Reviewerly/exHarmony huggingface dataset:
MODELING THE EFFECTS OF EXTERNAL DISTURBANCES ON A QUADROTOR MINI-UAV

With the progress of Technology, the use of Unmanned Aerial Vehicle called UAV is inescapable. Despite the fact that these machines are very useful, some problems can occur. There can be disturbances that can appears in different form. The main goals of this paper are to find the origin of some of the perturbations and to give solutions to avoid them. Indeed, when the UAV are in appropriate environment, they correctly receive the signals that are sent. So the UAV can carry out the required actions. However, if the environment contain disturbances, the UAV is affected. Therefore, it cant correctly receive the signals. These disturbances can be wind, rain, gravity or something else. In this article, some functioning principle of the quadrotor mini-UAV are explained. The analysis of the equation of movement is realized. The angles of Euler and the quaternions are therefore indispensable. In order to solve the problem of disturbances, we have to know something about it. Especially, we will see some information about how the wind comes about. So, we also give the effects of external perturbations like wind. We will see how it affects the UAV. Then, we will give some corrections to the effects of these external perturbations. All of that are possible thanks to the MATLAB software. As a matter of fact, an application is developed and written in M-files. The former equations are used in the programs. Graphic interfaces are built to facilitate the use of the application. The results are given as curves. These curves give first the operation of the UAV without perturbations. They also give the operation of the UAV with disturbances before and after the correction.
Control Strategy-Based Intelligent Planning of Service Composition

In this study, the intelligent planning technology for transportation service composition and the service selection process of service quality perception were investigated. Two outstanding contributions were achieved. First, a transportation service descriptive model which can embody classifying transportation domains and the effects of functional factors was constructed. Second, a hierarchical task network (HTN) planning method based on control strategy (CSHTN) was designed to overcome the shortcomings of the HTN planning method in selecting the decomposition method. The improved method expands the search range of the combination options by a decomposition method with full expansion, so that the optimal abstract service sequence can be accurately identified. In addition, to relieve the burden on the planner caused by the full expansion, short-term return factors and control factors were introduced, effectively controlling the search of the solution space and playing a regulatory role. The experimental verification shows that CSHTN not only can accurately find the abstract service plan that meets the traveler’s functional requirements, but also proves to be more economical in the selection of decomposition method with stronger engineering applicability.
Frank M. Oppenheim, SJ: A Model for Philosophical Interpretation and Reflection

Research Article| July 01 2019 Frank M. Oppenheim, SJ: A Model for Philosophical Interpretation and Reflection Jacquelyn Ann K. Kegley Jacquelyn Ann K. Kegley California State University, Bakersfield Search for other works by this author on: This Site Google The Pluralist (2019) 14 (2): 1–12. https://doi.org/10.5406/pluralist.14.2.0001 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Jacquelyn Ann K. Kegley; Frank M. Oppenheim, SJ: A Model for Philosophical Interpretation and Reflection. The Pluralist 1 January 2019; 14 (2): 1–12. doi: https://doi.org/10.5406/pluralist.14.2.0001 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressThe Pluralist Search Advanced Search The text of this article is only available as a PDF. Copyright 2019 by the Board of Trustees of the University of Illinois2019 Article PDF first page preview Close Modal You do not currently have access to this content.
2 Way Crawling

As we know that the deep web grows at very fast pace, there has been increased interest in techniques which help efficiently locate and check deep web interfaces. So, it is important to achieve wide coverage and high efficiency on the large volume of web resources. For this we propose a multistage framework, Smart crawler. Smart crawler is a two-stage crawler used to efficiently harvest deep web interfaces. In the first stage, the crawler performs site-based searching for center pages and avoids visiting non-relevant sites. In the second stage, an adaptive link ranking technique is used which helps to searching relevant site by excavating most relevant links. It is important to eliminate bias on visiting highly relevant links which is hidden in web directories, for this a link tree data structure is designed to achieve wider coverage for a website. The proposed framework gives experimental result on different domains and shows the agility and accuracy of the proposed framework, which retrieves deep-web interfaces from a large volume of sites and achieves higher harvest rates than other crawler.
A Case Study of Executive Functions in Real Process Modeling Sessions

Cognitive aspects like executive control functions, reasoning and abstraction have a crucial influence on modeling performance. Yet how are executive functions used in real modeling sessions and what individual differences exist? In this case study we analyse observations of three modeling sessions according to a coding scheme for behavioural observation of executive functions, reasoning and abstraction. We complement the findings with a qualitative, thick description of the sessions. We find that the modelers have unique styles in how they use executive control, that there appears to be a hierarchy in when specific executive functions are used, and that the use of executive control alone does not guarantee modeling progress. Greater awareness of the effects of executive control use in real modeling settings can be very helpful in training modelers to optimize their skills.
Anonymous Secure E-voting over a Network for Multiple Elections

