Reviewerly/exHarmony huggingface dataset:
A novel approach for segmentation and counting of overlapped leukocytes in microscopic blood images

Leukocytes count in the blood smear images plays an important role in identifying the overall health of the patient. The major steps involved in leukocytes counting system are segmentation and counting. However, the counting accuracy is greatly affected due to the morphological diversity of cells, the presence of staining artifacts and the overlapped cells. Therefore, this paper introduces a new framework to segment and counting of leukocytes. To segment leukocytes, an edge strength-based Grabcut method has been proposed. Later, the leukocyte region including the overlapped cells was counted using the novel gradient circular hough transform (GCHT) method. The research work was performed on ALL-IDB and Cellavision datasets. The proposed segmentation method has yielded high precision, recall and f-measure compared to the state-of-the-art methods. Additionally, comparison analysis was performed between the region count obtained using the existing and the GCHT method. The overall experimental results of the work showed that the proposed framework produced more accuracy in counting the leukocytes.
Comparison between Full Body Motion Recognition Camera Interaction and Hand Controllers Interaction used in Virtual Reality Exposure Therapy for Acrophobia

Virtual Reality has already been proven as a useful supplementary treatment tool for anxiety disorders. However, no specific technological importance has been given so far on how to apply Virtual Reality with a way that properly stimulates the phobic stimulus and provide the necessary means for lifelike experience. Thanks to technological advancements, there is now a variety of hardware that can help enhance stronger emotions generated by Virtual Reality systems. This study aims to evaluate the feeling of presence during different hardware setups of Virtual Reality Exposure Therapy, and, particularly how the user’s interaction with those setups can affects their sense of presence during the virtual simulation. An acrophobic virtual scenario is used as a case study by 20 phobic individuals and the Witmer–Singer presence questionnaire was used for presence evaluation by the users of the system. Statistical analysis on their answers revealed that the proposed full body Motion Recognition Cameras system generates a better feeling of presence compared to the Hand Controllers system. This is thanks to the Motion Recognition Cameras, which track and allow display of the user’s entire body within the virtual environment. Thus, the users are enabled to interact and confront the anxiety-provoking stimulus as in real world. Further studies are recommended, in which the proposed system could be used in Virtual Reality Exposure Therapy trials with acrophobic patients and other anxiety disorders as well, since the proposed system can provide natural interaction in various simulated environments.
Virtual Reality in Medical Education

The aim of this research is to examine student acceptance and use of virtual reality technologies in medical education. Within the scope of the research, a questionnaire consisting of 4 sub-dimensions and 21 items was developed by the researchers. This questionnaire consists of sub-dimensions of performance expectancy, effort expectancy, facilitating conditions, and social influence. The study was conducted on 421 university students who participated in courses and activities related to the use of virtual reality applications in medical education. The findings of the research demonstrated that the students' acceptance and use of virtual reality applications were high in medical education. Various suggestions were made for researchers and educators in accordance with the findings.
Physical exploration of a virtual reality environment: Effects on spatiotemporal associative recognition of episodic memory

Abstract Associative memory has been increasingly investigated in immersive virtual reality (VR) environments, but conditions that enable physical exploration remain heavily under-investigated. To address this issue, we designed two museum rooms in VR throughout which participants could physically walk (i.e., high immersive and interactive fidelity). Participants were instructed to memorize all room details, which each contained nine paintings and two stone sculptures. On a subsequent old/new recognition task, we examined to what extent shared associated context (i.e., spatial boundaries, ordinal proximity) and physically travelled distance between paintings facilitated recognition of paintings from the museum rooms. Participants more often correctly recognized a sequentially probed old painting when the directly preceding painting was encoded within the same room or in a proximal position, relative to those encoded across rooms or in a distal position. A novel finding was that sequentially probed paintings from the same room were also recognized better when the physically travelled spatial or temporal distance between the probed paintings was shorter, as compared with longer distances. Taken together, our results in highly immersive VR support the notion that spatiotemporal context facilitates recognition of associated event content.
Optimization-based k-anonymity algorithms

