Reviewerly/exHarmony huggingface dataset:
Hybrid incremental learning of new data and new classes for hand-held object recognition

Intelligence technology is an important research area. As a very special yet important case of object recognition, hand-held object recognition plays an important role in intelligence technology for its many applications such as visual question-answering and reasoning. In real-world scenarios, the datasets are open-ended and dynamic: new object samples and new object classes increase continuously. This requires the intelligence technology to enable hybrid incremental learning, which supports both data-incremental and class-incremental learning to efficiently learn the new information. However, existing work mainly focuses on one side of incremental learning, either data-incremental or class-incremental learning while do not handle two sides of incremental learning in a unified framework. To solve the problem, we present a Hybrid Incremental Learning (HIL) method based on Support Vector Machine (SVM), which can incrementally improve its recognition ability by learning new object samples and new object concepts during the interaction with humans. In order to integrate data-incremental and class-incremental learning into one unified framework, HIL adds the new classification-planes and adjusts existing classification-planes under the setting of SVM. As a result, our system can simultaneously improve the recognition quality of known concepts by minimizing the prediction error and transfer the previous model to recognize unknown objects. We apply the proposed method into hand-held object recognition and the experimental results demonstrated its advantage of HIL. In addition, we conducted extensive experiments on the subset of ImageNet and the experimental results further validated the effectiveness of the proposed method.
Revision of defeasible preferences

There are several contexts of non-monotonic reasoning where a priority between rules is established whose purpose is preventing conflicts.One formalism that has been widely employed for non-monotonic reasoning is the sceptical one known as Defeasible Logic.In Defeasible Logic the tool used for conflict resolution is a preference relation between rules, that establishes the priority among them.In this paper we investigate how to modify such a preference relation in a defeasible logic theory to change the conclusions of the theory itself.We argue that the approach we adopt is applicable to legal reasoning, where users, in general, cannot change facts or rules, but can propose their preferences about the relative strength of the rules.We provide a comprehensive study of the possible combinatorial cases and we identify and analyse the cases where the revision process is successful.After this analysis, we identify three revision/update operators and study them against the AGM postulates for belief revision operators, to discover that only a part of these postulates are satisfied by the three operators.
Feature fusions for 2.5D face recognition in Random Maxout Extreme Learning Machine

Contemporary face recognition system is often based on either 2D (texture) or 3D (texture + shape) face modality. An alternative modality that utilizes range (depth) facial images, namely 2.5D face recognition emerges. In this paper, we propose a 2.5D face descriptor that based on the Regional Covariance Matrix (RCM), a powerful means of feature fusion technique and a novel classifier dubbed Random Maxout Extreme Learning Machine (RMELM). The RCM of interest is constructed based on the Principal Component Analysis (PCA) filters responses of facial texture and/or range image, wherein the PCA filters are learned from a two-layer PCA network. The RMELM is an ELM variant where the activation function is based on the locally linear maxout function, in place of typical global non-linear functions in ELM. Since the RCM is a special case of symmetric positive definite matrix that resides on the Tensor manifold; a gap exists in between RCM and RMELM, which is a vector-based classifier. To bridge the gap, we flatten the manifold by transforming the RCM to a feature vector via a matrix logarithm operator. Experimental results from two public 3D face databases, FRGC v2.0 database and Gavab database, validated our proposed method is promising in 2.5D face recognition.
Cloud-based enterprise resource planning with elastic model–view–controller architecture for Internet realization

Companies currently encounter obstacles in networking, integration, and global manufacturing as mobile devices as well as computers increasingly facilitate commercial activities. Accordingly, we propose a new web-based enterprise resource planning (e-ERP) system that can be rapidly migrated from conventional on-site ERP systems. In this paper, by referring to Internet technologies and several key web topics, we explore the evolution of e-Commerce and ERP. We also discuss the applications of the proposed method based on an elastic model–view–controller e-ERP and software-as-a-service delivery. On the basis of information system architecture evaluation and realization results, we discuss the application of web technologies and web engineering models for the effective hosting of a reliable cloud ERP. In addition, on the basis of a review of the literature and related emerging technologies, we comparatively discuss several holistic frameworks of web application development from the technical and business perspectives. As indicated by the implementation results, the proposed approach can be used to effectively host a secure cloud ERP.
Incremental feature weighting for fuzzy feature selection

