Reviewerly/exHarmony huggingface dataset:
Improved DEA for motor’s model identification

Purpose The purpose of this paper is to propose an improved differential evolution algorithm (DEA) suitable for motor’s model identification. Design/methodology/approach The mutation operation of the standard DEA is improved, and the adaptive coefficient is designed to adjust the optimization process. Findings The application of motor model identification shows that the proposed improved DEA is more robust, with higher modeling accuracy and efficiency, and is more suitable for motor identification modeling applications. Compared with the ultrasonic motor model established by using particle swarm algorithm, the model established in this paper has higher precision. Originality/value This paper explores an improved DEA suitable for motor identification modeling. The algorithm can not only obtain the optimal solution but also effectively reduce the iterative generations and time required in the process of optimization identification.
Development of a novel line structured light measurement instrument for complex manufactured parts

This paper studies a novel structured light measurement instrument for complex manufactured parts. In this research, a quick and accurate light plane calibration method and a novel light strip center extraction method were used to establish the measurement instrument. First, the intrinsic and external parameters of the camera are calibrated by Zhang’s method. Second, a novel light plane calibration technique is proposed by using the original parameters and images in the process of camera calibration, which can be used to improve the accuracy of the measurement system because both the camera calibration and light plane calibration are based on the same target. Third, a subpixel extraction method is presented to extract the light strip center on the basis of the geometric center method. Finally, the constructed instrument is used to detect the geometric information of the complex manufactured parts. The experimental results show that the maximum measurement error is 0.1 mm under a measurement range of 50 mm and the accuracy is 0.2%, which indicates that the measurement instrument is fast, robust, and accurate enough to be applied to many kinds of measurements, such as railroad track deformation, the inner wall of a pipe, and so on.
Performance evaluation of different classification techniques using different datasets

&lt;span&gt;Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.&lt;/span&gt;
The Implementation of The E-Learning Concept In Education

Abstract Application of web technology (Web-Based Education) can help learning process for an educational process can be called e-learning (electronic learning). The technology offered is speed and unlimited place and time to access information. Teaching and learning activities can be done easily by students anytime and anywhere. The boundaries of space, distance and time are no longer complex problems to be solved. The way to learn through the web is that there is access to information sources via the internet. The development of e-learning is three possibilities in the development of an internet-based learning system, namely the web course, web centric course, and a web enhanced course. Learning or e-learning is also inseparable from various advantages and disadvantages. However behind it all, learning through e-learning is very supportive in the current learning process.
Deep Voice-Visual Cross-Modal Retrieval with Deep Feature Similarity Learning

Thanks to the development of deep learning, voice-visual cross-modal retrieval has made remarkable progress in recent years. However, there still exist some bottlenecks: how to establish effective correlation between voices and images to improve the retrieval precision and how to reduce data storage and speed up retrieval in large-scale cross-modal data. In this paper, we propose a novel Voice-Visual Cross-Modal Hashing (V2CMH) method, which can generate hash codes with low storage memory and fast retrieval properties. Specially, the proposed V2CMH method can leverage deep feature similarity to establish the semantic relationship between voices and images. In addition, for hash codes learning, our method attempts to preserve the semantic similarity of binary codes and reduce the information loss of binary codes generation. Experiments illustrate that V2CMH algorithm can achieve better retrieval performance than other state-of-the-art cross-modal retrieval algorithms.
Exploiting the Entity Type Sequence to Benefit Event Detection

Event Detection (ED) is one of the most important task in the field of information extraction. The goal of ED is to find triggers in sentences and classify them into different event types. In previous works, the information of entity types are commonly utilized to benefit event detection. However, the sequential features of entity types have not been well utilized yet in the existing ED methods. In this paper, we propose a novel ED approach which learns sequential features from word sequences and entity type sequences separately, and combines these two types of sequential features with the help of a trigger-entity interaction learning module. The experimental results demonstrate that our proposed approach outperforms the state-of-the-art methods.
Deep Learning for Breast Region and Pectoral Muscle Segmentation in Digital Mammography

