and 4.97% respectively.More importantly, OcuLock maintains a stable performance over a 2month period and is preferred by users when compared to other potential approaches.
An Automated Breast Micro-Calcification Detection and Classification Technique Using Temporal Subtraction of Mammograms

Radiologists worldwide use mammography as a reliable tool for breast cancer screening.However, mammography assessment is challenging even for well-trained radiologists, leading to a pressing need for Computer Aided Diagnosis (CAD) systems.In this work, a novel technique for the detection and classification of breast Micro-Calcifications (MCs), which are diagnostically significant but difficult to detect findings, is presented.The proposed method is based on the subtraction of temporally sequential mammogram pairs, after pre-processing and image registration, followed by machine-learning.The classification was performed using several features extracted from the subtracted mammograms and selected during training to optimize the accuracy of the results.Six classifiers were tested in a leave-one-patientout, 4, 5 and 10 fold cross-validation process.This technique was evaluated on a unique dataset, consisting of temporal sequences of mammograms from 80 patients taken between 1 to 6 years apart.The resulting 320 mammograms were reviewed by 2 radiologists who precisely marked each MC location.The accuracy of classifying MCs as benign or suspicious improved from 91.42% without temporal subtraction and an Ensemble of Decision Trees (EDT), to 99.55% with the use of sequential mammograms and Support Vector Machines (SVMs) with leave-one-patient-out validation.The improvement was statistically significant (pvalue < 0.005).These results verify the accuracy and the effectiveness of the proposed technique should to be further evaluated on a larger dataset.
Temporal-Logic-Based Semantic Fault Diagnosis With Time-Series Data From Industrial Internet of Things

The maturity of sensor network technologies has facilitated the emergence of an industrial Internet of Things (IIoT), which has collected an increasing volume of data. Converting these data into actionable intelligence for fault diagnosis is key to reducing unscheduled downtime and performance degradation, among other examples. This article formalizes a problem called semantic fault diagnosis- to construct the formal specifications of faults directly from data collected from IIoT-enabled systems. The specifications are written as signal temporal logic formulas, which can be easily interpreted by humans. To tackle the issue of the combinatorial explosion that arises, we propose an algorithm that combines ideas from agenda-based searching and imitation learning to train a policy that searches formulas in a strategic order. Specifically, we formulate the problem as a Markov decision process, which is further solved with a reinforcement learning algorithm. Our algorithm is applied to time-series data collected from an IIoT-enabled iron-making factory. The results show empirically that our proposed algorithm is both scalable to the size of the data set and interpretable, therefore allowing human users to take actions, for example, predictive maintenance.
Deep Critiquing for VAE-based Recommender Systems

Providing explanations for recommended items not only allows users to understand the reason for receiving recommendations but also provides users with an opportunity to refine recommendations by critiquing undesired parts of the explanation. While much research focuses on improving the explanation of recommendations, less effort has focused on interactive recommendation by allowing a user to critique explanations. Aside from traditional constraint- and utility-based critiquing systems, the only end-to-end deep learning based critiquing approach in the literature so far, CE-VNCF, suffers from unstable and inefficient training performance. In this paper, we propose a Variational Autoencoder (VAE) based critiquing system to mitigate these issues and improve overall performance. The proposed model generates keyphrase-based explanations of recommendations and allows users to critique the generated explanations to refine their personalized recommendations. Our experiments show promising results: (1) The proposed model is competitive in terms of general performance in comparison to state-of-the-art recommenders, despite having an augmented loss function to support explanation and critiquing. (2) The proposed model can generate high-quality explanations compared to user or item keyphrase popularity baselines. (3) The proposed model is more effective in refining recommendations based on critiquing than CE-VNCF, where the rank of critiquing-affected items drops while general recommendation performance remains stable. In summary, this paper presents a significantly improved method for multi-step deep critiquing based recommender systems based on the VAE framework.
Embarrassingly Simple Unsupervised Aspect Extraction

We present a simple but effective method for aspect identification in sentiment analysis.Our unsupervised method only requires word embeddings and a POS tagger, and is therefore straightforward to apply to new domains and languages.We introduce Contrastive Attention (CAt ), a novel single-head attention mechanism based on an RBF kernel, which gives a considerable boost in performance and makes the model interpretable.Previous work relied on syntactic features and complex neural models.We show that given the simplicity of current benchmark datasets for aspect extraction, such complex models are not needed.The code to reproduce the experiments reported in this paper is available at https://github.com/clips/cat.
Tripartite Genuine Non-Gaussian Entanglement in Three-Mode Spontaneous Parametric Down-Conversion

