Reviewerly/exHarmony huggingface dataset:
Mobile Augmented Reality for Craniotomy Planning

Augmented reality (AR) neuronavigation has been proposed to address the shortcomings of conventional neuron-avigators. Researchers have presented low-cost AR methods for craniotomy planning, but they lack navigation capabilities. Other studies introduce AR neuronavigation systems that are a step further in usability than traditional neuronavigators, but they may be hard to obtain or reproduce. AR neuronavigation was also implemented on mobile devices, but most systems have an undesired lag during the navigation. This work investigates the feasibility of creating an accurate and low-cost standalone mobile AR neuronavigator. Unlike other mobile approaches, this solution has no perceptible lag as the processing is efficiently performed on the device instead of an external computer. Results show that a neuronavigation system can be deployed on a mobile device, running smoothly at 60 frames per second, and achieving a smaller than 5 mm target registration error.
Field estimation using binary measurements

In this paper we consider the problem of field estimation using binary sensor measurements. A number of agents are equipped with sensors which can make binary measurements of the field. The agents are mobile and can move around to different locations to collect measurements. In estimating the field, we model the field as a sum of radial basis functions, whose parameters are then estimated using sequential Monte Carlo techniques. We incorporate an active sensing mechanism for the agents to adaptively choose their next measurement location, given the information currently collected. In the multi-agent case, we consider both a centralized setup, and a decentralized setup where agents share their past measurements with each other provided they are within communication range. Simulation studies illustrate the performance of the proposed algorithms.
Research on Density-Based K-means Clustering Algorithm

Abstract Cluster analysis is an unsupervised learning process, and its most classic algorithm K-means has the advantages of simple principle and easy implementation. In view of the K-means algorithm’s shortcoming, where is arbitrary processing of clusters k value, initial cluster center and outlier points. This paper discusses the improvement of traditional K-means algorithm and puts forward an improved algorithm with density clustering algorithm. First, it describes the basic principles and process of the K-means algorithm and the DBSCAN algorithm. Then summarizes improvement methods with the three aspects and their advantages and disadvantages, at the same time proposes a new density-based K-means improved algorithm. Finally, it prospects the development direction and trend of the density-based K-means clustering algorithm.
Modeling Global and Local Interactions for Online Conversation Recommendation

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.
Deep Image Restoration Model: A Defense Method Against Adversarial Attacks

These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely affects the performance or prediction. In this scenario, we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly again. We proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results evidence. We have used MNIST and CIFAR10 datasets for experiments and analysis of our defense method. In the end, we have compared our method to other state-of-the-art defense methods and proved that our results are better than other rival methods.
Joint enhancement and denoising in electronic speckle pattern interferometry fringe patterns with low contrast or uneven illumination via an oriented variational Retinex model

Simultaneous speckle reduction and contrast enhancement for electronic speckle pattern interferometry (ESPI) fringe patterns is a challenging task. In this paper, we propose a joint enhancement and denoising method based on the oriented variational Retinex model for ESPI fringe patterns with low contrast or uneven illumination. In our model, we use the structure prior to constrain the illumination and introduce a fractional-order differential to constrain the reflectance for enhancement, then use the second-order partial derivative of the reflectance as the denoising term to reduce noise. The proposed model is solved using the sequential method to obtain piecewise smoothed illumination and noise-suppressed reflectance sequentially, which avoids remaining noise in the illumination and reflectance map. After obtaining the refined illuminance and reflectance, we substitute the gamma-corrected illuminance into the camera response function to further adjust the reflectance as the final enhancement result. We test our proposed method on two non-uniform illumination computer-simulated and two low-contrast experimentally obtained ESPI fringe patterns. Finally, we compare our method with three other joint enhancement and denoising variational Retinex methods.
Person Re-identification Based on Improved Residual Neural Networks

Person re-identification is to search the same person from the picture gallery. The two key technologies of person re-identification are feature extraction and similarity measurements. The paper engages an improved residual neural network to make rank-1,5,10,20 accuracy and mAP be 1–5% higher than the original residual neural network in mainstream databases.
Fine-Grained Facial Ethnicity Recognition Based on Dual Convolutional Autoencoders

