Reviewerly/exHarmony huggingface dataset:
Online Path Planning of Messenger UAV in Air-Ground Collaborative System

This paper mainly solves an online path planning problem of messenger UAV in an air-ground collaborative system which is composed of a fixed-wing unmanned aerial vehicle(UAV) and multiple unmanned ground vehicles(UGVs). The UGVs play the role of mobile actuators, while the UAV acts as a messenger to transmit the information among the UGVs. The online path planning problem for the messenger UAV can be modeled as a Dynamic Dubins Traveling Salesman Problem (DDTSP). The goal of this problem is to find a shortest path which enables the UAV to access all UGVs periodically. This paper proposes an online path planning method based on heuristic rules. The method can be divided into two stages: firstly, the sequence for the UAV to access the UGVs are determined by the heuristic rules which consider the relative positions and movement characteristics of the UGVs and the UAV, and then the access positions of the UAV are determined. Computational results on instances with varying characteristics show that the proposed method can generate high-quality solutions in a very short time. The comparison experiments demonstrate that, compared with another advanced method, our method can achieve a better balance between the quality of the solution and time consumption.
Marathon Bib Number Recognition using Deep Learning

Bib number recognition (BNR) from unstructured marathon images can be a challenging task. This is because the images captured at these events are very inconsistent since, they are often captured by multiple photographers, at various locations and times. This results in images containing different backgrounds, angles and illumination. The images often contain multiple participants in various poses, where the bib numbers can be obstructed by the participants themselves. The bib numbers are often printed on flexible paper and can easily be deformed which distorts the printed numbers. In this work we present a BNR system based on deep learning which is able to locate bib numbers in unstructured, complex marathon images. Using the segmented bib numbers the system then, recognizes the digits and finally outputs the bib numbers that it was able to detect in the image. The first stage consists of a fully Convolutional Neural Network (CNN) to segment the bib numbers while the second stage consists of a Convolutional Recurrent Neural Network (CRNN) used to recognize the detected numbers. The proposed method obtained an F1 score of 0.69 which outperformed existing methods.
Security analysis of continuous-variable quantum key distribution with imperfect Faraday mirror

For the practical Faraday mirror (FM) in a continuous-variable quantum key distribution (CVQKD) system there exists a rotation angle deviation that can lead to a loss of photon number when a light message passes through the polarization beam splitter. Here we employ the entanglement-based model to research the influence of the FM's imperfection. Based on parameter estimation, the imperfection can introduce a loophole for Eve to obtain information. The secret key rate of the CVQKD will be impaired. Finally, a novel method is proposed to ensure system performance by compensating optical power, and we further study the relationship between the secret key rate and the modulation variance.
Deep Metric Learning with Dynamic Margin Hard Sampling Loss

In recent years, with the advent of large data sets and the adoption of end-to-end learning methods using convolutional networks, it is possible to verify and recognize faces on a large-scale dataset effectively, which has even surpassed that of humans. One of the most effective methods is the metric learning with triplet loss. However, when using a large-scale dataset to train the model, the number of the possible triplets will be explosive. Therefore, we propose an improved triplet loss based on deep neural network for end-to-end metric learning, which can effectively cut down the number of the possible triplets. It is a simplification of the traditional triplet loss function, but it has not degraded the performance of the model. Our method is evaluated on LFW and YTF datasets, which are most widely used benchmarks. The experimental results indicate that the proposed method can outperform some previous methods, and obtain the state-of-art performance with less time.
Real-Time Passive Magnetic Localization Based on Restricted Kinematic Property for Tongue-Computer-Interface*

For severe paralyzed individuals, free communications with the environment are of great importance. However, constrained by their body functions, available interaction technologies are limited. Tongue-computer-interface based on passive magnetic marking takes advantages of high flexibility and fatigue-resistance in physiological sense, and provides a basis for a self-sufficiency life. As control signals, the real-time tongue movements are required to be derived by solving a nonlinear inverse magnetic problem with forward magnetic dipole model. This paper proposes a combined localization algorithm with pre-calibrated location-orientation mapping database based on restricted kinematic property of target motion. The simulation results show that the developed localization method can provide improved tracking accuracy with good real-time capability.
Credit Card Fraud Detection

