Reviewerly/exHarmony huggingface dataset:
A Proximity Weighted Evidential k Nearest Neighbor Classifier for Imbalanced Data

In k Nearest Neighbor (kNN) classifier, a query instance is classified based on the most frequent class of its nearest neighbors among the training instances. In imbalanced datasets, kNN becomes biased towards the majority instances of the training space. To solve this problem, we propose a method called Proximity weighted Evidential kNN classifier. In this method, each neighbor of a query instance is considered as a piece of evidence from which we calculate the probability of class label given feature values to provide more preference to the minority instances. This is then discounted by the proximity of the neighbor to prioritize the closer instances in the local neighborhood. These evidences are then combined using Dempster-Shafer theory of evidence. A rigorous experiment over 30 benchmark imbalanced datasets shows that our method performs better compared to 12 popular methods. In pairwise comparison of these 12 methods with our method, in the best case, our method wins in 29 datasets, and in the worst case it wins in least 19 datasets. More importantly, according to Friedman test the proposed method ranks higher than all other methods in terms of AUC at 5% level of significance.
Prediction of forging dies wear with the modified Takagi–Sugeno fuzzy identification method

Modern design of technological processes employs various numerical tools. Artificial Intelligence is one of the most promising approaches. However, its application in industrial conditions is still limited. One of the important obstacles is a lack of large data sets, which are necessary for the most of AI approaches. In the paper, we present a modified Takagi–Sugeno method, one of Fuzzy Rule-Based Systems family, applied for prediction of forging dies wear. A technological process, as well as acquired data are briefly discussed. The modified Takagi-Sugeno approach is introduced. Its main advantage, acquiring knowledge from experts instead of datasets, is emphasized. Assumptions, model details, and prediction results are included.
Multi-Level Representation Learning for Chinese Medical Entity Recognition: Model Development and Validation

Medical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with six types of entities and proposed a multi-level representation learning model based on Bidirectional Encoder Representation from Transformers (BERT) for Chinese medical entity recognition.This study aimed to improve the performance of the language model by having it learn multi-level representation and recognize Chinese medical entities.In this paper, the pretraining language representation model was investigated; utilizing information not only from the final layer but from intermediate layers was found to affect the performance of the Chinese medical entity recognition task. Therefore, we proposed a multi-level representation learning model for entity recognition in Chinese EMRs. Specifically, we first used the BERT language model to extract semantic representations. Then, the multi-head attention mechanism was leveraged to automatically extract deeper semantic information from each layer. Finally, semantic representations from multi-level representation extraction were utilized as the final semantic context embedding for each token and we used softmax to predict the entity tags.The best F1 score reached by the experiment was 82.11% when using the CEMR dataset, and the F1 score when using the CCKS (China Conference on Knowledge Graph and Semantic Computing) 2018 benchmark dataset further increased to 83.18%. Various comparative experiments showed that our proposed method outperforms methods from previous work and performs as a new state-of-the-art method.The multi-level representation learning model is proposed as a method to perform the Chinese EMRs entity recognition task. Experiments on two clinical datasets demonstrate the usefulness of using the multi-head attention mechanism to extract multi-level representation as part of the language model.
Recent Metaheuristics Analysis of Path Planning Optimaztion Problems

In this paper we present different computational intelligent algorithms for solving piano mover problems also known as robot path planning problems. Path planning is one of the major problems in robotics technology. Since robots have no intelligent means of understanding its environment, it's usually fundamental to develop an obstacle free and costefficient path capable of navigating the robot within its environment. In line with this, we present a method for obstacle free robot path planning in an intermediate search environment using a novel Smell Agent Optimization (SAO), Particle Swarm Optimization (PSO) and Smell Detection Agent (SDA) algorithms. We implemented our proposed method in MATLAB R2019a simulation environment. Results showed that our proposed methods can find an obstacle free path efficiently with low computational cost.
Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition

Deep neural networks (DNNs) have shown a great achievement in acoustic modeling for speech recognition task. Of these networks, convolutional neural network (CNN) is an effective network for representing the local properties of the speech formants. However, CNN is not suitable for modeling the long-term context dependencies between speech signal frames. Recently, the recurrent neural networks (RNNs) have shown great abilities for modeling long-term context dependencies. However, the performance of RNNs is not good for low-resource speech recognition tasks, and is even worse than the conventional feed-forward neural networks. Moreover, these networks often overfit severely on the training corpus in the low-resource speech recognition tasks. This paper presents the results of our contributions to combine CNN and conventional RNN with gate, highway, and residual networks to reduce the above problems. The optimal neural network structures and training strategies for the proposed neural network models are explored. Experiments were conducted on the Amharic and Chaha datasets, as well as on the limited language packages (10-h) of the benchmark datasets released under the Intelligence Advanced Research Projects Activity (IARPA) Babel Program. The proposed neural network models achieve 0.1–42.79% relative performance improvements over their corresponding feed-forward DNN, CNN, bidirectional RNN (BRNN), or bidirectional gated recurrent unit (BGRU) baselines across six language collections. These approaches are promising candidates for developing better performance acoustic models for low-resource speech recognition tasks.
The left-behind human detection and tracking system based on vision with multi-model fusion and microwave radar inside the bus

Left-behind humans inside the car or bus have caused a lot of accidents, so it is essential to detect the humans in vehicle. Current human detection methods rely on wearable devices, oxygen sensors, and special seat designs in vehicles, but those sensors cannot adapt to ever-changing environments. To solve those problems and especially to improve passengers’ safety on the bus, we propose a method to accomplishing human detection by fusion vision and microwave radar information in various environments in vehicle. For vision information, we use different networks to extract human and human face features, and fusion of the detection results in different models to improve human detection accuracy. The human detection model is MobileNet-V2, and the human face detection model is MTCNN. A new matching schedule and tracking objects management rule based on the Kernelized Correlation Filter tracker are designed to track the human and human face detection boxes. The microwave radar information is used to detect moving objects. Finally, the fusion vision and microwave radar detection results are implemented. Experiments show that our method has improved the human detection accuracy in vehicle, and this method can be used for detection of left-behind children on the school bus.
A Variational Image Segmentation Model Based on Normalized Cut with Adaptive Similarity and Spatial Regularization

Image segmentation is a fundamental research topic in image processing and computer vision. In recent decades, researchers developed a large number of segmentation algorithms for various applications. Among these algorithms, the normalized cut (Ncut) segmentation method is widely applied due to its good performance. The Ncut segmentation model is an optimization problem whose energy is defined on a specifically designed graph. Thus, the segmentation results of the existing Ncut method are largely dependent on a preconstructed similarity measure on the graph since this measure is usually given empirically by users. This flaw will lead to some undesirable segmentation results. In this paper, we propose an Ncut-based segmentation algorithm by integrating an adaptive similarity measure and spatial regularization. The proposed model combines the Parzen--Rosenblatt window method, nonlocal weights entropy, Ncut energy, and regularizer of phase field in a variational framework. Our method can adaptively update the similarity measure function by estimating some parameters. This adaptive procedure enables the proposed algorithm to find a better similarity measure for classification than the Ncut method. We provide some mathematical interpretation of the proposed adaptive similarity from multiple viewpoints, such as statistics and convex optimization. In addition, the regularizer of phase field can guarantee that the proposed algorithm has a robust performance in the presence of noise, and it can also rectify the similarity measure with a spatial priori. The well-posed theory such as the existence of the minimizer for the proposed model is given in the paper. Compared with some existing segmentation methods such as the traditional Ncut-based model and the classical Chan--Vese model, the numerical experiments show that our method can provide promising segmentation results.
Face Emotion Identification by Fusing Neural Network and Texture Features: Facial Expression

An emotion describes the feeling of the person. The face is the reflection of the person's emotion. It plays a vital role in communication and expressing the feeling of someone. Face emotion expression make human different from other living beings. In this paper, we have tried to analyze human expression with the combination of neural networks with texture features. Traditionally to extract the face region bounding box is obtained, which contains the nearby information or crop some parts of the face. Therefore to overcome these facial landmarks are used to identify the face region. The proposed approach is divided into three parts. Initially, the face region is extracted; afterward, the face feature vector is obtained using the linear binary pattern to identify the texture feature. Finally, these features are passed in the neural network for the training and testing purpose. It is analyzed that the proposed work performs better as the landmark of the face extracts the specific face region.
Information fusion on the two-layer network for robust estimation of multiple geometric structures

