Reviewerly/exHarmony huggingface dataset:
Deviance Tests for Graph Estimation From Multi-Attribute Gaussian Data

We consider the problem of inferring the conditional independence graph (CIG) of Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. For single-attribute graphical models, considerable body of work exists where one first tests for exclusion of each edge from the saturated model, and then infers the CIG. In this paper, we propose and analyze a deviance test based on generalized likelihood ratio, for edge exclusion in multi-attribute Gaussian graphical models. The null distribution of the test statistic is derived to allow analytical calculation of the test threshold. An alternative computationally fast version of the deviance test statistic is also derived. Numerical results based on synthetic as well as real data are presented.
A hybrid pigeon inspired optimization algorithm based on Nelder-Mead simplex operations

As a recent swarm-based intelligent optimization algorithm, Pigeon Inspired Optimization (PIO), is motivated by the natural bio-mechanism of pigeons for their superior skills in destination finding and navigating. The standard PIO has been successfully implemented to solve complex optimization problems. Similar to other swarm intelligent techniques, PIO is suitable to solve global optimization problems for its robustness in adapting to dynamic environments, where the convergence rate is generally limited, since the algorithm does not employ much local information to establish a most promising direction for optima searching. In this paper, we propose a novel hybridized optimization algorithm, Nelder-Mead Pigeon Inspired Optimization (NMPIO), incorporating the global optimization ability of PIO and the capability of fast local convergent of the Nelder-Mead Simplex method. With the implementation of synthesizing a bio-inspired optimization algorithm and a direct local search method, feasible global optimal solution can be found with a faster convergence rate, compared with the original PIO algorithm. Numerical experiments for several well-known benchmarks are conducted to study the performance of this algorithm. The results reveal that our hybridization strategy is effective and efficient for solving global optimization problems.
Global Automatic Recognition and Localization of Multi Screws Based on Image Mosaic

In the tuning system based on vision sensor, the object recognition and localization is very important. In order to meet the practical tuning of the microwave cavity filter by the mechanical arm, a global recognition and localization method of multiple adjustable screws is proposed, which combines image mosaic and template matching. A high-definition global image of microwave cavity filter is composed of multiple local images according to feature point registration, and then all adjustable screws in the global image are recognized by template matching algorithm based on shape feature, which solves the problem of limited field of vision of a single industrial camera. It realizes the recognition, localization and numbering of all tuning objects in the whole tuning range, and transmits the coordinate information corresponding to the number to the mechanical arm. This method can be used to tune various types of microwave cavity filters, and it has certain versatility. The experimental results show that the recognition effect of the screws is favorable when the above method is applied to the actual tuning of 12-step microwave cavity filter with the hand-eye integrated four-axis mechanical arm. And it meets the requirements of tuning accuracy.
Effective Classification for Multi-modal Behavioral Authentication on Large-Scale Data

We propose an effective classification algorithm for machine learning to achieve higher performance for multi-modal behavioral authentication systems. Our algorithm uses a multiclass classification scheme that has a smaller number of classes than the number of users stored in the dataset. We also propose metrics, the self-mix-classified rate, other-single-classified rate, and equal-classified rate, for use with the proposed algorithm to determine an optimal number of classes for behavioral authentication. We conducted experiments using a large-scale dataset of activity histories that are stored when 10,000 users use commercial smartphone-applications to analyze performance measures such as false rejection rate, false acceptance rate, and equal error rate obtained with our proposed algorithm. The results indicate our algorithm achieved higher performance than that for previous ones.
The method on stacked particle image segmentation and particle size measurement

In the measurement of overlapping particle size, it is necessary to perform image processing on the stacked particle image to obtain accurate measurement results. In this paper morphological filtering is used to remove the isolated small area and fill the holes. The distance image combined with h-minima transform is used to get the seed points. Then the seed points and background are marked on the distance image. Finally, the distance image is segmented by watershed. Due to the partial missing after segmentation of conglutinated particles, according to the prior knowledge that the shape of particles is similar to ellipse, this paper reconstructs the contour of the incomplete particles by ellipse fitting technology. Finally, the measurement algorithm of particle shape and particle size characteristics is determined. Two groups experiments are carried out for particle size measurement, and the error of particle size measurement is analyzed. It is proved that the measurement is accurate.
Algebra for Complex Analysis of Data