Feature selection presents many challenges and difficulties during online learning. In this study, we focus on fuzzy feature selection for fuzzy data stream. We present a novel incremental feature weighting method with two main phases comprising offline fuzzy feature selection and online fuzzy feature selection. A sliding window is used to divide the fuzzy data set. Each fuzzy input feature is assigned a weight from [0,1] according to the mutual information shared between the input features and the output feature. These weights are employed to access the candidate fuzzy feature subsets in the current window and based on these subsets, the offline fuzzy features selection algorithm is applied to obtain the fuzzy feature subsets by combining the backward feature selection method with the fuzzy feature selection index in the first sliding window. The online feature selection algorithm is performed in each of the new sliding windows. The feature subset in the current window is updated by combining the fuzzy feature selection results from the previous sliding window with the current candidate fuzzy feature set according to the importance level of the fuzzy input feature. Finally, the evolving relationships of the fuzzy input features are found using the fuzzy feature weight between the sliding windows. Simulation results showed that the proposed algorithm obtains significantly improved adaptability and prediction accuracy compared with existing algorithms.
Multimodal behavior analysis in the wild: An introduction

The nature of human behavior is complex and multifaceted. Behavioral expressions vary significantly across individuals and are influenced by many factors. People act differently according to their physical and mental state, to their age, gender and socio-cultural background, to the nature of the tasks they are engaged in, to the environment where they operate, to the behavior of other individuals, etc. All these factors make the automatic analysis of human behavior an extremely challenging problem. Despite its complexity, human behavior understanding has attracted considerable attention due to its many applications, e.g., in health care, conflict and people management, sociology, marketing and surveillance, etc. In the last decades many researchers have invested efforts into developing computational approaches that enable one to automatically describe the behavior of individuals and groups. Generally speaking, the extraction of behavioral information involves methods operating at different levels of granularity, from low level (e.g., people detection, motion estimation) to high level (e.g., emotional states, personality traits). accurate methods operating at each level. This book describes some recent research efforts in the area of human behavior analysis, presenting methodologies for extracting behavioral cues at different levels. Special emphasis is given to recent approaches considering multimodal data to robustly extract behavioral information in real-world settings. Beside covering state-of-the-art research, the book also outlines some open challenges in the field as well as promising future research directions.
Intensity Inhomogeneity Suppressed Fuzzy C-Means for Infrared Pedestrian Segmentation

Pedestrians are highlighted regions in infrared images. Hence, infrared images could be used for pedestrian detection. Segmentation is an important step for detection and an accurate segmentation would be helpful for infrared pedestrian detection. However, intensity inhomogeneity is a common drawback in infrared images due to occlusion or uneven heat dissipation. This phenomenon would have negative influences on pedestrian segmentation in infrared images. To address this problem, an intensity inhomogeneity suppressed fuzzy C-means method is proposed in this paper for the segmentation of infrared pedestrians with intensity inhomogeneity. Two improvements are made in the proposed method to suppress intensity inhomogeneity: 1) a weight based on membership information and image intensity information of infrared pedestrians is added to the objective function and 2) neighborhood information is considered by adding a regularization term into the objective function to suppress the intensity inhomogeneity in infrared images. Eight famous segmentation methods are utilized as comparison methods in our experiment. The experimental results show that the proposed method could effectively suppress the intensity inhomogeneity in infrared pedestrian images and perform better for segmentation than the comparison methods.
Multipath Ensemble Convolutional Neural Network

Convolutional neural networks (CNNs) have achieved great success in the field of computer vision in recent years. In order to achieve higher recognition accuracy, the layers of CNNs are generally required to be deeper. However, the increase of CNNs depth will lead to gradient vanishing, which makes insufficient training of network parameters that are close to the input layer and further results in performance degradation. In addition, the simple deepening of layers has limited effects on classification accuracy improvement. Compared with shallow network, the deep network is inherently difficult to be optimized, and thus, converges slowly. To this end, a novel multipath ensemble CNN (ME-CNN) model is proposed in this paper. On one hand, the ME-CNN directly concatenates the low-level with that of high-level output features, and thus, the error gradient can be back propagated through a shorter path to achieve the ultradeep network training. On another, the ME-CNN broadens the network width by increasing the number of feature channels to further improve the network performance. Experimental results on CIFAR and tiny ImageNet datasets verify that the depth of ME-CNN can reach beyond one hundred layers, and the performance gradually increases as the network deepens and widens.
E-Voting System Evaluation Based on The Council of Europe Recommendations: Helios Voting