The accurate segmentation of a mammogram into different anatomical regions, such as breast or pectoral muscle, is a critical step in automated breast image analysis. This paper evaluates the performance of u-net deep learning architecture on segmenting breast area and pectoral muscle from digital mammograms and digital breast tomosynthesis. To minimise the image variations due to vendor and modality specifications, Volpara$$^\text {TM}$$ algorithm was used to normalise the raw image to a unity representation that is independent of imaging conditions. Four factors and their interactions were investigated for their effects on the performance of u-net segmentation: image normalisation; zero and extrapolated padding techniques for image size standarisation; different contrast between breast and background; and image resolution. By training u-net on 2,000 normalised images, we obtained median dice-similarity coefficients of 0.8879 and 0.9919, respectively for pectoral and breast segmentations from 825 testing images. The model training speed was boosted by using down sampled images without compromising segmentation accuracy. Using normalised breast images by Volpara$$^\text {TM}$$ algorithm, u-net was able to perform robust segmentation of breast area and pectoral muscle.
3D UAV Navigation with Moving-Obstacle Avoidance Using Barrier Lyapunov Functions

We address the safe-navigation problem for aerial robots in the presence of mobile obstacles. Our approach relies on an original dynamic model defined in a cylindrical-coordinate space. It is assumed that the environment contains moving obstacles, that are encoded as state constraints so that they are embedded in the control design: the controller is constructed so as to generate a force field which, in turn, is derived from a potential with negative gradient in the vicinity of stable equilibria and positive gradient in the vicinity of obstacles. In particular, we combine the so-called Barrier Lyapunov Functions (BLF) method with the backstepping technique to obtain a smooth time-invariant controller. It is guaranteed that the robot reaches its destination from any initial condition in the valid workspace (that is, the environment stripped of the obstacles’ safety neighborhoods) while avoiding collisions. Furthermore, the performance of our control approach is illustrated via simulations and experiments on a quadrotor benchmark.
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation

While broadly applicable to many natural language processing (NLP) tasks, variational autoencoders (VAEs) are hard to train due to the posterior collapse issue where the latent variable fails to encode the input data effectively.Various approaches have been proposed to alleviate this problem to improve the capability of the VAE.In this paper, we propose to introduce a mutual information (MI) term between the input and its latent variable to regularize the objective of the VAE.Since estimating the MI in the high-dimensional space is intractable, we employ neural networks for the estimation of the MI and provide a training algorithm based on the convex duality approach.Our experimental results on three benchmark datasets demonstrate that the proposed model, compared to the state-of-the-art baselines, exhibits less posterior collapse and has comparable or better performance in language modeling and text generation.We also qualitatively evaluate the inferred latent space and show that the proposed model can generate more reasonable and diverse sentences via linear interpolation in the latent space.
Kernel based locality – Sensitive discriminative sparse representation for face recognition

Kernel-based Sparse Representation (SR) has impacted positively on the classification performance in image recognition and has eradicated the problems attributed to the nonlinear distribution of face images and its implementation with Dictionary Learning (DL). However, the locality construction of image data containing more discriminative information, which is crucial for classification has not been fully examined by the current Kernel Sparse representation-based approaches. Furthermore, similar coding outcomes between test samples and neighbouring training data, restrained in the kernel space are not being fully realized from the image features with similar image groupings to effectively capture the embedded discriminative information. To handle these issues, we propose a novel DL method, Kernel Locality-Sensitive Discriminative SR (K-LSDSR) for face recognition. In the proposed K-LSDSR, a discriminative loss function for the groupings based on sparse coefficients is introduced into a locality-sensitive DL (LSDL). After solving the optimized dictionary, the sparse coefficients for the testing image feature samples are obtained, and then the classification results for face recognition is realized by reducing the error between the original and reassembled samples. Experimental results have shown that the proposed K-LSDSR significantly improves the performance of face recognition accuracies compared with competing methods and is robust to various diverse environments in image recognition.
End-to-End Multi-Task Machine Learning of Vehicle Dynamics for Steering Angle Prediction for Autonomous Driving

