Reviewerly/exHarmony huggingface dataset:
Localization and navigation of mobile robots in an environment with variable properties

A method of localization and navigation of a mobile robot in an environment with variable properties in conditions of limited possibilities was proposed for remote control which provides for a possibility of switching the mode of robot control to a state of autonomous navigation. The method is based on combined application of a fuzzy model and an RL-algorithm that makes it possible to improve the set of fuzzy rules using the signal of reinforcement. Improvement of the method of localization of mobile objects using iBeacon and NFC technologies in a space with known maps of premises was proposed which enables reduction of the number of transmitters necessary for localization. The method of identification of mobile object movement routes was modified with the use of the modified Jump Point Search algorithm. Essence of the modification consists in the use of the algorithm of Manhattan distance between coordinates of the route points. This makes it possible to reduce impact of individual surges on the results of calculations compared with the basic algorithm.The obtained results can be used in mobile robot control systems in an environment with variable properties at limited possibilities for remote control. The results of testing the proposed methods and corresponding computational procedures confirm their performance and prospects of practical application. Application of the above approach makes it possible to take into account obstacle configurations and adjust the navigation strategy to improve the system quality (in 95 % of the test experiments, the mobile robot reached the target in an environment with various types of obstacles)
Deep learning-based accurate diagnosis of eyelid malignant melanoma from gigapixel pathologic slides

Malignant melanoma (MM) of the eyelid is of high malignancy, high mortality, and easy to metastasize. Currently, the gold standard for MM treatment and prognosis is histopathology, but the diagnosis of different experts is often divergent. The computer-aided diagnosis based on deep learning helps to improve efficiency and accuracy. In this paper, a complete set of methods for MM diagnosis is proposed using the convolutional neural network (CNN) to classify the patch level pathological images. Hematoxylin and Eosin (H and E)-stained pathological images of the eyelids are classified as malignant melanoma and non-malignant melanoma (NMM). The prediction results are filled by location in the probabilistic map of the whole slide image level. Random forest classifier based on CNN inference results extract 31- dimensional features to achieve whole slide image-level classification. The color constancy method and the edge extraction mapping method based on the Sobel operator (EMBS) can significantly improve the performance of the model. The patch level classification results show that the balance accuracy is 93% on the Second Affiliated Hospital, Zhejiang University School of Medicine (ZJU-2) test set, and the balance accuracy is 89.4% on the Shanghai Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine (SJTU) test set. The corresponding area under curve (AUC) is 0.990 and 0.970. For whole slide image level classification results, the AUC for SJTU test set is 0.999, the sensitivity is 100%, and the specificity is 97.4%. As a result, our model can effectively tackle the challenge of clinicopathological diagnosis and relieve the pressure of pathologists.
A clean novel watermarking technique using chaotic logistic map and multiple embedding

Watermarking is widely used for copyright protection and authentic use of patented objects. Digital objects such as multimedia images, videos and data are watermarked with originator logo or the personal information of the purchaser or the both to issue authentic right to use such objects. The watermark embedded in the object changes the object quality to various amount depending on the technique used. We propose a watermarking technique in which the object remains unchanged, and hence called clean. The watermark is embedded multiple times in different pixel sequences of the object. The pixel sequences are generated using chaotic logistic maps. Multiple secret information sequences are derived after multiple embedding, which may be utilized against adversarial attacks for claiming rights or unauthorized usages.
Online Monitoring Automation Using Anomaly Detection in IoT/IT Environment

The increase of the IoT and the cloud environment have played a significant role of making our society knowledgeable and informative. Due to this trends the system environment gets more sophisticated and requires more system resources. In this paper, the monitoring automation without humans being involved has been proposed. It is noted that the 93.75% faults has been detected via the simulation using the proposed technique and the faults that the operators reported have been detected as well in datacenter.
An Ontology-Based Recommendation System for ADAS Design

AdvancedHuang, Hsun-Hui driver-assistance systems (ADAS) are an important componentYang, Horng-Chang in a vehicleYu, Yongjia for these systems to actively improve driving safety. Although technical development for ADAS is already mature, there are still a few areas that can be improved. In particular, the design of newer ADASs are mainly based on the experience and imagination of car designers and their tests are usually based on hypothetical situations and field models. Without user experience data, it is difficult for car makers to refine and improve their ADAS designs effectively and systematically. In order to help designers optimize their designs and shorten design cycles, a framework of collaborative filtering recommendation is proposed, in which domain ontologies, text mining, and machine learning techniques are used to produce multimedia summaries from data repositories for queries of real accidents, or design issues. The recommendation system framework aims to help designers and car makers improve their efficiency and produce safer vehicles. The data and knowledge bases constructed can be used as the basis of tutorial programs for new entrants and the car makers can reduce their managerial cost and manpower needs.
Computational Complexity of Core Fragments of Modal Logics T, K4, and S4