In this work we present an anonymous secure e-voting scheme over a network for multiple elections. We applied a variant of ElGamal signature to construct an electronic voting for t elections. The method is based on the RSA blind signature that ensures the anonymity of all transactions in the voting process. We also analyze the security of the proposed protocol.
Identifying low-profile objects from low-light UAS imagery using cascading deep learning

Unmanned aircraft systems (UAS) have gained utility in the Navy for many purposes, including facility needs, security, and intelligence, surveillance, and reconnaissance (ISR). UAS surveys can be employed in place of personnel to reduce safety risks, but they generate significant quantities of data that often require manual review. Research and development of automated methods to identify targets of interest in this type of imagery data can provide multiple benefits, including increasing efficiency, decreasing cost, and potentially saving lives through identification of hazards or threats. This paper presents a methodology to efficiently and effectively identify cryptic target objects from UAS imagery. The approach involves flight and processing of airborne imagery in low-light conditions to find low-profile objects (i.e., birds) in beach and desert-like environments. The object classification algorithms combat the low-light conditions and low-profile nature of the objects of interest using cascading models and a tailored deep convolutional neural network (CNN) architecture. Models were able to identify and count endangered birds (California least terns) and nesting sites on beaches from UAS survey data, achieving negative/positive classification accuracies from candidate images upwards of 97% and an <i>f</i><sub>1</sub> score for detection of 0:837.
Object detection using convolutional networks with adaptively adjusting receptive field of convolutional filter

The receptive field size of a convolutional filter in a deep convolutional network is a crucial issue for object detection task, as the output must response to a suitable size of area in the image to capture proper information. Receptive field size of convolutional filter is fixed due to the inherently fixed geometric structure in its building module. However, objects of interest vary significantly in size within the images for object detection. Different locations of images correspond to objects with different scales, and high level convolutional layers encode semantic features over spatial positions, thus adaptive determination of receptive field size of convolutional filter is desirable for object detection. The authors propose a new module to adaptively determine the receptive field size of convolutional filter, named adaptive convolution. It is based on the idea of dilating the convolutional filter with multiple dilation values and choosing the maximum activation as output, without adding any other parameters. The plain counterparts in existing convolutional neural networks can be easily replaced by adaptive convolution, giving rise to adaptive convolutional networks. Adequate experiments have proven the effectiveness of authors’ method.
Towards Improving Performance of Sigma Filter

Sigma filter is one of the widely used filters for noise removal. This is popular because of its simple implementation and computationally in-expensiveness and better filtering accuracy than many other existing filters. In this work, we have proposed an improved Sigma filter, whereTiwari, Mayank the filtering method is separately applied to high- and low-frequency segments of the image and then restored image isLamba, Subir Singh constructed by combining the two filtered images. The basic assumption of this work is the fact that effect of noise is different on high-frequency (more) Gupta, Bhupendra and low-frequency (analogously less) segments of the image and hence more amount of noise can be removed by separately applying a noise removal filter on high- and low-frequency segments of the image. Comparative results between the proposed noise removal filter and other noise removal filters show that proposed noise removal filter is capable of removing noise from images without affecting their visual details.
Brain Aneurysm Extraction in MRI Images

Medical image processing is the most demanding and emerging field which includes the processing of MRI images. Aneurysm can develop in any blood vessel in the body especially in the brain and abdominal aorta. In regards to brain aneurysm, it has been clarified that the MRI is a more preferable detection method to inspect this brain abnormality. This paper experiments MRI brain aneurysm images using image processing techniques in extracting a clearer image of the brain aneurysm. The method proposed includes preprocessing techniques and post processing techniques. Preprocessing techniques involves noise removal function and image enhancement function. The post processing techniques is segmentation and morphological function. All of these techniques are the basic concepts of image processing. Detection and extraction of brain aneurysm from MRI images of the brain is done by using MATLAB software.
CDIO Ideas in the Professional Training of Russian Language Teacher and Literature: Designing an Educational Program Based on the FSES 3 ++

The article analyzes the current theoretical and practical aspects of designing eff ective basic professional educational programs aimed at preparing a philologist teacher in the context of the requirements of professional standards.
Exploiting Symmetry of Independence in d-Separation