Despite the claimed benefits of e-voting initiatives, wider adoption of e-voting mechanisms and implementation processes is slower than expected. Several technical, social, and cultural challenges hinder generability and applicability of e-voting. Amongst them, the evaluation and harmonization of e-voting systems, given different legal and statutory frameworks, is still an important challenge to overcome. Yet, only a few works have addressed this topic in the field. This article aims to contribute to further understanding this unexplored topic by applying a practical evaluation framework to Helios Voting, one of the most widely used e-voting tools to date. Our framework, strongly based on the technical and security requirements issued by the Council of Europe in 2017, is a valuable source of information for election officials, researchers and voters to understand the strengths and weaknesses of Helios Voting and, as a result, to improve decision-making processes regarding the type and size of elections that can be securely handled by Helios Voting. The ultimate goal of our paper is to conceptually and practically support the gradual, secure and protocolized expansion of e-voting.
Boosted Convolutional Neural Network for object recognition at large scale

During the last few years, object recognition has received a big of interest in an attempt to make use of the large scale image datasets. Object recognition allows understanding image based on the objects that it contains. The success of the deep learning based methods in recognizing objects has encouraged recent works to follow this approach. In this paper, we propose a Boosted Convolutional Neural Network approach for object recognition. Our approach uses a very deep convolutional neural network reinforced by adding Boosted Blocks. These Boosted Blocks consist of a succession of convolutional layers boosted by using a Multi-Bias Nonlinear Activation function, that enriches the expressive power of the network, as well as a Concatenated Rectified Linear Unit function which ensures the preservation of all the information after the convolutional layer. Besides, inspired by the visual system of the human brain, our Boosted Convolutional Layer is designed following a recurrent structure. In addition, rather than using the classical max-pooling, our Boosted Convolutional Neural Network is improved by applying Generalizing pooling which allows pooling to adapt to complex and variable patterns. Furthermore, the Spatial Pyramid Pooling after the last Boosted Block has been conducted after the Boosted Block in order to remove the fixed-size constraint of input image. Our approach is evaluated on four different object recognition benchmarks: Pascal VOC 2007, Pascal VOC 2012, CIFAR-10 and the larger dataset ILSVRC-2012. These datasets are widely used by the recent object recognition methods. The experimental results validate the efficiency of our method in comparison with other methods from the literature.
Understanding flows in high-speed scientific networks: A Netflow data study

Complex science workflows involve very large data demands and resource-intensive computations. These demands need reliable high-speed networks, that can optimize performance for application data flows. Characterizing flows into large flows (elephant) versus small flows (mice) can allow networks to optimize performance by detecting and handling demands in real-time. However, predicting elephant versus mice flows is extremely difficult as their definition varies based on networks. Machine learning techniques can help classify flows into two distinct clusters to identify characteristics of transfers. In this paper, we investigate unsupervised and semi-supervised machine learning approaches to classify flows in real time. We develop a Gaussian Mixture Model combined with an initialization algorithm, to develop a novel general-purpose method to help classification based on network sites (in terms of data transfers, flow rates and durations). Our results show that the proposed algorithm is able to cluster elephants and mice with an accuracy rate of 90%. We analyzed NetFlow reports of 1 month from 3 ESnet site routers to train the model and predict clusters.
Predictors of visually induced motion sickness in women

Visually induced motion sickness (VIMS) is commonly experienced when visual motion information is in conflict with motion information provided by the vestibular system and/or tactile and kinesthetic senses. According to some studies, women experience higher levels of VIMS than men, whereas other studies do not find this to be the case. This study investigated whether gender differences can be attributed to changes in VIMS-susceptibility due to the menstrual cycle and perceived menstrual pain. We hypothesized that higher perceived menstrual pain correlates positively with VIMS susceptibility. We measured the influence of estimated concentrations of four sex hormones and menstrual pain, on the magnitude of self-reported VIMS levels and on measured postural stability in 56 participants. They were confronted with a continuous ten-minute recording of a bike ride across mountainous terrain. Subjective VIMS was measured at one-minute intervals during stimulus presentation using the Fast Motion Sickness (FMS) Scale; postural sway was recorded immediately before and after stimulus presentation. Correlation between peak FMS scores and perceived menstrual pain was high. Furthermore, significant differences between women with severe menstrual pain on the one hand and women with low pain and men on the other hand were found. Our findings suggest a shared link between VIMS and menstrual pain. Further research should investigate pain sensitivity as a general predictor for VIMS.
Supervised heterogeneous feature transfer via random forests