In data science, the process of development focuses on the improvement of methods for individual data analytical tasks. However, their combination is not properly researched. We believe that this situation is caused by a missing framework, that would focus solely on data analytical tasks, instead of complicated transformation between individual methods. In this paper, a new analytical algebra is defined. This algebra is based on a flat structure of transaction file and operations over it. As a part of the paper, definitions of several data analytical tasks are proposed. Algebra is recursive and extendable. As an example of usability of the algebra, one complex analytical task created by a combination of analytical operators is described.
KPML: A Novel Probabilistic Perspective Kernel Mahalanobis Distance Metric Learning Model for Semi-supervised Clustering

Metric learning aims to transform features of data into another based on some given distance relationships, which may improve the performances of distance-based machine learning models. Most existing methods use the difference between the distance of similar pairs and that of dissimilar pairs as loss functions for training. This kind of loss function may lack interpretability since people can only observe the distance or the difference of the distance, a number with no bounds, but have no idea about how large or small it is. To provide more explanation of these metric learning models, in this paper, we propose the probabilistic theoretical analysis of metric learning, design a special loss function, and propose the Kernelized Probabilistic Metric Learning (KPML) approach. With all the distance values transformed into probabilities, we can, therefore, compare and explain the results of the model. Besides, to effectively make use of both the labeled and unlabeled data to enhance the performance of semi-supervised clustering, we propose a KPML-based approach that leverages metric learning and semi-supervised learning effectively in a novel way. Finally, we use our model to do experiments about kNN-based semi-supervised clustering and the results show that our model significantly outperforms baselines across various datasets.
An Overview of Mutation Strategies in Particle Swarm Optimization

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.
Decomposition/Aggregation K-means for Big Data

Well-known and widely applied k-means clustering heuristic is used for solving Minimum Sum-of-Square Clustering problem. In solving large size problems, there are two major drawbacks of this technique: (i) since it has to process the large input dataset, it has heavy computational costs and (ii) it has a tendency to converge to one of the local minima of poor quality. In order to reduce the computational complexity, we propose a clustering technique that works on subsets of the entire dataset in a stream like fashion. Using different heuristics the algorithm transforms the Big Data into Small Data, clusters it and uses obtained centroids to initialize the original Big Data. It is especially sensitive for Big Data as the better initialization gives the faster convergence. This approach allows effective parallelization. The proposed technique evaluates dynamically parameters of clusters from sequential data portions (windows) by aggregating corresponding criteria estimates. With fixed clustering time our approach makes progress through a number of partial solutions and aggregates them in a better one. This is done in comparing to a single solution which can be obtained by regular k-means-type clustering on the whole dataset in the same time limits. Promising results are reported on instances from the literature and synthetically generated data with several millions of entities.
Vision-based Lifting of 2D Object Detections for Automated Driving

Image-based 3D object detection is an inevitable part of autonomous driving because cheap onboard cameras are already available in most modern cars. Because of the accurate depth information, currently most state-of-the-art 3D object detectors heavily rely on LiDAR data. In this paper, we propose a pipeline which lifts the results of existing vision-based 2D algorithms to 3D detections using only cameras as a cost-effective alternative to LiDAR. In contrast to existing approaches, we focus not only on cars but on all types of road users. To the best of our knowledge, we are the first using a 2D CNN to process the point cloud for each 2D detection to keep the computational effort as low as possible. Our evaluation on the challenging KITTI 3D object detection benchmark shows results comparable to state-of-the-art image-based approaches while having a runtime of only a third.
Genetic algorithm-based intelligent watermarking for security of medical images in telemedicine applications

