Reviewerly/exHarmony huggingface dataset:
Collision Avoidance in OpenUxAS

This paper presents an approach for evaluating collision avoidance performance for fixed-wing UAVs with AFRL’s autonomous vehicle software frame-work, OpenUxAS. Specific situations that provide airspace violations were tested to explore OpenUxAS’ response and capabilities. The process of detecting a potential aerial collision was considered by range. OpenUxAS was evaluated through simulated scenarios of direct, indirect, and following non-cooperative vehicles. Additional pathfinding algorithms were used to supplement OpenUxAS for conflict free paths using escape trajectories.
A Comparative Study of Information Retrieval Using Machine Learning

Information retrieval is the study of finding relevant information from the semi-unstructured type of data. This paper surveys the insightful techniques and shows the comparative analysis between several information retrieval systems using machine learning approach. Information that we get from the web portal should be with high recall and high precision and F-measure. In this paper, in order to compare information retrieval techniques, we use these parameters like recall, precision, accuracy, and so on. Recall means the ability of the system to retrieve all relevant documents. High precision means the ability of the system to retrieve only relevant documents. F-measure is a proportion of a test’s accuracy. To compute the score it considers both the recall and the precision.
A Model-Free Algorithm of Moving Ball Interception by Holonomic Robot Using Geometric Approach

In this paper, one common problem for the teams competing in the RoboCup Small Size League (SSL) is addressed, namely the interception of a moving ball at an arbitrary aspect angle relative to the direction of the shot. We present a simple, robust and efficient algorithm for the interception of a moving ball by an omnidirectional SSL robot. The algorithm, designed on the basis of a heuristic approach, requires minimal knowledge of robot dynamics and relies on two key ideas. The first idea is the consideration of ball motion via transition to a reference frame where the ball is static, and the second one is planning the motion of the robot in such a reference frame from the geometric viewpoint. Experiments conducted in a real SSL environment confirmed the beneficial properties of the algorithm: it provides successful interception in a variety of scenarios, characterized by different directions of ball motion and the positional relationships between the ball, robot and goal.
Improving Structural Test and Analysis Correlation Using Digital Image Correlation Boundary Measurements

Simplified idealizations, such as clamped or simply supported, are commonly used as support boundary conditions in modeling and analysis of tested aerospace structures. However, these simplifications are not always appropriate and characterization of the response of non-rigid boundaries is often required to improve test and analysis correlation. While analysis models are typically modified to better represent the test, this modification is often not practical due to the complexities of the boundary response. A study is conducted to examine the use of digital image correlation (DIC) to measure the boundary support structure response and then to adjust the test data to remove the effects of the boundary response in order to improve test and analysis correlation. A high-aspect ratio composite wing test is the subject of the study. Three sources of boundary flexibility contributing to rigid body rotation of the test wing are identified and quantified using both DIC and conventional test data. The wing displacement test data are adjusted using DIC data for these sources. Wing displacement test data is also adjusted using direct measurement of wing rotation from a DIC system that monitored the top surface, root region of the wing, and using available conventional instrumentation. The adjusted data exhibits improved test and analysis correlation and demonstrated the benefit of using DIC along with conventional instrumentation to collect and interpret test data.
Decision Support System Employee Recommendation using Fuzzy Sugeno Method as a Job Search Service

Based on data from the Indonesian Central Bureau of Statistics which was updated in February 2018, the percentage of the workforce towards the working age population reaches 69.20%. The Open Unemployment Rate in Indonesia reaches 5.13% or 6.87 million people. Prospective workers, especially new graduates who have a small level of work experience will have difficulty in marketing their expertise because they are unable to compete with experienced workers. This research is used to facilitate the selection of prospective workers/job seekers. It developed and equipped with a Decision Support System Employee Recommendation using Fuzzy Sugeno Method. Fuzzy Sugeno method is used to process job data and job search data based on variables of age, education level, certification of expertise, and work experience. Job data processing determines the maximum and minimum value limit for job seekers to get recommendations for applying jobs. The same variables of job seeker data and job data produce a value of received percentage in job openings. Job seekers will get job recommendations accompanied by a value of recieved percentage . Job seekers who have applied for a job opening will be displayed sequentially based on the value of received percentage at the job opening therefore they can find the recommendations of the workers.
Tiny Image Classification using Four-Block Convolutional Neural Network

