Reviewerly/exHarmony huggingface dataset:
Effects of Sampling Rate on Amplified Spontaneous Emission Based Single-Bit Quantum Random Number Generation

We report the sampling rate dependency of a single-bit quantum random number generation scheme using a filtered and amplified source of amplified spontaneous emission (ASE). The quality of randomness is measured by the number of passed NIST statistical randomness tests. The number decreases for almost all cases when increasing the sampling rate. We apply a simple self-delay XOR post-processing technique, drastically improving the quality for low sampling rates. However, sampling rates higher than the analog bandwidth of the sampled intensity fluctuations are found to be ineligible for the generation scheme.
Incoherent Resolution of Unresolved Surface Multipath

The reflection off the ground or sea is known to produce a strong multipath signal that can be unresolvable in both angle and range from that of the direct path. While the measured range is relatively unaffected, the apparent elevation obtained from monopulse or using digital beamforming to locate the energy maxima often strongly deviates from the correct value. While means for correcting the readings using the coherent subarray or sum/difference matched filter outputs, this can prove inconvenient for retrofitting and subject to unmodeled errors, and very closely spaced reflections are in any case unresolvable with a single dwell. This paper discusses means for resolving the true elevation using only the returns (after target-associated detections) of the apparent elevation angle. Results of the technique on test data from a 5-MHz bandwidth radar at 3.35 GHz on drone targets are promising.
An Unmanned Aerial Vehicle Framework Design for Autonomous Flight Path

Unmanned Aerial Vehicle (UAV) has become a preferred choice due to it robustness, reliability and efficiency, at an economically lower cost. The present study is intended to develop a UAV that is capable of navigating autonomously, using path planning algorithm, incorporating a collision avoidance approach at the lowest possible cost. This paper addresses the current issues of auto-piloting UAVs with collision avoidance mechanisms, that are commercially available, however, these systems are too large and expensive. The framework design of the UAV consists of a quadcopter frame, a flight controller, four motors, a Global Positioning System (GPS) system, an on-board telemetry system, a battery source and a raspberry pi camera processor board with a camera module. In the proposed UAV design, a preliminary software is designed to enable the UAV to navigate autonomously, on a pre-set designated flight path. The designed UAV provides a birds-eye view of its designated flight path to monitor the traffic flow.
Collaborative training of far infrared and visible models for human detection

This paper is about the collaborative training of a far infrared and a visible spectrum human detector; the idea is to use the strengths of one detector to fill the weaknesses of the other detector and vice versa. At first infrared and visible human detectors are pre-trained using initial training datasets. Then, the detectors are used to collect as many detections as possible. The validity of each detection is tested using a low-level criteria based on an objectness measure. New training data are generated in a coupled way based on these detections and thus reinforce both the infrared and the visible human detectors in the same time. In this paper, we showed that this semi-supervised approach can significantly improve the performance of the detectors. This approach is a good solution to generate infrared training data, this kind of data being rarely available in the community.
Face Set Recognition

In this paper we present a Cluster Aggregation Network (CAN) for face set recognition. This network takes a set of face images, which could be either face videos or clusters with a different number of face images as its input, and then it is able to produce a compact and fixed-dimensional feature representation for the face set for the purpose of recognition. The whole network is made up of two modules, among which the first one is a face feature embedding module and the second one is the face feature aggregation module. The first module is a deep Convolutional Neural Network (CNN) which maps each of the face images to a fixed-dimensional vector. The second module is also a CNN which is trained to be able to automatically assess the quality of input face images and thus assign various weights to the images&#x2019; corresponding feature vectors. Then the one aggregated feature vector representing the input set is formed inside the convex hull formed by the input single face image features. Due to the mechanism that quality assessment is invariant to the order of one image in a set and the number of images in the set, the aggregation is invariant to these factors. Our CAN is trained with standard classification loss without any other supervision information and we found that our network is automatically attracted to high quality face images, while repelling low quality images, such as blurred, blocked, and non-frontal face images. We trained our networks with CASIA and YouTube Face datasets and the experiments on IJB-C video face recognition benchmark show that our method outperforms the current state-of-the-art feature aggregation methods and our challenging baseline aggregation method.
4D scanning system for measurement of human body in motion

In this paper we propose 4DBODY system which realizes full body 4D scanning of human in motion. It consists eight measurement heads based on single frame structured light method. All heads works synchronously with frequency 120 Hz with spectral separation to eliminate crosstalk. Single frame method is based on projection of sinusoidal fringe pattern with special marker allowing for absolute phase reconstruction. We develop custom made unwrapping procedure adjusted for human body analysis. We could achieve 0.5 mm spatial resolution and 0.3 mm of accuracy within a 1.5 x 1.5 x 2.0 m3 working volume. We integrated treadmill into system which allows to perform walking experiments. We present some results of measurements with initial validation of 4DBODY system.
Automatic shadow detection using hyperspectral data for terrain classification

