Reviewerly/exHarmony huggingface dataset:
Visual feature fusion and its application to support unsupervised clustering tasks

The concept of involving users in the loop of analytic workflows refers to the ability to replace heuristics with user input in machine learning and data mining tasks. For supervised tasks, user engagement generally occurs via the manipulation of training data. But for unsupervised tasks, user involvement is limited to changes in the algorithm parametrization or the input data representation, also known as features. Typically, different types of features can be extracted from raw data, and the careful selection of the extraction strategy allows users to have more control over unsupervised tasks. Nevertheless, since there is no perfect feature extractor, the combination of multiple sets of features has been explored through a process called feature fusion. Feature fusion can be readily performed when the machine learning or data mining algorithms have a cost function, such as accuracy for classification tasks. However, when such a function does not exist, user support needs to be provided, otherwise the process is impractical. In this article, we present a novel feature fusion approach that employs data samples and visualization to allow users to not only effortlessly control the combination of different feature sets but also understand the attained results. The effectiveness of our approach is confirmed by a comprehensive set of qualitative and quantitative experiments, opening up different possibilities for user-guided analytical scenarios. The ability of our approach to provide real-time feedback for feature fusion is exploited in the context of unsupervised clustering techniques, where users can perform an exploratory process to discover the best combination of features that reflects their individual perceptions about similarity.
Fine-Grained Classification of Internet Video Traffic From QoS Perspective Using Fractal Spectrum

Internet video traffic exhibits considerable variation as new video services continue to emerge. Some videos require strict real-time performance, while others may aim for a minimal packet loss rate or sufficient bandwidth. Therefore, it is important to develop fine-grained classification mechanisms to realize effective resource management and quality of service (QoS) provisioning. However, the existing methods for classifying video traffic always suffer from two problems: payload inspection and feature selection. In this paper, we propose a novel method that uses fractal characteristics to achieve traffic classification at a fine-grained level. This method requires neither payload signatures nor statistical features. Through rigorous analysis, we prove the feasibility of employing fractal characteristics for video traffic classification and further develop a theoretical framework for the proposed scheme. For the specific scenario of video flow classification, we improve the theory of fractals in terms of estimated spectrum, core domain, segmentation, and threshold setting. The results of an extensive experimental study on several real-world video traffic datasets show that the classification accuracy of the proposed scheme is higher than that of existing methods.
Landmark-Guided Deformation Transfer of Template Facial Expressions for Automatic Generation of Avatar Blendshapes

Blendshape models are commonly used to track and re-target facial expressions to virtual avatars using RGB-D cameras and without using any facial marker. When using blendshape models, the target avatar model must possess a set of key-shapes that can be blended depending on the estimated facial expression. Creating realistic set of key-shapes is extremely difficult and requires time and professional expertise. As a consequence, blendshape-based re-targeting technology can only be used with a limited amount of pre-built avatar models, which is not attractive for the large public. In this paper, we propose an automatic method to easily generate realistic key-shapes of any avatar that map directly to the source blendshape model (the user is only required to select a few facial landmarks on the avatar mesh). By doing so, captured facial motion can be easily re-targeted to any avatar, even when the avatar has largely different shape and topology compared with the source template mesh. Our experimental results show the accuracy of our proposed method compared with the state-of-the-art method for mesh deformation transfer.
An efficient certificateless public key encryption with equality test toward Internet of Vehicles

Abstract With the rapid development of the Internet of Vehicles, a great amount of vehicles are connected to Internet of Vehicles environment. These vehicles can exchange data to each other and upload the data to a cloud server for backup. Since the data contains some sensitive information, it is necessary to encrypt the data before uploading to the cloud server. However, this method severely hampers the utilization of data because of the tremendous difficulty in the search for encrypted data. For addressing the above issue, we propose a novel certificateless public key encryption supporting the equality test on ciphertexts that are performed by the cloud server while revealing nothing about the corresponding plaintexts. Our scheme has proved to be secure in bilinear Diffie‐Hellman assumption under the model of random oracle. At the same time, both theoretical analysis and experimental evaluation indicate the security, efficiency, as well as effectiveness of the proposed scheme.
The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies

Surgical pathology forms the cornerstone of modern oncological medicine, owing to the wealth of clinically relevant information that can be obtained from tissue morphology. Although several ancillary testing modalities have been added to surgical pathology, the way in which we view and interpret tissue morphology has remained largely unchanged since the inception of our profession. In this review, we discuss new technological advances that promise to transform the way in which we access tissue morphology and how we use it to guide patient care.
Interactive Bicluster Aggregation in Bipartite Graphs

Exploring coordinated relationships is important for sense making of data in various fields, such as intelligence analysis. To support such investigations, visual analysis tools use biclustering to mine relationships in bipartite graphs and visualize the resulting biclusters with standard graph visualization techniques. Due to overlaps among biclusters, such visualizations can be cluttered (e.g., with many edge crossings), when there are a large number of biclusters. Prior work attempted to resolve this problem by automatically ordering nodes in a bipartite graph. However, visual clutter is still a serious problem, since the number of displayed biclusters remains unchanged. We propose bicluster aggregation as an alternative approach, and have developed two methods of interactively merging biclusters. These interactive bicluster aggregations help organize similar biclusters and reduce the number of displayed biclusters. Initial expert feedback indicates potential usefulness of these techniques in practice.
Design of a Convolutional Neural Network Based Smart Waste Disposal System

In recent times, waste management problem has become a crucial challenge for Bangladesh, which is having a detrimental impact on the environment. This paper presents the proposition of designing a smart dustbin similar to an Automated Teller Machine (ATM) along with an intelligent embedded system, which has been dubbed as Automated Teller Dustbin (ATD). An efficient convolutional neural network (CNN) based image classifier is developed, which is able to detect and recognize any object regarded as garbage by analyzing training features. Additionally, it can also count the number of labeled objects and assign a price value to each object. The waste brought by any individual to the ATD will readily be recognized by the image classifier and the recycle value, which has been assigned for that object can be withdrawn by that individual. Therefore, a direct exchange of waste and its equivalent price is possible, which will incentivize people to use our proposed smart dustbin. After the installation cost, the operation and maintenance cost can be gained by recycling the garbage in it. A pre-trained CNN-based model ALexNet has been utilized to train and test the model with a dataset of 20 images for each of the 10 categorized objects collected from different waste management shops in Dhaka, Bangladesh. The model that has been trained for object recognition has attained an accuracy of 96%, which bears testimony to the feasibility of our proposal.
An effective approach to unmanned aerial vehicle navigation using visual topological map in outdoor and indoor environments

Unmanned Aerial Vehicles are constantly being using in professional activities that require higher precision in navigating and positioning the aircraft during operation. Advanced location technologies such as Global Navigation Satellite System and Real-Time Kinematic are widely used, however, they depend on an area with transmission coverage. In this approach, this article presents a visual navigation methodology based on topological maps. We compared the performance of consolidated classifiers such as Bayesian classifier, k-nearest neighbor, Multilayer Perceptron, Optimal Path Forest and Support Vector Machines (SVM). They are evaluated with attributes returned by last generation resource extractors such as Fourier, Gray Level Co-Occurrence and Local Binary Patterns (LBP). After analyzing the results we found that the combination of LBP and SVM obtained the best values in the evaluation metrics considered, among them, 99.99% Specificity and 99.98% Precision in the navigation process. SVM reached 5.49787 s in combination with LBP completes the training in 5.49787 s. Concerning the testing time, SVM achieving 80.91 ms in association with LBP.
Feature Fusion Based Deep Spatiotemporal Model for Violence Detection in Videos

It is essential for public monitoring and security to detect violent behavior in surveillance videos. However, it requires constant human observation and attention, which is a challenging task. Autonomous detection of violent activities is essential for continuous, uninterrupted video surveillance systems. This paper proposed a novel method to detect violent activities in videos, using fused spatial feature maps, based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) units. The spatial features are extracted through CNN, and multi-level spatial features fusion method is used to combine the spatial features maps from two equally spaced sequential input video frames to incorporate motion characteristics. The additional residual layer blocks are used to further learn these fused spatial features to increase the classification accuracy of the network. The combined spatial features of input frames are then fed to LSTM units to learn the global temporal information. The output of this network classifies the violent or non-violent category present in the input video frame. Experimental results on three different standard benchmark datasets: Hockey Fight, Crowd Violence and BEHAVE show that the proposed algorithm provides better ability to recognize violent actions in different scenarios and results in improved performance compared to the state-of-the-art methods.
Interpreting Layered Neural Networks via Hierarchical Modular Representation

