Reviewerly/exHarmony huggingface dataset:
Multi-Layer Selector(MLS): Dynamic selection based on filtering some competence measures

Multi-Classifier Systems (MCSs) have been widely studied as committee methods for increasing accuracy in pattern recognition. Recently, dynamic selection (DS) techniques have attracted undivided attention as effective types of MCSs in application. In DS techniques, the selection process is accomplished in two steps including measurement and selection. Normally, most DS methods in the literature have tried to focus on approaches adopted based on only one of the two options that are to offer a new measurement or a new selection method. On the other hand, some other papers have proposed frameworks in which both options were applied in combination with each other making use of previously existing measurement methods. For the first time, the idea of combining DS methods via multi-layer selectors is offered in this paper. In specific, two main tasks are performed in each layer of the multi-layer selector. First, the competence-level of classifiers are calculated. Then, the classifiers with high competence-level are passed to the next layer, and this way, best classifiers will be passed to the output in the last layer. During this procedure, some competence criteria are combined to measure the competence-level of classifiers. Furthermore, this paper introduces a novel taxonomy for dynamic selection methods and a new technique, called Probability Based (PB) method, to find the competence level of classifier. Experimental results show that the suggested framework improves the classification accuracy when compared against current state-of-the-art dynamic ensemble selection techniques. The Quade non-parametric statistical test confirms the capability of our proposed method to deal with classification problems.
Circular Fields and Predictive Multi-Agents for Online Global Trajectory Planning

Safe and efficient trajectory planning for autonomous robots is becoming increasingly important in both industrial applications and everyday life. The demands on a robot which has to react quickly and precisely to changes in cluttered, unknown and dynamic environments are particularly high. Towards this end, based on the initial idea proposed in [27] we propose the Circular Field Predictions approach, which unifies reactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planningreactive collision avoidance and global trajectory planning while providing smooth, fast and collision free trajectories for robotic motion planning. The proposed approach is inspired by electromagnetic fields, free of local minima and extended with artificial multi-agents to efficiently explore the environment. The algorithm is extensively analysed in complex simulation environments where it is shown to be able to generate smooth trajectories around arbitrarily shaped obstacles. Moreover, we experimentally verified the approach with a 7 Degree-of-Freedom (DoF) Franka Emika robot.
LeukoX: Leukocyte Classification Using Least Entropy Combiner (LEC) for Ensemble Learning

This brief proposes a scheme to detect and classify across four different classes of white blood cells (WBC) based on their morphological features by ensemble learning applied over multiple deep neural networks (DNN). Contrary to the DNN based WBC classification schemes, this method makes use of a novel least entropy combiner (LEC) network that effectively combines the individual classifier decisions in order to minimize the cross entropy cost. In this brief, two pretrained DNNs, namely DenseNet121 and ResNet50 are used along with a custom-designed convolutional neural network (CNN) to individually produce the class confidence scores which are fed to the proposed LEC network for the ensemble learning purpose. A dataset comprising of around 12, 500 images of four different WBC types, namely Eosinophil, Lymphocyte, Monocyte, and Neutrophil are considered for training and testing the proposed network. The proposed model yields superior performance than the individual networks. Our results demonstrate that the proposed model achieves an overall test accuracy of 96.67%, with precision and sensitivity scores of 96.73%, and 96.62% respectively and the Matthews correlation coefficient (MCC) and kappa scores being 0.9334 and 0.9550, respectively. The overall model is termed as LeukoX (X: Extended).
The dynamic of body and brain co-evolution

We introduce a method that permits to co-evolve the body and the control properties of robots. It can be used to adapt the morphological traits of robots with a hand-designed morphological bauplan or to evolve the morphological bauplan as well. Our results indicate that robots with co-adapted body and control traits outperform robots with fixed hand-designed morphologies. Interestingly, the advantage is not due to the selection of better morphologies but rather to the mutual scaffolding process that results from the possibility to co-adapt the morphological traits to the control traits and vice versa. Our results also demonstrate that morphological variations do not necessarily have destructive effects on robots’ skills.
Research on the Hand-eye calibration Method Based on Monocular Robot