Hyperspectral image classification has received more attention from researchers in recent years. Hyperspectral imaging systems utilize sensors, which acquire data mostly from the visible through the near infrared wavelength ranges and capture tens up to hundreds of spectral bands. Using the detailed spectral information, the possibility of accurately classifying materials is increased. Unfortunately conventional spectral cameras sensors use spatial or spectral scanning during acquisition which is only suitable for static scenes like earth observation. In dynamic scenarios, such as in autonomous driving applications, the acquisition of the entire hyperspectral cube in one step is mandatory. To allow hyperspectral classification and enhance terrain drivability analysis for autonomous driving we investigate the eligibility of novel mosaic-snapshot based hyperspectral cameras. These cameras capture an entire hyperspectral cube without requiring moving parts or line-scanning. The sensor is mounted on a vehicle in a driving scenario in rough terrain with dynamic scenes. The captured hyperspectral data is used for terrain classification utilizing machine learning techniques. A major problem, however, is the presence of shadows in captured scenes, which degrades the classification results. We present and test methods to automatically detect shadows by taking advantage of the near-infrared (NIR) part of spectrum to build shadow maps. By utilizing these shadow maps a classifier may be able to produce better results and avoid misclassifications due to shadows. The approaches are tested on our new hand-labeled hyperspectral dataset, acquired by driving through suburban areas, with several hyperspectral snapshotmosaic cameras.
Monte Carlo Strategies for Exploiting Fairness in N-player Ultimatum Games

The Ultimatum Game (UG) is studied to see how people respond in bargaining situations. In the 2-player version each round a player can be a proposer or a responder. As a proposer an offer is made on how to split a monetary amount. The responder either accepts or rejects the offer. If accepted, the money is split as proposed; if rejected both players get nothing. Studies have found over time the offers decrease but are still accepted (getting something is better than nothing) until a subgame perfect Nash equilibrium is reached where the lowest possible offer is accepted. In the N-player version the object is to see if the population can reach a state of fairness where, on average, offers are accepted. We have previously shown that a (µ/µ,λ) evolution strategy can evolve offers and acceptance thresholds that promote fairness. In this paper we report an extension to this previous work. One player is added to the population who interacts in the same manner with the other N players. However, this new player is rational—i.e., he ignores fairness and instead exploits the other players by maximizing his payoffs. We used three different versions of Monte Carlo Tree Search (MCTS) to adaptively control this rational player’s offer levels during the game. The results indicate payoffs for this player can be as much as 40% higher than the population average payoff. Our MCTS introduces a novel rollout approach making it ideally suited for the play of mathematical games.
Getting Started with Python

Getting Python on your computer is obviously the first step for the reader of this book, in order to be able to run your Python code. In this chapter we’ll talk about what tools you might want to use to edit and manage your Python code, after we’ve dealt with the primary issue of actually getting a Python distribution on your computer.
SRDT: A Novel Robust RGB-D Tracker Based on Siamese Region Proposal Network and Depth Information

Visual tracking is still a challenging fundamental task in the field of computer vision, especially in complex scenes such as long-term occlusion, nonrigid deformation and fast movement. In this paper, we presented an RGB-D tracker based on the Siamese Region Proposal Network and Depth Information. First, Siamese Network with shared parameters was constructed to perform feature extraction on the target patch and search area. Second, Region Proposal Network was constructed to estimate the target position in the RGB channels. At the same time, the depth information in the RGB-D video was used to determine the target occlusion state and fine-tune the target position. Finally, the tracker used depth information to achieve occlusion recovery when the target was fully occluded. The experimental result shows that the method has better performance in tracking accuracy and tracking speed on the large-scale Princeton RGB-D Tracking Benchmark (PTB) dataset.
Data Placement Oriented Scheduling Algorithm for Scheduling Scientific Workflow in Cloud: A Budget-Aware Approach

