Reviewerly/exHarmony huggingface dataset:
Neutrosophic hough transform for blood cells nuclei detection

Automatic blood cell (BCs) detection and nuclei detection are still challenging tasks in medical microscopic imaging systems due to their embedding in the cluttered and intricate smear. Because the BCs have an ellipsoid/circular shape, a circular Hough transform (HT) shape detector was proposed based on a neutrosophic set to extract the descriptive features of BC circular shapes in microscopic images. First, the Canny edge detector was employed to detect the edges in the microscopic image to form an edge image for further use of the HT to detect the BC. Afterward, the Hough transformed image was mapped in the neutrosophic set (NS) domain followed by the k-means clustering method to detect the nuclei within the BC images. Thus, the proposed method of two phases is called Hough transform with neutrosophic k-means (HNK) nuclei detection. The experimental results established the superiority of the proposed HNK compared to the classic HT with k-means without NS on 50 BC images in terms of the JAC, dice, sensitivity, specificity, and accuracy; it achieved 87%, 93%, 95%, 99%, and 98%, respectively.
A Hierarchical Saliency Based Target Detection Method for High-Resolution Sar Images

The traditional target detection methods for high-resolution synthetic aperture radar (SAR) images rely on the high intensity contrast between the targets and clutter, which are usually effective on the homogeneous background; however, they may lose effectiveness in the heterogeneous background. Compared with the clutter, the targets of interest usually have specific size and shape characteristics in high-resolution SAR images. Based on what mentioned above, we propose a target detection method based on the hierarchical saliency (HS) for high-resolution SAR images. The proposed method constructs the Bayesian saliency map to obtain the complete structures of both the targets of interest and strong clutter with superpixels as units firstly. Then, the morphological saliency map is constructed to retain the targets of interest while suppressing the strong clutter via the introduction of prior size information. The experimental results on the MiniSAR images show the effectiveness of the proposed target detection method.
Quality Solution of Logic Programming in Hopfield Neural Network

Abstract The dynamical behaviours of Artificial neural network (ANN) system are strongly dependent by its network structure. In that sense, the output of ANN has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of ANN in doing logic programming. The work presents an integrated representation of 2 Satisfiability (2SAT) in different Hopfield Neural Network (HNN) models. Neuron states of HNN always converge to minimum energy but the solution produced always confined in limited number of solution space. The main purpose of this study is to explore the quality of the solution obtained from HNN. It has been shown that HNN only retrieves limited neuron states with the lowest minimum energy. This finding will lead to a better understand of logic programming in HNN.
Feature Fusion for Image Retrieval With Adaptive Bitrate Allocation and Hard Negative Mining

By combining Convolutional Neural Network (CNN) descriptor and Compact Descriptors for Visual Search (CDVS), the visual search performance can be boosted. However, some redundancies still exist in the CDVS representation and the hard negative mining is not very accurate when training CNN embeddings. In this paper, we propose a high performance image retrieval scheme based on descriptor fusion. In detail, we first propose a more compact CDVS descriptor database building scheme through bitrate allocation, which can reduce information redundancy and boost image retrieval performance. We then propose a highly accurate CDVS-guided hard negative mining scheme when training CNN embeddings. In the hard negative selection, the CDVS descriptor and the CNN embedding are adaptively weighted together to achieve more precise decisions. Finally, the retrieval result is further refined through CDVS local descriptor matching by removing the irrelevant targets from the top positions. Extensive experimental results show that the proposed method outperforms the recent hybrid method and several other anchors remarkably, and produces better visual search performance. Codes and some models are available at https://github.com/WendyDong/ImageRetrieval_DF_CDVS.
Structure-Aware Image Expansion with Global Attention

We present a novel structure-aware strategy for image expansion which aims to complete an image from a small patch. Different from image inpainting, the majority of the pixels are absent here. Hence, there are higher requirements for global structure-aware prediction to produce visually plausible results. Thus, treating the expansion tasks as inpainting from the outside is ill-posed. Therefore, we propose a learning-based method combining structure-aware and visual attention strategies to make better prediction. Our architecture consists of two stages. Since visual attention cannot be taken full advantage of when the global structure is absent, we first use the ImageNet-pre-trained VGG-19 to make the structure-aware prediction on the pre-training stage. Then, we implement a non-local attention layer on the coarsely-completed results on the refining stage. Our network can well predict the global structures and semantic details from small input image patches, and generate full images with structural consistency. We apply our method on a human face dataset, which containing rich semantic and structural details. The results show its stability and effectiveness.
VROffice