The task of classifying images into predefined classes is a major problem in computer vision and artificial intelligence. Deep neural network such as Convolutional Neural Network (CNN) have shown great success in large-scale dataset of high resolution image classification. Here, it is important to note that most real time images may not have high resolution. With the increasing demand of surveillance camera-based applications, the low resolution images are major problem. To overcome this problem, we propose two Four-Block CNN model; one with four-layers and the other one with three-layers. Our proposed Four-block four-layer CNN model contains four convolution layers, first three layers contains 3 × 3 kernel size with stride-1 and fourth layer used with stride-2 for dimensionality reduction.The Four-block three-layer has two convolutional layers with stride-1 and third layer with stride-2. We trained our model on CINIC-10 and CIFAR-10 datasets having low-resolution images. For taking average of whole feature map we are using Global Average Pooling layer as a classifier in both models. To reduce training time complexity, we use non-saturating neurons. Overfitting problem has been addressed by dropout and batch normalization methods. On the validation data, we achieved the best accuracy of 81.62%, 92.21% for CINIC-10 and CIFAR-10 respectively, using Four-block four-layer model.
A Vertex Concavity-Convexity Detection Method for Three-Dimensional Spatial Objects Based on Geometric Algebra

Vertex concavity-convexity detection for spatial objects is a basic algorithm of computer graphics, as well as the foundation for the implementation of other graphics algorithms. In recent years, the importance of the vertex concavity-convexity detection algorithm for three-dimensional (3D) spatial objects has been increasingly highlighted, with the development of 3D modeling, artificial intelligence, and other graphics technologies. Nonetheless, the currently available vertex concavity-convexity detection algorithms mostly use two-dimensional (2D) polygons, with limited research on vertex concavity-convexity detection algorithms for 3D polyhedrons. This study investigates the correlation between the outer product and the topology of the spatial object based on the unique characteristic that the outer product operation in the geometric algebra has unified and definitive geometric implications in space, and with varied dimensionality. Moreover, a multi-dimensional unified vertex concavity-convexity detection algorithm framework for spatial objects is proposed, and this framework is capable of detecting vertex concavity-convexity for both 2D simple polygons and 3D simple polyhedrons.
Comparative Study of Ranking Algorithms

The world is surrounded with tons of data, and extraction of the required information from such colossal volume of information is majorly convoluted. Internet contains raw data and to transform this data into knowledge, mining has to be done. Whenever user puts some query on any search engine, the results come in certain order based on the importance of the document produced by search engine. Certainly, the order is because of the algorithms they are implementing in background. Popular search engines like Yahoo, Bing, Google, MSN, etc. contain their own way to compute ranking, different results are obtained on different search engines for the same query because the way of determining the scores of the web pages differs from algorithm to algorithm. In this paper we have tried to compare popular web page ranking algorithm on the basis of there strength and weakness. This paper puts light on some of the very popular ranking algorithms and tries to find out a better hybrid solution which can cut down the time spent on searching a page.
Cryptography Survey of DSS and DSA

Digital signatures are the basis for identifying who sends file identities in modern society today. Digital signatures are a way to provide integrity and reliability for digital data signatures. Signatures use a set of principles and parameters that allow a computer to verify the identity and authentication of data. There are three algorithms for digital signature generation in the DSS. The first one is the RSA, the second is the ElGamal algorithm, and the third is the elliptic curve digital signature algorithm (ECDSA). This paper discusses the cryptography including different types of digital signatures based on the key type and some algorithms of digital signature, RSA, ElGamal, and ECDSA.
Adaptive Differentially Private Data Stream Publishing in Spatio-temporal Monitoring of IoT

Spatio-temporal monitoring of the Internet of Things (IoT) has enabled the development and proliferation of third-party computing services by extensively exploiting the massive amount of sensing data. In particular, continuously generated data stream are monitored in real-time and exploited to facilitate people’s daily lives, such as traffic monitoring and epidemic prevention. In its simplest way of deployment, the direct publishing of various streams could seriously compromise the privacy of participating users. Hence, a more sophisticated scheme is needed to regulate the privately publishing of data streams, which may possibly require control to be applied dynamically. However, most existing solutions are non-adaptive to dynamic changes of the streams due to constraints of predefined parameters, thus are vulnerable to low data utility. In this paper, we present AdaPub, a data-adaptive framework for infinite multidimensional stream real-time publishing with ω-event differential privacy while ensuring high data utility. Without predefining the parameters, AdaPub could learn and update the parameters that reflect the spatio-temporal correlations of the stream in a data-adaptive manner. Specifically, we propose two modules DimParti and AdaCluster which are seamlessly incorporated into AdaPub to simultaneously learn dimension correlations and time correlations in a data-adaptive way, thus greatly improving the data utility of the sanitized streams. Extensive experiments on real-world datasets demonstrate that our solution substantially outperforms state-of-the-art solutions with much lower errors while achieving strong privacy guarantees.
Compiling VR/AR Trainings from Business Process Models