Background: A large number of communities and enterprises deploy numerous scientific workflow applications on cloud service. Aims: The main aim of the cloud service provider is to execute the workflows with a minimal budget and makespan. Most of the existing techniques for budget and makespan are employed for the traditional platform of computing and are not applicable to cloud computing platforms with unique resource management methods and pricing strategies based on service. Methods: In this paper, we studied the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and proposed a novel workflow scheduling scheme. Also, data placement is included in the proposed algorithm. Results: In this scheme, DPO-HEFT (Data Placement Oriented HEFT) algorithm is developed which closely integrates the data placement mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflow demonstrate the efficacy of our scheme. Conclusion: Our scheme can achieve significantly better cost and makespan trade-off fronts with remarkably higher hypervolume and can run up to hundreds times faster than the state-of-the-art algorithms.
A Comprehensive Head-mounted Eye Tracking Review: Software Solutions, Applications, and Challenges

Head-mounted eye-tracking (E-T) technology allows for monitoring and recording people&#x2019;s eye movements in a completely unobtrusive fashion. It has been effectively used to accelerate human behavior research in a wide variety of areas. Motivated by the growing importance/applications of head-mounted eye tracking technology, this work provides a comprehensive review for researchers who are trying to find the most effective strategies for their research and applications. The goal of the paper is to provide the reader with a survey of state-of-art head-mounted eye tracking technology, present commercially available wearable eye tracking equipment, software solutions for eye tracking data analysis techniques reported in the literature, and large-scale eye tracking data visualization methods for researchers from varying backgrounds. Example applications adopting head-mounted eye tracking are described, such as human-machine interaction, cognitive psychology, medical applications, and augmented reality. Finally, discussion of limitations and expectations for headmounted eye tracking analysis methodology is also investigated.
Google Compute Engine (GCE)

Google Compute Engine (GCE) makes available to users virtual machines (VMs) that are running on Google’s data centers around the world. These machines take advantage of Google’s state-of-the-art fiber optic powered network capabilities to offer fast and high-performance machines that can scale based on usage and automatically deal with issues of load balancing.
Feasibility Study of Energy Self-sufficiency on Chuja Island, Korea using Wind Energy

A study on the possibility of energy self-sufficiency was conducted on Chuja Island, South Korea. The one-year wind data from 40 m to 80 m above ground level were collected from the Light Detection and Ranging (LiDAR) device. Fundamental characteristics of the wind data were analyzed in detail. Then, the Measure-Correlate-Predict (MCP) technique was carried out to adjust the one-year wind data to long-term wind climate on the island using a nearby 25-year reanalysis wind data set. The power curves of three commercial wind turbines were applied to calculate the Annual Energy Productions (AEPs) for 25 years. Finally, an economic feasibility analysis was performed and then the possibility of energy self-sufficiency was assessed on the basis of actual electricity consumption of the island. As a result, the estimated annual Capacity Factors (CFs) were about 40% to 50%, which is similar to those of generic offshore wind farms. Also, the Internal Rate of Return (IRR) and Benefit-Cost Ratio (BCR) were 13.63% and 1.77, respectively. The possibility of energy self-sufficiency on Chuja Island is revealed in this paper.
Status of Mobile Agricultural Apps in the Global Mobile Ecosystem.

Smart phones are the greatest invention of this century that has now became an inseparable gadget from every one’s pocket. The diffusion of smartphones has been widening more rapidly due to its means for communication, Gaming, Education, Entertainment, Technical supports, Information, Day to day news coverage, Trading and Business etc. Mobile apps are software programs designed to run on smartphones, tablets and other devices. There is a need for the design and development of a mobile application for farmers, students and agri experts with improvisation of content/features in both educational and advisory services. This study aims to explore the present scenario of mobile agricultural applications in global mobile ecosystem available in various platforms and to bring out the challenges in the up scaling of the mobile agricultural apps and pointing out certain additional context to strengthen the mobile agricultural applications. Developers and agricultural experts must look carefully to avoid the stereotypes and cliches followed and the upcoming mobile applications must be user friendly to the farmers in advisory services and educational aspects.
Validity of Geochemical Evidence for Abiotic Alkane Gas: A Revisit

Preview this conference paper: Validity of Geochemical Evidence for Abiotic Alkane Gas: A Revisit, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2019/P85-1.gif
Comparison of Awareness and Computer Literacy in Classes through Creation of Storytelling and Product Introduction Slides

In order to ensure that university students have a sound base of computer knowledge and operating ability, and in order to improve their awareness related to their abilities, two types of classes were offered in which storytelling and product slide presentations were performed by creating slides using PowerPoint, entering awareness and evaluations by others using Excel, and creating and submitting reports using Word. This paper investigates differences in the outcomes resulting from differences in the themes, and explores potential reasons for these differences. It compares and examines the amount of time taken to complete the works and the awareness of the students with regard to these abilities. Their abilities to use software functions are surveyed and differences between two classes are compared and revealed. After comparing usage abilities of individual software functions, the results of variance analysis of the average usage ability for each of the three types of software for two classes are shown.
Predicting the Amount and Timing of Rehabilitation in Acute Care Using Electronic Health Records Data