Faces contain abundant biological and sociological information. Inter-ethnicity identification using facial images has been intensively studied, while intra-ethnicity classification has received less attention. In this paper, we propose an Ensemble of Convolutional Autoencoders (E-CAE) model to attempt to distinguish Chinese, Japanese, and Korean faces and individuals from different regions of China. To accomplish this task, CJK and RoC datasets are built and E-CAE yields a classification accuracy of 80.69% on CJK dataset and 61.81% on RoC dataset. The experimental results demonstrate that our model outperforms existing methods for fine-grained ethnicity recognition in terms of accuracy and robustness. To our knowledge, this is the first work that performs fine-grained ethnicity recognition at the scale of provinces.
LRFNet: An Occlusion Robust Fusion Network for Semantic Segmentation with Light Field

Semantic segmentation, aiming to assign a categorical label to each pixel in an image, has drawn a lot of attention and has made significant achievements in recent years. However semantic segmentation remains a challenging problem caused by occlusion due to the lack of view information. In practice, the result of pixel assignment is greatly influenced by the scene information, especially in complex occlusion areas. One way to address the pixel assignment problem of occlusion areas is to train a feature representation that is extracted with enough scene information. To this end, we present light robust features(LRFs) through light field(LF), which contains all the light information of the scene. In addition, LRF consists of two components: 1)light color features(LCFs) and 2)light spatial features(LSFs). On the one hand, LCF is the expert in exploring the comprehensive RGB characteristic of LF images. When training LCF, we adopt a ResNet based feature extraction module. Moreover, a cross-entropy loss is deployed in LCF network. On the other hand, we design a LSF feature extraction module, which has a similar architecture to a robust depth estimation network. The difference between LCF and LSF is that LSF represents the depth characteristic of LF images. Finally, by combining LCF and LSF through pyramid-pooling and conditional random field(CRF) module, LRF can alleviate the influence of the occlusion, even in scenes of multi scales and complex categories. Experiments are conducted on the LF Semantic Segmentation data sets. We show that LRF produces competitive performance compared with state-of-the- art approaches.
Machine Learning in Gaze-Based Interaction: A Survey of Eye Movements Events Detection

Spontaneous gaze-based input offers faster and more intuitive human-computer interaction as people naturally look at their desired destination. Developing a spontaneous gaze-based application faces many challenges, one of them is detecting events of eye movements. Most events detection methods are based on velocity or dispersion threshold. Unfortunately, these approaches depend on manual setting of threshold parameters. Despite previous attempts to review various techniques used in spontaneous gaze-based interaction, there is no survey paper that takes into account various machine learning techniques used for events detection. Here we present a brief overview of spontaneous gaze-based interactive applications and some machine learning approaches used for eye movement events detection. First, we explored major development of spontaneous gaze-based interaction by reviewing some examples of its applications. Next, we presented some state-of-the-art of threshold-based and machine learning-based events detection techniques. Finally, we discussed future research on spontaneous gaze-based interaction. Our brief survey paper maybe used by entry level researchers interested in developing uncalibrated interactive applications based on eye tracking.
Continuous self-adaptive optimization to learn multi-task multi-agent

Abstract Multi-agent reinforcement learning (MARL) methods have shown superior performance to solve a variety of real-world problems focusing on learning distinct policies for individual tasks. These approaches face problems when applied to the non-stationary real-world: agents trained in specialized tasks cannot achieve satisfied generalization performance across multiple tasks; agents have to learn and store specialized policies for individual task and reliable identities of tasks are hardly observable in practice. To address the challenge continuously adapting to multiple tasks in MARL, we formalize the problem into a two-stage curriculum. Single-task policies are learned with MARL approaches, after that we develop a gradient-based Self-Adaptive Meta-Learning algorithm, SAML, that cannot only distill single-task policies into a unified policy but also can facilitate the unified policy to continuously adapt to new incoming tasks. In addition, to validate the continuous adaptation performance on complex task, we extend the widely adopted StarCraft benchmark SMAC and develop a new multi-task multi-agent StarCraft environment, Meta-SMAC, for testing various aspects of continuous adaptation method. Our experiments with a population of agents show that our method enables significantly more efficient adaptation than reactive baselines across different scenarios.
Data augmentation of JavaScript dataset using DCGAN and random seed