Worldwide billions of dollars per year goes into vain because of credit card fraud which is a major on growing problem. High tech advanced classification methods provide the ability to detect these fraudulent transactions without much disturbance to legal transactions. However, some key risks are involved namely, (a) imbalance learning and (b) concept drift. This paper involves some hybrid sampling for handling class imbalance at the data level and algorithm level which is being examined on European credit card transactions over a period of two days. The results are compared with the three main algorithms which have high performance in the task of fraud detection: (a) linear support vector machine, random forest, and K-NN. Thus, it has been proven from the results that sophisticated generative sampling methods will fall short in generalizing the minority class in the presence of extreme class imbalance. The work is implemented in Python. The execution of the method is evaluated on the basis of accuracy. The outcomes show of optimal accuracy for support vector machine, k-nearest neighbor and random forest classifiers are 5, 79, and 84% separately. The similar outcomes demonstrate that random forest performs superior to support vector machine and k-nearest neighbor algorithms.
A New Optimization Algorithm and Its Comparison on Traditional Optimization Algorithms

Bio-inspired algorithm is a kind of algorithm proposed by simulating biological evolution or social behavior in nature. This kind of algorithm can solve many complex optimization problems, such as assignment problem, knapsack problem, travel path problem and so on, which are difficult to solve by traditional methods. Presently, bio-inspired algorithms have received a significant amount of attention in both academic and engineering societies. This paper presents a new bio-inspired algorithm, termed beetle antennae search (BAS) algorithm. First, inspired by the foraging behavior of beetles, the principle of BAS algorithm is presented. Then, two application examples are conducted to evaluate the effectiveness of BAS algorithm. Importantly, to substantiate the advantage of BAS algorithm, abundant comparative experiments are provided. Experiment results reveal that the BAS algorithm is superior to the other algorithms.
Setting Sail for Tipasa: Preparing for an Interlibrary Loan System Transition

This paper is the first segment of a two-part article detailing the transition process of Northeastern State University Library’s migration from OCLC’s interlibrary loan automation system, ILLiad to its first cloud-based ILL management system, Tipasa. The transition planning process began in the fall of 2018 with the transition period beginning in early 2019. This article provides a framework for the successful implementation process of shift to a new interlibrary system in one semester. The authors explore the challenges and lessons learned while providing a structure that may be beneficial for other libraries preparing for a similar transition process.
A Framework for 3D Object Detection and Pose Estimation in Unstructured Environment Using Single Shot Detector and Refined LineMOD Template Matching

In order to improve the robot's perception ability in the complicated environment, especially the unstructured environment, a framework of 3D object detection and pose estimation using single shot detector (SSD) and modified LineMOD template matching is proposed, which can detect multiple objects and estimate their pose simultaneously. Firstly, the initial object detection (the first detection) is realized by single shot detector network and therefore the region of interest (RoI) of target objects are generated. LineMOD template matching is then applied to provide candidate templates. These calculated templates are grouped by the designed clustering algorithm. After sorting the clusters according to the descending order of the average similarity, non-maximum suppression removes the similar results and provide the further multiple detection results (the second detection). Finally, based on the results from the second detection, the pose of the object is estimated by using iterative closest point (ICP) algorithm. The object detection experiments show that on Tejani dataset, the average recognition rate of six objects reaches 99.25%. For the object pose estimation, F1 of the proposed method is 21.7% higher than the conventional method in the pose estimation experiments. Also, F1 of the presented algorithm is 9.5% higher than Deep-6Dpose method. Both comparison experiments verify the effectiveness of the proposed framework. Further, this framework for object detection and pose estimation is employed to do robotic grasping. In particular, the workpiece of steel plates is grabbed, which is a necessary procedure of the polishing technique.
Mixed-Reality Exhibition for Museum of Peace Corps Experiences using AHMED toolset

We present a mixed-reality exhibition for the Museum of Peace Corps Experiences designed using the Ad-Hoc Mixed-reality Exhibition Designer (AHMED) toolset. AHMED enables visitors to experience mixed-reality museum or art exhibitions created ad-hoc at any location. The system democratizes access to exhibitions for populations that cannot visit these exhibitions in person for reasons of disability, time-constraints, travel restrictions, or socio-economic status.
Polymorphic computation systems: Theory and practice of confluence with call-by-value

We present a new framework of polymorphic computation rules that can accommodate a distinction between values and non-values. It is suitable for analysing fundamental calculi of programming languages. We develop a type inference algorithm and new criteria to check the confluence property. These techniques are then implemented in our automated confluence checking tool PolySOL. Its effectiveness is demonstrated through examination of various calculi, including the call-by-need lambda-calculus, Moggi's computational lambda-calculus, and skew-monoidal categories.
Detecting phishing website using Pattern Mining

Abstract Nowadays users are purchasing products and payments through online. While using these websites they will ask for the information of the users like username and password etc. There are some websites which are used to hack the details of the user. This type of websites is called phishing websites. For detecting these websites, they are proving an effective, intelligent data mining algorithm is used. In this paper, they are surveying the detection of phishing websites by using pattern mining technique.
Evaluation of content adaptation