Virtual trainings are probably the most popular application of virtual reality (VR) in industry. Such applications are usually hand crafted by computer graphics experts, so generating new trainings is timeintensive and expensive. Even when a training application has been built, any subsequent changes require the inclusion of Software Developers and 3D modeling experts. In this paper, we propose a solution that separates these concerns and makes the generation of AR or VR/AR Training applications easier and cheaper. Our approach is based on a Business Process Model (BPM) of the task to be trained. For technical experts it is common to use such process models to describe tasks. Numerous BPM tools are available, and often models of tasks to be trained are already available. We thus present a compiler that directly translates such process models and a corresponding CAD Model of a machine to a ready-to-use VR/AR machine Training application. By feeding different process models to the compiler, different VR/AR Training applications can be generated quickly, without programming or 3D modeling expertise. We also show how knowledge about the possible interactions is exploitable within a particular training to optimize rendering and simulation performance, which allows us to generate VR/AR Training applications even for machines of high geometric complexity.
Recurrent Neural Network for Content Based Image Retrieval Using Image Captioning Model

With the tremendous growth in the collection of digital images by social media, ecommerce applications, medial applications and so on, there is a need for Content based image retrieval. Automatic retrieval process is one of the main focus of CBIR, whereas traditional keyword based search approach is time consuming. Semantic based image retrieval is performed using CBIR where the user query is matched based on the perception of the contents of the image rather than the query which is in text format. One of the main research issue in CBIR is the semantic gap which can be reduced by deep learning. This paper focuses on image captioning model for image retrieval based on the content using recurrent and convolutional neural network in deep learning.
Document Network Projection in Pretrained Word Embedding Space

We present Regularized Linear Embedding (RLE), a novel method that projects a collection of linked documents (e.g., citation network) into a pretrained word embedding space. In addition to the textual content, we leverage a matrix of pairwise similarities providing complementary information (e.g., the network proximity of two documents in a citation graph). We first build a simple word vector average for each document, and we use the similarities to alter this average representation. The document representations can help to solve many information retrieval tasks, such as recommendation, classification and clustering. We demonstrate that our approach outperforms or matches existing document network embedding methods on node classification and link prediction tasks. Furthermore, we show that it helps identifying relevant keywords to describe document classes.
Ambiguity Function Based Radar Waveform Classification and Unsupervised Adaptation Using Deep CNN Models

We present a robust generalized approach to phase and frequency modulated LPI Radar waveform classification and adaptation, inspired by deep convolutional neural architectures. We use a complex Ambiguity Function matrix as a pre-processing step, following which, a waveform classification, or adaptation to unlabeled reference target domains, is performed. We test our method on a wide range of tasks, datasets, and different signal distributions. Our method surpasses the state-of-the-art performance on classification problems on multi-encoding, multi-feature datasets, in diverse and challenging conditions. Our novel approach to an unlabeled Radar waveform adaptation reveals impressive classification improvements to domain shifted unlabeled signals.
Responding to individual fraud

In today’s society, fraud poses significant challenges. The evolution of technology and the proliferation of online fraudulent approaches, has amplified the issues faced by individuals who are victims as well as the agencies who seek to respond. Fraud is a unique offence, in that there are a multitude of agencies that victims can report to, including police, banks, remittance agencies, consumer protection, and other government and non-government agencies, termed the “fraud justice network” (FJN). Much of the previous research addressing fraud (particularly online approaches) has been from the perspective of victims and clearly outlined the often negative challenges and large degree of secondary victimisation that they experience. The current chapter seeks to address this issue from the perspective of organisations across the FJN. Based on interviews with thirty-one professionals across the FJN in London (United Kingdom) and Toronto (Canada), this chapter examines how these organisations understand and view individual fraud victimisation and the challenges that they face in working with individuals. It highlights specific areas of difference from the victim and organisational perspective. The analysis demonstrates several tensions that exist and identifies areas that require further attention into the future in order to improve victim experiences.
Reversible Database Watermarking Based on Differential Evolution Algorithm