A lot of research on medical image watermarking has been done to fulfill the requirements of robustness and imperceptibility. Also, the security of watermark logos has been considered in many studies. In these studies, the value of weight or scaling factor is taken as user-defined or fixed. Sometimes due to the wrong selection of this value, the watermarking scheme has not given the desired results for the given medical dataset. Therefore optimization of this value is required to overcome this limitation in existing medical image watermarking schemes. Also, in the literature there are fewer watermarking schemes using bioinspired algorithms for the security of medical images. These two points motivate the development of new watermarking schemes using a bioinspired algorithm for the security of medical images. In this chapter, an intelligent watermarking scheme based on the genetic algorithm (GA) is discussed, including its different features. Also in this chapter, an image watermarking scheme using hybridization of discrete cosine transform-singular value decomposition and a GA is developed and analyzed for the security of medical images for telemedical applications. Here, GA is used for optimization of the scaling factor based on the nature of the cover medical image and watermark logo.
A Novel Hybrid Optimization Algorithm Combined with BBO and ACO

This paper proposes a novel hybrid optimization algorithm based biogeography-based optimization (BBO) and ant colony optimization (ACO). Specifically, BBO is utilized for preliminary search and then the final search is conducted by using ACO on the preliminary results, which makes the hybrid algorithm have powerful search ability in the solution space. Moreover, the algorithm is tested on 6 well-known benchmarks for comparing with the 4 others existing optimization algorithms, including 2 algorithms were proposed by the coauthor earlier. Obviously, the simulation results show that the proposed hybrid BBO-ACO has better performance than the existing others.
ECO-HC Based Tracking for Ground Moving Target Using Single UAV

To overcome the problems of target missing and serious target occlusion in ground target tracking with UAV, this paper introduces the tracking confidence and detect module on the basis of ECO-HC. We use the Peak to Sidelobe Ratio(PSR) to express the track confidence. The YOLOv3 is taken as the detect module, the position and feature of detecting results are used to match with the target. The algorithm descreases the frequence of calling YOLOv3 by PSR, and reduces the influence of YOLOv3 to real-time through the way of offline training to enhance the speed. At the same time the accuracy of detecting is guranteed with the target relevance matching. The results show the algorithm can find the target-lost back and has obvious improvement in tracking accuracy.
Prostate Localization in 2D Sequence MR with Fusion of Center Position Prior and Sequence Correlation

A prostate organ localization algorithm based on the state-of-art object detection framework Faster R-CNN is proposed in this paper for Magnetic Resonance (MR) slice sequence. Using the prior information of the central position of the organ in the image, ResNet-50 with spatial attention mechanism is introduced as the network's feature extraction module to enhance the sensitivity of the network to the spatial location features. In addition, inspired by the correlation between neighboring slice images in the position and morphological size of the organ, spatial curve fitting of key points of the object bounding boxes based on the sequence direction is applied to further improve the detection performance of the algorithm. Compared with the original Faster R-CNN framework, the algorithm we proposed has achieved better performance of prostate localization on the PROMISE12 dataset, which is mainly reflected in the area recall rate and localization success rate of the organs increased by 7.1% and 6.5%, respectively. It establishes a good foundation for subsequent medical image processing tasks such as organ segmentation and lesion detection.
Toddler Tracking System with Face Recognition and Object Tracking Using Deep Neural Network

Toddlers tend to have approach objects that may be dangerous to them at home because of their natural curiosity. When families are sleeping or doing housework, unpredictable injuries may be happening. Horrible, irreversible accidents may occur in less than no time. Not only for this reason but also with the technological evolution, systems related digital parenting have gained importance. Smart technology is adopted by modern parents to provide their children safety. Considering all these situations, the need to design a toddler tracking system for helping the parents has emerged. In this article, a toddler tracking system using Deep Neural Network has been proposed. The proposed system is based on face recognition and object tracking algorithms and created using pre-trained neural networks. The system is based on recognizing the toddlers' faces and following all toddler's movements in the house. When the toddler is getting close dangerous places, tools, furniture, etc, the system alerts the user with the warning system. The proposed system is tested by using the toddlers' data which is collected before and 80.7% test accuracy has been obtained. The experimental result showed that the proposed method has achieved sufficient performance to be compared with state-of-the-art studies.
Remote Sensing Image Segmentation Based on U-shaped Network with Atrous Spatial Pyramid