Purpose Students have learning difficulties, mainly in processes that involve attention and interpretation of written or spoken language. Technological tools allow to create computational platforms with adaptation aspects depending on the student’s characteristics. It is also important to highlight the progress of the measurement of cognitive processes such as attention through NeuroSky’s MindWave EEG sensors. This paper aims to present the results of analyzing attention levels of children with learning difficulties, based on the acquired brain waves. As a final result, an adaptive computational system that displays educational activities regarding educational profiles of children is obtained. Design/methodology/approach The Kamachiy–Idukay platform was chosen to make the validation. The platform generates the educational activities according to the students’ profile. The validation phases were identification of the test environment, the first environment required a scenario that involved students with learning difficulties, to verify the functionality of the system, when analyzing cases of the students with learning difficulties; identification of two validation criteria, type of educational activity and attention difficulties of the students; and analysis of the brain signal when children interact with the educational content. Findings The adaptation of contents that include music and animations generate higher levels of attention in students with difficulty. The analysis of signals from the NeuroSky sensor to determine the attentional levels in children allowed a generation of content adapted to the characteristics of the difficulty in each child. Research limitations/implications For the validation, it was necessary at the beginning of the activity to determine the stability of the signal emitted by the NeuroSky sensor. Two cases were studied in children with difficulty and their measure of attention versus adaptive contents. Practical implications A k-means algorithm was used to establish the attention levels of the children. Social implications Children with learning difficulties have different learning styles, which implies an adaptation of content that generates an attentional process according to their characteristics. Originality/value Evaluation content adaptation taking into account the signal brain sensor NeuroSky for learning process. The signal brain of the student when interacting with the activities is include in the student profile.
Bag Semantics of DL-Lite with Functionality Axioms

Ontology-based data access (OBDA) is a popular approach for integrating and querying multiple data sources by means of an ontology, which is usually expressed in a description logic (DL) of DL-Lite family. The conventional semantics of OBDA and DLs is set-based—that is, duplicates are disregarded. This disagrees with the standard database bag (multiset) semantics, which is especially important for the correct evaluation of aggregate queries. In this article, we study two variants of bag semantics for query answering over $$\textit{DL-Lite} _{\mathcal{F}}$$ , extending basic $$\textit{DL-Lite} _{\textit{core}}$$ with functional roles. For our first semantics, which follows the semantics of primary keys in SQL, conjunctive query (CQ) answering is coNP-hard in data complexity in general, but it is in TC $$^0$$ for the restricted class of rooted CQs; such CQs are also rewritable to the bag relational algebra. For our second semantics, the results are the same except that TC $$^0$$ membership and rewritability hold only for the restricted class of ontologies identified by a new notion of functional weak acyclicity.
A Novel Stochastic Optimization Algorithm Inspired From The Biology Of Plant Reproduction

This paper proposes a novel stochastic optimization algorithm based on the biology of plant reproduction system. The plants are taken as solution variables and distributed in the solution space. The plant with highest fitness value is assumed to be in a fertile land with suitable climatic conditions and is made to disperse several seeds randomly in the solution space. The other lesser fit plants scatter fewer seeds around themselves, the number of seeds being made directly proportional to the plant's fitness. The process is repeated till convergence is attained. The new method is tested on standard test functions and the results obtained are promising. A comparison of the new method with Particle swarm Optimization (PSO) and Genetic Algorithm (GA) shows relatively improved convergence characteristics.
ReMap

ReMap is a multimodal interface that enables searching for learning videos using speech and in-task pointing. ReMap extends multimodal interaction to help-seeking for complex tasks. Users can speak search queries, adding app-specific terms deictically. Users can navigate ReMap's search results via speech or mouse. These features allow people to stay focused on their task while simultaneously searching for and using help resources. Future work should explore how to implement more robust deictic resolution and more modalities.
Optical Neural Network by Disordered Tumor Spheroids

Optical neuromorphic computing processes information at the speed of light, but requires a careful design and fabrication of the deep layers, which strongly hampers the development of large-scale photonic learning machines [1,2]. New paradigms, as reservoir computing [3], suggest that brain-inspired complex systems such as disordered and biological materials may realize artificial neural networks with thousands of computational nodes trained only at the input and at the readout.
A Novel Linear Collaborative Discriminant Regression Classification and L1 Norm Based Algorithm for On-Chip Realization of Uncontrolled Face Recognition