In this paper, we exploit the symmetry of independence in the implementation of d-separation. We show that it can matter whether the search is conducted from start to goal or vice versa. Analysis reveals it is preferable to approach observed v-structure nodes from the bottom. Hence, a measure, called depth, is suggested to decide whether the search should run from start to goal or from goal to start. One salient feature is that depth can be computed during a pruning optimization step widely implemented. An empirical comparison is conducted against a clever implementation of d-separation. The experimental results are promising in two aspects. The effectiveness of our method increases with network size, as well as with the amount of observed evidence, culminating with an average time savings of 9% in the 9 largest BNs used in our experiments.
Genetic Algorithm VS Simulated Evolution: A Comparative Study of Evolutionary Optimization Techniques for Object Recognition

One of the fundamental fields of computer vision is Object recognition and it has a plethora of applications associated with it. The primary goal of the object recognition is to identify the objects of the same type even when they are viewed from different viewpoints. However, this goal is still a very challenging research problem in the field of computer vision because of different phenomena that can modify an image such as translation, rotation, and scaling. It has been proven that shape descriptors like Fourier and Moments are invariant with respect to transformation, rotation, and scaling. However, one of the most important and challenging tasks regarding the object recognition is how to find number of descriptors of a given object. As the main objective is to maximize the recognition rate therefore another challenging question is that what is the optimum number of descriptors to be used for achieving the maximum recognition rate? Another important question is that whether all the descriptors have equal importance or not? Due to all these reasons, selection of the appropriate descriptors is of immense importance therefore applying different optimization techniques for selection of best descriptors is a key to success. In this work we do a comparative analysis of two well-known evolutionary optimization techniques known as Genetic Algorithm (GA) and Simulated Evolution (SimE). By extensive simulations in MATLAB, it is observed that Genetic Algorithm provides better performance in terms of recognition rate as compared to Simulated Evolution.
New Fibonacci Recurrent Systems Applied to Transmission Lines Input Impedance and Admittance

In this paper, new Fibonacci wave functions are introduced for the first time. These transfer functions are derived from a first order transfer function source. Their characteristics and behaviors are quiet impressive and their output signals present multiple intermediate steady-states. Their polynomial coefficients follow specific distribution with respect to the Golden ratio. The pole locations of these transfer functions follow the Fibonacci pattern and they can be created to an infinite degree following a simple recurrent process. They have multiple resonance and anti-resonance frequencies organized in a perfect way with respect to each other. Each Fibonacci system has two well defined Fibonacci boundary systems using Pascal's triangle. These systems describe perfectly transmission lines LC ladder model input impedance and admittance.
Using Hashtags to Capture Fine Emotion Categories from Tweets

Despite recent successes of deep learning in many fields of natural language processing, previous studies of emotion recognition on Twitter mainly focused on the use of lexicons and simple classifiers on bag-of-words models. The central question of our study is whether we can improve their performance using deep learning. To this end, we exploit hashtags to create three large emotion-labelled data sets corresponding to different classifications of emotions. We then compare the performance of several word and character-based recurrent and convolutional neural networks with the performance on bag-of-words and latent semantic indexing models. We also investigate the transferability of the final hidden state representations between different classifications of emotions, and whether it is possible to build a unison model for predicting all of them using a shared representation. We show that recurrent neural networks,especially character-based ones, can improve over bag-of-words and latent semantic indexing models. Although the transfer capabilities of these models are poor, the newly proposed training heuristic produces a unison model with performance comparable to that of the three single models.
Measuring Human Emotion in Short Documents to Improve Social Robot and Agent Interactions

Social robots and agents can interact with people better if they can infer their affective state (emotions). While they cannot yet recognise affective state from tone and body language, they can use the fragments of speech that they (over)hear. We show that emotions – as conventionally framed – are difficult to detect. We suggest, from empirical results, that this is because emotions are the wrong granularity; and that emotions contain subemotions that are much more clearly separated from one another, and so are both easier to detect and to exploit.
CT Image Secret Sharing Based on Chaotic Map and Singular Value Decomposition

In order to prevent CT images from being stolen and tampered in telemedicine, a secret sharing method of (n, n) structure is designed and combined with the classical chaotic digital image encryption method. The method consists of three parts: In the first part, singular value decomposition is used to generate the key of the Henon chaotic map and CT subgraphs; in the second part, the Henon map is used to generate chaotic sequences of scramble CT subgraphs. Then a valueless image selected by the user is used as an impurity image, the diffusing operation is completed by a XOR operation between the impurity image and the image obtained by the scrambling operation. Finally, the image obtained by the diffusing operation is divided into multiple parts by the secret sharing method designed in this paper and embedded into the cover images selected by the user to generate several shadow images. The third part is the decryption part, and the decryptor completes the decryption when all the shadow images, impurity image and keys are collected. Several experiments have shown that this method has excellent performance in the test of ciphertext statistical characteristics, sensitivity, PSNR and other typical indicators for measuring the performance of image encryption methods.
Quality evaluation for adaptive optical image through a logarithmic deviation of Rényi entropy