Abstract The traditional approaches to autonomous, vision-based vehicle systems are limited by their dependency on robust algorithms, sensor fusion, detailed scene construction, and high-quality maps. End-to-end models offer a means of circumventing these limitations by directly mapping an image input to a steering angle output for lateral control. Existing end-to-end models, however, either fail to capture temporally dynamic information or rely on computationally expensive Recurrent Neural Networks (RNN), which are prone to error accumulation via feedback. This paper proposes a Multi-Task Learning (MTL) network architecture that leverages available dynamic sensor data as a target for auxiliary tasks. This method improves steering angle prediction by facilitating the extraction of temporal dependencies from sequentially stacked image inputs. Evaluations performed on the publicly available Comma.ai dataset show a 28.6% improvement in steering angle prediction over existing end-to-end methods.
Modeling a Supply Chain Reference Ontology Based on a Top-Level Ontology

Abstract Several supply-chain ontologies have been introduced in the past decade with the promise of enabling supply chain interoperability. However, the existing supply-chain ontologies have several gaps with respect to completeness, logical consistency, domain accuracy, and the development approach. In this work, we propose a new, supply-chain, reference ontology that is 1) based on an existing top-level ontology and 2) developed using a collaborative, ontology-development, best practice. We chose this approach because empirical studies have shown the usefulness of adopting a top-level ontology both for improving the efficiency of the development process and enhancing the quality of the resulting ontology. The proposed proof-of-concept reference ontology is developed in the context of the Industrial Ontology Foundry (IOF). IOF is an international effort aimed at providing a coherent set of modular and publicly-available ontologies for the manufacturing sector. Although the proposed reference ontology is still at the draft stage, this paper shows that it has already benefited from the collaborative development process that involves inputs from the other working groups within IOF. Additionally, as a way to validate the proposed reference ontology, an application ontology related to a supplier discovery and evaluation use case is derived from the reference ontology and tested.
Final Report of the NTCIR-14 FinNum Task: Challenges and Current Status of Fine-Grained Numeral Understanding in Financial Social Media Data

NTCIR-14 FinNum task aims to disambiguate the meanings of the numerals in financial social media data from both coarse-grained taxonomy and fine-grained taxonomy. This task attracted 13 teams from 15 institutions in 6 different countries to register, received 16 submissions from 9 participants, and finally accepted 6 papers from participants. In this paper, we provide the analysis of the proposed dataset in depth, and discuss the challenges of fine-grained numeral understanding in financial social media data.
Design of Digital Campus Somatosensory Interactive System Based on Unity3D and VR Technology

Abstract With the rapid development of science and technology, new science and technology are constantly impacting on all areas of our lives. As a new technology of human-computer interaction, somatosensory interaction technology is bound to set off a new revolution in the field of education. Based on Unity3D and VR technology, this paper will study the design of digital campus somatosensory interaction system. The application of somatosensory technology in multimedia interactive teaching system can make the teaching system solve many problems of current man-machine system. This article understands the somatosensory interaction and analyzes its application in the teaching model. The results show that the somatosensory interaction technology has more powerful interactive functions and simpler control, and can more intuitively demonstrate the teaching content. So that students can experience the new teaching experience of somatosensory technology in multimedia teaching, making the abstract content in teaching more vivid, visual and concrete.
A Comparative Analysis of TF-IDF, LSI and LDA in Semantic Information Retrieval Approach for Paper-Reviewer Assignment

The intelligent task of semantically assigning a paper to a reviewer with respect to his knowledge domain remains a challenging task in academic conferences. From literature, a number of automated reviewer assignment systems have been presented which are based on distributional semantic models such as Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) have been used to capture semantics. Thus, this study presents the comparative study of the three models based on their derived suitability scores between a paper meant for review and a reviewer’s representation papers. From the experimental results obtained, it shows that TF-IDF outperformed the accuracy level of the other two models by a substantial margin.
Bundled Crossings Revisited