This paper proposes the novel algorithm that suits the on-chip realization of uncontrolled face recognition system. Face recognition has its own advantage and occupied all possible venues. It plays a key role in the identification or authentication of a person in wide range applications from unlocking the mobile phone to forensics. The biggest hindering factor is its speed when it is implemented along with other systems. The systems should be implemented as standalone devices. Design of Face recognition algorithms that fit for on-chip realization is far more important. With the existence of sophisticated System on Chip hardware and software platforms IP based system design is very much possible. In this work, Collaborative discriminates classification based face recognition is designed and implemented on Xilinx Zynq SoC platform. This proposed algorithm can be converted as an IP and the same can be reused for any design using VIVADO IP integrator. The proposed algorithm and architecture shows the improvements in speed and reduction of power consumption.
Large-Scale Ising Machine Based on Spatial Light Modulation

Physical systems that evolve according to an Ising Hamiltonian are currently attracting an enormous research interest as novel computing architectures for solving optimization problems that cannot be tackled efficiently on large scales by conventional hardware. In particular, it has been recently demonstrated that photonic platforms such as coherent Ising machines [1,2] and nanophotonic circuits [3] can find the ground state of a classical system of spins with programmable interactions. However, all the proposed optical machines either involve a limited number of spins or fail to properly solve large-scale systems. Although spatial optical waves can present spin-glass dynamics [4] and matrix operations can also be performed by spatially shaped fields [5], the use of spatial optical modulation to solve Ising spin models has remained unexplored. Here, we exploit the spatial degree of freedom of light to realize an optical Ising machine with an unprecedented number of interacting spins [6].
A Memory Efficient, Multiplierless &amp; Modular VLSI Architecture of 1D/2D Re-Configurable 9/7 &amp; 5/3 DWT Filters Using Distributed Arithmetic

Dedicated hardware for “Discrete Wavelet Transform” (DWT) is at high demand for real-time imaging operations in any standalone electronic devices, as DWT is being extensively utilized for most of the transform-domain imagery applications. Various DWT algorithms exist in the literature facilitating its software implementations which are generally unsuitable for real-time imaging in any stand-alone devices due to their power intensiveness and huge computation time. In this paper, a convolutional DWT-based pipelined and tunable VLSI architecture of Daubechies 9/7 and 5/3 DWT filter is presented. Our proposed architecture, which mingles the advantages of convolutional and lifting DWT while discarding their notable disadvantages, is made area and memory efficient by exploiting “Distributed Arithmetic’ (DA) in our own ingenious way. Almost 90% reduction in the memory size than other notable architectures is reported. In our proposed architecture, both the 9/7 and 5/3 DWT filters can be realized with a selection input, “mode”. With the introduction of DA, pipelining and parallelism are easily incorporated into our proposed 1D/2D DWT architectures. The area requirement and critical path delay are reduced to almost 38.3% and 50% than that of the latest remarkable designs. The performance of the proposed VLSI architecture also excels in real-time applications.
Beyond Earthworm: Keeping the Promise

Abstract The founding of the Advanced National Seismic System (ANSS) vision was originally presented in U.S. Geological Survey Circular 1188 (U.S. Geological Survey [USGS], 1999), after many years of discussions and workshops, described in detail by Filson and Arabasz (2016). Much has been accomplished in the ensuing two decades. Disparate and sometimes divergent developments that had been previously explored at individual private and public universities were finally centralized with increased efficiency and coherency of effort. The stated mission of the ANSS is to “… provide accurate and timely data and information products for seismic events, including their effects on buildings and structures, employing modern monitoring methods and technologies.” In this article, an approach (xQuake) is proposed that does not interfere in any way with the mission of the National Earthquake Information Center and ANSS but instead restores much of the community focus and international collaboration that has been lost over the past two decades. xQuake uses an executable graph framework in a pipeline architecture; this framework can be seamlessly integrated into current ANSS quake monitoring systems. This new approach incorporates modern approaches to computer analytics, including multitopic Kafka exchange rings, cloud computing, a self-configuring phase associator, and machine learning. The xGraph system is free for noncommercial use, open source, hardware agnostic (Windows, Linux, Mac), with no requirement for commercial datastores.
Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. Such methods efficiently find solutions even for high degree-of-freedom robots. However, a globally optimal solution is often intractable in practice and state-of-the-art trajectory optimization methods are thus prone to local minima, especially in cluttered environments. In this paper, we propose a novel motion planning algorithm that employs stochastic optimization based on the cross-entropy method in order to tackle the local minima problem. We represent trajectories as samples from a continuous-time Gaussian process and introduce heteroscedasticity to generate powerful trajectory priors better suited for collision avoidance in motion planning problems. Our experimental evaluation shows that the proposed approach yields a more thorough exploration of the solution space and a higher success rate in complex environments than a current Gaussian process based state-of-the-art trajectory optimization method, namely GPMP2, while having comparable execution time.