We show that the satisfiability problem in core fragments of modal logics T, K4, and S4 in whose languages diamond modal operators are disallowed is NL-complete. Moreover, we provide deterministic procedures for satisfiability checking. We show that the above fragments correspond to certain core fragments of linear temporal logic, hence our results imply NL-completeness of the latter.
A sampling-based optimized algorithm for task-constrained motion planning

We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.
Realizing parallelism in quantum MISD architecture

We propose an idea to speed up instruction execution through a probabilistic approach, using the parallelism offered by quantum computers. For this, we divide the instruction set of an arbitrary quantum instruction set architecture (QISA) into separate groups and then bias certain qubits representing the group so that only the instructions within the group have a high probability of getting executed in a quantum processor. Therefore, the result generated will be the superimposition of the qubits as if all the instructions within the group were executed simultaneously. We show that we can achieve a significant design improvement compared to classical computer.
A sparse and decomposed particle swarm optimization for inferring gene regulatory networks based on fuzzy cognitive maps

Inferring gene regulatory networks (GRNs) is vital to understand the complex cellular processes and reveal the regulatory mechanisms among genes. Although various methods have been developed, more accurate algorithms which can control the sparseness of GRNs still need to be developed. In this work, we model GRNs by fuzzy cognitive maps (FCMs), and a node in an FCM means a gene. Then, a new sparse and decomposed particle swarm optimization, termed as SDPSOFCM-GRN, is proposed to train FCMs, which employs the least absolute shrinkage and selection operator (Lasso) to control the network sparseness with a decomposed strategy. In the experiments, the performance of SDPSOFCM-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that SDPSOFCM-GRN can well control the sparseness of GRNs, and infer directed GRNs with high accuracy and efficiency.
Sensor Registration and Tracking from Heterogeneous Sensors with Belief Propagation

Modern sensor platforms carry an increasingly diverse range of sensors onboard, in order to estimate target positions inside their common surveillance region. Off-the-shelf sensors often provide measurements at different rates, and with different and potentially varying levels of uncertainty. Heterogeneous and asynchronous sensor networks make the sensor fusion problem more challenging as multiple measurement models and a dynamic prediction model are required. Moreover, a key challenge to address in fusion systems is that of sensor bias. Any relative bias between sensors could result in measurements not correlating with one another in a common frame of reference, and therefore vastly reducing track accuracy. This work presents novel results on a joint method that estimates both external angular bias between a radar and a camera, and the states of multiple targets. The proposed technique uses a particle-based implementation of Belief Propagation (BP), and compares with Random Finite Set (RFS)-based approaches. Initial results show that the BP approach outperforms the RFS approaches in terms of accuracy by around 50&#x0025; when using the Optimal Sub-Pattern Assignment (OSPA) metric.
Research on Frequency Hopping Synchronization Strategies based on TOPSIS Method

The synchronization of hopping frequency is a key step in frequency hopping communication systems. During system synchronization, the contradiction emerges between synchronization duration and credibility in the capture confirmation process after frequency acquisition, which make it difficult for the system to establish synchronization quickly and steadily. Thus, a solution is presented based on the classic Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to solve this problem. Firstly, a mathematic model for recognition process is built by Markov chains. Secondly, TOPSIS is employed to make analysis and suggestions to the recognition strategies. Results show that the presented mothed can shorten the synchronization time, ensure the reliability of the synchronization systems, and achieve an optimal effect in frequency hopping capture confirmation process.
The Application of Convolutional Neural Network in Security Code Recognition