This paper proposes a novel and effective model fitting method to estimate multiple geometric structures on a two-layer network, where vertices in the first layer denote model hypotheses and vertices in the second layer denote data points. Instead of only considering the consensus information on model hypotheses or the preference information on data points, we combine these two kinds of information into a two-layer network. Based on this formulation, we first distinguish vertices with the quantities of information they contain in both layers by using an information theoretic algorithm. We then fuse the retained model hypotheses in the first layer together with the generated hypotheses from the retained data points in the second layer. Finally, the proposed method, namely Information Fusion on Two-Layer Network (IFTLN), detects model instances from the fused model hypotheses vertices according to three key elements (i.e., the local maximum value of weighting score, the distance between vertices, and the local density). Overall, IFTLN can not only automatically and simultaneously estimate the number and parameters of model instances with a large number of outliers, but also effectively handle significantly unbalanced distribution of data points among model instances. Comprehensive experiments are performed on both synthetic data and real images, and superior performances are achieved by the proposed method in comparison with some state-of-the-art model fitting methods.
Long-Lived Agents on the Web: Continuous Acquisition of Behaviors in Hypermedia Environments

We demonstrate how autonomous goal-directed agents can exploit hypermedia to acquire and execute new behaviors at run time. In addition to behaviors programmed into the agents, in our system agents can discover and reuse behaviors extracted from machine-readable resource manuals. Such manuals can be published by developers, synthesized by agents through automated planning, or even specified by human users at run time. Agents can then discover and use physical and virtual resources in flexible ways, which allows them to better cope with the rapid evolution of open and dynamic Web environments.
Fragile Watermarking of 3D Models in a Transformed Domain

This paper presents an algorithm aimed at the integrity protection of 3D models represented as a set of vertices and polygons. The proposed method defines a procedure to perform a fragile watermarking of the vertices’ data, namely 3D coordinates and polygons, introducing a very small error in the vertices’ coordinates. The watermark bit string is embedded into a secret vector space defined by the Karhunen–Loève transform derived from a key image. Experimental results show the good performance of the method and its security.
Faster Cofactorization with ECM Using Mixed Representations

This paper introduces a novel implementation of the elliptic curve factoring method specifically designed for medium-size integers such as those arising by billions in the cofactorization step of the Number Field Sieve. In this context, our algorithm requires fewer modular multiplications than any other publicly available implementation. The main ingredients are: the use of batches of primes, fast point tripling, optimal double-base decompositions and Lucas chains, and a good mix of Edwards and Montgomery representations.
Language model based interactive estimation of distribution algorithm

It is very hard, if not impossible to use analytical objective functions for optimization of personalized search due to the difficulties in mathematically describing qualitative problems. To solve such optimization problems, interactive evolutionary algorithms, which can make use of human preferences, are highly desirable. However, due to the lack of effective encoding methods, interactive evolutionary algorithms have been limited to numerically encoded optimization problems. In practice, however, linguistic terms (words) are the most natural expression of human preferences, and they are also commonly used to describe items in personalized search or E-commerce; therefore, language models better suit encoding, and the optimization of personalized search is converted into a dynamic document matching problem. To optimize word-described personalized search, we propose a novel interactive estimation of distribution algorithm. This algorithm combines a language model-based encoding approach, a Dirichlet-Multinomial compound distribution-based preference expression, and a Bayesian inference mechanism. The proposed algorithm is applied to two personalized search cases to demonstrate the capability of the algorithm in ensuring a more efficient and accurate search with less user fatigue.
The Development of Real-time Large Data Processing Platform Based On Reactive Micro-Service Architecture

The big data technologies has been used in all the field of industry, and now the automatic controller system also has the requirement to use big data techniques to analysis the read time big data to support the business need. This paper provides the solution for real time big data processing in industry controller system by combination of Spring Cloud Stream micro service, Kafka and Apache Spark. The paper also discusses how to develop event driven system with stream API of Spark and Spring for Apache Kafka Stream for real time big data processing and analysis. The construction of real time big data platform infrastructure and configuration in this platform has also been discussed.
Maximum-relevance and maximum-diversity of positive ranks: A novel feature selection method

With the existing abundance of intelligent and expert systems, there is a need for selecting a subset of highly relevant features with low redundancy. In filter approaches, the feature subsets are iteratively computed by evaluating the candidate features in terms of their relevance with the target class and pairwise redundancies. The use mutual information-based metrics has been extensively studied as an approach to quantifying the relevance and redundancy of candidate features. In this study, a novel filter approach based on ranks of positive instances is proposed. In this approach, redundancy is replaced by diversity to quantify the complementarity of a candidate feature with respect to the already selected subset. Both relevance and diversity are computed in terms of the ranks of positive instances, which is analogous to the computation of the area under the receiver operating characteristic curve (AUC). Experiments conducted on 15 UCI and microarray gene expression data sets have confirmed that the proposed multivariate filter feature selection approach provides better performance scores when compared to other competing multivariate methods as well as benchmark univariate filters.
Eyes on URLs: Relating Visual Behavior to Safety Decisions

