Reviewerly/exHarmony huggingface dataset:
Nuclei Detection with Local Threshold Processing in DAB&amp;H Stained Breast Cancer Biopsy Images

Histopathological sections allow pathologists to evaluate a wide range of specimens, including breast cancer, obtained from biopsies and surgical procedures. The accuracy of the employed automated cell detection technique is critical in obtaining efficient diagnostic performance. In this paper we investigate 18 different adaptive threshold methods based on various approaches. We validate the methods on a set of histopathological images of breast cancer, where immunohistochemical staining of FOXP3 was performed with 3,3’-diaminobenzidine and hematoxylin. The thresholding is performed on monochromatic images derived from original images: separate channels of Red-Green-Blue and Hue-Saturation-Value, layers of results of color deconvolution, ‘brown’ channel, ‘blue-ratio’ layer. The main objective of the evaluation is to determine if the detected objects obtained by the tested methods of thresholding are consistent with the manually labeled ones. The performance is evaluated using precision, sensitivity and F1 score measures. It appears that satisfactory results were achieved only by 6 methods. It was found that bradley method is the best performing method for nuclei detection in this type of stained tissue samples. It has best sensitivity value for images after color deconvolution and Value layer (of Hue-Saturation-Value color space), 0.970 and 0.975 respectively. As a result, we recommend a most efficient local threshold technique in the case of nuclei detection in digitized immunohistochemically stained tissue sections. This initial detection of objects followed by texture, size and shape analysis will give a collection of cells’ nuclei to perform further accurate segmentation. The proposed detection method will be used in a framework focused on computer-aided diagnosis.
Robust and Accurate Representation Learning for High-dimensional and Sparse Matrices in Recommender Systems

How to accurately represent a high-dimensional and sparse (HiDS) user-item rating matrix is a crucial issue in implementing a recommender system. A latent factor (LF) model is one of the most popular and successful approaches to address this issue. It is developed by minimizing the errors between the observed entries and the estimated ones on an HiDS matrix. Current studies commonly employ L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm to minimize the errors because it has a smooth gradient, making a resultant LF model can accurately represent an HiDS matrix. As is well known, however, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm is very sensitive to the outlier data or called unreliable ratings in the context of the recommender system. Unfortunately, the unreliable ratings often exist in an HiDS matrix due to some malicious users. To address this issue, this paper proposes a Smooth L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm-oriented Latent Factor (SL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -LF) model. Its main idea is to employ smooth L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm rather than L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm to minimize the errors, making it have both high robustness and accuracy in representing an HiDS matrix. Experimental results on four HiDS matrices generated by industrial recommender systems demonstrate that the proposed SL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -LF model is robust to the outlier data and has significantly higher prediction accuracy than state-of-the-art models for the missing data of an HiDS matrix.
Post-Click Behaviors Enhanced Recommendation System

To predict users' interests, the traditional recommendation system (RS) relies on exploring the explicit user-item ratings and macro implicit feedbacks (e.g., whether or not a user clicks the item). In this work, fine-grained post-click behaviors (e.g., mouse behaviors, keyboard events, and page scrolling events) are integrated to alleviate the data sparsity problem of explicit feedback and the data accuracy problem of macro implicit feedback. In the deployed article recommendation pipeline, a variety of post-click behaviors are combined to create a reading pattern model. The reading patterns are leveraged by the recommendation system to estimate users' preference levels. As compared with existing click-based (macro implicit feedback) and dwell time-based (single micro implicit feedback) recommendation systems, the test performance of our designed reading pattern-based RS has been significantly improved in terms of rating prediction and ranking.
Bearing-Only Navigation With Field of View Constraints

This paper addresses the problem of navigation using only relative direction measurements (i.e., relative distances are unknown) under field of view constraints. We present a novel navigation vector field for the bearing-based visual homing problem with respect to static visual landmarks in 2-D and 3-D environments. Our method employs two control fields that are tangent and normal to ellipsoids having landmarks as their foci. The tangent field steers the robot to a set of points where the average of observed bearings is parallel to the average of the desired bearings, and the normal field uses the angle between a pair of bearings as a proxy to adjust the robot's distance from landmarks and to satisfy the field of view constraints. Both fields are blended together to construct an almost globally stable control law. Our method is easy to implement, as it requires only comparisons between average bearings, and between angles of pairs of vectors. We provide simulations that demonstrate the performance of our approach for a double integrator system and unicycles.
A New Emulation Platform for Real-time Machine Learning in Substance Use Data Streams