An effective way to reduce clutter in a graph drawing that has (many) crossings is to group edges that travel in parallel into bundles. Each edge can participate in many such bundles. Any crossing in this bundled graph occurs between two bundles, i.e., as a bundled crossing. We consider the problem of bundled crossing minimization: A graph is given and the goal is to find a bundled drawing with at most k bundled crossings. We show that the problem is NP-hard when we require a simple drawing. Our main result is an FPT algorithm (in k) when we require a simple circular layout. These results make use of the connection between bundled crossings and graph genus.
Sentiment Analysis of Customer Reviews in Zomato Bangalore Restaurants Using Random Forest Classifier

Natural Language Processing is one part of Artificial Intelligence and Machine Learning tomake an understanding of the interactions between computers and human (natural) languages.Sentiment analysis is one part of Natural Language Processing, that often used to analyzewords based on the patterns of people in writing to find positive, negative, or neutralsentiments. Sentiment analysis is useful for knowing how users like something or not.Zomato is an application for rating restaurants. The rating has a review of the restaurantwhich can be used for sentiment analysis. Based on this, writers want to discuss the sentimentof the review to be predicted. The method used for preprocessing the review is to make allwords lowercase, tokenization, remove numbers and punctuation, stop words, andlemmatization. Then after that, we create word to vector with the term frequency-inversedocument frequency (TF-IDF). The data that we process are 150,000 reviews. After thatmake positive with reviews that have a rating of 3 and above, negative with reviews that havea rating of 3 and below, and neutral who have a rating of 3. The author uses Split Test, 80%Data Training and 20% Data Testing. The metrics used to determine random forest classifiersare precision, recall, and accuracy. The accuracy of this research is 92%. The precision ofpositive, negative, and neutral sentiment are 92%, 93%, 96%. The recall of positive, negative,and neutral sentiment are 99%, 89%, 73%. Average precision and recall are 93% and 87%.The 10 words that affect the results are: “bad”, “good”, “average”, “best”, “place”, “love”,“order”, “food”, “try”, and “nice”.
Robust Reinforcement Learning for Continuous Control with Model Misspecification

We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms. We specifically focus on incorporating robustness into a state-of-the-art continuous control RL algorithm called Maximum a-posteriori Policy Optimization (MPO). We achieve this by learning a policy that optimizes for a worst case, entropy-regularized, expected return objective and derive a corresponding robust entropy-regularized Bellman contraction operator. In addition, we introduce a less conservative, soft-robust, entropy-regularized objective with a corresponding Bellman operator. We show that both, robust and soft-robust policies, outperform their non-robust counterparts in nine Mujoco domains with environment perturbations. In addition, we show improved robust performance on a challenging, simulated, dexterous robotic hand. Finally, we present multiple investigative experiments that provide a deeper insight into the robustness framework; including an adaptation to another continuous control RL algorithm. Performance videos can be found online at https://sites.google.com/view/robust-rl.
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning

While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent’s policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard constraints. In this setting, the agent is attempting to maximize cumulative rewards subject to these given constraints on their behavior. We reformulate the problem of IRL on Markov Decision Processes (MDPs) such that, given a nominal model of the environment and a nominal reward function, we seek to estimate state, action, and feature constraints in the environment that motivate an agent’s behavior. Our approach is based on the Maximum Entropy IRL framework, which allows us to reason about the likelihood of an expert agent’s demonstrations given our knowledge of an MDP. Using our method, we can infer which constraints can be added to the MDP to most increase the likelihood of observing these demonstrations. We present an algorithm which iteratively infers the Maximum Likelihood Constraint to best explain observed behavior, and we evaluate its efficacy using both simulated behavior and recorded data of humans navigating around an obstacle.
DisSim: A Discourse-Aware Syntactic Text Simplification Framework for English and German

We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, we turn input sentences into a two-layered semantic hierarchy in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them. In that way, we preserve the coherence structure of the input and, hence, its interpretability for downstream tasks.
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep RL

Saliency maps are often used to suggest explanations of the behavior of deep rein- forcement learning (RL) agents. However, the explanations derived from saliency maps are often unfalsifiable and can be highly subjective. We introduce an empirical approach grounded in counterfactual reasoning to test the hypotheses generated from saliency maps and show that explanations suggested by saliency maps are often not supported by experiments. Our experiments suggest that saliency maps are best viewed as an exploratory tool rather than an explanatory tool.