Abstract The measurement system is composed of a single camera and a manipulator, and the conversion relationship between the manipulator tool coordinate system and the camera coordinate system is one of the key technologies of the system. This paper proposes a unit octet hand-eye calibration algorithm, which has better accuracy and anti-noise ability. This algorithm combines the characteristics of data on SO(4) on the basis of traditional dual quaternion, and establishes the conversion equation of AX=XB in combination with robot kinematics, and realizes the eye calibration of the manipulator. Compared with the traditional dual quaternion algorithm, the speed is faster, the robustness is good, and it has better stability and practicability.
A State-of-the-Art Survey of Tasks for Tree Design and Evaluation With a Curated Task Dataset

In the field of information visualization, the concept of "tasks" is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this article, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization articles and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations. These abstracted tasks can benefit visualization researchers and practitioners as they design evaluation studies or compare their analytical tasks with ones previously studied in the literature to make informed decisions about their design. We also reflect on our novel methodology and advocate more broadly for the creation of task-based knowledge repositories for different types of visualizations. The Supplemental Material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TVCG.2021.3064037, will be maintained on OSF: https://osf.io/u5ehs/.
Novel Multifold Secured System by Combining Multimodal Mask Steganography and Naive Based Random Visual Cryptography System for Digital Communication

With increase in growth of data and digital threat, demand of securing the data communicated over the internet is an essential play in the digital world. In the vision of digitalizing services with the next generation of security to the sensitive data transmitted over the internet by hiding the existence of the data using next generation cryptography by fusing cryptography techniques is one the major technique adopted. With this the aim in traditional Least Significant Bit (LSB) is one of the widely used technique. Where the secret message or image are placed in the cover image in the least significant bits of RGB Channels resulting in a stego image. But the drawback is, on suspecting the differences in the pixels of original and stegoimage in the secret data embedded can be guessed and extracted by attacker. The Proposed visual crypto-mask steganography method overcomes this drawback and support good payload capacity with multi modal approach of embedding biometrics, resulting in ∞ PSNR. The authenticated person face and fingerprint information is transmitted in a cover image and mask image (magic sheet) using proposed steganography and is combined with Random Visual Crypto Technique. Which results in enhanced and advance visual crypto steganography secured model in communicating sensitive (biometric features) information over the internet. Where the complete information cannot be extracted using only cover image. Mask image (magic sheet) is used along with cover image that reveals the secret data in the receiving end.
A Handwritten Chinese Character Recognition based on Convolutional Neural Network and Median Filtering

Abstract With the rapid growth of researches toward computer vision and pattern recognition, methods that based on convolutional neural network (CNN) have shown unique advantages on handwritten characters recognition, also provided impressive results. This paper proposes a model based on CNN to deal with matters of handwritten Chinese character recognition. Different with conventional recognition system, in this model, input images are preprocessed by median filtering to smooth and reduce noise. For testing the stability and performance of the model, two testes are managed respectively. In integral test, experimental results show that the accuracy rate of recognition approach to 90.91% after 5000 times training, mean square error is decreased to 0.0079 at last. Meanwhile, this system also has a good performance at real-time test.
Align R-CNN: A Pairwise Head Network for Visual Relationship Detection

Scene graphs connect individual objects with visual relationships. They serve as a comprehensive scene representation for downstream multimodal tasks. However, by exploring recent progress in Scene Graph Generation (SGG), we find that the performance of recent works is highly limited by the pairwise relationship modeling by naive feature concatenation. Such pairwise features lack sufficient object interaction due to the mis-aligned object parts, resulting in non-discriminative pairwise features for visual relationship prediction. For example, naive concatenated pairwise feature usually make the model fail to discriminate between <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">riding</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feeding</monospace> for object pair <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">person</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">horse</monospace> . To this end, we design a meta-architecture— learning-to-align — for dynamic object feature concatenation. We call our model: <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Align R-CNN</b> . Specifically, we introduce a novel attention-based multiple region alignment module that can be jointly optimized with SGG. Experiments on the large-scale SGG benchmark Visual Genome show that the proposed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Align R-CNN</b> can replace the naive feature concatenation and thus boost all the existing SGG methods.
9.4 PIU: A 248GOPS/W Stream-Based Processor for Irregular Probabilistic Inference Networks Using Precision-Scalable Posit Arithmetic in 28nm