Interpreting the prediction mechanism of complex models is currently one of the most important tasks in the machine learning field, especially with layered neural networks, which have achieved high predictive performance with various practical data sets. To reveal the global structure of a trained neural network in an interpretable way, a series of clustering methods have been proposed, which decompose the units into clusters according to the similarity of their inference roles. The main problems in these studies were that (1) we have no prior knowledge about the optimal resolution for the decomposition, or the appropriate number of clusters, and (2) there was no method for acquiring knowledge about whether the outputs of each cluster have a positive or negative correlation with the input and output unit values. In this paper, to solve these problems, we propose a method for obtaining a hierarchical modular representation of a layered neural network. The application of a hierarchical clustering method to a trained network reveals a tree-structured relationship among hidden layer units, based on their feature vectors defined by their correlation with the input and output unit values.
Quantumness protection for open systems in a double-layer environment

We study the dynamics of two-level atomic systems (qubits) subject to a double-layer environment that consists of a network of single-mode cavities coupled to a common reservoir. A general exact master equation for the dynamics of a qubit system can be obtained by the quantum-state-diffusion (QSD) approach, which is extended to our spin-cavity-boson model. The quantumness of the atoms comprising coherence and entanglement is investigated for various configurations of the double-layer environment. The findings indicate that parametric control is available for the preservation and generation of system-quantumness by regulating the cavity network. Moreover the underlying physics is profoundly revealed by an effective model obtained by a unitary transformation. Therefore, our work provides an interesting proposal to protect the quantumness of open systems in the framework of a double-layer environment containing bosonic modes.
Pairwise registration of TLS point clouds by deep multi-scale local features

Because of the mechanism of TLS system, noise, outliers, various occlusions, varying cloud densities, etc. inevitably exist in the collection of TLS point clouds. To achieve automatic TLS point cloud registration, many methods, based on the hand-crafted features of keypoints, have been proposed. Despite significant progress, the current methods still face great challenges in accomplishing TLS point cloud registration. In this paper, we propose a multi-scale neural network to learn local shape descriptors for establishing correspondences between pairwise TLS point clouds. To train our model, data augmentation, developed on pairwise semi-synthetic 3D local patches, is to extend our network to be robust to rotation transformation. Then, based on varying local neighborhoods, multi-scale subnetworks are constructed and fused to learn robust local features. Experimental results demonstrate that our proposed method successfully registers two TLS point clouds and outperforms state-of-the-art methods. Besides, our learned descriptors are invariant to translation and tolerant to changes in rotation.
Enriching textbooks by Question-Answers using cQA

The two major sources of information for the knowledge seekers are the community question answers (cQA) blogs and textbooks. Textbooks play a vital role in any educational system. Many times, the textbooks that are available in the market are not adequate to fulfill the curiosity of the students, they frequently use the online question answering systems to acquire more knowledge. Due to the high volume of data, there will be high variance in the quality of questions and available answers in cQA forums, hence it takes additional effort to go through all possible question-answers for a better insight. To address this issue, this paper presents a technological solution-“A sentence-level text enrichment process” for a textbook with cQA content. We used techniques of natural language processing and data mining to extract the high-quality question-answers (QA) sets and corresponding links of cQA to enrich the textbooks. Experiments were carried out on the National Council of Educational Research and Training (NCERT) textbooks from India and Pattern Recognition and Machine Learning textbook by Christopher M Bishop, proves that we succeed to enrich textbooks on various subjects and across different grades with high-quality reference materials using automated techniques. The performance of the proposed system is evaluated using precision scores states that our method is competitive.
Tag-Based Semantic Features for Scene Image Classification

The existing image feature extraction methods are primarily based on the content and structure information of images, and rarely consider the contextual semantic information. Regarding some types of images such as scenes and objects, the annotations and descriptions of them available on the web may provide reliable contextual semantic information for feature extraction. In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web. Specifically, we propose a new method which consists of two consecutive steps to extract our semantic features. For each image in the training set, we initially search the top $k$ most similar images from the internet and extract their annotations/descriptions (e.g., tags or keywords). The annotation information is employed to design a filter bank for each image category and generate filter words (codebook). Finally, each image is represented by the histogram of the occurrences of filter words in all categories. We evaluate the performance of the proposed features in scene image classification on three commonly-used scene image datasets (i.e., MIT-67, Scene15 and Event8). Our method typically produces a lower feature dimension than existing feature extraction methods. Experimental results show that the proposed features generate better classification accuracies than vision based and tag based features, and comparable results to deep learning based features.
Recognition of Transrectal Ultrasound Prostate Image Based on HOG-LBP