In this paper we present a formulation of k-anonymity as a mathematical optimization problem. In solving this formulated problem, k-anonymity is achieved while maximizing the utility of the resulting dataset. Our formulation has the advantage of incorporating different weights for attributes in order to achieve customized utility to suit different research purposes. The resulting formulation is a Mixed Integer Linear Program (MILP), which is NP-complete in general. Recognizing the complexity of the problem, we propose two practical algorithms which can provide near-optimal utility. Our experimental evaluation confirms that our algorithms are scalable when used for datasets containing large numbers of records.
Precise iterative closest point algorithm for RGB-D data registration with noise and outliers

In this paper, we propose a precise Iterative Closest Point (ICP) algorithm which is a RGB information supplemented registration method based on correntropy measurement, which can overcome noise and outliers to complete precise point cloud registration. Firstly, in order to achieve accurate point set registration of single structure geometry point cloud with a large number of planes information rather than geometry information, we add the RGB information to our initial method as information compensation, which greatly ensure our algorithm could achieve accurate registration. Secondly, due to the noise and outliers existed in RGB-D data can easily cause deviations in registration, in order to achieve precise registration in RGB-D data, we introduced the correntropy measurement method into the initial model. Experimental results on our scene dataset demonstrate the proposed algorithm can achieve precise point set registration in RGB-D scene data well.
Improved butterfly optimisation algorithm based on guiding weight and population restart

Butterfly Optimisation Algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution.
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Discrete ill-posed problems arise in many areas of science and engineering. Their solutions, if they exist, are very sensitive to perturbations in the data. Regularization aims to reduce this sensitivity. Many regularization methods replace the original problem by a minimization problem with a fidelity term and a regularization term. Recently, the use of a p-norm to measure the fidelity term and a q-norm to measure the regularization term has received considerable attention. The relative importance of these terms is determined by a regularization parameter. When the perturbation in the available data is made up of impulse noise and a sparse solution is desired, it often is beneficial to let 0<p,q<1. Then the p- and q-norms are not norms. The choice of a suitable regularization parameter is crucial for the quality of the computed solution. It therefore is important to develop methods for determining this parameter automatically, without user-interaction. However, the latter has so far not received much attention when the data is contaminated by impulse noise. This paper discusses two approaches based on cross validation for determining the regularization parameter in this situation. Computed examples that illustrate the performance of these approaches when applied to the restoration of impulse noise contaminated images are presented.
IFDTC4.5: Intuitionistic fuzzy logic based decision tree for E-transactional fraud detection

As the on-line transaction has become one of most important day-to-day life activities, security issues related with the on-line transactions has become most crucial part of the financial institutions. We have proposed here fraud detection algorithm for on-line transactional frauds. We developed a decision tree IFDTC4.5 using intuitionistic fuzzy logic and C4.5 decision tree to detect the frauds. Here intuitionistic fuzzy logic helps in considering cognitive properties of attributes, so that the legitimate transactions are not treated as frauds and fraudulent transactions are not treated as legitimate. For optimality of detection process we have considered both the derived attributes and normal attributes. The result obtained here shows that detection rate is high and efficient, compared to the other recent works.
An efficient implementation of multiple weak targets tracking filter with labeled random finite sets for marine radar

The detection and tracking of multiple weak targets with time-varying and unknown number has become a hot spot and challenging problem in marine radar application. This paper integrates δ-generalized labeled multi-Bernoulli (δ-GLMB) density and labeled multi-Bernoulli (LMB) density, which are two important special cases of labeled random finite set, to propose an effective method for multiple weak target tracking with track-before-detect strategy based on Bayes framework including prediction and update step. The proposed method can deal with multi-target tracking problem with tractable computational complexity due to prediction step with dynamic grouping procedure albeit with slightly precision loss compared with standard δ-GLMB filter. In general, the conjugate prior property based GLMB does not hold on for generic observation model in update step. In order to solve this problem, this paper applies a tractable principled approximation involving GLMB density, which was firstly proposed in the literature, to make whole Bayes filtering with labeled RFS to perform iteratively for each group with TBD model. Finally, the numerical simulation results of Swerling type 1 target tracking in K-distributed sea clutter illustrate that the proposed method is superior to the methods including standard δ-GLMB-TBD filter, LMB-TBD filter and DP-TBD algorithm in comprehensive performance.
Heterogeneous Coded Computation across Heterogeneous Workers