Many of the malicious JavaScript detection techniques focus on training a machine learning model with balanced dataset. However, real-world data only has a small fraction of malicious JavaScript, making it an imbalanced dataset. Training and testing the model with an imbalanced dataset can help evaluate the practical uses of that model. This paper aims to build a filter model that can quickly classify JavaScript malware using natural language processing (NLP) and machine learning. The feature of words inside the JavaScript file will be converted into vector form and used to train the SVM classifier. Different NLP models and oversampling methods are tested to achieve high recall score, such as Doc2Vec and Latent semantic indexing (LSI) model. In this paper, DCGAN model will be used to generate new training malicious data based on original training dataset. The goal is to let the DCGAN model learn the feature of the training malicious data and generate more reliable data than previous experiment which use random seed for oversampling. We evaluate our models with a dataset of over 20,000 samples obtained from top popular websites, PhishTank and other source. The experimental result shows that the best recall score achieves 0.78 with the LSI model.
Towards the Black-Box Aggregation of Language Components

Engineering software languages demands considering many different aspects, including syntax, semantics, tooling, variability, usability, and many more. Engineering efficiency depends largely on our capability of reusing language parts in other contexts. Currently, most language reuse focuses on few isolated aspects. We have devised the component-based SCOLAR framework for the novel integrated reuse of concrete syntax, abstract syntax, well-formedness rules, and code generators. SCOLAR supports black-box embedding of language parts, yielding composed languages, and, consequently, models (e.g., expressions embedded into Statecharts). This is insufficient when aiming to integrate languages such that their models should be loosely coupled only. We, therefore, present an extension of SCOLAR towards the black-box integration of language components through language aggregation, yielding a loose coupling between their models (e.g., Statecharts referring to class diagram types). To this end, we analyzed the requirements for language aggregation and devised compact extensions to SCOLAR to also support the latter. This enables a novel integration of language components through aggregation, which can facilitate reusing software languages and, hence, advance their application.
Missing Data Imputation

Many real world datasets may contain missing values for various reasons. These incomplete datasets can pose severe issues to the underlying machine learning algorithms and decision support systems. It may result in high computational cost, skewed output and invalid deductions. Various solutions exist to mitigate this issue; the most popular strategy is to estimate the missing values by applying inferential techniques such as linear regression, decision trees or Bayesian inference. In this paper, the missing data problem is discussed in detail with a comprehensive review of the approaches to tackle it. The paper concludes with a discussion on the effectiveness of three imputation methods namely, imputation based on Multiple Linear Regression (MLR), Predictive Mean Matching (PMM) and Classification And Regression Tree (CART) in the context of subspace clustering. The experimental results obtained on real benchmark datasets and high-dimensional synthetic datasets highlight that, MLR based imputation method is more efficient on high-dimensional incomplete datasets.
A dynamic population reduction differential evolution algorithm combining linear and nonlinear strategy piecewise functions

Abstract The population size has a great impact on the performance of the Differential Evolution (DE) algorithm, but in many classic DE algorithms, the population size is usually determined by the user based on the experience value, and it remains unchanged during the evolution process, which greatly affect the performance of DE. To this end, a dynamic population reduction differential evolution algorithm (DPSHADE) that combines linear and nonlinear strategy piecewise functions is proposed. The algorithm uses a dynamic population size reduction method to dynamically adjust the population size during operation, and construct a combination of linear and nonlinear piecewise functions for dynamic scale adaptive adjustment. In this paper, we proposed the DPSHADE algorithm and is compared with the four traditional algorithms in the CEC2017 benchmark set. The experimental results show that DPSHADE performs better in overall performance, which is significant better than the performance of SHADE.
Efficient Algorithm for CSP Selection Based on Three-Level Architecture

Trust management has been described as an essential part of active partnership sharing in the cloud environment between different trading partners. For the trading partners, it is challenging and confusing on how to opt for the trusted cloud environment among the various cloud service providers. There are multiple methods given by numerous researchers for selecting the appropriate cloud service provider (CSP). In this frame, this paper put forward a proposed three-level architecture for the efficient selection of appropriate CSP. This paper aims to present a smart and effective architecture system that can be helpful for the selection of CSPs from the pool of various available providers.
Privacy Preserving Scheme for Smart Transportation in 5G Integrated IoT