While deep neural networks have become an indispensable tool in today's smart devices, their usage is also criticized due to lack of explainability, inability to include domain knowledge, and a need for large volumes of training data. To overcome this, researchers are increasingly using probabilistic models as a part of the system [1] [2] [3] (Fig. 9.4.1). For example, Stelzner et al. [2] complements neural networks with probabilistic models for efficient unsupervised scene understanding robust to noise. Zheng et al. [3] uses a probabilistic model for end-to-end semantic environment mapping during robotic navigation. While sampling techniques are usually used for approximate inference with probabilistic models, fast exact inference is often tractable by using Sum-Product Networks (SPN, also called probabilistic circuits) [4]. This SPN-based inference is preferred over sampling techniques because it provides deterministic results, avoids error-accumulation, and can enable inference in discrete probabilistic programs.
Knowledge graph embedding with shared latent semantic units

Knowledge graph embedding (KGE) aims to project both entities and relations into a continuous low-dimensional space. However, for a given knowledge graph (KG), only a small number of entities and relations occur many times, while the vast majority of entities and relations occur less frequently. This data sparsity problem has largely been ignored by most of the existing KGE models. To this end, in this paper, we propose a general technique to enable knowledge transfer among semantically similar entities or relations. Specifically, we define latent semantic units (LSUs), which are the sub-components of entity and relation embeddings. Semantically similar entities or relations are supposed to share the same LSUs, and thus knowledge can be transferred among entities or relations. Finally, extensive experiments show that the proposed technique is able to enhance existing KGE models and can provide better representations of KGs.
Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition

A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata with billions of statements describing millions of entities. The aim of Question Answering systems is to allow lay users to access such data using natural language without needing to write formal queries. However, users often submit questions that are complex and require a certain level of abstraction and reasoning to decompose them into basic graph patterns. In this short paper, we explore the use of architectures based on Neural Machine Translation called Neural SPARQL Machines to learn pattern compositions. We show that sequence-to-sequence models are a viable and promising option to transform long utterances into complex SPARQL queries.
A gradient-based method for multilevel thresholding

At present, the research about fast algorithm for multi-threshold image segmentation is mostly focused on bionic algorithm. Many of bionic algorithms, such as particle swarm optimization algorithm (PSO), artificial bee colony algorithm (ABC), bat algorithm (BA), firefly algorithm (FA) and grey wolf optimization algorithm (GWO), have achieved good results and are being further improved. In this paper, a gradient-based method different from bionic algorithm is proposed to search optimal multilevel thresholds. For most realistic images, the objective functions in mostly-used multi-threshold methods, such as Kapur's entropy and Otsu function, have good convexity. On this basis, gradient descent method can be used to solve the optimization problem more simply and directly. Based on gradient descent method, our method is tailored for discrete objection function, and the process of searching optimal threshold is divided into two stages, which makes algorithm convergence efficient and accurate. The multi-thresholding experiments of above mentioned algorithms have been conducted, whose results show that the gradient-based method is efficient in computation and has equal or even better performance of segmentation than other algorithms.
Predicting of Open Source Software Component Reusability Level Using Object-Oriented Metrics by Taguchi Approach

Component-based software development (CBSD) is an efficient approach used by software developers to develop new software. The commercial off the shelf (COTS) and open-source software (OSS) are two styles to implement CBSD. The COTS provides the interface and depicts the black-box behavior, but does not support several software quality characteristics. On the other hard, OSS is a more efficient approach compared to COTS due to its source code availability. This research aims to identify the reusability level of OSS components from an online repository of OSS. The OSS components are classified based on Chidamber and Kemerer reusability metrics (CK-metrics). This paper proposed a mathematical model to establish the relationship between the reusability of CK-metrics. Reusability level of OSS component has been measured and most effective CK-metrics obtained by applying the Taguchi design and analysis of variance (ANOVA). The input parameters for the experimental design are evaluated based on the OSS repository. Performance analysis has been carried out based upon the interaction effect between the reusability of CK-metrics. Main effect plots are created to identify the most reusable component of the OSS. The genetic algorithm (GA) is used to predict the optimized value of the different control parameters. The results indicate that the OSS component reusability level is 0.698194. The reusability of software has a significant effect on the quality of software. The quality of software can be improved by increasing the reusability of software components.
Simultaneous Buckling, Contact, and Load-Carrying Capacity