Coded distributed computing framework enables large-scale machine learning (ML) models to be trained efficiently in a distributed manner, while mitigating the straggler effect. In this work, we consider a multi-task assignment problem in a coded distributed computing system, where multiple masters, each with a different matrix multiplication task, assign computation tasks to workers with heterogeneous computing capabilities. Both dedicated and probabilistic worker assignment models are considered, with the objective of minimizing the average completion time of all tasks. For dedicated worker assignment, greedy algorithms are proposed and the corresponding optimal load allocation is derived based on the Lagrange multiplier method. For probabilistic assignment, successive convex approximation method is used to solve the non-convex optimization problem. Simulation results show that the proposed algorithms reduce the completion time by 80% over uncoded scheme, and 49% over an unbalanced coded scheme.
AutoToon: Automatic Geometric Warping for Face Cartoon Generation

Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces. This task is typically left to artists, as it has proven difficult to capture subjects' unique characteristics well using automated methods. Recent development of deep end-to-end methods has achieved promising results in capturing style and higher-level exaggerations. However, a key part of caricatures, face warping, has remained challenging for these systems. In this work, we propose AutoToon, the first supervised deep learning method that yields high-quality warps for the warping component of caricatures. Completely disentangled from style, it can be paired with any stylization method to create diverse caricatures. In contrast to prior art, we leverage an SENet and spatial transformer module and train directly on artist warping fields, applying losses both prior to and after warping. As shown by our user studies, we achieve appealing exaggerations that amplify distinguishing features of the face while preserving facial detail.
Quantum experiments and hypergraphs: Multiphoton sources for quantum interference, quantum computation, and quantum entanglement

We introduce the concept of hypergraphs to describe quantum optical experiments with probabilistic multiphoton sources. Every hyperedge represents a correlated photon source, and every vertex stands for an optical output path. Such a general graph description provides new insights for producing complex high-dimensional multiphoton quantum entangled states, which go beyond limitations imposed by pair creation via spontaneous parametric down-conversion. Furthermore, the properties of hypergraphs can be investigated experimentally. For example, the NP-complete problem of deciding whether a hypergraph has a perfect matching can be answered by experimentally detecting multiphoton events in quantum experiments. By introducing complex weights in hypergraphs, we show a general many-particle quantum interference and manipulating entanglement in a pictorial way. Our work paves the path for the development of multiphoton high-dimensional state generation and might inspire new applications of quantum computations using hypergraph mappings.
Broken lines path following algorithm for a water-jet propulsion USV with disturbance uncertainties

To fulfill autonomous navigation of unmanned surface vehicle, it is pivotal to resolve unmanned surface vehicle path following issue. However, the problem that we are concerned with is hindered by unmanned surface vehicle’s nonlinearity and the disturbance uncertainties brought by the navigation environment. In this paper, we focus on path following problem for a jet driven underactuated unmanned surface vehicle. The stressed followed paths in practical application are broken lines connected by continuous straight lines rather than smooth lines. By calculating the virtual target on each straight line in following the unmanned surface vehicle dynamically, we propose a broken lines path following algorithm for the unmanned surface vehicle in an uncertain environment. By employing our unmanned surface vehicle experiment platform that we developed a series of numerical simulations and physical tests have been carried out. Results verify the superiority and effectiveness of our broken lines path following algorithm for unmanned surface vehicle especially in continuous broken lines following scenarios.
Improved SERPENT Algorithm: Design to RGB Image Encryption Implementation

Gigantic data transfer over networks and internet turned in developing a large number of encryption techniques.As a consequence, in parallel, it gave birth to new plans of attacks.In facet of data security, the algorithms specifically Rijndael (Advanced Encryption Standard-AES), SERPENT and TWOFISH are equally considered to be the best.However, they pay less attention in some cryptographic areas like digital image encryption schemes because of their time consumption inferiority.In this paper, we introduce a time diminishing improved version of SERPENT algorithm depending upon chain ring-based substitution boxes (S-boxes) dealing with 8-bit vector instead of 4-bit.The multiplicative substructures in a chain ring have multiple generators and thus the chain ring based S-boxes improve the algebraic complexity of the cipher.Apart from it, the algorithm is utilized in a digital RGB image encryption application where throughout the chain ring operations are performed.The digital image analyses reveal that the proposed scheme consumes less time (i.e.8.2 microseconds for encryption and 5.8 microseconds for decryption of a 128-bit block) as compared to the existing prominent RGB image encryption techniques.Furthermore, the investigation on the suggested RGB image encryption scheme it is observed that it has a great resistance against the statistical and the differential attacks.
Probabilistic Topic Modeling for Comparative Analysis of Document Collections