Prostate biopsy is a gold standard for diagnosing prostate cancer. In clinic, multi-needle saturation puncture is often used in the diagnosis of prostate cancer. Although it can improve the positive rate of diagnosis, it also increases the probability of postoperative infection, hematuria and other complications. This paper presents a method to identif prostate cancer by histogram of oriented gradient (HOG) and local binary pattern (LBP) feature extraction. Firstly, Gaussian filtering, gradient transformation function and other algorithms are used to preprocess the transrectal ultrasound prostate images to filter out image noise and improve contrast. Then, the local and global texture feature information of the image is extracted by using HOG and LBP. Finally, support vector machine (SVM) is used to classify features and identify positive regions. The results show that the proposed method is superior to other methods. The transrectal ultrasound prostate images exhibit superior diagnostic performance with an accuracy of 72.2% and a specificity of 75%. Experiments show that this method can provide the necessary auxiliary information for doctor diagnosis and reduce the number of puncture needles.
Lost in spotify: folksonomy and wayfinding functions in spotify’s interface and companion apps

Music streaming sites are growing rapidly and the novel ways in which site users can organize, explore and present their music are important in music discovery. This paper focuses on the organization and presentation of music through folksonomy in the visual interface of Spotify. The site's interface is evaluated to determine its overall folksonomy-friendliness using a framework based upon an analysis of wayfinding features. Folksonomy is a social tagging strategy that exemplifies the innovation of dynamic web interfaces but is surprisingly scarce in music streaming interfaces. It can be found in sites composed of user-made content, where users categorize music in their own words. How music is organized in streaming interfaces, and whether it reflects a user's own musical vocabulary, impacts upon a user's access to a variety of music, their modes of interaction, and overall power dynamics, in which one path of listening may have more influence than another.
Analysis and evaluation of eye behavior for marine operation training - A pilot study

This paper presents a new analysis approach for evaluating situation awareness in marine operation training. Taking advantage of eye tracking technology, the situation awareness reflected by visual attention can be visualized and analyzed. A scanpath similarity comparison method that allows group-wise comparisons is proposed. The term ‘Expert zone’ is introduced to evaluate the performance of novice operator based on expert operators’ eye movement. It is used to evaluate performance of novice operators in groups in certain segment of marine operation. A pilot study of crane lifting experiment was carried out. Two target stages of operation for the load descending until total immersion to the seabed were selected and analyzed for both novice and expert operators. The group-wise evaluation method is proven to be able to access the performance of the operator. Besides that, from data analysis of fixation-related source and scanpath, the similarities and dissimilarities of eye behavior between novice and expert is concluded with the scanpath mode in target segment.
Algorithmic and language-based optimization of Marsa-LFIB4 pseudorandom number generator using OpenMP, OpenACC and CUDA

The aim of this paper is to present new high-performance implementations of Marsa-LFIB4 which is an example of high-quality multiple recursive pseudorandom number generators. We propose an algorithmic approach that combines language-based vectorization techniques together with a new divide-and-conquer parallel method that exploits a special sparse structure of the matrix obtained from the recursive formula that defines the generator. Our portable OpenACC implementation achieves the performance comparable to those achieved by our CUDA-based and OpenMP-based implementations on GPUs and multicore CPUs, respectively.
A Strong Baseline for Tiger Re-ID and its Bag of Tricks

As an instance-level recognition task, person re-identification methods always calculate local features by horizontal pooling. It is based on a simple assumption that pedestrians always stand vertically. But as to wildlife re-identification task, we can not make similar assumption since the various view-angles of wildlife. In this paper, we propose a novel dynamic partial matching method. In our module, global feature learning benefits greatly from local feature learning, which performs an alignment/matching by flipping local features and calculating the shortest path between them. Besides the partial matching method, we also consider a series of data augmentation methods such as flip as new id, random whitening, random crop and so on. And we also use an example sampling strategy, i.e., hard negative mining, for training. In addition, we ensemble the models with different backbones and epochs using imagenet pre-trained models. Extensive experiments validate the superiority of our method for tiger Re-ID. Code has been released at https://github.com/vvictoryuki/tiger_reid_pytorch.