Semantic segmentation in remote sensing images has always been an important research direction of computer vision, and is widely used in land and resources related fields such as land mapping, hydrology and environmental testing, urban and rural construction. This paper focuses on remote sensing image segmentation problem, which is still challenging for current semantic segmentation networks. To address this problem, this paper takes the DeepLabv3+[1] network as the baseline structure, and attaches a multi-level feature fusion module to well utilize the low-level information of the network. The proposed network performs better on multiple evaluations such as Precision, Recall, and Mean Absolute Error (MAE) compared to several state-of-the-art methods, and is able to extract details of the segmentation more precisely.
Improved-YOLOv3 network for object detection in simulated Space Solar Power Systems images

In this paper, the object detection technology based on deep learning is applied to the assembly process of space power station simulation, which can provide assistance for the attitude adjustment and navigation of the aircraft through the detection of some components. Firstly, the 3D modeling and rendering of the space power station are carried out, on which the image dataset is collected and established. Then, based on the YOLOv3 network, we improve the structure of feature extraction. By fusing the information of shallow and deep features, we can improve the detection ability of the network for different scale objects. Qualitative and quantitative experimental results show that the improved YOLOv3 network can accurately and effectively detect the key components of the Space solar power station.
Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems

Online recommendation services, such as e-commerce sites, rely on a vast amount of knowledge about users/items that represent an invaluable resource. Part of this acquired knowledge is public and can be accessed by anyone through the Internet. Unfortunately, that same knowledge can be used by competitors or malicious users. A large body of research proposes methods to attack recommender systems, but most of these works assume that the attacker knows or can easily access the rating matrix. In practice, this information is not directly accessible, but can only be gathered via crawling. Considering such real-life limitations, in this paper, we assess the impact of different crawling approaches when attacking a recommendation service. From the crawled information, we mount different shilling attacks. We determine the value of the collected knowledge through the reconstruction of the user/item neighborhood. Our results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction), this will not be enough to mount a successful shilling attack in practice.
Wireless Signal Based Elderly Fall Detection Using XGboost Algorithm

With the rapid population ageing and increase of the elderly who live alone, there is a growing demand for intelligent monitoring, especially fall detection systems. In this paper, based on received signal strength (RSS) and machine learning algorithm, a fall detection method is proposed. It using multi-domain features, including time-domain and wavelet-domain, and Boost algorithm trains a model to discriminate fall and other actions, such as, sit, stand and squat. The experimental results show that the proposed method can identify falls well.
Automatic Derivation of Conceptual Database Model from a Set of Business Process Models

The paper presents an approach aimed at automatically deriving the conceptual database model from a set of business process models. The approach proposes the incremental synthesis of the target model by iteratively composing the partial conceptual database models that are derived from the models contained in the source set. The approach is implemented by the AMADEOS tool, which is the first online web-based tool enabling the automatic derivation of the conceptual database model from a set of business process models.
Utility and Privacy in Object Tracking from Video Stream using Kalman Filter

Tracking objects in Computer Vision is a hard problem. Privacy and utility concerns adds an extra layer of complexity over this problem. In this work we consider the problem of maintaining privacy and utility while tracking an object in a video stream using Kalman filtering. Our first proposed method ensures that the localization accuracy of this object will not improve beyond a certain level. Our second method ensures that the localization accuracy of the same object will always remain under a certain threshold.
Generating Dialogue Sentences to Promote Critical Thinking

Conventional dialogue generating methods focus on generating fluent sentences in context, but insufficient consideration of speaker emotions. They often generate sentences with the same sentimental polarity of the speakers. Such that sentences are hard to change the mood or viewpoints of the speakers, i.e., from a negative mood to positive one, etc. In this paper, we propose a method to generate dialogue sentences to provide different viewpoints. To this end, we propose two novel concepts, polarity co-occurrence (P-Cooc) and modification co-occurrence (M-Cooc). P-Cooc is used to find aspects providing different and comfortable viewpoints, and M-Cooc is used to find proper modification terms to such that aspects. The experimental results demonstrate that our method could provide supplementary viewpoints to promote critical thinking.