The adaptive optical telescopes play a more and more important role in astronomy and satellite detection, and the space target images are so many that we need find a suitable method of quality evaluation to choose good quality images automatically in order to study the restorate image. It is well known that the adaptive optical images are no-reference images. In this paper, a new evaluation method based on the use of the logarithmic standard deviation of Rényi Entropy for the adaptive optical images is proposed. Through the discrete cosine transform using one dimension window, the statistical property of Rényi entropy for images is studied. The different directional Rényi entropy maps of an input image containing different information content are obtained. The mean values of different directional Rényi entropy maps are calculated. For image quality evaluation, the different directional Rényi entropy and its standard deviation corresponding to region of interest is selected as an indicator for the anisotropy of the images. In this paper, we define a logarithmic measure the standard deviation based on Rényi entropy can be selected as an indicator of quality evaluation for adaptive optical images. Experimental results show the proposed method that the sorting quality matches well with the visual inspection.
TKDN: Scene Text Detection via Keypoints Detection

In the past few years, great efforts have been devoted to scene text detection. Nevertheless, efficient text detection in the wild remains a challenging problem. Methods for general object detection usually have limitations in handling the arbitrary orientations and large aspect ratios of scene text. In this paper, we present a novel scene text detection method which treats text detection as a text keypoint detection task performed in a coarse-to-fine scheme (text keypoint detection network, TKDN). Specifically, in TKDN we first generate the coarse text instance regions using feature pyramid network (FPN) as well as region proposal network (RPN) and ResNet50. Within the coarse text regions, we then perform text keypoint detection, bounding box classification and regression, and text region segmentation in a multi-task way. In the inference stage, an effective post-processing algorithm is designed to combine the outputs from three branches and obtain the final text keypoint detection results. The proposed TKDN approach outperforms the state-of-the-art approach and achieves an F-measure of 82.0% on the public-domain ICDAR2015 database.
Additive Trees for Fitting Three-Way (Multiple Source) Proximity Data

Additive trees are graph-theoretic models that can be used for constructing network representations of pairwise proximity data observed on a set of N objects. Each object is represented as a terminal node in a connected graph; the length of the paths connecting the nodes reflects the inter-object proximities. Carroll, Clark, and DeSarbo (J Classif 1:25–74, 1984) developed the INDTREES algorithm for fitting additive trees to analyze individual differences of proximity data collected from multiple sources. INDTREES is a mathematical programming algorithm that uses a conjugate gradient strategy for minimizing a least-squares loss function augmented by a penalty term to account for violations of the constraints as imposed by the underlying tree model. This article presents an alternative method for fitting additive trees to three-way two-mode proximity data that does not rely on gradient-based optimization nor on penalty terms, but uses an iterative projection algorithm. A real-world data set consisting of 22 proximity matrices illustrated that the proposed method gave virtually identical results as the INDTREES method.
Real-time high dynamic range 3D scanning with RGB camera

This paper introduces a novel real-time high dynamic range 3D scanning method with RGB camera, which utilizes the cameras varying color sensitivity and the projector’s dark time to alleviate saturation-induced measurement error. The varying color responses in R, G, and B channels creates three different intensity levels, and an additional capture at the projectors bright to dark transition state doubles the total intensity levels to six. Finally, saturation-induced errors can be alleviated by choosing the unsaturated pixel of best quality among the images with six intensity levels. Experimental results will be presented to demonstrate the success of such method.
An Overview of Business Writing: Challenges and Solutions

Abstract Bad writing can affect individuals and companies alike. It can cause confusion and misunderstanding, waste important resources of time and money, erode credibility and trust. On the other hand, clear writing will increase productivity, promote goodwill, and cement relations. This article discusses some of the most common problems encountered in business writing, together with their causes and the solutions proposed in the literature. Using too many words, an abstract vocabulary, and passive constructions will obscure the message and tire the reader. Conversely, the elimination of clutter, the use of plain words and personal pronouns, and the construction of sentences with clear subjects and verbs will convey the message clearly and effectively and will make your writing stand out.