Individual and organizational computer security rests on how people interpret and use the security information they are presented. One challenge is determining whether a given URL is safe or not. This paper explores the visual behaviors that users employ to gauge URL safety. We conducted a user study on 20 participants wherein participants classified URLs as safe or unsafe while wearing an eye tracker that recorded eye gaze (where they look) and pupil dilation (a proxy for cognitive effort). Among other things, our findings suggest that: users have a cap on the amount of cognitive resources they are willing to expend on vetting a URL; they tend to believe that the presence of www in the domain name indicates that the URL is safe; and they do not carefully parse the URL beyond what they perceive as the domain name.
Augmented Reality as a tool for providing informational content in different production domains

Augmented Reality (AR) is a versatile tool for enriching the real world with digital content. By presenting information virtually on head-mounted devices, the user can perform different tasks hands-free. Despite the benefits of AR, this kind of information representation is a niche application in the production industry. It is not yet clear, whether the reasons are about the available AR technology or about level of acceptance by the user. The aim of the presented paper is to investigate the potential of AR in production applications. Three different use-cases generated data regarding the acceptance of the user by applying different types of AR support, influence of different AR hardware, and the potential of AR support in different production-related disciplines (manufacture, assembly, and maintenance). The study revealed that the impact of AR increased with the required time for the task. In addition, benefit of AR was related to the technological skills of the user. However, AR seems to enable a better task time planning which is reflected by the smaller standard deviations of required time in all settings.
A Genetic-Based Incremental Local Outlier Factor Algorithm for Efficient Data Stream Processing

Interest in outlier detection methods is increasing because detecting outliers is an important operation for many applications such as detecting fraud transactions in credit card, network intrusion detection and data analysis in different domains. We are now in the big data era, and an important type of big data is data stream. With the increasing necessity for analyzing high-velocity data streams, it becomes difficult to apply older outlier detection methods efficiently. Local Outlier Factor (LOF) is a well-known outlier algorithm. A major challenge of LOF is that it requires the entire dataset and the distance values to be stored in memory. Another issue with LOF is that it needs to be recalculated from the beginning if any change occurs in the dataset. This research paper proposes a novel local outlier detection algorithm for data streams, called Genetic-based Incremental Local Outlier Factor (GILOF). The algorithm works without any previous knowledge of data distribution, and it executes in limited memory. The outcomes of our experiments with various real-world datasets demonstrate that GILOF has better performance in execution time and accuracy than other state-of-the-art LOF algorithms.
How Not to Create an Isogeny-Based PAKE

Isogeny-based key establishment protocols are believed to be resistant to quantum cryptanalysis. Two such protocols—supersingular isogeny Diffie-Hellman (SIDH) and commutative supersingular isogeny Diffie-Hellman (CSIDH)—are of particular interest because of their extremely small public key sizes compared with other post-quantum candidates. Although SIDH and CSIDH allow us to achieve key establishment against passive adversaries and authenticated key establishment (using generic constructions), there has been little progress in the creation of provably-secure isogeny-based password-authenticated key establishment protocols (PAKEs). This is in stark contrast with the classical setting, where the Diffie-Hellman protocol can be tweaked in a number of straightforward ways to construct PAKEs, such as EKE, SPEKE, PAK (and variants), J-PAKE, and Dragonfly. Although SIDH and CSIDH superficially resemble Diffie-Hellman, it is often difficult or impossible to "translate" these Diffie-Hellman-based protocols to the SIDH or CSIDH setting; worse still, even when the construction can be "translated," the resultant protocol may be insecure, even if the Diffie-Hellman based protocol is secure. In particular, a recent paper of Terada and Yoneyama and ProvSec 2019 purports to instantiate encrypted key exchange (EKE) over SIDH and CSIDH; however, there is a subtle problem which leads to an offline dictionary attack on the protocol, rendering it insecure. In this work we present man-in-the-middle and offline dictionary attacks on isogeny-based PAKEs from the literature, and explain why other classical constructions do not "translate" securely to the isogeny-based setting.