In conventional work environments, visualization and integration of correlated data has always been limited by the amount of software windows a regular PC can display. On the other hand, immersive virtual environments present possibilities of interaction that fit scalable and adaptable three-dimensional space, allowing the integration of different types of data, and relating information. This article describes VROffice, an immersive virtual reality office to handle and correlate georeferenced data. VROffice is an immersive and interactive virtual reality office used to handle and correlate georeferenced data, providing access to 2D and 3D elements (i.e rocks, lab samples, organisms) organized as a virtual library. These objects work as data containers ready to store and display data from its location. In the virtual office, data is visualized in a way that allows different forms of interactions, such as observation, free-hand manipulation, inspection of details and object location in a 3D georeferenced space. Users can also organize datasets, perform analysis between different types of objects, combine characteristics, and interpret data in a Geographic Information System (GIS) environment. It enables different forms of interaction with potential to improve insights over observations, instantly turning multivariable datasets into intuitive immersive displays. Different forms of interaction, as well as UI (user interface) and UX (user experience) decisions have been customized to ensure that the experience in using the system is as comfortable as possible. Therefore, in order to demonstrate the possibilities of its use, case studies were created in two different fields of knowledge, geology and biology, which have georeferenced data available.
Solar resource assessment of modern parking machines in an urban environment

This study presents a methodology for assessing the potential solar resource available at solar-powered parking machines installed in a typical city center. It is based on capturing fisheye images through an appropriate camera/lens. These images are then processed to classify the sky and non-sky pixels using a semi-automated sky detection method, also developed in this study. The processed images are then used to calculate the diffuse and beam solar potential of the site by estimating the visible fraction of the sky and the hours of the year during which the sun can be seen from the site, respectively. For the case study, 15 existing parking machines were analyzed in the central business district of Auckland (New Zealand). It was found that most of these machines were able to capture less than 50% of the available solar potential. Only two machines crossed the yearly irradiation threshold that represents the criterion for solar panel installation. If modern high-power solar-powered machines are used, selecting suitable locations for installation is crucial.
UAV Low Altitude Flight Threat Perception Based on Improved SSD and KCF

Threat perception is a key problem of UAV low altitude flight safety. UAVs fly in a relatively high speed and the aerial threats have small sizes, which means the threat perception method needs to have a high detection speed and could detect small targets as well. Aiming at the above problems, a parallel detection and tracking frame of UAV threat perception is proposed. Firstly, referring to SSD and DSSD, an improved SSD is proposed to improve the small target detection precision, meanwhile, the detection speed is also guaranteed. Moreover, in order to improve the perception speed further, KCF tracking method is combined to track the detected dynamic threat. Finally, effectiveness of the proposed method is demonstrated by experiments.
3-D Structure-from-Motion Retrieval Based on Circular Videosar Sequences

Video synthetic aperture radar (VideoSAR) provides a continuously multidimensional phase-history acquisition, proved to be significantly attractive in the remote sensing-based information extraction field. In this paper, we propose a 3-D structure-from-motion (SfM) retrieval method based on the cylinder model from circular VideoSAR sequences. First, VideoSAR imaging characteristics on the cylinder model are analyzed. Then, SfM retrieval method with geometric prior knowledge is proposed using robust shadow information. Finally, experimental results utilizing circular VideoSAR fragment published by the Sandia National Laboratories indicate that the proposed method can achieve a higher accuracy, hence, the validity has been demonstrated.
Modulation Classification of VHF Communication System based on CNN and Cyclic Spectrum Graphs

Modulation classification is the technological basis of adaptive interference mitigation in communication system. This paper proposes a modulation classification method for very high frequency (VHF) signals, which is based on deep convolutional neural network (CNN) and cyclic spectrum graphs. First, the cyclic spectrum of VHF signals is analyzed. Then, a deep learning method based on CNN is proposed, down-sampling and clipping technologies are used for preprocessing cyclic spectrum images, parameters of the proposed neural network are optimized, and finally the modulation classification is realized. The experimental results show that, the proposed method has high modulation classification accuracy and less computation burden in low SNR.
Efficient design of quaternary quantum comparator with only a single ancillary input

Circuit design with no energy dissipation is possible in the reversible logic. Also, using the quantum multiple-valued logic in the design of circuits due to the reduction of circuit width leads to the smaller chip area and lower power consumption. Quaternary reversible logic potentially has several advantages in comparison to the equivalent ternary and binary quantum technologies. Hence, the quaternary quantum technology is a promising choice for the future digital system design. Here, the authors proposed an efficient design of 1-qudit and n-qudit quantum comparators. The comparator module is widely used in search conditions, such as Grover's quantum search algorithm in quantum computing. The proposed circuits were better than their existing counterparts in terms of quantum cost, the number of constant inputs, the number of garbage outputs, and hardware complexity. To design these circuits, they have used 1-qudit gates and 2-qudit Muthukrishnan–Stroud gates. The efficiency of their proposed circuits was reported.
A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data