Identify patient characteristics which predict acute care rehabilitation utilization.
Active contour model for medical sequence image segmentation based on spatial similarity

Background and Objectives: Image segmentation is the basic problem in computer vision and pattern recognition. This study mainly focuses on the segmentation of medical sequence images. Materials and Methods: In this article, we considered the spatial similarity of the medical sequence image in active contour model (ACM) for segmentation. First, by utilizing the similarity of object contour between adjacent slices of medical images, and then using the segment result of the former slice as the initial contour of the next image to segmentation. The proposed model can automatically obtain a better initial contour location and reduce the computing cost for segment processing. Second, to improve the accuracy of image segmentation, we considered the similarity of the object contour between adjacent slices, and introduce a punishment term in localized ACM. Results: We compared our model and other methods for segmenting medical brain magnetic resonance slices, and the experimental results on synthetic medical sequence images validate the effectiveness of the proposed method. Conclusions: By utilizing the similarity of object contour between adjacent slices of medical images, and using the segment result of former slice as the initial contour of the next image to segment, the proposed model can obtain better initial contour location for segmentation sequence images and reduce the computing cost for whole medical sequence image segmentation process.
Skew Correction and Text Line Extraction of Arabic Historical Documents

The field of optical character recognition for the Arabic text is not getting much attention by researchers comparing to Latin text. It is only in the last two decades that this field was being exploited, due to the complexity of Arabic writing and the fact that it demands a critical step which is segmentation; first from text to lines, then from lines to words and finally from words to characters. In case of historical documents, the segmentation is more complicated because of the absence of writing rules and the poor quality of documents. In this paper we present a projection-based technique for the segmentation of text into lines of ancient Arabic documents. To override the problem of overlapping and touching lines which is the most challenging problem facing the segmentation systems, firstly, pre-processing operations are applied for binarization and noise reduction. Secondly a skew correction technique is proposed beside a space following algorithm which is performed to separate lines from each other. The segmentation method is applied on four representations of the text image, including an original binary image and other three representations obtained by transforming the input image into: (1) smeared image with RLSA algorithm, (2) up-to-down transitions, (3) smoothed image by gaussian filter. The obtained results are promising and they are compared in term of accuracy and time cost. These methods are evaluated on a private set of 129 historical documents images provided by Al-Qaraouiyine Library.
A Unified Approach on Active Learning Dual Supervision

Active Learning (AL) is a machine learning framework that aims to efficiently select a limited labeled data to construct an effective model given huge amount of unlabeled data on the side. Most studies in AL focus on how to select the unlabeled data to be labeled by a human oracle in order to maximize the performance gain of the model with as little labeling effort as possible. In this work, however, we focus not only on how to select appropriate data instances but also how to select informative features, more specifically, categorical features to be labeled by the oracle in a unified manner. The unification means that we select the best choice of item to label where the item can be either a feature or an instance on each iteration given a unified scoring function to make the decision. The method that we propose is by synthesizing new instances that represent a set of features. By utilizing synthesized instances, we can treat this set of features as if they are regular instances. Therefore they could be compared on an equal ground when the model tries to select which instances to be labeled by the oracle. The features used to build the synthesized instances need to be carefully selected so the resulting synthesized instances could improve the model and not introducing any contradicting information. We utilize hierarchical clustering in order to group features that own similar content. This is done first by picking clusters whose label purity are estimated to be high. Then we score a feature based on how common the feature is in the cluster and how related the feature is to the estimated majority label. The top scoring features will then be used to synthesize instances. We demonstrate the effectiveness of the proposed method through a few data sets that consist of only categorical features where the feature labeling makes more sense to labeling oracles. The experiment results show that adopting the unified approach creates clear benefit to model construction, especially in the early stage where we can efficiently obtain an effective model through only a few iterations, compared to the one using only instance labeling for model construction.
Engineering Reliable Multiagent Systems (Dagstuhl Seminar 19112)

This report documents the program and outcomes of Dagstuhl Seminar 19112 Engineering Reliable Multiagent Systems. The aim of this seminar was to bring together researchers from various scientific disciplines, such as software engineering of autonomous systems, software verification, and relevant subareas of AI, such as ethics and machine learning, to discuss the emerging topic of the reliability of (multi-)agent systems and autonomous systems in particular. The ultimate aim of the seminar was to establish a new research agenda for engineering reliable autonomous systems.