With 5G networks on the rise, it becomes more and more important to grant researchers access to tools that allow for development and experimentation in the field of 5G transmission. Healthcare can benefit greatly from these developments. In this paper a real-time transmission technique is described and tested that, if implemented, allows wearable devices to transmit multiple streams of data on various frequencies. These tests will be used to explain how this presented platform works, what drawbacks and benefits exist with the proposed scheme, and how to further develop the solution of real-time transmission of sensitive data, such as substance-use data, at higher frequencies.
MaskS R-CNN Text Detector

Scene text detection and scene text recognition are important components of scene text recognition system. Scene text detection, the initial stage of scene text recognition, aims to find out text area in the picture. Recently the target detection method Mask R-CNN has been employed scene text detection and achieved good performance. In this paper, we set forth a model, MaskS R-CNN text detector, based on Mask R-CNN, which attempts to detect scene text. In this model, a network block of Mask Scoring R-CNN is introduced to learn the high quality of the predicted instance mask scores. The mask scoring mechanism correct the inconformity between mask quality and mask score, at the same time improves instance segmentation performance by attaching great importance to more accurate mask predictions. The method put forward in this paper can achieve multi-directional and multi-language natural scene text detection. Compared with some existing traditional location methods based on edge, color and texture and some location methods based on deep learning, it is a relatively innovative method.
Clifford Algebras: A Proposal Towards Improved Image Recognition in Machine Learning

Machine learning algorithms are designed to learn autonomously to learn general rules from a set of examples. The importance of this task lies in its potential to provide future and past predictions, as well as to improve the interpretability of the data. RGB images have shown themselves to be a challenging topic to neural networks as their 3 dimensions (Red, Green and Blue) have to be processed using mathematical techniques designed for 1-dimensional inputs. However, an implementation of neural networks using Clifford algebras can speed up the processing time and improve the performance, as the resulting network is based on a 4-dimensional space.
An End-to-end Speech Recognition Algorithm based on Attention Mechanism

End-to-end speech recognition system is a major research field in speech recognition. The most typical model is the end-to-end speech recognition system based on CTC where RNN mining to and time sequence information are adopted, and a series of assumptions of HMM are discarded to obtain a good recognition rate. However, the CTC-based model is more dependent on the speech model and have a longer training cycle. Therefore, in the framework of traditional acoustic model, this paper proposes to train a feature extraction network of spectrogram based on attention mechanism by using prior knowledge. Firstly, it was spliced in the front end based on CTC model, and then the number of layers of cyclic neural network based on CTC model was reduced. Finally, it was combined to retrain. The experimental results show that the training time of the combined model is effectively reduced, and the accuracy of speech recognition is further improved.
Variational Optimization of Informational Privacy

The datasets containing sensitive information can't be publicly shared as a privacy-risk posed by several types of attacks exists. The data perturbation approach uses a random noise adding mechanism to preserve privacy, however, results in distortion of useful data. There remains the challenge of studying and optimizing privacy-utility tradeoff especially in the case when statistical distributions of data are unknown. This study introduces a novel information theoretic framework for studying privacy-utility tradeoff suitable for multivariate data and for the cases with unknown statistical distributions. We consider an information theoretic approach of quantifying privacy-leakage by the mutual information between sensitive data and released data. At the core of privacy-preserving framework lies a variational Bayesian fuzzy model approximating the uncertain mapping between released noise added data and private data such that the model is employed for variational approximation of informational privacy. The suggested privacy-preserving framework consists of three components: 1) Optimal Noise Adding Mechanism; 2) Modeling of Uncertain Mapping Between Released Noise Added Data and Private Data; and 3) Variational Approximation of Information Privacy.
Decentralized Architecture for Energy-Aware Service Assembly

Contemporary application domains make more and more appealing the vision of applications built as a dynamic and opportunistic assembly of autonomous and independent resources. However, the adoption of such paradigm is challenged by: (i) the openness and scalability needs of the operating environment, which rule out approaches based on centralized architectures and, (ii) the increasing concern for sustainability issues, which makes particularly relevant, in addition to QoS constraints, the goal of reducing the application energy footprint. In this context, we contribute by proposing a decentralized architecture to build a fully functional assembly of distributed services, able to optimize its energy consumption, paying also attention to issues concerning the delivered quality of service. We suggest suitable indexes to measure from different perspectives the energy efficiency of the resulting assembly, and present the results of extensive simulation experiments to assess the effectiveness of our approach.
Comparison of early and late fusion techniques for movie trailer genre labelling