Vast amounts of data, especially in biomedical research, are being published as Linked Data. Being able to analyze these data sets is essential for creating new knowledge and better decision support solutions. Many of the current analytics solutions require continuous access to these data sets. However, accessing Linked Data at query time is prohibitive due to high latency in searching the content and the limited capacity of current tools to connect to these databases. To reduce this overhead cost, modern database systems maintain a cache of previously searched content. The challenge with Linked Data is that databases are constantly evolving and cached content quickly becomes outdated. To overcome this challenge, we propose a Change-Aware Maintenance Policy (CAMP) for updating cached content. We propose a Change Metric that quantifies the evolution of a Linked Dataset and determines when to update cached content. We evaluate our approach on two datasets and show that CAMP can reduce maintenance costs, improve maintenance quality and increase cache hit rates compared to standard approaches.
A Generalized and Parallelized SSIM-Based Multilevel Thresholding Algorithm

Multilevel thresholding is a widely used technique to perform image segmentation. It consists of dividing an input image into several distinct regions by finding the optimal thresholds according to a certain objective function. In this work, we generalize the use of the SSIM quality measure as an objective function to solve the multilevel thresholding problem using empirically tuned swarm intelligence algorithms. The experimental study we have conducted shows that our approach, producing near-exact solutions, is more effective compared to the state-of-the-art methods. Moreover, we show that the computation complexity has been significantly reduced by adopting a shared-memory parallel programming paradigm for all the algorithms we have implemented.
Transparent Watermarking QR Code Authentication for Mobile Banking Applications

Mobile banking is a service provided by the bank and other financial institutions, which accepts their users to do a remote online transactions by using smart devices such as mobile or tab. With the help of Mobile Banking, customer can pay bills, check account balance and recent transaction details. To keep mobile banking application secure in E-commerce transaction or a banking transaction, we require proper Authentication so that the intruder or attacker cannot do Eavesdropping.This paper provides a novel approach of watermarking QR Code authentication for mobile banking applications. The idea is to develop an Android Mobile Banking Application which will give Authentication at Sign-in part & Transaction part. This proposed method will provide high security while doing online transactions along with HTTPS Protocol.
The limits of generalizability: Why good theory can have bad outcomes

Generalizability is the goal for scientists (both social and natural). In the social sciences invoking generalizability is more problematic as it is often based on assessment of a mainstream population, but may not apply to groups outside of the mainstream. This is a significant challenge to developing appropriate policy and interventions suitable for Other groups. An example is given of how this concern was overcome through consideration of the Voice Of the Customer (VOC) and utilizing cognitive maps to consider the challenge of diabetes in two Canadian First Nations Communities. An understanding of the limits to generalizability is important in cases where cultural, geographical, or political differences exist. This is not only an important consideration for practitioners in search of successful interventions, but also academics trying to understand and contribute to knowledge in a variety of fields; including the diffusion and adoption of innovation.
Design and Prototyping of a New Device for Supplying Oxygen and other Pharmaceuticals to Newborn Babies

More than fifteen million premature babies are born each year in the whole world. In order to guarantee a proper maturation and development process, most of those cases require the use of oxygen therapy techniques because of the immature status of their respiratory system. Besides that, respiratory illnesses are one of the main causes of death in babies/children between zero and two years old. As a consequence, the use of devices for supplying oxygen and other pharmaceuticals affects especially to those patients, because in most cases those devices are not adapted to their physiognomy. Thus, their use might cause issues such as pressure ulcers, oxygen leaks, and impossibility to access specific face areas, besides complicating the placement and manipulation of the devices themselves. In order to ease those issues, this work addresses the conception and design of a new device that provides a feasible solution to the problem described. Starting with a detailed analysis of the users' needs, from which the corresponding collection of technical specification of the requirements is built, a viable three-dimensional model for the product is defined in detail. Such model will be materialized by means of additive manufacturing, aiming to its further testing by control groups.
Frequent Pattern-based Graph Exploration