We show that the states generated by a three-mode spontaneous parametric down-conversion (SPDC) interaction Hamiltonian possess tripartite entanglement of a different nature to other paradigmatic three-mode entangled states generated by the combination of two-mode SPDC interactions. While two-mode SPDC generates Gaussian states whose entanglement can be characterized by standard criteria based on two-mode quantum correlations, these criteria fail to capture the entanglement generated by three-mode SPDC. We use criteria built from three-mode correlation functions to show that the class of states recently generated in a superconducting-circuit implementation of three-mode SPDC ideally have tripartite entanglement, contrary to recent claims in the literature. These criteria are suitable for triple SPDC but we show that they fail to detect tripartite entanglement in other states which are known to possess it, which illustrates the existence of two fundamentally different notions of tripartite entanglement in three-mode continuous-variable systems.
Feature Engineering and Selection: A Practical Approach for Predictive Models

In recent years, the terms machine learning, data science, and predictive modeling have become ubiquitous in nearly every discipline in which data analysis plays a central role. When first diving i...
Publish or perish, but do not forget your software artifacts

Abstract Open-science initiatives have gained substantial momentum in computer science, and particularly in software-engineering research. A critical aspect of open-science is the public availability of artifacts (e.g., tools), which facilitates the replication, reproduction, extension, and verification of results. While we experienced that many artifacts are not publicly available, we are not aware of empirical evidence supporting this subjective claim. In this article, we report an empirical study on software artifact papers (SAPs) published at the International Conference on Software Engineering (ICSE), in which we investigated whether and how researchers have published their software artifacts, and whether this had scientific impact. Our dataset comprises 789 ICSE research track papers, including 604 SAPs (76.6 %), from the years 2007 to 2017. While showing a positive trend towards artifact availability, our results are still sobering. Even in 2017, only 58.5 % of the papers that stated to have developed a software artifact made that artifact publicly available. As we did find a small, but statistically significant, positive correlation between linking to artifacts in a paper and its scientific impact in terms of citations, we hope to motivate the research community to share more artifacts. With our insights, we aim to support the advancement of open science by discussing our results in the context of existing initiatives and guidelines. In particular, our findings advocate the need for clearly communicating artifacts and the use of non-commercial, persistent archives to provide replication packages.
A Traditional Analysis for Efficient Data Mining with Integrated Association Mining into Regression Techniques

A Wal-Mart salesman was trying to surge the sales data of the store by combining the commodities together and putting discounts on those products. The goods are clearly distinct thus he found that nurturing kids is exhausting. And to release pain, guardian decided to buy beer. Data Mining, also known as KDD, to find irregularities, associations, arrangements, and tendencies to forecast consequences. Apriori algorithm is a standard process in data mining. It is utilised for mining recurrent sets of items and related association rubrics. It is formulated to work on a database comprising of a lot of transactions. It is very vital for operative Market Basket Investigation and this assistance the patrons in buying their substances with more effortlessness which escalates the sales of the markets. While finding goods to be associated together, it is imperative to have some association on which the commodities can be listed together. In this research work a hybrid method has been proposed to attenuate association rules using optimization algorithm Differential Evolution with Apriori Algorithm. Firstly, Apriori algorithm is applied to get frequent itemsets and association rules. Then, AMO is employed to scale back the amount of association rules with a brand new fitness function that comes with frequent rules. it’s observed from the experiments that, as compared with the opposite relevant techniques, ARMAMO greatly reduce the computational time for frequent item set generation, memory for association rule generation, and also the number of rules generated. Data mining could be a process that uses a spread of information analysis tools to find patterns and relationships in data which will be accustomed make valid predictions. Association rule is one in every of the favored techniques used for mining data for pattern discovery is that the KDD. Rule mining is a very important component of information mining. to seek out regularities/patterns in data, the foremost effective class is association rule mining. Mining has been utilized in many application domains. during this paper, an efficient mining based algorithm for rule generation is presented.