In this paper we explore automatic genre labelling of motion picture previews using audio-visual features present in movie trailers and the focus is on fusion techniques (early fusion and late fusion) and the resultant improvement on classification accuracy. This paper proposes a novel combination of deep learned features (from a pretrained VGG-16 model) obtained using a state-of-the-art shot detector and hand-crafted audio features. This combination of features and an associated comparison of early and late fusion with these features has not been attempted in the literature before. Furthermore, two popular fusion techniques and three distinct classification algorithms are investigated to determine the optimal fusion technique and classifier combination. The study uses a subset of the LMTD-9 movie trailer dataset with selected genres (action, comedy, drama and horror). The best performing low-level audio features are comprised of timbre features extracted using the MIRtoolbox followed by standalone mel-frequency cepstral coefficients. The best performing high-level audio feature is tonality. Audio features are augmented by visual features extracted using a pre-trained convolutional neural network (VGG-16). Feature fusion (early and late fusion) methods are investigated together with classification methods such as extreme gradient boosting, support vector machine and a neural network. Evaluation metrics such as precision, recall, confusion matrices and F1 score are used to measure classification accuracy. Early fusion methods outperform late fusion methods with a classification performance gain of approximately 10% for a four class classification problem. The best classification performance for early fusion obtained with a support vector machine is (73.12% accuracy), followed by the extreme gradient boosting classifier (69.37% accuracy) and neural network classifier (67.50% accuracy), whereas chance is 25%. It is shown that superior classification performance can be achieved by employing early feature fusion of low-level audio descriptors, high-level audio descriptors and high-level visual feature descriptors together with suitable classifiers.
Tag-Extended Collaborative Filtering Recommendation Algorithm

For most of recommendation algorithms that use the relationship between tags, users, and items have to face the problem of tag sparse. Aiming at the problem of sparse tag data that caused by the user's subjective arbitrariness when labeling items, this paper proposes a tag extension collaborative filtering recommendation algorithm. We test the tag similarity in terms of user behavior and tag semantics, and use the tag similarity to expand the item-tag vector to reduce the sparseness of the matrix generated by the association between items and tags. Then we calculate the item similarity for item-based collaborative filtering recommendation. The core of collaborative filtering recommendation algorithm is to calculate the similarity of users or resources. However, it is necessary to consider the question that the items labeled by a user are not necessarily what the user likes and interest offset when using tag information to calculate the similarity of items. So for improving the accuracy of the recommendation algorithm, we improve the calculation method of similarity. We integrate user rating information and time information into the calculation of similarity to increase the weight of the item that tagged by the user and has a high rating and to reflect the change of user's preference. Finally, the MovieLens dataset is employed to test different algorithms and results show that the proposed algorithm has a higher accuracy.
Observability Analysis for Target Tracking Systems with Bearing Measurements

This paper is concerned with the problem of estimating the position and velocity of targets with noise-corrupted bearing information. Instead of traditional models on targets with a constant velocity, a more general model for the bearings-only target tracking problem is discussed. Firstly, a equivalent transformation is proposed such that the nonlinearity of measurements can be transferred to the dynamical systems. Then, some sufficient and necessary conditions for the locally weak observability of the systems are given by using Lie algebra tools. Finally, the Consistent Extended Kalman Filter(CEKF) algorithm is designed to estimate the state vector and some numerical simulations are carried out.
The Effects of Major Selection Motivations on Dropout, Academic Achievement and Major Satisfactions of College Students Majoring in Foodservice and Culinary Arts

This study is aimed at figuring out the effects of major selecting motivation on dropout, academic achievement, and major satisfactions of college students majoring in foodservice and culinary arts. To accomplish this, an empirical survey was conducted through a structural equation model. These findings showed that students are likely to drop out of college due to a career change or major maladjustment if they decide their major in consideration of college reputation or department recognition rather than their aptitude. Unlike existing studies, this study has practical implications concerning the importance of these factors in that their academic achievement is affected by their relationship and perception of their major satisfactions rather than their major selection motivations.
Experiences and Lessons Learned Creating and Validating Concept Inventories for Cybersecurity