Visual graph exploration can help the users have an intuitive impression about a dataset for the first time. However, it is better to provide some guidance so that the users can quickly locate the "interesting" or "informative" area in the graph. Then they can start the next stage of sense-making. We propose a visual exploration system which provides some frequent sub-graphs extracted from the whole graph, and the relationship between them. Thus, users can raise their study questions more quickly when they first confront a graph dataset. We evaluate our exploration system with two datasets. Specifically, we demonstrate how to raise a question and find the answer with our system, which validates the effectiveness of this project.
Periodic behavior of a class of nonlinear dynamic systems based on the Runge-Kutta algorithm

In this paper, the periodic state of a class of nonlinear hydrodynamic models is studied. This study is applicable to many fields, such as engineering, cybernetics and so on. We use the Runge Kutta algorithm. First, we give a four order nonlinear hydrodynamic model based on the Runge Kutta algorith m. Then, we study the periodic dynamic characteristics of the nonlinear model and establish some new sufficient conditions. The results of our study promote and improve the existing results. And the numerical results are also verified by the numerical experiments. Therefore, the study of this paper has a great help to master the periodic motion of the fluid.
SAR Images Enhancement Via Deep Multi-Scale Encoder-Decoder Neural Network

In this paper, we propose to apply a deep multi-scale encoder-decoder neural network (MsEN-Net) for SAR images enhancement method based on scale-recurrent network (SRN), which consists of encoder and decoder modules trained by the coarse to fine strategy. Simulation with fixed speed errors and experiments with real data are implemented and evaluated by peak signal to noise ratio and structural similarity. Experimental results with both visual and quantitative analysis demonstrate the competitive performance of our proposed method.
Characteristics of bidikmisi’s scholarship awardee in FMIPA UNP using chi-squared automatic interaction detection

Abstract Bidikmisi’s Scholarships is a funding assistance from government to help students in the university who lack economically but good in academic. Number of awardee in FMIPA UNP increases yearly. So, we have to know classification of awardee to make sure that scholarship is received by the right one. We use CHAID-Chi-Squared Autimatic Interaaction Detection analysis. Result of the research, there are five groups awardee. Characteristics of awardee is father’s jobs (farmers, motor driver, jobless) and area of their house approximately 50 m2.
Outlier Detection Forest for Large-Scale Categorical Data Sets

Outlier detection is one of the most important data mining problems, which has attracted much attention over the past years. So far, there have been a variety of different schemes for outlier detection. However, most of the existing methods work with numeric data sets. And these methods cannot be directly applied to categorical data sets because it is not straightforward to define a practical similarity measure for categorical data. Furthermore, the existing outlier detection schemes that are tailored for categorical data tend to result in poor scalability, which makes them infeasible for large-scale data sets. In this paper, we propose a tree-based outlier detection algorithm for large-scale categorical data sets, Outlier Detection Forest (ODF). Our experimental results indicate that, compared with the state-of-the-art outlier detection schemes, ODF can achieve the same level of outlier detection precision and much better scalability.
A sim2real framework enabling decentralized agents to execute MADDPG tasks

Multi-agent RL is a process of training the agents to collaborate with others. We argue that an additional 'reality gap' in the system aspects occurs when applying sim2real to the multi-agent RL, especially when performing the 'transferred' collaborative task in the real-world environment. In this paper, we propose an ADO framework enabling decentralized agents to participate in performing collaborative tasks without suffering from the reality gap. Our contribution is threefold. First, we clearly identify and summarize the reality gaps in the context of the sim2real of multi-agent RL. Second, we propose a new system model to deal with system issues derived from when executing collaborative tasks. Third, we design and implement a software framework to support system issues required in developing and executing collaborative tasks in the real world.
Live migration in Greenplum database based on SDN via improved gray wolf optimization algorithm

Data migration has become one of the key technologies for load balance and high performance services in the distributed systems. In past years, the data migration researches focused on several factors including resource utilization, data quality or security improvement and migration time reduction, whereas the investigation on how to provide continuous system service during the data migration process has not attracted enough attention. In this paper, the migration operation cost and the user service quality are both weighted in the proposed migration scheme so as to guarantee a continuous high quality of user service during the data migration procedure. First, a cost-based migration model is built. Based on this model, the migration operation among the nodes of the data cluster is formulated as an optimization problem which targets to search for the optimal execution sequence. Then, an improved grey wolf optimization algorithm (IGWO) is proposed to search for the optimized migration solutions. In addition, to guarantee the high quality service for some critical user requests, the bandwidth reserved for the migration operations is dynamically managed through the on-demand programming of SDN (Software Defined Network) controller. We conducted the experiments on the Greenplum database, and the results showed the effectiveness of the proposed migration scheme.