To battle against the problem of invisibility and robustness of existing reversible database watermarking algorithms, we propose a reversible database watermarking method based on differential evolution algorithm. In the image preprocessing stage, the edge detection operator is introduced, and the image edge feature points are extracted to guide the watermark embedding, which improves the invisibility of the database watermark. In addition, by introducing a differential evolution algorithm to select the optimal embedding position of the database, the watermark is embedded in the case of the minimum fitness function, which improves the watermark invisibility. Finally, in the coding mode, the set of sites consisting of database tuples and attribute columns is used as a population individual to improve the robustness of the watermark. The experiment proves the effectiveness of the proposed method and the proposed algorithm has good performance than the reversible database watermark based on genetic algorithm and Simulated annealing algorithm.
A Method of Extracting Metadata Information in Digital Books

Metadata is the most important application format in current digital book resources. This paper mainly studies the key technologies and applications of metadata extraction in digital books, and proposes a metadata extraction method using information compensation from web. Experiments show that this method can correct errors of the original extracted metadata to a certain extent, and can also complement the missing metadata.
Computational phase correction algorithms for multi-aperture systems

Aperture cophasing can increase the resolution of a multi-aperture array while reducing optical system size and accounting for phase errors from hardware misalignments and atmospheric turbulence. Remapping apertures using an anamorphic pupil relay can separate the spatial frequency spectrum of a previously continuous modulation transfer function to facilitate a posteriori piston corrections between apertures [ J. Opt. Soc. Am. A 34 , A47 ( 2017 ) JOAOD6 0740-3232 10.1364/JOSAA.34.000A47 ]. This study expands this method from two apertures to six using maximum likelihood estimation to calculate piston phase errors and least squares for tip and tilt. Perturbed test targets were generated for specific turbulence and noise values, providing random realizations of pistons, tips, tilts, and atmospheric phase screens. Results show improved Strehl ratios for corrected images in the presence of noise and atmospheric turbulence.
On the Union Closed Fragment of Existential Second-Order Logic and Logics with Team Semantics.

We present syntactic characterisations for the union closed fragments of existential second-order logic and of logics with team semantics. Since union closure is a semantical and undecidable property, the normal form we introduce enables the handling and provides a better understanding of this fragment. We also introduce inclusion-exclusion games that turn out to be precisely the corresponding model-checking games. These games are not only interesting in their own right, but they also are a key factor towards building a bridge between the semantic and syntactic fragments. On the level of logics with team semantics we additionally present restrictions of inclusion-exclusion logic to capture the union closed fragment. Moreover, we define a team based atom that when adding it to first-order logic also precisely captures the union closed fragment of existential second-order logic which answers an open question by Galliani and Hella.
A survey on generative adversarial networks and their variants methods

Data science becomes creative with generative adversarial networks (GANs) which have had a big success since they were introduced in 2014 by Ian J. Goodfellow and co-authors. In technical term the GANs are based on the unsupervised learning of two artificial neural networks called Generator and Discriminator both trained under the adversarial learning idea. The major goal of GAN is to generate new samples that estimate the potential distribution of real data. Due to its huge success, many modified versions have been proposed in the last two years. We summarize in this review paper GAN’s background, architecture and its application fields. Then, we discuss the different extensions of GAN over the original model and provide a comparative analysis of these techniques.
Recovering Analog-Tape Seismograms from the 1980 Mount St. Helens Pre-Eruption Period

Abstract Mount St. Helens in Washington State erupted violently (Volcano Explosivity Index = 5) on 18 May 1980. During the previous two months, intense seismic activity at the volcano was recorded by a combination of continuous analog-tape recordings, paper drum recordings, and a recently installed triggered digital event computer system. Because of the technological constraints of the time, the digital data available cover only a little more than 1% of the two-month period. The paper drum records only exist for a few of the seismic stations and are also quite incomplete. However, the analog-tape data from some stations is near complete for almost the whole two months. During the period 2005–2014, these old analog tapes were recovered from storage and digitized to generate standard digital data for archiving at the Incorporated Research Institutions for Seismology Data Management Center. This recovery process was long and complicated but, for the most part, was fairly successful. Although the quality of these recovered data is nowhere near as good as modern digital seismograms, this dataset does provide a near-continuous record of the significant seismic sequence that led up to the major volcanic eruption. It includes the large variety of seismic signals from different types of volcanic earthquakes and harmonic tremor and should be a valuable resource for those studying volcanic seismicity.