We reflect on our ongoing journey in the educational Cybersecurity Assessment Tools (CATS) Project to create two concept inventories for cybersecurity. We identify key steps in this journey and important questions we faced. We explain the decisions we made and discuss the consequences of those decisions, highlighting what worked well and what might have gone better. The CATS Project is creating and validating two concept inventories—conceptual tests of understanding—that can be used to measure the effectiveness of various approaches to teaching and learning cybersecurity. The Cybersecurity Concept Inventory (CCI) is for students who have recently completed any first course in cybersecurity; the Cybersecurity Curriculum Assessment (CCA) is for students who have recently completed an undergraduate major or track in cybersecurity. Each assessment tool comprises 25 multiple-choice questions (MCQs) of various difficulties that target the same five core concepts, but the CCA assumes greater technical background. Key steps include defining project scope, identifying the core concepts, uncovering student misconceptions, creating scenarios, drafting question stems, developing distractor answer choices, generating educational materials, performing expert reviews, recruiting student subjects, organizing workshops, building community acceptance, forming a team and nurturing collaboration, adopting tools, and obtaining and using funding. Creating effective MCQs is difficult and time-consuming, and cybersecurity presents special challenges. Because cybersecurity issues are often subtle, where the adversarial model and details matter greatly, it is challenging to construct MCQs for which there is exactly one best but non-obvious answer. We hope that our experiences and lessons learned may help others create more effective concept inventories and assessments in STEM.
Face Retrieval in Videos using Face Quality Assessment and Convolution Neural Networks

With the large amount of videos produced every day, Content-Based Video Retrieval (CBVR) has become a necessity by describing each video with a compact and significant signature in order to efficiently retrieve the desired video from a large collection. In this work, we present a CBVR system applied on face recognition based on key frames. The first step in this system consists of extracting key frames from videos using Face Quality Assessment (FQA) and Convolution Neural Networks (CNN). Starting by generating face quality scores for each face image using three face feature descriptors (Gabor, Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HoG)). Then, we train a Convolution Neural Network (CNN) in a supervised manner in order to select frames having the best face quality. Experiments on several datasets has shown that the proposed “DeepFQA” method gives promising results in terms of accuracy and precision/recall curve.
Towards Learning Travelers’ Preferences in a Context-Aware Fashion

Providing personalized offers, and services in general, for the users of a system requires perceiving the context in which the users' preferences are rooted. Accordingly, context modeling is becoming a relevant issue and an expanding research field. Moreover, the frequent changes of context may induce a change in the current preferences; thus, appropriate learning methods should be employed for the system to adapt automatically. In this work, we introduce a methodology based on the so-called Context Dimension Tree—a model for representing the possible contexts in the very first stages of Application Design—as well as an appropriate conceptual architecture to build a recommender system for travelers.
The High-speed Rotorcraft UAV Trajectory Planning Based on the Beetle Antennae Search Optimization Algorithm

In this study, a novel exponential artificial potential field is applied to the three-dimensional trajectory planning of high-speed rotor drones. It perfectly avoids obstacles and accurately reaches the target position, and is difficult to fall into the local minimum. However, the selection of gravitational gain coefficient and repulsive gain coefficient in the artificial potential field method is a very difficult problem. In this paper, we choose to use the beetle antennae search optimization algorithm to filter out the most suitable gain coefficient. The BAS optimization algorithm is used for the first time to optimize the gain coefficient of the artificial potential field. Due to its individual search mechanism and update strategy, the optimization algorithm accelerates the speed of iterative convergence and reduces the possibility of falling into the local optimal solution of the algorithm. In the simulation experiment, it can converge to the optimal value after the number of iterations less than ten times. The results well prove that the algorithm can realize the global path planning of the high-speed rotor drones. Moreover, the optimized trajectory planning method has a fast convergence speed and a stable convergence effect, and is suitable for the three-dimensional real-time trajectory planning of high-speed rotor drones.
Linking the Present and Past in Virtual Worlds: A Case Study of Development and Navigation

Virtual worlds are unique 3D spaces, which are interactive, collaborative, coherent, persistent and social in nature. Users, an integral part of these environments are represented using digital characters, called avatars, and they are allowed to design and develop the content according to their own desires. This paper presents the virtual world presences, developed using OpenSimulator framework and blender, for the present and past of the University of Science and Technology Bannu - Pakistan. The developed spaces are explored through different navigation techniques and they are equipped with interactive maps and signboards for guidance and supporting advanced navigation technique called the teleporting. The developed environment not only imparts online education and helps youngsters to get acquainted with glorious past but also offers virtual tour of the campus to the prospective students of the University. The users were found more motivated when they explored the spaces using flying and teleporting - the navigation means not practical in the physical world. The virtual world presence, developed in this work, is compared with traditional techniques, currently in practice, for imparting education and preserving culture heritage, using a number of parameters.