Transfer learning across heterogeneous feature spaces can, in general, be a very difficult problem in practice due to the heterogeneity of features and lack of correspondence between data points of different domains. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that transfers knowledge from a data-rich source domain to a target domain with only few training instances. The proposed method makes use of random forests to identify pivot features that bridge the two domains. The key idea of the proposed feature transfer approach is that every path in a decision tree leading to a partition of the data is associated with a certain label distribution and the label distributions that appear both in the source and target random forest models can be used as pivots for bridging the two domains. This information is used to generate a sparse feature transformation matrix, which maps patterns from the source feature space to the target feature space. The target model is then retrained along with the projected source data. We conduct extensive experiments on diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches.
A vague set approach for identifying shot transition in videos using multiple feature amalgamation

Shot boundary detection (SBD) is the preliminary and most significant step in Content Based Video Retrieval (CBVR). As such the effectiveness of a CBVR system depends heavily on reliable detection of shot boundaries. In this work, a simple yet effective technique for amalgamating several distance features extracted from video frames has been proposed. The aim here is to develop a technique which is able to produce a better distance feature from the existing ones by hybridizing several distance metrics. In the proposed model, any number of distance features can be incorporated and fused together. The resultant feature is not only more robust but also immune to features which are inefficient. Robustness of the proposed method is tested by combining several low performing features with the more efficient ones. Several statistical amalgamation functions are also tested for determining the most efficient one in terms of F1 score. The power of vague sets has been harnessed to detect the shot boundaries effectively using the resultant distance feature. The proposed method is proved to be effective by means of the results obtained, which show that multiple feature amalgamation can lead to a hybrid distance feature which performs better than the best feature incorporated for SBD. The proposed technique is analyzed using ANOVA. A comparison with the other existing methods portray the efficacy of the proposed approach. This method can also be applied for other research problems where several features are to be fused together for producing superior results than the ones obtained by individual methods.
High-Throughput Architecture for Both Lossless and Near-lossless Compression Modes of LOCO-I Algorithm

Real-time image lossless and near-lossless compression based on LOCO-I algorithm is in great demand in many critical missions, such as satellite remote sensing, space exploration, and nuclear medical imaging, for its excellent complexity/compression rate tradeoff. However, the real-time implementation of LOCO-I encounters two bottlenecks in error prediction: the context conflict in context update and the pixel reconstruction loop in near-lossless mode, which always limit the overall throughput in practice. This paper adopts a patch-wise compression method and proposes a high-performance globally pipelined hardware architecture with spatial parallelism in local, in which patch-wise dual parallel error prediction (PEP) modules are designed. The PEP gains an extra pixel cycle and thus, alleviates both the two bottlenecks. Besides, an equivalent simplification scheme is designed to accelerate the error quantization computation, the most time and resource consuming procedure in the pixel reconstruction loop, saving the resource usage, and reducing computing delay. The proposed encoder is able to compress each image patch independently in either lossless or near-lossless mode by parameter setting. It facilitates the Region of Interests compression so as to improve the overall compression ratio and achieves excellent information fidelity. Moreover, the possible error propagation can be prevented and constrained within one single patch. The proposed architecture is implemented on a XILINX Virtex6-75t FPGA and achieves a maximum throughput up to 51.684 MPixel/s. It is the fastest architecture reported in the literature which implements both lossless and near-lossless compression modes.
Heuristic target class selection for advancing performance of coverage-based rule learning

Rule learning is a popular branch of machine learning, which can provide accurate and interpretable classification results. In general, two main strategies of rule learning are referred to as ‘divide and conquer’ and ‘separate and conquer’. Decision tree generation that follows the former strategy has a serious drawback, which is known as the replicated sub-tree problem, resulting from the constraint that all branches of a decision tree must have one or more common attributes. The above problem is likely to result in high computational complexity and the risk of overfitting, which leads to the necessity to develop rule learning algorithms (e.g., Prism) that follow the separate and conquer strategy. The replicated sub-tree problem can be effectively solved using the Prism algorithm, but the trained models are still complex due to the need of training an independent rule set for each selected target class. In order to reduce the risk of overfitting and the model complexity, we propose in this paper a variant of the Prism algorithm referred to as PrismCTC. The experimental results show that the PrismCTC algorithm leads to advances in classification performance and reduction of model complexity, in comparison with the C4.5 and Prism algorithms.