This paper considers the case of a relatively large number of parallel columns that buckle simultaneously. The close proximity between columns results in the possibility of contact between adjacent columns as buckling proceeds, and this brings with it some interesting observations on load-carrying capacity. Some experimental results verify the theoretical development based on the versatility of high-fidelity 3D printing, but also highlights the difficulty of applying purely axial loading. The sensitive nature of initial geometric imperfections (slight lack of straightness) and load eccentricity strongly influence postbuckled contact, load-carrying capacity, and, as the number of columns is increased, a statistically based evaluation of anticipated behavior is explored and ultimately found to be unrealistic.
On Perfect Obfuscation: Local Information Geometry Analysis

We consider the problem of privacy-preserving data release for a specific utility task under perfect obfuscation constraint.We establish the necessary and sufficient condition to extract features of the original data that carry as much information about a utility attribute as possible, while not revealing any information about the sensitive attribute.This problem formulation generalizes both the information bottleneck and privacy funnel problems.We adopt a local information geometry analysis that provides useful insight into information coupling and trajectory construction of spherical perturbation of probability mass functions.This analysis allows us to construct the modal decomposition of the joint distributions, divergence transfer matrices, and mutual information.By decomposing the mutual information into orthogonal modes, we obtain the locally sufficient statistics for inferences about the utility attribute, while satisfying perfect obfuscation constraint.Furthermore, we develop the notion of perfect obfuscation based on χ 2 -divergence and Kullback-Leibler divergence in the Euclidean information space.
Long-term Cognitive Network-based architecture for multi-label classification

This paper presents a neural system to deal with multi-label classification problems that might involve sparse features. The architecture of this model involves three sequential blocks with well-defined functions. The first block consists of a multilayered feed-forward structure that extracts hidden features, thus reducing the problem dimensionality. This block is useful when dealing with sparse problems. The second block consists of a Long-term Cognitive Network-based model that operates on features extracted by the first block. The activation rule of this recurrent neural network is modified to prevent the vanishing of the input signal during the recurrent inference process. The modified activation rule combines the neurons' state in the previous abstract layer (iteration) with the initial state. Moreover, we add a bias component to shift the transfer functions as needed to obtain good approximations. Finally, the third block consists of an output layer that adapts the second block's outputs to the label space. We propose a backpropagation learning algorithm that uses a squared hinge loss function to maximize the margins between labels to train this network. The results show that our model outperforms the state-of-the-art algorithms in most datasets.
A robust anomaly detection algorithm based on principal component analysis

Quantifying the abnormal degree of each instance within data sets to detect outlying instances, is an issue in unsupervised anomaly detection research. In this paper, we propose a robust anomaly detection method based on principal component analysis (PCA). Traditional PCA-based detection algorithms commonly obtain a high false alarm for the outliers. The main reason is that ignores the difference of location and scale to each component of the outlier score, this leads to the cumulated outlier score deviates from the true values. To address the issue, we introduce the median and the Median Absolute Deviation (MAD) to rescale each outlier score that mapped onto the corresponding principal direction. And then, the true outlier scores of instances can be obtained as the sum of weighted squares of the rescaled scores. Also, the issue that the assignment of the weight for each outlier score will be solved. The main advantage of our new approach is easy to build with unsupervised data and the recognition performance is better than the classical PCA-based methods. We compare our method to the five different anomaly detection techniques, including two traditional PCA-based methods, in our experiment analysis. The experimental results show that the proposed method has a good performance for effectiveness, efficiency, and robustness.