Security Code or CAPTCHA, which represents Completely Automated Public Turing test to tell Computers and Humans Apart, is used to determine whether the user is human or not. It is widely applied in website management to prevent people from maliciously registering and spreading spam. With the development of machine learning algorithms and artificial intelligence technologies, it is possible to recognize the security codes with machine so that people can have access to the website without registration limits. There are many traditional machine learning algorithms such as Support Vector Machine (SVM) and Random forest are used to recognize security codes, but they have various disadvantages like low efficiency and low learning ability, inability to extract features automatically and difficulty in handling 2-dimension pictures directly. In machine learning area, the convolution neural network (CNN) is famous for its strong learning ability and automatic feature extraction, as well as high learning ability and efficiency, which makes it suitable for 2-dimension image data. Thus, we construct a convolutional neural network for security code recognition. The proposed CNN model is made up with 3 convolutional layers, a flatten layer and a full-connected layer. With the proposed model, we achieve an accuracy of 80.5% in the validation set.
DEVELOPING FISCAL DATA PROCESSING ANALYTICAL SYSTEM

The paper deals with the task of designing an analytical system for processing fiscal data. From a business point of view, such a system should solve the problem of analyzing a market basket, that is, finding the most typical patterns of purchases. From the point of view of data mining, the task of searching for association rules is solved, which consists of two stages: the search for all frequent sets with their support values and the acquisition of association rules based on the sets found. The first stage is provided by various search algorithms for frequent sets. In the paper, the algorithm chosen is the Frequent Pattern Growth Strategy (FPG) as the optimal one. The mathematical formulation of the task and the method for implementing the selected algorithm within the target system are given. The result of the work is a description of the fault‑tolerant and scalable model of the analytical system.
Experimental Data Processing

Conducting an experiment is related to providing certain experimental results, mostly numerical data. The chapter discusses the possibilities for adequate processing of such data, as well as the appropriate interpretation of results and assessments from the analysis conducted. In its main part, the processing of experimental data is based on conducted statistical analysis and graphical interpretation of the estimates. This defines the three main parts included in the chapter. In the first part, an approach is proposed for primary processing of experimental data (determination of definitional areas, individual criteria for evaluation, logical completeness of the evaluation and the need for preliminary filtering of the sample). The second part is dedicated to the basic processing of experimental data, where statistical approaches for this are discussed (basic statistics, correlation and regression analysis, logistic regression, variance analysis). In the last part, the basic methods are tabular and graphical interpretation of estimates from the analysis of experimental data.
Distillability of Sudden Death in Qutrit-Qutrit Systems under Global Mixed Noise*

Considering the influences of collective dephasing, multilocal qutrit-flip, local qutrit-flip, and the combination of global mixed noise, we study the dynamics of entanglement and the phenomenon of distillability of sudden death (DSD) in a qutrit-qutrit system under various decoherent noises. It is shown that the system always undergoes DSD when it interacts with multilocal and local qutrit-flip noise, and the time-determined bound entangled state is more dependent on different noises. Comparing with the cases of global mixed and collective dephasing noise, we conclude that the qutrit-flip noise is responsible for the DSD .
Weight or Density Corrected Value? Using Density Derived Key Ratio for Additional Accuracy to Intercomparability of Medieval and Historical Artifact Groups

In this paper, I propose a method to improve the intercomparability of different artifact groups by narrowing the error caused by the differences in material densities. The products, especially of mixed materials, have not been previously analyzed as regards whether or not they have statistically significant differences in material densities or whether the densities affect comparative studies. The results showed that in addition to the obvious material groups like different metals and stones, there is also a significant difference in material densities when comparing redware and clay pipes by their weight. Therefore, the method presented here eliminates the effect of material density, but in a way that it is still intuitive and can be compared with values that have not been density corrected.
A Camouflage Effect Detection Model for Fixed Targets

The traditional camouflage effect detection mainly implements the evaluation process for a single image, and cannot effectively reflect the statistical characteristics of the target. In order to better simulate the dynamic interpretation process of reconnaissance personnel on the target, a dynamic and statistically characteristic camouflage effect evaluation model is proposed for the problem of camouflage effect detection of fixed targets. Combined with the Mean shift target tracking technology, the model statistically correlates the target with the background eight-link domain and establishes a normalized joint Gaussian distribution. The target's camouflage effect is then evaluated using the distribution of probability density. The experiment performs complete camouflage, partial camouflage and non-disguise on the exit target of a simulated cavern, and calculates the logarithmic amplification probability and statistics of the curve after collecting the data. According to the 3σ criterion, the mean value is compared with a preset threshold value, and corresponds to the original camouflage state, the better camouflage state, and the invalid camouflage state, respectively. The experimental results show that the model can clearly distinguish the different camouflage states.
Nonlinear Contextual Face Hallucination