Probabilistic topic models, which can discover hidden patterns in documents, have been extensively studied. However, rather than learning from a single document collection, numerous real-world applications demand a comprehensive understanding of the relationships among various document sets. To address such needs, this article proposes a new model that can identify the common and discriminative aspects of multiple datasets. Specifically, our proposed method is a Bayesian approach that represents each document as a combination of common topics (shared across all document sets) and distinctive topics (distributions over words that are exclusive to a particular dataset). Through extensive experiments, we demonstrate the effectiveness of our method compared with state-of-the-art models. The proposed model can be useful for “comparative thinking” analysis in real-world document collections.
Generative Model with Semantic Embedding and Integrated Classifier for Generalized Zero-Shot Learning

Generative models have achieved impressive performance for the generalized zero-shot learning task by learning the mapping from attributes to feature space. In this work, we propose to derive semantic inferences from images and use them for the generation, which enables us to capture the bidirectional information i.e., visual to semantic and semantic to visual spaces. Specifically, we propose a Semantic Embedding module which not only gives image specific semantic information to the generative model for generation of better features, but also makes sure that the generated features can be mapped to the correct semantic space. We also propose an Integrated Classifier, which is trained along with the generator. This module not only eliminates the requirement of additional classifier for new object categories which is required by the existing generative approaches, but also facilitates the generation of more discriminative and useful features. This approach can be used seamlessly for the task of few-shot learning. Extensive experiments on four benchmark datasets, namely, CUB, SUN, AWA1, AWA2 for both zero-shot learning and few-shot setting show the effectiveness of the proposed approach.
Hidden Markov model-based activity recognition for toddlers

Abstract Objective : Physical activity has been shown to impact future health outcomes in adults, but little is known about the long-term impact of physical activity in toddlers. Accurately measuring the specific types and amounts of physical activity in toddlers will help us to understand, predict, and better affect their future health outcomes. Although activity recognition has been extensively developed for adults as well as older children, toddlers move in ways that are significantly different from older children, indicating the need for a more tailored approach. Approach : In this study, 22 toddlers wore Actigraph waist-worn accelerometers which recorded their movements during guided play. The toddlers were videotaped and their activities were later annotated for the following eight distinct activity classes: lying down, being carried, riding in a stroller, sitting, standing, running/walking, crawling, and climbing up/down. Accelerometer data were extracted in 2 s signal windows and paired with the activities the toddlers were performing during that time interval. Main results : A variety of classifiers were tuned to a validation set. A random forest classifier was found to achieve the highest accuracy of 63.8% in a test set. To improve the accuracy, a hidden Markov model (HMM) was applied by providing the predictions of the static classifiers as observations. The HMM was able to improve the accuracy to 64.8% with all five classifiers increasing the accuracy an average of 1.3% points (95% confidence interval = 0.7–1.9, p &lt; 0.01). When the three most misclassified activities (sitting, standing, and riding in a stroller) were collapsed together, the accuracy increased to 79.3%. Significance : Further refinement of the toddler activity recognition classifier will enable more accurate measurements of toddler activity and improve future health outcomes of toddlers.
The effect of numerical and textual information on visual engagement and perceptions of AI-driven persona interfaces

In an experiment, we present 38 marketing and data analysts professionals with two online AI-driven persona interfaces, one using numbers and the other using text. We employ eye tracking, think-aloud, and a post-engagement survey for data collection to measure perception and visual engagement with the personas along 7 constructs. Results show that the use of numbers has a mixed effect on the perceptions and visual engagement of the persona profile, with job role as a determining factor on whether numbers/text affect end users for 2 of the constructs. The use of numbers has a significant positive effect on user perceptions of usefulness by analysts but a significantly negative effect on user perceptions of completeness for both marketers and analysts. The use of numbers decreases the perceived completeness of the personas for both marketer and analysts. This research has both theoretical and practical consequences for AI-driven persona development and their interface design, suggesting that the inclusion of numbers can have a desirable effect for certain roles but with possible negative effects on user perceptions.