The Internet of Things (IoT) is widely used in smart transportation and 5G technology is used as a mode of communication. Many devices are involved in monitoring, collecting and processing of information from various users and also sharing the information between devices as well as users. Security and privacy preservation is the biggest challenge in smart transportation because devices are more in number and also connected wirelessly. In this paper, we consider security and privacy issues in smart transportation and focus on improving privacy preservation. Also we analyzed the technique using the formal verification tool Scyther. The result shows that privacy is preserved.
A Robot, Meteorologist, and Amateur Forecaster Walk into A Bar: Examining Qualitative Responses to A Weather Forecast Delivered via Social Robot

The current study examines qualitative responses from participants about their opinions on a weather forecast delivered by a professional meteorologist, an amateur meteorologist, and a social robot standing in for a meteorologist. Through thematic analyses, this study identified that people evaluated each weather forecast agent’s performance based on two major criteria: message factors and messenger factors. Two themes emerged from the category of message factors: an enjoyable/entertaining experience and commentary on the forecast content and information. Five themes were identified under the category of messenger factors: an expectancy violation or uncertainty which made participants uncomfortable, a distraction to the point it impeded focus on the forecast, vocalization concerns, unprofessional delivery, and commentary on the stimuli being “cute”/perceiving it as a joke. Findings further illustrate the expectancy violation people experienced while encountering a robot and/or amateur forecaster. Such knowledge will contribute to human-machine communication literature and offer pragmatic implications for technology development.
Implementation of gradient gravitational search algorithm towards conformational search

The conformational search is a highly complex optimization problem and the solution to such problem depends on the employed simulation techniques. The adaptation of novel techniques such as evolutionary algorithms could be more effective to solve the relevant problems specific to conformational search and even extended to global optimization. The advantages of using meta-heuristic stochastic algorithms are due to their flexibility and fitness to the objective function with a robust behavior. In this paper, Gradient Gravitational Search (GGS) algorithm has been implemented as the conformational search techniques by using C++ in MATLAB. The total potential energy of each conformer within the search space is minimized based on the CHARMM force field parameters. The contribution from both bonded and non-bonded terms have been taken care by using Cartesian coordinates. We also compare the efficiency of the GGS algorithm by considering smaller peptides with other reported simulation methods: DE/Best, DE/Rand, JAYA, CRO and PSO. Among these simulation techniques, GGS is found to be robust algorithm as compared to others, so as to explore conformational searches of small peptides, with regard to small iteration steps and computational accuracy at low cost.
Digital Competence of a Foreign Language Teacher: E-learning in Work

The article deals with the formation of digital competence of a foreign language teacher in the context of modern educational policy and digital transformation of educational system. The authors substantiate the need to master this type of competence for the teachers in order to comply with the Federal State Educational Standard and the Professional Standard. They are analyzed various approaches to determining the digital competence components, and it is proposed its structure considered as the unity of value-motivational, operational-technological, communicative-cognitive, and reflexive-evaluative components. The key characteristics of the technologies used in the educational process and their classification are also considered. It is argued that the introduction of these technologies in the process of training future foreign language teachers will enhance the students' cognitive activity, positively influencing the formation of digital competence.
Adversarial example defense based on image reconstruction

The rapid development of deep neural networks (DNN) has promoted the widespread application of image recognition, natural language processing, and autonomous driving. However, DNN is vulnerable to adversarial examples, such as an input sample with imperceptible perturbation which can easily invalidate the DNN and even deliberately modify the classification results. Therefore, this article proposes a preprocessing defense framework based on image compression reconstruction to achieve adversarial example defense. Firstly, the defense framework performs pixel depth compression on the input image based on the sensitivity of the adversarial example to eliminate adversarial perturbations. Secondly, we use the super-resolution image reconstruction network to restore the image quality and then map the adversarial example to the clean image. Therefore, there is no need to modify the network structure of the classifier model, and it can be easily combined with other defense methods. Finally, we evaluate the algorithm with MNIST, Fashion-MNIST, and CIFAR-10 datasets; the experimental results show that our approach outperforms current techniques in the task of defending against adversarial example attacks.