Face hallucination, which refers to the restoration of single or multiple low-resolution face images into clear high-resolution one, is a challenging research issue. Most existing methods use local or global patches for image representation and achieve good performance. However, they ignore that the local patch limits the area of the receptive field and the priori information used in reconstruction is limited. And the global patch expands the receptive field but introduces irrelevant information to degrade the reconstruction performance. In order to improve the performance of reconstruction, we propose a nonlinear contextual face hallucination method. First, contextual information can effectively improve the receptive field area to make full use of priori information. Then, the nonlinear model can make the proposed model more suitable for practical application and make the correlation of data in kernel space more compact. Finally, combining contextual and residual learning can improve the stability of the solution of the super-resolution model and the accuracy of reconstruction performance. The experimental results show that the proposed face hallucination method has superior performance than the state-of-the-art method.
Multi-frame image restoration based on a new degradation model of hazy and blurred image

The quality of outdoor images is significantly influenced by atmospheric environment and the vibration of imaging system. In this paper, we propose a novel model to describe the process of image degradation due to the above two factors, and a blind image restoration method based on this model. We derive the degradation model from the hypothesis that the process of atmospheric degradation and the process of vibration causing motion blur are independent. The contrast and brightness of images is decreased due to atmospheric absorption and scattering. Besides, multiple scattering and camera vibration lead to image blurring. We estimate the atmospheric light and transmittance, therefore the image restoration problem is converted to a blind deconvolution problem. A deblurring method of multi-frame image is proposed under the Bayesian posterior probability model, where the latent images are regularized by a natural image gradient prior and the point spread functions are regularized by an <i>l</i><sub>1</sub>-norm based prior. An alternating minimization approach is used to solve the optimization problem. Experimental results of synthetic images and real world images demonstrate that our model can well describe the degradation process and the proposed method can recover images effectively.
FGFIREM: A feature generation framework based on information retrieval evaluation measures

Learning to rank has become one of the most popular research areas in recent years. A series of learning to rank algorithms have been proposed to improve the ranking performance. In this work, we propose three learning to rank algorithms by directly optimizing evaluation measures based on the AdaRank algorithms. We name the three algorithms as AdaRank-ERR, AdaRank-MRR and AdaRank-Q, which optimize three evaluation measures, Expected Reciprocal Rank (ERR), Mean Reciprocal Rank (MRR), and Q-measure (Q), based on AdaRank, respectively. Furthermore, we propose a novel feature generation framework FGFIREM to enhance the ranking performance. The framework generates effective document ranking features based on the ranking scores assigned by the proposed algorithms, and enriches the original feature space of learning to rank using the generated features for improving the ranking performance. We evaluate the proposed framework on three datasets from LETOR3.0 and the web dataset MSLR-WEB10K. The experimental results demonstrate that our framework can effectively improve the ranking performance.
The Resurgence of Hate: Introductory Notes on the 2016 US Presidential Campaign

The Journal of Hate Studies is an annual peer-reviewed publication of the Gonzaga University Institute for Hate Studies.The Journal of Hate Studies is an international scholarly journal promoting the sharing of interdisciplinary ideas and research relating to the study of what hate is, where it comes from, and how to combat it. It presents cutting-edge essays, theory, and research that deepen the understanding of the development and expression of hate.View the complete list of issues by theme.
Potential of Creative Areas in Digitalized Modern Cities

The authors of the article regard creative spaces of urban areas, agglomerations and megalopolises as a recent and specific way of communication in a contemporary postmodern society. They operationalize the concept of an urban creative space and develop communication model for it. The concept reveals patterns of significant socio-structural transformations of cities, aiming at development of their marketing strategies with regard to transregional and intercountry global competition.
Hot Topic Detection Based on VSM and Improved LDA Hybrid Model

In consideration of the features of microblog content such as short text, sparse feature words, and the huge scale, a hot topic detection method was suggested by this paper based on VSM and improved LDA hybrid model. This method first uses LDA model incorporating the PageRank algorithm, deeply mines the structure of the social network. Then the VSM model and the improved LDA model are used for hybrid modeling and to calculate the similarity of microblog. Finally, the hot topic clustering results are obtained through the single-pass clustering algorithm based on the hybrid model. The experimental results show that the proposed method can effectively improve the microblogging hot topic detection efficiency.